diff --git a/CNN_DEMO/README.md b/CNN_DEMO/README.md
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+++ b/CNN_DEMO/README.md
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+# Step 0: Get going
+To get going right away, train a LeNet-5 model on MNIST using Alt-Min by executing:
+```
+python train_cnn_14jan.py 
+```
+# Step 1: HYPERPARAMETER TESTING
+Write a hyperparameter grid search in `experiments` sub-folder.
+e.g.:
+* `./all_lenetFashionMnist_18jan.sh`    does both alt-min (am) and SGD for fashion-mnist
+* `./am_lenetMnist_18jan.sh`            does am on mnist
+* `./sgd_lenetMnist_18jan.sh`           does sgd on mnist
+
+These files call `../train_cnn_14jan.py` in a loop, which trains LeNet architectures on training sets and record error on validation set. Make sure to set "savePath" etc. within that file to an appropriate sub-directory.
+
+
+# Step 2: ANALYSIS
+1. Still from the experiments folder, use `cnnMnistViz19jan.py` to determine the hyperparameters which performed the best, on average. E.g.:
+```
+python -i ../cnnMnistViz19jan.py --loadPath='./lenetMnist_18jan/'
+```
+
+This will automatically save the results in `bestHypers` and `ims` subfolders within the folder specific via argument `--loadPath`.
+
+2. Copy the values saved in `bestHypers` subfolder (in `.pt` format) to the corresponding subfolder of `am2-paper-results/postHPexps/`. `postHPexps` stands for "POST-HYPER-PARAMETER-EXPERIMENTS", e.g. testing and visualization.
+
+3. To print the selected hyperparameters to terminal, execute from `am2-paper-results` e.g.:
+```
+python -i printHPOrez_cnn.py --loadPath='postHPexps/lenet/lenet_mnist/'
+```
+
+# Step 3: TESTING
+In the `postHPexps` subfolders, run the executables to initiate testing. Each one executes:
+1. Load the best hyperparameters determined in Step (2a) and copied in Step (2b).
+2. Train corresponding network using entire training set.
+3. Evaluate on the test.
+4. Repeat for many random intializations.
+
+# Step 4: VISUALIZATION
+From the `postHPexps` subfolder, use `testRezViz_19jan.py` to generate figures displaying testing results, e.g.
+```
+python ../../../testRezViz_19jan.py --loadPath='./testResults/'
+```
+
diff --git a/CNN_DEMO/__pycache__/altmin14jan.cpython-36.pyc b/CNN_DEMO/__pycache__/altmin14jan.cpython-36.pyc
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diff --git a/CNN_DEMO/__pycache__/models.cpython-36.pyc b/CNN_DEMO/__pycache__/models.cpython-36.pyc
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diff --git a/CNN_DEMO/altmin14jan.py b/CNN_DEMO/altmin14jan.py
new file mode 100644
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+++ b/CNN_DEMO/altmin14jan.py
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+"""
+@author Mattia Rigotti (augmented by Benjamin Cowen)
+@date 15 Jan 2019
+"""
+from __future__ import print_function, division
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+import numpy as np
+
+from utils.nn import Flatten
+
+
+def compute_codes_loss(code, nmod, lin, loss_fn, codes_target, mu, lambda_c):
+    ''' codes: outputs of the linear modules
+        nmod: non-linear module downstream from linear module
+        lin:  linear module (Conv2d or Linear)
+    '''
+    output = lin(nmod(code))
+    loss = (1/mu)*loss_fn(output) + F.mse_loss(code, codes_target)
+    if lambda_c>0.0:
+        loss += (lambda_c/mu)*code.abs().mean()
+    return loss
+
+
+def update_memory(As, Bs, inputs, codes, model_mods, eta=0.0):
+    "Updates the bookkeeping matrices using codes as in Mairal2009"
+    if hasattr(model_mods, 'n_inputs'):
+        x = inputs.view(-1, model_mods.n_inputs)
+    else:
+        x = inputs
+
+    with torch.no_grad():
+        id_codes = [i for i,m in enumerate(model_mods) if hasattr(m, 'has_codes') and getattr(m, 'has_codes')]
+        for i, (idx, c_in, c_out) in enumerate(zip(id_codes, [x]+codes[:-1], codes)):
+            try:
+                nmod = model_mods[idx-1]
+            except IndexError:
+                nmod = lambda x: x
+
+            a = nmod(c_in)
+            if eta == 0.0:
+                As[i] += a.t().mm(a)
+                Bs[i] += c_out.t().mm(a)
+            else:
+                As[i] = (1-eta)*As[i] + eta*a.t().mm(a)
+                Bs[i] = (1-eta)*Bs[i] + eta*c_out.t().mm(a)
+    return As, Bs
+
+
+def update_hidden_weights_bcd_(model_mods, As, Bs, lambda_w):
+    # Use BCD to update intermediate weights
+    id_codes = [i for i,m in enumerate(model_mods) if hasattr(m, 'has_codes') and getattr(m, 'has_codes')]
+    for i, A, B in zip(id_codes, As, Bs):
+        model_mods[i].weight.data = BCD(model_mods[i].weight.data, A, B, lambda_w)
+
+
+def BCD(w, A, B, lambda_w, eps=1e-3, max_iter=20, return_errors=False):
+    # lambda_w is referenced to A_jj in every column
+    B = B.div(A.diag() + 1e-10)
+    A = A.div(A.diag() + 1e-10)
+
+    errors = []
+    with torch.no_grad():
+        for i in range(max_iter):
+            w_pre = w.clone()
+            error = 0
+            for j in range(A.shape[1]):
+                delta_j = (B[:,j] - w.mv(A[:,j]))
+                w[:,j].add_(delta_j)
+                #  u_j /= max(u_j.norm(), 1.0) # This was in Mairal2009, but assumes that B has spectral radius smaller than A
+                # Shrinkage step (sparsity regularizer)
+                if lambda_w > 0.0:
+                    sign_w = w[:,j].sign()
+                    w[:,j].abs_().add_(-lambda_w).clamp_(min=0.0).mul_(sign_w)
+                error += delta_j.abs().mean().item()
+            errors.append(error)
+            # Converged is there is no change between steps
+            if (w - w_pre).abs().max().item() < eps:
+                break
+
+    if return_errors:
+        return w, errors
+    else:
+        return w
+
+
+def post_processing_step(model, data, target, criterion, lambda_w, n_iter=1):
+        with torch.no_grad():
+            output, codes = get_codes(model, data)
+
+        update_last_layer_(model, codes[-1], target, criterion, lambda_w=args.lambda_w, n_iter=n_iter)
+
+
+def insert_mod(model_mods, mod, has_codes):
+    "If a mod is not empty, close it, include it, and start a new mod"
+    if len(mod) == 1:
+        model_mods.add_module(str(len(model_mods)), mod[0])
+        model_mods[-1].has_codes = has_codes
+    elif len(mod) > 1:
+        model_mods.add_module(str(len(model_mods)), mod)
+        model_mods[-1].has_codes = has_codes
+    mod = nn.Sequential()
+    return mod
+
+# EXTRACTED FROM get_mods 2 Jan 2019:
+def set_mod_optimizers_(model_mods, optimizer=None, optimizer_params={}, data_parallel=False):
+    '''
+      Sets/resets model optimizer(s).
+    '''
+    if optimizer is not None:
+        # Include an optimizer in modules with codes
+        for m in model_mods:
+            if m.has_codes:
+                m.optimizer = getattr(optim, optimizer)(m.parameters(), **optimizer_params)
+
+        # Add optimizer to the last layer
+        model_mods[-1].optimizer = getattr(optim, optimizer)(model_mods[-1].parameters(), **optimizer_params)
+
+    if data_parallel:
+        data_parallel_mods_(model_mods)
+
+    return model_mods
+
+def get_mods(model, optimizer=None, optimizer_params={}, data_parallel=False):
+    '''Returns all the modules in a nn.Sequential alternating linear and non-linear modules
+        Arguments:
+            optimizer: if not None, each module will be given an optimizer of the indicated type
+            with parameters in the dictionary optimizer_params
+      EDIT BY BEN: 2 Jan 2019
+      --- if model is already a nn.Sequential, just resets the optimizers.
+    '''
+    if not isinstance(model, nn.Sequential):
+      model_mods = nn.Sequential()
+      if hasattr(model, 'n_inputs'):
+          model_mods.n_inputs = model.n_inputs
+
+      nmod, lmod = nn.Sequential(), nn.Sequential()
+      for m in list(model.features) + [Flatten()] + list(model.classifier):
+          if any([isinstance(m, t) for t in [nn.Conv2d, nn.Linear, nn.BatchNorm2d, nn.BatchNorm1d]]) or hasattr(m, 'has_codes'):
+              nmod = insert_mod(model_mods, nmod, has_codes=False)
+              lmod.add_module(str(len(lmod)), m)
+          else:
+              lmod = insert_mod(model_mods, lmod, has_codes=True)
+              nmod.add_module(str(len(nmod)), m)
+
+      insert_mod(model_mods, nmod, has_codes=False)
+      insert_mod(model_mods, lmod, has_codes=True)
+
+      # Last layer that generates codes is lumped together with adjacent modules to produce the last layer
+      id_codes = [i for i,m in enumerate(model_mods) if hasattr(m, 'has_codes') and getattr(m, 'has_codes')]
+
+      model_tmp = model_mods[:id_codes[-2]+1]
+      model_tmp.add_module(str(len(model_tmp)), model_mods[id_codes[-2]+1:])
+      model_tmp[-1].has_codes = False
+      model_mods = model_tmp
+
+      # Added by Ben.
+      if optimizer is not None:
+        set_mod_optimizers_(model, optimizer = optimizer,
+                         optimizer_params = optimizer_params, data_parallel=data_parallel)
+      return model_mods
+
+def data_parallel_mods_(model_mods):
+    for i,m in enumerate(model_mods):
+        model_mods[i] = torch.nn.DataParallel(m)
+        model_mods[i].has_codes = m.has_codes
+        if hasattr(m, 'optimizer'):
+            model_mods[i].optimizer = m.optimizer
+
+
+# EDIT BY BEN 12/12/2018:
+# all RNN stuff...
+def get_codes(model_mods, inputs):
+    '''Runs the architecture forward using `inputs` as inputs, and returns outputs and intermediate codes
+    '''
+    # First mess with the input shape like this.
+    # Probably have to change for the RNN.
+    if hasattr(model_mods, 'n_inputs'):
+        x = inputs.view(-1, model_mods.n_inputs)
+    else:
+        x = inputs
+
+    # If doing RNN, codes have to be computed a certain way.
+    if hasattr(model_mods, 'isRNN') and getattr(model_mods, 'isRNN'):
+      codes = model_mods.forward(x, get_codes=True)
+
+    else:
+      # As codes only return outputs of linear layers
+      codes = []
+      for m in model_mods:
+          x = m(x)
+          if hasattr(m, 'has_codes') and getattr(m, 'has_codes'):
+              codes.append(x.clone())
+    # Do not include output of very last linear layer (not counted among codes)
+    return x, codes
+
+
+def update_codes(codes, model_mods, targets, criterion, mu, lambda_c, n_iter, lr):
+    id_codes = [i for i,m in enumerate(model_mods) if hasattr(m, 'has_codes') and getattr(m, 'has_codes')]
+    for l in range(1, len(codes)+1):
+        idx = id_codes[-l]
+
+        codes[-l].requires_grad_(True)
+        optimizer = optim.SGD([codes[-l]], lr=lr)
+        codes_initial = codes[-l].clone()
+
+        try:
+            nmod = model_mods[idx+1]
+        except IndexError:
+            nmod = lambda x: x
+        try:
+            lin = model_mods[idx+2]
+        except IndexError:
+            lin = lambda x: x
+
+        if l == 1:  # last layer
+            loss_fn = lambda x: criterion(x, targets)
+        else:       # intermediate layers
+            loss_fn = lambda x: F.mse_loss(x, codes[-l+1].detach())
+
+        # EDIT BY BEN 12/6:
+        # This optionally exits if converged.
+#        chgTol = 1e-4
+#        chg    = chgTol+1
+        # first iteration is out here.
+        optimizer.zero_grad()
+        loss = compute_codes_loss(codes[-l], nmod, lin, loss_fn, codes_initial, mu, lambda_c)
+        loss.backward()
+        optimizer.step() 
+        it     = 1
+        while ( (it < n_iter)):# and (chg>chgTol)):
+            it +=1
+            # For tracking convergence:
+            prevLoss = loss.item()
+            # Gradient Descent Step:
+            optimizer.zero_grad()
+            loss = compute_codes_loss(codes[-l], nmod, lin, loss_fn, codes_initial, mu, lambda_c)
+            loss.backward()
+            optimizer.step()
+            # Check convergence:
+#            chg = (np.abs(loss.item() - prevLoss)/np.abs(prevLoss))
+    return codes
+
+
+def update_last_layer_(mod_out, inputs, targets, criterion, n_iter):
+    for it in range(n_iter):
+        mod_out.optimizer.zero_grad()
+        outputs = mod_out(inputs)
+        loss = criterion(outputs, targets)
+        loss.backward()
+        mod_out.optimizer.step()
+
+
+def update_hidden_weights_adam_(model_mods, inputs, codes, lambda_w, n_iter, dropout_perc=0):
+    id_codes = [i for i,m in enumerate(model_mods) if hasattr(m, 'has_codes') and getattr(m, 'has_codes')]
+
+    if hasattr(model_mods, 'n_inputs'):
+        x = inputs.view(-1, model_mods.n_inputs)
+    else:
+        x = inputs
+
+    for idx, c_in, c_out in zip(id_codes, [x]+codes[:-1], codes):
+        # Randomly skip this layer according to dropout_perc:
+        if (torch.rand(1).item() <= dropout_perc) and dropout_perc>0.00001:
+          continue
+
+        lin = model_mods[idx]
+        if idx >= 1:
+            nmod = model_mods[idx-1]
+        else:
+            nmod = lambda x: x
+
+        # EDIT BY BEN 12/6:
+        # This optionally exits if converged.
+#        chgTol = 1e-4
+#        chg    = chgTol+1
+        # first iteration is out here.
+        lin.optimizer.zero_grad()
+        loss = F.mse_loss(lin(nmod(c_in)), c_out.detach())
+        if lambda_w > 0.0:
+            loss += lambda_w*lin.weight.abs().mean()
+        loss.backward()
+        lin.optimizer.step()
+        it     = 1
+        while ( (it < n_iter)):# and (chg>chgTol)):
+            it +=1
+            # For tracking convergence:
+            prevLoss = loss.item()
+            # Gradient Descent Step:
+            lin.optimizer.zero_grad()
+            loss = F.mse_loss(lin(nmod(c_in)), c_out.detach())
+            if lambda_w > 0.0:
+                loss += lambda_w*lin.weight.abs().mean()
+            loss.backward()
+            lin.optimizer.step()
+            # Check convergence:
+#            chg = (np.abs(loss.item() - prevLoss)/np.abs(prevLoss))
+
+
+
+# ------------------------------------------------------------------------
+# Misc
+# ------------------------------------------------------------------------
+def update_codes_TANH(model, codes, targets, mu, n_iter, lr):
+    '''Update codes approximating sign non-linearity with the soft version tanh
+    '''
+    #  Last layer codes
+    codes[-1].requires_grad_(True)
+    codes_target = codes[-1].clone()
+    optimizer = optim.SGD([codes[-1]], lr=lr)
+    for it in range(n_iter):
+        optimizer.zero_grad()
+        loss = compute_codes_loss(codes[-1], F.nll_loss, targets, torch.tanh, model.w_out, codes_target, mu, 0)
+        loss.backward()
+        optimizer.step()
+
+    # Hidden layers codes
+    for l in range(2, len(codes)):
+        codes[-l].requires_grad_(True)
+        codes_target = codes[-l].clone()
+        optimizer = optim.SGD([codes[-l]], lr=lr)
+        for it in range(n_iter):
+            optimizer.zero_grad()
+            loss = compute_codes_loss(codes[-l], F.mse_loss, codes[-l+1].data, torch.tanh, model.w[-l+1], codes_target, mu, 0)
+            loss.backward()
+            optimizer.step()
+
+    return codes
+
+
+class OnlineCov(nn.Module):
+    '''Online covariance class
+        Online calcularion of covariance matrix between a stream x and y, updated as in the Online paragraph of https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Covariance"
+    '''
+    def __init__(self, in_features, out_features=None):
+        super(OnlineCov, self).__init__()
+        if out_features is None:
+            out_features = in_features
+        # Covariance
+        self.register_buffer('cc', torch.zeros(out_features, in_features))
+        # Means
+        self.register_buffer('mX', torch.zeros(in_features))
+        self.register_buffer('mY', torch.zeros(out_features))
+        # Iteration number
+        self.n_iter = 0
+
+        self.in_features = in_features
+        self.out_features = out_features
+
+    def reset(self):
+        self.cc.zero_()
+        self.mX.zero_()
+        self.mY.zero_()
+        self.n_iter = 0
+
+    def update_cov(self, x, y):
+        # Checks
+        if x.dim() < 2:
+            x = x.view(1,-1)
+        if y.dim() < 2:
+            y = y.view(1,-1)
+        assert x.size(0) == y.size(0)
+
+        # Start update
+        self.n_iter += 1
+
+        self.mX  += (x - self.mX).mean(0)/self.n_iter
+        dx = x - self.mX
+        dy = y - self.mY # Note: it's correct that dy is computed with previous mean
+
+        self.cc += dy.t().mm(dx)
+
+        self.mY += (y - self.mY).mean(0)/self.n_iter
+
+    def get_cc(self):
+        return self.cc/self.n_iter
+
+    def __len__(self):
+        return self.n_iter
+
+    def extra_repr(self):
+        return '{in_features}, {out_features}, n_iter={n_iter}'.format(**self.__dict__)
+
+
+def update_memory_cov(As, Bs, codes, nlin=F.relu):
+    '''Updates the bookkeeping matrices using codes as in Mairal2009, but using centered variance and covariance
+    Arguments:
+        As, Bs: lists of OnlineCov objects
+    '''
+    a = codes[0].data
+    for i in range(len(codes)-1):
+        As[i].update_cov(a, a)
+        Bs[i].update_cov(a, codes[i+1].data)
+        a = nlin(codes[i+1].data)
+    return As, Bs
+
+
+def update_hidden_weights_cov_(Ws, As, Bs, lambda_w):
+    # Use BCD to update intermediate weights
+    for i, (A, B) in enumerate(zip(As, Bs)):
+        Ws[i].weight.data = BCD(Ws[i].weight.data, A.get_cc(), B.get_cc(), lambda_w)
+        Ws[i].bias.data = B.mY - Ws[i].weight.data.mv(B.mX)
+
diff --git a/CNN_DEMO/cnnMnistViz19jan.py b/CNN_DEMO/cnnMnistViz19jan.py
new file mode 100644
index 0000000..c068368
--- /dev/null
+++ b/CNN_DEMO/cnnMnistViz19jan.py
@@ -0,0 +1,423 @@
+"""
+Creates visualizations for CNN hyperparameter search results.
+
+@author Benjamin Cowen
+@date 17 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--saveBestHyperParameterPath', type=str, default=None, metavar='path/to/savedHyperParameters/',
+          help='Path to directory to save the BEST hyperparameters according to the exp.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+p.add_argument('--reduced', type=bool, default=False, metavar='T/F',
+          help='skip methods...')
+
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+else:
+  savePath = args.savePath
+if not os.path.exists(savePath):
+  os.mkdir(savePath)
+# For saving the best hyperparameters
+if args.saveBestHyperParameterPath is None:
+  saveHPpath = loadPaths[0] + "bestHypers/"
+else:
+  saveHPpath = args.saveBestHyperParameterPath
+if not os.path.exists(saveHPpath):
+  os.mkdir(saveHPpath)
+
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('Saving best hyperparameters to '+saveHPpath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"sgd":{}, "adam":{},   # Backprop-based methods
+          "amAdam1_1_me1":{},
+          "amAdam5_1_me1":{},
+          "amAdam1_5_me1":{},
+          "amAdam5_5_me1":{}
+         }
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    if not hasattr(DD,'perf'):
+      continue
+
+    # Backprop-type.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    # Altmin-type.
+    elif algBase=='altmin':
+      nit1 = str(DD.args['n_iter_codes'])
+      nit2 = str(DD.args['n_iter_weights'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit1 + '_' + nit2 + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Now we have to determine the parameter Combination ID.
+    # Location of the first digit of Combo ID. (comes right after "combo" string.)
+    numStart = fileName.find('combo')+len('combo')  
+    n        = 1               # Number of digits in the Combo ID.
+    nextChar = fileName[numStart+n]
+    while not(nextChar=="t"):  # Scrolling thru filename until the number ends.
+      n += 1
+      nextChar = fileName[numStart+n]
+    comboNum = fileName[numStart:(numStart+n)]   # this better be an integer combo ID :)
+    useKey   = "combo"+ comboNum +'_'+ str(k)        # Combination ID !!!
+
+    # Finally, add it to results. If it's a new key, make it. 
+    #  If it's just new trial, append it
+    if useKey in allSh[expID]:
+      # Append
+      allSh[expID][useKey].append(ddict()._load(filepath))
+    else:
+      # Or make new
+      allSh[expID][useKey] = [ddict()._load(filepath)]
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+############################################
+# Print loss figures -----------------------
+bestRez  = {}
+allMeanTes={}
+for expID, sh in allSh.items():
+  bestRez[expID] = {"acc":-1} # Initialize each Exp ID.
+  allMeanTes[expID] = {}
+  for comboID, rez in sh.items():
+    numTrials       = len(rez)
+    numFailedTrials = 0
+    
+    # (0.) Collect the Training/Testing histories.
+    # Compute the mean and std dev across trials.
+    numEpochs = rez[0].args['epochs']              #should be same for all trials.
+    allTr = None
+    allTe = None
+    for trial in range(numTrials):
+      # Shelf for current trial.
+      tShelf = rez[trial]
+
+      # CHECK VALIDITY:-----------------
+      # If no 'perf' field, then performance wasn't yet recorded.
+      if not hasattr(tShelf,'perf'):
+        continue
+      else:
+        expData = tShelf.perf
+
+      #-------------------------------------  
+      # If the number of recorded epochs << number allowed epochs,
+      #  then add zeros to the end. EG force the length to be numEpochs.
+      currTrAcc = th.Tensor(expData['tr'])
+      currTeAcc = th.Tensor(expData['te'])
+
+      # Double check failure to train.
+#      if currTeAcc.mean()<0.2:
+#        numFailedTrials+=1
+#        continue
+      if len(expData['tr'])<=2:
+        continue
+      # Otherwise you can still visualize incomplete results.
+      if (len(expData['tr'])< numEpochs): 
+        currTrAcc = th.cat([currTrAcc, th.zeros(numEpochs-len(expData['tr']))])
+        currTeAcc = th.cat([currTeAcc, th.zeros(numEpochs-len(expData['te']))])
+
+      # If it checks out, extract the data.
+      currTrAcc = currTrAcc.view(numEpochs, 1)
+      currTeAcc = currTeAcc.view(numEpochs, 1)
+
+      # Append into a numpy array so we can get mean/std later.
+      if allTr is None:
+        allTr = currTrAcc
+        allTe = currTeAcc
+      else:
+        allTr = th.cat([allTr, currTrAcc], 1)
+        allTe = th.cat([allTe, currTeAcc], 1)
+      # End of loop through the trials.
+    # If allTr is None, NO trials were valid (for various reasons).
+    # if numFailedTrials > numTrials/2, then more than half
+    #    of the trials failed due to initialization randomness.
+    if allTr is None or (numFailedTrials > numTrials/2):
+      continue
+    # (1.) Else, compute mean and std devs.
+    # Update the number of trials actually used in this analysis.
+    numTrials = allTr.size(1)
+    # Only average the nonzero entries.
+    meansTr = (allTr.sum(1)/allTr.gt(0).sum(1).float())
+    meansTe = (allTe.sum(1)/allTe.gt(0).sum(1).float())
+    # Initialize with zero.
+    stdsTr  = th.zeros(meansTr.size()).numpy()
+    stdsTe  = th.zeros(meansTe.size()).numpy()
+    # If there were actually multiple trials, fill in using NONZERO entries only.
+    if numTrials>1:
+      for epoch, tnsr in enumerate(allTr):
+        # This is my stupid way of doing ONLY NONZERO std dev's...
+        stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+        if np.isnan(stdtr):
+          stdtr=np.array([0])
+        stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+        if np.isnan(stdte):
+          stdte=np.array([0])
+        # Finally...
+        stdsTr[epoch] = stdtr
+        stdsTe[epoch] = stdte
+    meansTr = meansTr.numpy()
+    meansTe = meansTe.numpy()
+
+
+    # (1.5) for plotting, we REMOVE the zeros added during mean-computation.
+    while np.isnan(meansTr[-1]) or np.isnan(meansTe[-1]) or meansTr[-1]<1e-6 or meansTe[-1]<1e-6:
+      meansTr = meansTr[:-1]
+      meansTe = meansTe[:-1]
+      stdsTr  = stdsTr[:-1]
+      stdsTe  = stdsTe[:-1]
+
+    if epoch0_meanTr is not None:
+      meansTr = np.insert(meansTr, 0, epoch0_meanTr)
+      meansTe = np.insert(meansTe, 0, epoch0_meanTe)
+      stdsTr = np.insert(stdsTr, 0, epoch0_stdTr)
+      stdsTe = np.insert(stdsTe, 0, epoch0_stdTe)
+      x_axis = range(0, len(meansTr))
+    else:
+      x_axis = range(1, len(meansTr)+1)
+
+    allMeanTes[expID][comboID]=meansTe
+    ## (2.) If requested, plot the results for each parameter combination.
+    if not args.onlyBest:
+#    if expID=='amAdam5_me1':
+      # (2.a) Extract information about this setting for titles etc.
+      ar = rez[0].args
+      # Model Size.
+
+      # Alg name.
+      algName = ar['opt_method']
+      modelName=ar['model']
+      TITLE  =modelName+' trained with '+algName
+
+      # Print other hyperparameters
+      LR      = ar['lr_weights']
+
+      if algName in ['sgd', 'adam']:
+        l1_w    = ar['lambda_w']
+        TITLE2  = '\nWeight LR={}, L1={}; '.format(LR,l1_w)
+      else:
+        algName += '-{}_me{}'.format(ar['n_iter_weights'] , ar['mini_epochs'])
+        codeLR  = ar['lr_codes']
+        l1_c    = ar['lambda_c']
+        muMult  = ar['mult_mu']
+        muMax   = ar['max_mu']
+        l1wFact = ar['lambda_w_muFact']
+        l1cFact = ar['lambda_c_muFact']
+
+        TITLE2  = '\nWeight LR={},L1={}\mu; '.format(LR,l1wFact)
+        TITLE2 +=     'Code LR={},L1={}\mu; '.format(codeLR,l1cFact)
+        TITLE2 += '\n\mu factor = {}; '.format(muMult)
+        TITLE2 +=   '\mu-max={}'.format(muMax)
+
+      plt.figure(1)
+      plt.clf()
+      plt.errorbar(x_axis, meansTr, stdsTr, marker = '.', label='Training')
+      plt.errorbar(x_axis, meansTe, stdsTe, marker = '.', label='Testing')
+      plt.legend()
+      plt.ylim([.8,1])
+      plt.xlabel('Epochs')
+      plt.ylabel('Classification Accuracy')
+      plt.title(TITLE+TITLE2)
+      plt.savefig(savePath + expID + comboID + 'allTrials.png')
+
+    ## (3.) Finally, record the best results for each setting.
+    #(3.a) determine the final (nonzero) accuracy:
+    if meansTe[-1] > bestRez[expID]['acc']:# and len(meansTe)>9:
+      # For Plotting
+      bestRez[expID]['xax']     = x_axis
+      bestRez[expID]['meansTr'] = meansTr
+      bestRez[expID]['stdsTr']  = stdsTr
+      bestRez[expID]['meansTe'] = meansTe
+      bestRez[expID]['stdsTe']  = stdsTe
+      bestRez[expID]['numTr']   = numTrials
+      # For later.
+      bestRez[expID]['comboID'] = comboID
+      bestRez[expID]['acc']     = meansTe[-1]
+      bestRez[expID]['lr_weights'] = rez[0].args['lr_weights']
+      bestRez[expID]['bsz'] = rez[0].args['batch_size']
+      if expID.startswith('am'):
+        bestRez[expID]['lr_codes'] = rez[0].args['lr_codes']
+        bestRez[expID]['d_mu']     = rez[0].args['d_mu']
+        bestRez[expID]['muMult']     = rez[0].args['mult_mu']
+        bestRez[expID]['muMax']     = rez[0].args['max_mu']
+
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',   # Backprop-based methods
+          "amAdam1_1_me1":'r',
+          "amAdam5_1_me1":'y',
+          "amAdam1_5_me1":'m',
+          "amAdam5_5_me1":'c'
+          }
+markers = {"sgd":'o', "adam":'o',   
+          "amAdam1_1_me1":'*',
+          "amAdam5_1_me1":'d',
+          "amAdam1_5_me1":'P',
+          "amAdam5_5_me1":'x'
+          }
+plotNames={"amAdam1_1_me1":'AM-Adam11',
+           "amAdam5_1_me1":'AM-Adam51',
+           "amAdam1_5_me1":'AM-Adam15',
+           "amAdam5_5_me1":'AM-Adam55',
+           'sgd':'SGD', 'adam':"Adam"
+          }
+#colors = ['b','g','r','c','m','y','k']
+
+markerSize = 4
+modelName = rez[0].args['model']
+resUsableTitle = modelName
+
+dataName = rez[0].args['dataset']
+trmark='.'
+temark='^'
+numEpochs=10
+######## ALL PLOTS
+for i, xLims in enumerate([[-0.1, numEpochs+0.1]]):
+  for k,yLims in enumerate([[0., 1], [0.6,0.93], [0.85,1], [0.75,1]]):
+    plt.figure(i*k + k)
+    plt.clf()
+    colorID=0
+    if yLims[0]>0 or yLims[1]<1 or xLims[1]<numEpochs:
+      titleZoom = ' (zoomed) '
+    else:
+      titleZoom = ''
+    for expID, rez in bestRez.items():
+  #    print('expID = '+expID+': '+str(rez['stdsTr']))
+      if rez['acc']==-1:
+        continue 
+      # Else, add to the axes.
+      plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+                      marker = trmark, color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=plotNames[expID]+' Train')
+      plt.errorbar(rez['xax'], rez['meansTe'], rez['stdsTe'], 
+                      marker = temark, color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=plotNames[expID]+' Valid (N={})'.format(rez['numTr']))
+    plt.xlim(xLims)
+    plt.ylim(yLims)
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.title('Optimization methods using best parameters\n'+resUsableTitle +titleZoom)
+    # Get the legend off of the main plot:
+    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+    plt.savefig(savePath+'BEST-'+modelName+dataName+'lim'+str(i)+str(k)+'.png', bbox_inches='tight')
+
+
+######################################################
+# Finally, print out the best hyperparameters found.
+
+print('***************************************************')
+print('PRINTING OFFICIAL BEST HYPERPARAMETERS FOR '+resUsableTitle)
+for expID, rez in bestRez.items():
+  if rez['acc']==-1:
+    continue 
+  print('--------------------')
+  print('Optimizer = '+expID)
+  print('Batch-size = ' + str(bestRez[expID]['bsz']))
+  print('learning rate (weights) = ' + str(bestRez[expID]['lr_weights']))
+  if expID.startswith('am'):
+    print('learning rate (codes) = ' + str(bestRez[expID]['lr_codes']))
+    print('delta-mu= ' + str(bestRez[expID]['d_mu']))
+    print('mu-Mult= ' + str(bestRez[expID]['muMult']))
+    print('mu-Max= ' + str(bestRez[expID]['muMax']))
+  # I was gonna just save bestRez, but it seems silly to use both pickle
+  #   and ddict() even though pickle is simpler...
+  # Args from an example of the winning parameter combo.
+  winningArgs = allSh[expID][rez['comboID']][0].args
+  SH = ddict(args=winningArgs)
+  # Finally, save it.
+  rezFileName = expID
+  SH._save(saveHPpath + rezFileName, date=False)
+
+
+
+
+
+
+
+
+
diff --git a/CNN_DEMO/experiments/README.md b/CNN_DEMO/experiments/README.md
new file mode 100644
index 0000000..65e6fb7
--- /dev/null
+++ b/CNN_DEMO/experiments/README.md
@@ -0,0 +1,9 @@
+This folder contains .sh executables which perform hyperparameter grid searches on a 
+    validation set.
+
+All results get dumped in the subfolders here.
+
+After this, the next step is to use visualization file in previous folder to compile results and
+   determine best hyperparameter combination on average.
+
+Those results can then be used to evaluate on test set etc.
diff --git a/CNN_DEMO/experiments/all_lenetFashionMnist_18jan.sh b/CNN_DEMO/experiments/all_lenetFashionMnist_18jan.sh
new file mode 100755
index 0000000..108ad56
--- /dev/null
+++ b/CNN_DEMO/experiments/all_lenetFashionMnist_18jan.sh
@@ -0,0 +1,88 @@
+#!/bin/bash
+
+# Which GPU to run it on?
+gpuID=0
+
+# Where to save results?
+savePath='./lenetFM_bsz200/'
+
+# These are fixed for all experiments.
+max_epochs=10
+dataName='fashion_mnist'
+model='lenet'
+numTrials=100
+# It seems like I have to set this to get a preliminary result...?
+save_interval=2000
+
+# We loop through these
+niters=(1)
+n_weight_it=1
+
+# LOOPED HYPERPARAMETERS---------------------------
+muMults=(1 1.1 1.25)
+muMaxes=(1.5)
+muStart=0.01
+muUpdateFreq=999999 # only update between epochs
+delta_mus=(0.01 0.00001 0.0000001)             # small additive increments
+
+# Code learning rates.
+code_LRs=(0.1 1)
+
+# batches/epoch = (600, 120, 6)
+bszs=(200)
+
+# weight learning rates for batchsize 50
+#             1   12   123   1234   12345
+weight_LRs=(0.2 0.02 0.002 0.0002 0.00002)
+
+for ((trial=1; trial<=numTrials; trial++)); do
+  # Get trial ID
+  trialNum=$trial #((gpuID*numTrials + trial))
+  trialID='trial'$trialNum
+
+  # Reset Counters
+  amComboInc=0
+  sgdComboInc=0
+
+  for BSZ in "${bszs[@]}"; do
+    for weightLR in ${weight_LRs[@]}; do
+      # Compute parameter combination ID
+      let "(sgdcomboInc++)"
+      sgdComboID='combo'$sgdComboInc
+
+      # Compute save name
+      savename=$savePath'sgd_'$sgdComboID$trialID
+
+      # Run SGD experiment!
+      CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_cnn_14jan.py --opt-method='sgd' --dataset=$dataName --seed=$trialNum --lr-weights=$weightLR --epochs=$max_epochs --save-filename=$savename --save-interval=$save_interval --model=$model --batch-size=$BSZ --use-validation-size=10000
+
+
+      # Compute save name
+      savename=$savePath'adam_'$sgdComboID$trialID
+
+      # Run Adam experiment!
+      CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_cnn_14jan.py --opt-method='adam' --dataset=$dataName --seed=$trialNum --lr-weights=$weightLR --epochs=$max_epochs --save-filename=$savename --save-interval=$save_interval --model=$model --batch-size=$BSZ --use-validation-size=10000
+
+
+      for n_code_it in "${niters[@]}"; do
+        for codeLR in "${code_LRs[@]}"; do
+          for muMult in "${muMults[@]}"; do
+            for muFin in "${muMaxes[@]}"; do
+              for dMu in "${delta_mus[@]}"; do
+                # Compute parameter combination ID
+                let "(amComboInc++)"
+                comboID='combo'$amComboInc
+          
+                # Compute save name
+                savename=$savePath'am_'$comboID$trialID
+          
+                # Run alt-min experiment!
+                CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_cnn_14jan.py --dataset=$dataName --seed=$trialNum --lr-codes=$codeLR --lr-weights=$weightLR --epochs=$max_epochs --n-iter-codes=$n_code_it --save-filename=$savename --save-interval=$save_interval --model=$model --batch-size=$BSZ --n-iter-weights=$n_weight_it --min-mu=$muStart --max-mu=$muFin --d-mu=$dMu --mu-update-freq=$muUpdateFreq --mult-mu=$muMult --use-validation-size=10000
+              done
+            done
+          done
+        done
+      done
+    done
+  done
+done
diff --git a/CNN_DEMO/experiments/am_lenetMnist_18jan.sh b/CNN_DEMO/experiments/am_lenetMnist_18jan.sh
new file mode 100755
index 0000000..751a6d4
--- /dev/null
+++ b/CNN_DEMO/experiments/am_lenetMnist_18jan.sh
@@ -0,0 +1,72 @@
+#!/bin/bash
+
+# Which GPU to run it on?
+gpuID=3
+
+# Where to save results?
+savePath='./lenetMnist_18jan/'
+
+# These are fixed for all experiments.
+max_epochs=10
+dataName='mnist'
+model='lenet'
+numTrials=100
+# It seems like I have to set this to get a preliminary result...?
+save_interval=2000
+
+# We loop through these
+niters=(1)
+n_weight_it=1
+
+# LOOPED HYPERPARAMETERS---------------------------
+muMults=(1 1.1)
+muMaxes=(1.5)
+muStart=0.01
+muUpdateFreq=999999 # only update between epochs
+delta_mus=(0.01 0.00001 0.0000001)             # small additive increments
+
+# Code learning rates.
+code_LRs=(0.1 1)
+
+# batches/epoch = (600, 120, 6)
+bszs=(128)
+
+# weight learning rates for batchsize 50
+#             12   123   1234   12345
+weight_LRs=(0.02 0.002 0.0002 0.00002)
+
+for ((trial=1; trial<=numTrials; trial++)); do
+  # Get trial ID
+  trialNum=$trial #((gpuID*numTrials + trial))
+  trialID='trial'$trialNum
+
+  # Reset Counters
+  amComboInc=0
+
+  for n_code_it in "${niters[@]}"; do
+    for BSZ in "${bszs[@]}"; do
+      for weightLR in ${weight_LRs[@]}; do
+        for codeLR in "${code_LRs[@]}"; do
+          for muMult in "${muMults[@]}"; do
+            for muFin in "${muMaxes[@]}"; do
+              for dMu in "${delta_mus[@]}"; do
+                # Compute parameter combination ID
+                let "(amComboInc++)"
+                comboID='combo'$amComboInc
+          
+                if [ $amComboInc -gt 19 ] || [ $trial -gt 1 ];
+                then
+                  # Compute save name
+                  savename=$savePath'am_'$comboID$trialID
+            
+                  # Run alt-min experiment!
+                  CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_cnn_14jan.py --dataset=$dataName --seed=$trialNum --lr-codes=$codeLR --lr-weights=$weightLR --epochs=$max_epochs --n-iter-codes=$n_code_it --save-filename=$savename --save-interval=$save_interval --model=$model --batch-size=$BSZ --n-iter-weights=$n_weight_it --min-mu=$muStart --max-mu=$muFin --d-mu=$dMu --mu-update-freq=$muUpdateFreq --mult-mu=$muMult --use-validation-size=10000
+                fi
+              done
+            done
+          done
+        done
+      done
+    done
+  done
+done
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diff --git a/CNN_DEMO/experiments/lenetFashionMnist_18jan/ims/cnnMnistViz19jan.py b/CNN_DEMO/experiments/lenetFashionMnist_18jan/ims/cnnMnistViz19jan.py
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--- /dev/null
+++ b/CNN_DEMO/experiments/lenetFashionMnist_18jan/ims/cnnMnistViz19jan.py
@@ -0,0 +1,423 @@
+"""
+Creates visualizations for CNN hyperparameter search results.
+
+@author Benjamin Cowen
+@date 17 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--saveBestHyperParameterPath', type=str, default=None, metavar='path/to/savedHyperParameters/',
+          help='Path to directory to save the BEST hyperparameters according to the exp.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+p.add_argument('--reduced', type=bool, default=False, metavar='T/F',
+          help='skip methods...')
+
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+else:
+  savePath = args.savePath
+if not os.path.exists(savePath):
+  os.mkdir(savePath)
+# For saving the best hyperparameters
+if args.saveBestHyperParameterPath is None:
+  saveHPpath = loadPaths[0] + "bestHypers/"
+else:
+  saveHPpath = args.saveBestHyperParameterPath
+if not os.path.exists(saveHPpath):
+  os.mkdir(saveHPpath)
+
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('Saving best hyperparameters to '+saveHPpath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"sgd":{}, "adam":{},   # Backprop-based methods
+          "amAdam1_1_me1":{},
+          "amAdam5_1_me1":{},
+          "amAdam1_5_me1":{},
+          "amAdam5_5_me1":{}
+         }
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    if not hasattr(DD,'perf'):
+      continue
+
+    # Backprop-type.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    # Altmin-type.
+    elif algBase=='altmin':
+      nit1 = str(DD.args['n_iter_codes'])
+      nit2 = str(DD.args['n_iter_weights'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit1 + '_' + nit2 + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Now we have to determine the parameter Combination ID.
+    # Location of the first digit of Combo ID. (comes right after "combo" string.)
+    numStart = fileName.find('combo')+len('combo')  
+    n        = 1               # Number of digits in the Combo ID.
+    nextChar = fileName[numStart+n]
+    while not(nextChar=="t"):  # Scrolling thru filename until the number ends.
+      n += 1
+      nextChar = fileName[numStart+n]
+    comboNum = fileName[numStart:(numStart+n)]   # this better be an integer combo ID :)
+    useKey   = "combo"+ comboNum +'_'+ str(k)        # Combination ID !!!
+
+    # Finally, add it to results. If it's a new key, make it. 
+    #  If it's just new trial, append it
+    if useKey in allSh[expID]:
+      # Append
+      allSh[expID][useKey].append(ddict()._load(filepath))
+    else:
+      # Or make new
+      allSh[expID][useKey] = [ddict()._load(filepath)]
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+############################################
+# Print loss figures -----------------------
+bestRez  = {}
+allMeanTes={}
+for expID, sh in allSh.items():
+  bestRez[expID] = {"acc":-1} # Initialize each Exp ID.
+  allMeanTes[expID] = {}
+  for comboID, rez in sh.items():
+    numTrials       = len(rez)
+    numFailedTrials = 0
+    
+    # (0.) Collect the Training/Testing histories.
+    # Compute the mean and std dev across trials.
+    numEpochs = rez[0].args['epochs']              #should be same for all trials.
+    allTr = None
+    allTe = None
+    for trial in range(numTrials):
+      # Shelf for current trial.
+      tShelf = rez[trial]
+
+      # CHECK VALIDITY:-----------------
+      # If no 'perf' field, then performance wasn't yet recorded.
+      if not hasattr(tShelf,'perf'):
+        continue
+      else:
+        expData = tShelf.perf
+
+      #-------------------------------------  
+      # If the number of recorded epochs << number allowed epochs,
+      #  then add zeros to the end. EG force the length to be numEpochs.
+      currTrAcc = th.Tensor(expData['tr'])
+      currTeAcc = th.Tensor(expData['te'])
+
+      # Double check failure to train.
+#      if currTeAcc.mean()<0.2:
+#        numFailedTrials+=1
+#        continue
+      if len(expData['tr'])<=2:
+        continue
+      # Otherwise you can still visualize incomplete results.
+      if (len(expData['tr'])< numEpochs): 
+        currTrAcc = th.cat([currTrAcc, th.zeros(numEpochs-len(expData['tr']))])
+        currTeAcc = th.cat([currTeAcc, th.zeros(numEpochs-len(expData['te']))])
+
+      # If it checks out, extract the data.
+      currTrAcc = currTrAcc.view(numEpochs, 1)
+      currTeAcc = currTeAcc.view(numEpochs, 1)
+
+      # Append into a numpy array so we can get mean/std later.
+      if allTr is None:
+        allTr = currTrAcc
+        allTe = currTeAcc
+      else:
+        allTr = th.cat([allTr, currTrAcc], 1)
+        allTe = th.cat([allTe, currTeAcc], 1)
+      # End of loop through the trials.
+    # If allTr is None, NO trials were valid (for various reasons).
+    # if numFailedTrials > numTrials/2, then more than half
+    #    of the trials failed due to initialization randomness.
+    if allTr is None or (numFailedTrials > numTrials/2):
+      continue
+    # (1.) Else, compute mean and std devs.
+    # Update the number of trials actually used in this analysis.
+    numTrials = allTr.size(1)
+    # Only average the nonzero entries.
+    meansTr = (allTr.sum(1)/allTr.gt(0).sum(1).float())
+    meansTe = (allTe.sum(1)/allTe.gt(0).sum(1).float())
+    # Initialize with zero.
+    stdsTr  = th.zeros(meansTr.size()).numpy()
+    stdsTe  = th.zeros(meansTe.size()).numpy()
+    # If there were actually multiple trials, fill in using NONZERO entries only.
+    if numTrials>1:
+      for epoch, tnsr in enumerate(allTr):
+        # This is my stupid way of doing ONLY NONZERO std dev's...
+        stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+        if np.isnan(stdtr):
+          stdtr=np.array([0])
+        stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+        if np.isnan(stdte):
+          stdte=np.array([0])
+        # Finally...
+        stdsTr[epoch] = stdtr
+        stdsTe[epoch] = stdte
+    meansTr = meansTr.numpy()
+    meansTe = meansTe.numpy()
+
+
+    # (1.5) for plotting, we REMOVE the zeros added during mean-computation.
+    while np.isnan(meansTr[-1]) or np.isnan(meansTe[-1]) or meansTr[-1]<1e-6 or meansTe[-1]<1e-6:
+      meansTr = meansTr[:-1]
+      meansTe = meansTe[:-1]
+      stdsTr  = stdsTr[:-1]
+      stdsTe  = stdsTe[:-1]
+
+    if epoch0_meanTr is not None:
+      meansTr = np.insert(meansTr, 0, epoch0_meanTr)
+      meansTe = np.insert(meansTe, 0, epoch0_meanTe)
+      stdsTr = np.insert(stdsTr, 0, epoch0_stdTr)
+      stdsTe = np.insert(stdsTe, 0, epoch0_stdTe)
+      x_axis = range(0, len(meansTr))
+    else:
+      x_axis = range(1, len(meansTr)+1)
+
+    allMeanTes[expID][comboID]=meansTe
+    ## (2.) If requested, plot the results for each parameter combination.
+    if not args.onlyBest:
+#    if expID=='amAdam5_me1':
+      # (2.a) Extract information about this setting for titles etc.
+      ar = rez[0].args
+      # Model Size.
+
+      # Alg name.
+      algName = ar['opt_method']
+      modelName=ar['model']
+      TITLE  =modelName+' trained with '+algName
+
+      # Print other hyperparameters
+      LR      = ar['lr_weights']
+
+      if algName in ['sgd', 'adam']:
+        l1_w    = ar['lambda_w']
+        TITLE2  = '\nWeight LR={}, L1={}; '.format(LR,l1_w)
+      else:
+        algName += '-{}_me{}'.format(ar['n_iter_weights'] , ar['mini_epochs'])
+        codeLR  = ar['lr_codes']
+        l1_c    = ar['lambda_c']
+        muMult  = ar['mult_mu']
+        muMax   = ar['max_mu']
+        l1wFact = ar['lambda_w_muFact']
+        l1cFact = ar['lambda_c_muFact']
+
+        TITLE2  = '\nWeight LR={},L1={}\mu; '.format(LR,l1wFact)
+        TITLE2 +=     'Code LR={},L1={}\mu; '.format(codeLR,l1cFact)
+        TITLE2 += '\n\mu factor = {}; '.format(muMult)
+        TITLE2 +=   '\mu-max={}'.format(muMax)
+
+      plt.figure(1)
+      plt.clf()
+      plt.errorbar(x_axis, meansTr, stdsTr, marker = '.', label='Training')
+      plt.errorbar(x_axis, meansTe, stdsTe, marker = '.', label='Testing')
+      plt.legend()
+      plt.ylim([.05,0.5])
+      plt.xlabel('Epochs')
+      plt.ylabel('Classification Accuracy')
+      plt.title(TITLE+TITLE2)
+      plt.savefig(savePath + expID + comboID + 'allTrials.png')
+
+    ## (3.) Finally, record the best results for each setting.
+    #(3.a) determine the final (nonzero) accuracy:
+    if meansTe[-1] > bestRez[expID]['acc']:# and len(meansTe)>9:
+      # For Plotting
+      bestRez[expID]['xax']     = x_axis
+      bestRez[expID]['meansTr'] = meansTr
+      bestRez[expID]['stdsTr']  = stdsTr
+      bestRez[expID]['meansTe'] = meansTe
+      bestRez[expID]['stdsTe']  = stdsTe
+      bestRez[expID]['numTr']   = numTrials
+      # For later.
+      bestRez[expID]['comboID'] = comboID
+      bestRez[expID]['acc']     = meansTe[-1]
+      bestRez[expID]['lr_weights'] = rez[0].args['lr_weights']
+      bestRez[expID]['bsz'] = rez[0].args['batch_size']
+      if expID.startswith('am'):
+        bestRez[expID]['lr_codes'] = rez[0].args['lr_codes']
+        bestRez[expID]['d_mu']     = rez[0].args['d_mu']
+        bestRez[expID]['muMult']     = rez[0].args['mult_mu']
+        bestRez[expID]['muMax']     = rez[0].args['max_mu']
+
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',   # Backprop-based methods
+          "amAdam1_1_me1":'r',
+          "amAdam5_1_me1":'y',
+          "amAdam1_5_me1":'m',
+          "amAdam5_5_me1":'c'
+          }
+markers = {"sgd":'o', "adam":'o',   
+          "amAdam1_1_me1":'*',
+          "amAdam5_1_me1":'d',
+          "amAdam1_5_me1":'P',
+          "amAdam5_5_me1":'x'
+          }
+plotNames={"amAdam1_1_me1":'AM-Adam11',
+           "amAdam5_1_me1":'AM-Adam51',
+           "amAdam1_5_me1":'AM-Adam15',
+           "amAdam5_5_me1":'AM-Adam55',
+           'sgd':'SGD', 'adam':"Adam"
+          }
+#colors = ['b','g','r','c','m','y','k']
+
+markerSize = 4
+modelName = rez[0].args['model']
+resUsableTitle = modelName
+
+dataName = rez[0].args['dataset']
+trmark='.'
+temark='^'
+numEpochs=10
+######## ALL PLOTS
+for i, xLims in enumerate([[-0.1, numEpochs+0.1]]):
+  for k,yLims in enumerate([[0., 1], [0.6,0.93], [0.85,1]]):
+    plt.figure(i*k + k)
+    plt.clf()
+    colorID=0
+    if yLims[0]>0 or yLims[1]<1 or xLims[1]<numEpochs:
+      titleZoom = ' (zoomed) '
+    else:
+      titleZoom = ''
+    for expID, rez in bestRez.items():
+  #    print('expID = '+expID+': '+str(rez['stdsTr']))
+      if rez['acc']==-1:
+        continue 
+      # Else, add to the axes.
+      plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+                      marker = trmark, color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=plotNames[expID]+' Train')
+      plt.errorbar(rez['xax'], rez['meansTe'], rez['stdsTe'], 
+                      marker = temark, color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=plotNames[expID]+' Valid (N={})'.format(rez['numTr']))
+    plt.xlim(xLims)
+    plt.ylim(yLims)
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.title('Optimization methods using best parameters\n'+resUsableTitle +titleZoom)
+    # Get the legend off of the main plot:
+    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+    plt.savefig(savePath+'BEST-'+modelName+dataName+'lim'+str(i)+str(k)+'.png', bbox_inches='tight')
+
+
+######################################################
+# Finally, print out the best hyperparameters found.
+
+print('***************************************************')
+print('PRINTING OFFICIAL BEST HYPERPARAMETERS FOR '+resUsableTitle)
+for expID, rez in bestRez.items():
+  if rez['acc']==-1:
+    continue 
+  print('--------------------')
+  print('Optimizer = '+expID)
+  print('Batch-size = ' + str(bestRez[expID]['bsz']))
+  print('learning rate (weights) = ' + str(bestRez[expID]['lr_weights']))
+  if expID.startswith('am'):
+    print('learning rate (codes) = ' + str(bestRez[expID]['lr_codes']))
+    print('delta-mu= ' + str(bestRez[expID]['d_mu']))
+    print('mu-Mult= ' + str(bestRez[expID]['muMult']))
+    print('mu-Max= ' + str(bestRez[expID]['muMax']))
+  # I was gonna just save bestRez, but it seems silly to use both pickle
+  #   and ddict() even though pickle is simpler...
+  # Args from an example of the winning parameter combo.
+  winningArgs = allSh[expID][rez['comboID']][0].args
+  SH = ddict(args=winningArgs)
+  # Finally, save it.
+  rezFileName = expID
+  SH._save(saveHPpath + rezFileName, date=False)
+
+
+
+
+
+
+
+
+
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diff --git a/CNN_DEMO/experiments/lenetMnist_18jan/ims/cnnMnistViz19jan.py b/CNN_DEMO/experiments/lenetMnist_18jan/ims/cnnMnistViz19jan.py
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--- /dev/null
+++ b/CNN_DEMO/experiments/lenetMnist_18jan/ims/cnnMnistViz19jan.py
@@ -0,0 +1,423 @@
+"""
+Creates visualizations for CNN hyperparameter search results.
+
+@author Benjamin Cowen
+@date 17 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--saveBestHyperParameterPath', type=str, default=None, metavar='path/to/savedHyperParameters/',
+          help='Path to directory to save the BEST hyperparameters according to the exp.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+p.add_argument('--reduced', type=bool, default=False, metavar='T/F',
+          help='skip methods...')
+
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+else:
+  savePath = args.savePath
+if not os.path.exists(savePath):
+  os.mkdir(savePath)
+# For saving the best hyperparameters
+if args.saveBestHyperParameterPath is None:
+  saveHPpath = loadPaths[0] + "bestHypers/"
+else:
+  saveHPpath = args.saveBestHyperParameterPath
+if not os.path.exists(saveHPpath):
+  os.mkdir(saveHPpath)
+
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('Saving best hyperparameters to '+saveHPpath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"sgd":{}, "adam":{},   # Backprop-based methods
+          "amAdam1_1_me1":{},
+          "amAdam5_1_me1":{},
+          "amAdam1_5_me1":{},
+          "amAdam5_5_me1":{}
+         }
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    if not hasattr(DD,'perf'):
+      continue
+
+    # Backprop-type.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    # Altmin-type.
+    elif algBase=='altmin':
+      nit1 = str(DD.args['n_iter_codes'])
+      nit2 = str(DD.args['n_iter_weights'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit1 + '_' + nit2 + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Now we have to determine the parameter Combination ID.
+    # Location of the first digit of Combo ID. (comes right after "combo" string.)
+    numStart = fileName.find('combo')+len('combo')  
+    n        = 1               # Number of digits in the Combo ID.
+    nextChar = fileName[numStart+n]
+    while not(nextChar=="t"):  # Scrolling thru filename until the number ends.
+      n += 1
+      nextChar = fileName[numStart+n]
+    comboNum = fileName[numStart:(numStart+n)]   # this better be an integer combo ID :)
+    useKey   = "combo"+ comboNum +'_'+ str(k)        # Combination ID !!!
+
+    # Finally, add it to results. If it's a new key, make it. 
+    #  If it's just new trial, append it
+    if useKey in allSh[expID]:
+      # Append
+      allSh[expID][useKey].append(ddict()._load(filepath))
+    else:
+      # Or make new
+      allSh[expID][useKey] = [ddict()._load(filepath)]
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+############################################
+# Print loss figures -----------------------
+bestRez  = {}
+allMeanTes={}
+for expID, sh in allSh.items():
+  bestRez[expID] = {"acc":-1} # Initialize each Exp ID.
+  allMeanTes[expID] = {}
+  for comboID, rez in sh.items():
+    numTrials       = len(rez)
+    numFailedTrials = 0
+    
+    # (0.) Collect the Training/Testing histories.
+    # Compute the mean and std dev across trials.
+    numEpochs = rez[0].args['epochs']              #should be same for all trials.
+    allTr = None
+    allTe = None
+    for trial in range(numTrials):
+      # Shelf for current trial.
+      tShelf = rez[trial]
+
+      # CHECK VALIDITY:-----------------
+      # If no 'perf' field, then performance wasn't yet recorded.
+      if not hasattr(tShelf,'perf'):
+        continue
+      else:
+        expData = tShelf.perf
+
+      #-------------------------------------  
+      # If the number of recorded epochs << number allowed epochs,
+      #  then add zeros to the end. EG force the length to be numEpochs.
+      currTrAcc = th.Tensor(expData['tr'])
+      currTeAcc = th.Tensor(expData['te'])
+
+      # Double check failure to train.
+#      if currTeAcc.mean()<0.2:
+#        numFailedTrials+=1
+#        continue
+      if len(expData['tr'])<=2:
+        continue
+      # Otherwise you can still visualize incomplete results.
+      if (len(expData['tr'])< numEpochs): 
+        currTrAcc = th.cat([currTrAcc, th.zeros(numEpochs-len(expData['tr']))])
+        currTeAcc = th.cat([currTeAcc, th.zeros(numEpochs-len(expData['te']))])
+
+      # If it checks out, extract the data.
+      currTrAcc = currTrAcc.view(numEpochs, 1)
+      currTeAcc = currTeAcc.view(numEpochs, 1)
+
+      # Append into a numpy array so we can get mean/std later.
+      if allTr is None:
+        allTr = currTrAcc
+        allTe = currTeAcc
+      else:
+        allTr = th.cat([allTr, currTrAcc], 1)
+        allTe = th.cat([allTe, currTeAcc], 1)
+      # End of loop through the trials.
+    # If allTr is None, NO trials were valid (for various reasons).
+    # if numFailedTrials > numTrials/2, then more than half
+    #    of the trials failed due to initialization randomness.
+    if allTr is None or (numFailedTrials > numTrials/2):
+      continue
+    # (1.) Else, compute mean and std devs.
+    # Update the number of trials actually used in this analysis.
+    numTrials = allTr.size(1)
+    # Only average the nonzero entries.
+    meansTr = (allTr.sum(1)/allTr.gt(0).sum(1).float())
+    meansTe = (allTe.sum(1)/allTe.gt(0).sum(1).float())
+    # Initialize with zero.
+    stdsTr  = th.zeros(meansTr.size()).numpy()
+    stdsTe  = th.zeros(meansTe.size()).numpy()
+    # If there were actually multiple trials, fill in using NONZERO entries only.
+    if numTrials>1:
+      for epoch, tnsr in enumerate(allTr):
+        # This is my stupid way of doing ONLY NONZERO std dev's...
+        stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+        if np.isnan(stdtr):
+          stdtr=np.array([0])
+        stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+        if np.isnan(stdte):
+          stdte=np.array([0])
+        # Finally...
+        stdsTr[epoch] = stdtr
+        stdsTe[epoch] = stdte
+    meansTr = meansTr.numpy()
+    meansTe = meansTe.numpy()
+
+
+    # (1.5) for plotting, we REMOVE the zeros added during mean-computation.
+    while np.isnan(meansTr[-1]) or np.isnan(meansTe[-1]) or meansTr[-1]<1e-6 or meansTe[-1]<1e-6:
+      meansTr = meansTr[:-1]
+      meansTe = meansTe[:-1]
+      stdsTr  = stdsTr[:-1]
+      stdsTe  = stdsTe[:-1]
+
+    if epoch0_meanTr is not None:
+      meansTr = np.insert(meansTr, 0, epoch0_meanTr)
+      meansTe = np.insert(meansTe, 0, epoch0_meanTe)
+      stdsTr = np.insert(stdsTr, 0, epoch0_stdTr)
+      stdsTe = np.insert(stdsTe, 0, epoch0_stdTe)
+      x_axis = range(0, len(meansTr))
+    else:
+      x_axis = range(1, len(meansTr)+1)
+
+    allMeanTes[expID][comboID]=meansTe
+    ## (2.) If requested, plot the results for each parameter combination.
+    if not args.onlyBest:
+#    if expID=='amAdam5_me1':
+      # (2.a) Extract information about this setting for titles etc.
+      ar = rez[0].args
+      # Model Size.
+
+      # Alg name.
+      algName = ar['opt_method']
+      modelName=ar['model']
+      TITLE  =modelName+' trained with '+algName
+
+      # Print other hyperparameters
+      LR      = ar['lr_weights']
+
+      if algName in ['sgd', 'adam']:
+        l1_w    = ar['lambda_w']
+        TITLE2  = '\nWeight LR={}, L1={}; '.format(LR,l1_w)
+      else:
+        algName += '-{}_me{}'.format(ar['n_iter_weights'] , ar['mini_epochs'])
+        codeLR  = ar['lr_codes']
+        l1_c    = ar['lambda_c']
+        muMult  = ar['mult_mu']
+        muMax   = ar['max_mu']
+        l1wFact = ar['lambda_w_muFact']
+        l1cFact = ar['lambda_c_muFact']
+
+        TITLE2  = '\nWeight LR={},L1={}\mu; '.format(LR,l1wFact)
+        TITLE2 +=     'Code LR={},L1={}\mu; '.format(codeLR,l1cFact)
+        TITLE2 += '\n\mu factor = {}; '.format(muMult)
+        TITLE2 +=   '\mu-max={}'.format(muMax)
+
+      plt.figure(1)
+      plt.clf()
+      plt.errorbar(x_axis, meansTr, stdsTr, marker = '.', label='Training')
+      plt.errorbar(x_axis, meansTe, stdsTe, marker = '.', label='Testing')
+      plt.legend()
+      plt.ylim([.8,1])
+      plt.xlabel('Epochs')
+      plt.ylabel('Classification Accuracy')
+      plt.title(TITLE+TITLE2)
+      plt.savefig(savePath + expID + comboID + 'allTrials.png')
+
+    ## (3.) Finally, record the best results for each setting.
+    #(3.a) determine the final (nonzero) accuracy:
+    if meansTe[-1] > bestRez[expID]['acc']:# and len(meansTe)>9:
+      # For Plotting
+      bestRez[expID]['xax']     = x_axis
+      bestRez[expID]['meansTr'] = meansTr
+      bestRez[expID]['stdsTr']  = stdsTr
+      bestRez[expID]['meansTe'] = meansTe
+      bestRez[expID]['stdsTe']  = stdsTe
+      bestRez[expID]['numTr']   = numTrials
+      # For later.
+      bestRez[expID]['comboID'] = comboID
+      bestRez[expID]['acc']     = meansTe[-1]
+      bestRez[expID]['lr_weights'] = rez[0].args['lr_weights']
+      bestRez[expID]['bsz'] = rez[0].args['batch_size']
+      if expID.startswith('am'):
+        bestRez[expID]['lr_codes'] = rez[0].args['lr_codes']
+        bestRez[expID]['d_mu']     = rez[0].args['d_mu']
+        bestRez[expID]['muMult']     = rez[0].args['mult_mu']
+        bestRez[expID]['muMax']     = rez[0].args['max_mu']
+
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',   # Backprop-based methods
+          "amAdam1_1_me1":'r',
+          "amAdam5_1_me1":'y',
+          "amAdam1_5_me1":'m',
+          "amAdam5_5_me1":'c'
+          }
+markers = {"sgd":'o', "adam":'o',   
+          "amAdam1_1_me1":'*',
+          "amAdam5_1_me1":'d',
+          "amAdam1_5_me1":'P',
+          "amAdam5_5_me1":'x'
+          }
+plotNames={"amAdam1_1_me1":'AM-Adam11',
+           "amAdam5_1_me1":'AM-Adam51',
+           "amAdam1_5_me1":'AM-Adam15',
+           "amAdam5_5_me1":'AM-Adam55',
+           'sgd':'SGD', 'adam':"Adam"
+          }
+#colors = ['b','g','r','c','m','y','k']
+
+markerSize = 4
+modelName = rez[0].args['model']
+resUsableTitle = modelName
+
+dataName = rez[0].args['dataset']
+trmark='.'
+temark='^'
+numEpochs=10
+######## ALL PLOTS
+for i, xLims in enumerate([[-0.1, numEpochs+0.1]]):
+  for k,yLims in enumerate([[0., 1], [0.6,0.93], [0.85,1], [0.75,1]]):
+    plt.figure(i*k + k)
+    plt.clf()
+    colorID=0
+    if yLims[0]>0 or yLims[1]<1 or xLims[1]<numEpochs:
+      titleZoom = ' (zoomed) '
+    else:
+      titleZoom = ''
+    for expID, rez in bestRez.items():
+  #    print('expID = '+expID+': '+str(rez['stdsTr']))
+      if rez['acc']==-1:
+        continue 
+      # Else, add to the axes.
+      plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+                      marker = trmark, color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=plotNames[expID]+' Train')
+      plt.errorbar(rez['xax'], rez['meansTe'], rez['stdsTe'], 
+                      marker = temark, color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=plotNames[expID]+' Valid (N={})'.format(rez['numTr']))
+    plt.xlim(xLims)
+    plt.ylim(yLims)
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.title('Optimization methods using best parameters\n'+resUsableTitle +titleZoom)
+    # Get the legend off of the main plot:
+    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+    plt.savefig(savePath+'BEST-'+modelName+dataName+'lim'+str(i)+str(k)+'.png', bbox_inches='tight')
+
+
+######################################################
+# Finally, print out the best hyperparameters found.
+
+print('***************************************************')
+print('PRINTING OFFICIAL BEST HYPERPARAMETERS FOR '+resUsableTitle)
+for expID, rez in bestRez.items():
+  if rez['acc']==-1:
+    continue 
+  print('--------------------')
+  print('Optimizer = '+expID)
+  print('Batch-size = ' + str(bestRez[expID]['bsz']))
+  print('learning rate (weights) = ' + str(bestRez[expID]['lr_weights']))
+  if expID.startswith('am'):
+    print('learning rate (codes) = ' + str(bestRez[expID]['lr_codes']))
+    print('delta-mu= ' + str(bestRez[expID]['d_mu']))
+    print('mu-Mult= ' + str(bestRez[expID]['muMult']))
+    print('mu-Max= ' + str(bestRez[expID]['muMax']))
+  # I was gonna just save bestRez, but it seems silly to use both pickle
+  #   and ddict() even though pickle is simpler...
+  # Args from an example of the winning parameter combo.
+  winningArgs = allSh[expID][rez['comboID']][0].args
+  SH = ddict(args=winningArgs)
+  # Finally, save it.
+  rezFileName = expID
+  SH._save(saveHPpath + rezFileName, date=False)
+
+
+
+
+
+
+
+
+
diff --git a/CNN_DEMO/experiments/lenetMnist_18jan/ims/rnnviz17jan.py b/CNN_DEMO/experiments/lenetMnist_18jan/ims/rnnviz17jan.py
new file mode 100644
index 0000000..a138b3b
--- /dev/null
+++ b/CNN_DEMO/experiments/lenetMnist_18jan/ims/rnnviz17jan.py
@@ -0,0 +1,453 @@
+"""
+Creates visualizations for RNN hyperparameter search results.
+
+@author Benjamin Cowen
+@date 17 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--saveBestHyperParameterPath', type=str, default=None, metavar='path/to/savedHyperParameters/',
+          help='Path to directory to save the BEST hyperparameters according to the exp.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+p.add_argument('--reduced', type=bool, default=False, metavar='T/F',
+          help='skip methods...')
+
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+else:
+  savePath = args.savePath
+if not os.path.exists(savePath):
+  os.mkdir(savePath)
+# For saving the best hyperparameters
+if args.saveBestHyperParameterPath is None:
+  saveHPpath = loadPaths[0] + "bestHypers/"
+else:
+  saveHPpath = args.saveBestHyperParameterPath
+if not os.path.exists(saveHPpath):
+  os.mkdir(saveHPpath)
+
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('Saving best hyperparameters to '+saveHPpath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"sgd":{}, "adam":{},   # Backprop-based methods
+          "amAdam1_me1":{},
+         }
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    if not hasattr(DD,'perf'):
+      continue
+
+    # Backprop-type.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    # Altmin-type.
+    elif algBase=='altmin':
+      nit = str(DD.args['n_iter_codes'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Now we have to determine the parameter Combination ID.
+    # Location of the first digit of Combo ID. (comes right after "combo" string.)
+    numStart = fileName.find('combo')+len('combo')  
+    n        = 1               # Number of digits in the Combo ID.
+    nextChar = fileName[numStart+n]
+    while not(nextChar=="t"):  # Scrolling thru filename until the number ends.
+      n += 1
+      nextChar = fileName[numStart+n]
+    comboNum = fileName[numStart:(numStart+n)]   # this better be an integer combo ID :)
+    useKey   = "combo"+ comboNum +'_'+ str(k)        # Combination ID !!!
+
+    # Finally, add it to results. If it's a new key, make it. 
+    #  If it's just new trial, append it
+    if useKey in allSh[expID]:
+      # Append
+      allSh[expID][useKey].append(ddict()._load(filepath))
+    else:
+      # Or make new
+      allSh[expID][useKey] = [ddict()._load(filepath)]
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+############################################
+# Print loss figures -----------------------
+bestRez  = {}
+allMeanTes={}
+for expID, sh in allSh.items():
+  bestRez[expID] = {"acc":-1} # Initialize each Exp ID.
+  allMeanTes[expID] = {}
+  for comboID, rez in sh.items():
+    numTrials       = len(rez)
+    numFailedTrials = 0
+    
+    # (0.) Collect the Training/Testing histories.
+    # Compute the mean and std dev across trials.
+    numEpochs = rez[0].args['epochs']              #should be same for all trials.
+    allTr = None
+    allTe = None
+    for trial in range(numTrials):
+      # Shelf for current trial.
+      tShelf = rez[trial]
+
+      # CHECK VALIDITY:-----------------
+      # If no 'perf' field, then performance wasn't yet recorded.
+      if not hasattr(tShelf,'perf'):
+        continue
+      else:
+        expData = tShelf.perf
+
+      #-------------------------------------  
+      # If the number of recorded epochs << number allowed epochs,
+      #  then add zeros to the end. EG force the length to be numEpochs.
+      currTrAcc = th.Tensor(expData['tr'])
+      currTeAcc = th.Tensor(expData['te'])
+
+      # Double check failure to train.
+#      if currTeAcc.mean()<0.2:
+#        numFailedTrials+=1
+#        continue
+      if len(expData['tr'])<=2:
+        continue
+      # Otherwise you can still visualize incomplete results.
+      if (len(expData['tr'])< numEpochs): 
+        currTrAcc = th.cat([currTrAcc, th.zeros(numEpochs-len(expData['tr']))])
+        currTeAcc = th.cat([currTeAcc, th.zeros(numEpochs-len(expData['te']))])
+
+      # If it checks out, extract the data.
+      currTrAcc = currTrAcc.view(numEpochs, 1)
+      currTeAcc = currTeAcc.view(numEpochs, 1)
+
+      # Append into a numpy array so we can get mean/std later.
+      if allTr is None:
+        allTr = currTrAcc
+        allTe = currTeAcc
+      else:
+        allTr = th.cat([allTr, currTrAcc], 1)
+        allTe = th.cat([allTe, currTeAcc], 1)
+      # End of loop through the trials.
+    # If allTr is None, NO trials were valid (for various reasons).
+    # if numFailedTrials > numTrials/2, then more than half
+    #    of the trials failed due to initialization randomness.
+    if allTr is None or (numFailedTrials > numTrials/2):
+      continue
+    # (1.) Else, compute mean and std devs.
+    # Update the number of trials actually used in this analysis.
+    numTrials = allTr.size(1)
+    # Only average the nonzero entries.
+    meansTr = (allTr.sum(1)/allTr.gt(0).sum(1).float())
+    meansTe = (allTe.sum(1)/allTe.gt(0).sum(1).float())
+    # Initialize with zero.
+    stdsTr  = th.zeros(meansTr.size()).numpy()
+    stdsTe  = th.zeros(meansTe.size()).numpy()
+    # If there were actually multiple trials, fill in using NONZERO entries only.
+    if numTrials>1:
+      for epoch, tnsr in enumerate(allTr):
+        # This is my stupid way of doing ONLY NONZERO std dev's...
+        stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+        if np.isnan(stdtr):
+          stdtr=np.array([0])
+        stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+        if np.isnan(stdte):
+          stdte=np.array([0])
+        # Finally...
+        stdsTr[epoch] = stdtr
+        stdsTe[epoch] = stdte
+    meansTr = meansTr.numpy()
+    meansTe = meansTe.numpy()
+
+
+    # (1.5) for plotting, we REMOVE the zeros added during mean-computation.
+    while np.isnan(meansTr[-1]) or np.isnan(meansTe[-1]) or meansTr[-1]<1e-6 or meansTe[-1]<1e-6:
+      meansTr = meansTr[:-1]
+      meansTe = meansTe[:-1]
+      stdsTr  = stdsTr[:-1]
+      stdsTe  = stdsTe[:-1]
+
+    if epoch0_meanTr is not None:
+      meansTr = np.insert(meansTr, 0, epoch0_meanTr)
+      meansTe = np.insert(meansTe, 0, epoch0_meanTe)
+      stdsTr = np.insert(stdsTr, 0, epoch0_stdTr)
+      stdsTe = np.insert(stdsTe, 0, epoch0_stdTe)
+      x_axis = range(0, len(meansTr))
+    else:
+      x_axis = range(1, len(meansTr)+1)
+
+    allMeanTes[expID][comboID]=meansTe
+    ## (2.) If requested, plot the results for each parameter combination.
+    if not args.onlyBest:
+#    if expID=='amAdam5_me1':
+      # (2.a) Extract information about this setting for titles etc.
+      ar = rez[0].args
+      # Model Size.
+      hidSize = str(ar['rnnHidSize'])
+
+      # Alg name.
+      algName = ar['opt_method']
+      TITLE  = 'RNN 1-'+hidSize +'; trained with '+algName
+
+      # Print other hyperparameters
+      LR      = ar['lr_weights']
+
+      if algName in ['sgd', 'adam']:
+        l1_w    = ar['lambda_w']
+        TITLE2  = '\nWeight LR={}, L1={}; '.format(LR,l1_w)
+      else:
+        algName += '-{}_me{}'.format(ar['n_iter_weights'] , ar['mini_epochs'])
+        codeLR  = ar['lr_codes']
+        l1_c    = ar['lambda_c']
+        muMult  = ar['mult_mu']
+        muMax   = ar['max_mu']
+        l1wFact = ar['lambda_w_muFact']
+        l1cFact = ar['lambda_c_muFact']
+
+        TITLE2  = '\nWeight LR={},L1={}\mu; '.format(LR,l1wFact)
+        TITLE2 +=     'Code LR={},L1={}\mu; '.format(codeLR,l1cFact)
+        TITLE2 += '\n\mu factor = {}; '.format(muMult)
+        TITLE2 +=   '\mu-max={}'.format(muMax)
+
+      plt.figure(1)
+      plt.clf()
+      plt.errorbar(x_axis, meansTr, stdsTr, marker = '.', label='Training')
+      plt.errorbar(x_axis, meansTe, stdsTe, marker = '.', label='Testing')
+      plt.legend()
+      plt.ylim([.05,0.5])
+      plt.xlabel('Epochs')
+      plt.ylabel('Classification Accuracy')
+      plt.title(TITLE+TITLE2)
+      plt.savefig(savePath + expID + comboID + 'allTrials.png')
+
+    ## (3.) Finally, record the best results for each setting.
+    #(3.a) determine the final (nonzero) accuracy:
+    if meansTe[-1] > bestRez[expID]['acc']:# and len(meansTe)>9:
+      # For Plotting
+      bestRez[expID]['xax']     = x_axis
+      bestRez[expID]['meansTr'] = meansTr
+      bestRez[expID]['meansTe'] = meansTe
+      bestRez[expID]['stdsTr']  = stdsTr
+      bestRez[expID]['stdsTe']  = stdsTe
+      bestRez[expID]['numTr']   = numTrials
+      # For later.
+      bestRez[expID]['comboID'] = comboID
+      bestRez[expID]['acc']     = meansTe[-1]
+      bestRez[expID]['lr_weights'] = rez[0].args['lr_weights']
+      if expID.startswith('am'):
+        bestRez[expID]['lr_codes'] = rez[0].args['lr_codes']
+        bestRez[expID]['d_mu']     = rez[0].args['d_mu']
+        bestRez[expID]['muMult']     = rez[0].args['mult_mu']
+        bestRez[expID]['muMax']     = rez[0].args['max_mu']
+
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+hidSize = str(100)#str(rez[0].args['rnnHidSize'])
+
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',   # Backprop-based methods
+            "amAdam5_me1":'r',      # 5 subproblem iter, 1 miniepoch.
+            "amAdam5_me2":'r',      # 5 subproblem iter, 2 miniepoch.
+            "amAdam5_me3":'r',      # 5 subproblem iter, 3 miniepoch.
+            "amAdam1_me1":'y',      # 1 subproblem iter, 1 miniepoch.
+            "amAdam1_me2":'y',      # 1 subproblem iter, 2 miniepoch.
+            "amAdam1_me5":'y',      # 1 subproblem iter, 5 miniepoch.
+            "amAdam20_me1":'m',     # 20 subproblem iter (codes only), 1 miniepoch.
+            "amAdam15_me2":'m',     # 1 subproblem iter (codes only), 2 miniepoch.
+            "amAdam15_me3":'m',     # 15 subproblem iter (codes only), 3 miniepoch.
+            "amAdam15_me5":'m',     # 15 subproblem iter (codes only), 5 miniepoch.
+          }
+markers = {"sgd":'o', "adam":'o',   # Backprop-based methods
+            "amAdam5_me1":'o',      # 5 subproblem iter, 1 miniepoch.
+            "amAdam1_me1":'o',      # 1 subproblem iter, 1 miniepoch.
+            "amAdam20_me1":'o',     # 20 subproblem iter (codes only), 1 miniepoch.
+            "amAdam5_me2":'*',      # 5 subproblem iter, 2 miniepoch.
+            "amAdam1_me2":'*',      # 1 subproblem iter, 2 miniepoch.
+            "amAdam15_me2":'*',     # 1 subproblem iter (codes only), 2 miniepoch.
+            "amAdam5_me3":'^',      # 5 subproblem iter, 3 miniepoch.
+            "amAdam15_me3":'^',     # 15 subproblem iter (codes only), 3 miniepoch.
+            "amAdam1_me5":'_',      # 1 subproblem iter, 5 miniepoch.
+            "amAdam15_me5":'_',     # 15 subproblem iter (codes only), 5 miniepoch.
+          }
+
+#colors = ['b','g','r','c','m','y','k']
+
+markerSize = 4
+resUsableTitle = 'no-skip RNN: 1-'+hidSize
+numEpochs=10
+######## ALL PLOTS
+for i, xLims in enumerate([[-0.1, numEpochs+0.1]]):
+  for k,yLims in enumerate([[0.05, 0.5]]):
+    plt.figure(i*k + k)
+    plt.clf()
+    colorID=0
+    if yLims[0]>0 or yLims[1]<1 or xLims[1]<numEpochs:
+      titleZoom = ' (zoomed) '
+    else:
+      titleZoom = ''
+    for expID, rez in bestRez.items():
+  #    print('expID = '+expID+': '+str(rez['stdsTr']))
+      if rez['acc']==-1:
+        continue 
+      # Else, add to the axes.
+#      plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                      marker = trmark, color=colors[expID],
+#                      markersize = markerSize, linewidth = 1,
+#                      label=plotNames[expID]+' Train')
+      plt.errorbar(rez['xax'], rez['meansTe'], rez['stdsTe'], 
+                      marker = markers[expID], color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=expID+' Test (N={})'.format(rez['numTr']))
+    plt.xlim(xLims)
+    plt.ylim(yLims)
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.title('Optimization methods using best parameters\n'+resUsableTitle +titleZoom)
+    # Get the legend off of the main plot:
+    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+    plt.savefig(savePath+'ALL_METHODS_best_jan2_1-'+str(hidSize)+'lim'+str(i)+str(k)+'.png', bbox_inches='tight')
+
+
+######## RELEVANT PLOTS
+keepThese = ['amAdam5_me1','amAdam1_me1', 'amAdam1_me2','sgd', 'adam',]
+for i, xLims in enumerate([[-0.1, numEpochs+0.1]]):
+  for k,yLims in enumerate([[0.05, 0.5]]):
+    plt.figure(i*k + k)
+    plt.clf()
+    colorID=0
+    if yLims[0]>0 or yLims[1]<1 or xLims[1]<numEpochs:
+      titleZoom = ' (zoomed) '
+    else:
+      titleZoom = ''
+    for expID, rez in bestRez.items():
+  #    print('expID = '+expID+': '+str(rez['stdsTr']))
+      if rez['acc']==-1 or not (expID in keepThese):
+        continue 
+      # Else, add to the axes.
+#      plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                      marker = trmark, color=colors[expID],
+#                      markersize = markerSize, linewidth = 1,
+#                      label=plotNames[expID]+' Train')
+      plt.errorbar(rez['xax'], rez['meansTe'], rez['stdsTe'], 
+                      marker = markers[expID], color=colors[expID],
+                      markersize = markerSize, linewidth = 1,
+                      label=expID+' Test (N={})'.format(rez['numTr']))
+    plt.xlim(xLims)
+    plt.ylim(yLims)
+    plt.xlabel('Epochs')
+    plt.ylabel('Accuracy')
+    plt.title('Optimization methods using best parameters\n'+resUsableTitle +titleZoom)
+    # Get the legend off of the main plot:
+    plt.legend(loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+    plt.savefig(savePath+'RELEVANT_best_jan2_1-'+str(hidSize)+'lim'+str(i)+str(k)+'.png', bbox_inches='tight')
+
+######################################################
+# Finally, print out the best hyperparameters found.
+
+print('***************************************************')
+print('PRINTING OFFICIAL BEST HYPERPARAMETERS FOR '+resUsableTitle)
+for expID, rez in bestRez.items():
+  if rez['acc']==-1:
+    continue 
+  print('--------------------')
+  print('Optimizer = '+expID)
+  print('learning rate (weights) = ' + str(bestRez[expID]['lr_weights']))
+  if expID.startswith('am'):
+    print('learning rate (codes) = ' + str(bestRez[expID]['lr_codes']))
+    print('delta-mu= ' + str(bestRez[expID]['d_mu']))
+    print('mu-Mult= ' + str(bestRez[expID]['muMult']))
+    print('mu-Max= ' + str(bestRez[expID]['muMax']))
+  # I was gonna just save bestRez, but it seems silly to use both pickle
+  #   and ddict() even though pickle is simpler...
+  # Args from an example of the winning parameter combo.
+  winningArgs = allSh[expID][rez['comboID']][0].args
+  SH = ddict(args=winningArgs)
+  # Finally, save it.
+  rezFileName = expID
+  SH._save(saveHPpath + rezFileName, date=False)
+
+
+
+
+
+
+
+
+
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diff --git a/CNN_DEMO/experiments/sgd_lenetMnist_18jan.sh b/CNN_DEMO/experiments/sgd_lenetMnist_18jan.sh
new file mode 100755
index 0000000..e9f1706
--- /dev/null
+++ b/CNN_DEMO/experiments/sgd_lenetMnist_18jan.sh
@@ -0,0 +1,56 @@
+#!/bin/bash
+
+# Which GPU to run it on?
+gpuID=2
+
+# Where to save results?
+savePath='./lenetMnist_18jan/'
+
+# These are fixed for all experiments.
+max_epochs=10
+dataName='mnist'
+model='lenet'
+numTrials=100
+# It seems like I have to set this to get a preliminary result...?
+save_interval=2000
+
+# batches/epoch = (600, 120, 6)
+bszs=(128)
+
+# weight learning rates for batchsize 50
+#             12   123   1234   12345
+weight_LRs=(0.02 0.002 0.0002 0.00002)
+
+for ((trial=1; trial<=numTrials; trial++)); do
+  # Get trial ID
+  trialNum=$trial #((gpuID*numTrials + trial))
+  trialID='trial'$trialNum
+
+  # Reset Counters
+  comboInc=0
+
+  for BSZ in "${bszs[@]}"; do
+    for weightLR in ${weight_LRs[@]}; do
+
+      if [ $trial -gt 2 ];
+      then
+        # Compute parameter combination ID
+        let "(comboInc++)"
+        comboID='combo'$comboInc
+        
+        # Compute save name
+        savename=$savePath'sgd_'$comboID$trialID
+        
+        # Run SGD experiment!
+        CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_cnn_14jan.py --opt-method='sgd' --dataset=$dataName --seed=$trialNum --lr-weights=$weightLR --epochs=$max_epochs --save-filename=$savename --save-interval=$save_interval --model=$model --batch-size=$BSZ --use-validation-size=10000
+  
+  
+        # Compute save name
+        savename=$savePath'adam_'$comboID$trialID
+  
+        # Run alt-min experiment!
+        CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_cnn_14jan.py --opt-method='adam' --dataset=$dataName --seed=$trialNum --lr-weights=$weightLR --epochs=$max_epochs --save-filename=$savename --save-interval=$save_interval --model=$model --batch-size=$BSZ --use-validation-size=10000
+      fi
+    done
+  done
+done
diff --git a/CNN_DEMO/models.py b/CNN_DEMO/models.py
new file mode 100644
index 0000000..c4cf35c
--- /dev/null
+++ b/CNN_DEMO/models.py
@@ -0,0 +1,288 @@
+from __future__ import print_function, division
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class LinMod(nn.Linear):
+    '''Linear modules with or without batchnorm, all in one module
+    '''
+    def __init__(self, n_inputs, n_outputs, bias=False, batchnorm=False):
+        super(LinMod, self).__init__(n_inputs, n_outputs, bias=bias)
+        if batchnorm:
+            self.bn = nn.BatchNorm1d(n_outputs, affine=False)
+
+        self.n_inputs = n_inputs
+        self.n_outputs = n_outputs
+        self.batchnorm = batchnorm
+        self.bias_flag = bias
+
+    def forward(self, inputs):
+        outputs = super(LinMod, self).forward(inputs)
+        if hasattr(self, 'bn'):
+            outputs = self.bn(outputs)
+        return outputs
+
+    def extra_repr(self):
+        return '{n_inputs}, {n_outputs}, bias={bias_flag}, batchnorm={batchnorm}'.format(**self.__dict__)
+
+
+class FFNet(nn.Module):
+    '''Feed-forward all-to-all connected network
+    '''
+    def __init__(self, n_inputs, n_hiddens, n_hidden_layers=2, n_outputs=10, nlin=nn.ReLU, bias=False, batchnorm=False):
+        super(FFNet, self).__init__()
+
+        self.features = () # Skip convolutional features
+
+        self.classifier = nn.Sequential(LinMod(n_inputs, n_hiddens, bias=bias, batchnorm=batchnorm), nlin())
+        for i in range(n_hidden_layers-1):
+            self.classifier.add_module(str(2*i+2), LinMod(n_hiddens, n_hiddens, bias=bias, batchnorm=batchnorm))
+            self.classifier.add_module(str(2*i+3), nlin())
+        self.classifier.add_module(str(len(self.classifier)), nn.Linear(n_hiddens, n_outputs))
+
+        self.batchnorm = batchnorm
+        self.n_inputs = n_inputs
+        self.n_hiddens = n_hiddens
+        self.n_outputs = n_outputs
+
+    def forward(self, x):
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+
+def test(model, data_loader, criterion=nn.CrossEntropyLoss(), label=''):
+    '''Compute model accuracy
+    '''
+    model.eval()
+    device = next(model.parameters()).device
+
+    # Added by Ben 11 Jan for Validation stuff...
+    if hasattr(data_loader, 'numSamples'):
+      N = data_loader.numSamples
+    else:
+      N = len(data_loader.dataset)
+    test_loss, correct = 0.0, 0.0
+    with torch.no_grad():
+        for data, target in data_loader:
+            data, target = data.to(device), target.to(device)
+            output = model(data)
+            if isinstance(output, tuple):
+                output = output[0]
+            test_loss += criterion(output, target).item()
+            pred = output.data.max(1)[1] # get the index of the max log-probability
+            correct += pred.eq(target.data).cpu().sum()
+
+    accuracy = float(correct)/N
+    test_loss /= len(data_loader) # loss function already averages over batch size
+    if label:
+        print('{}: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
+            label, test_loss, correct, N, 100. * accuracy ))
+    return accuracy
+
+
+class LeNet(nn.Module):
+    '''Based on https://github.com/kuangliu/pytorch-cifar/blob/master/models/lenet.py
+    '''
+    def __init__(self, num_input_channels=3, num_classes=10, window_size=32, bias=True):
+        super(LeNet, self).__init__()
+        self.bias = bias
+        self.window_size = window_size
+        self.features = nn.Sequential(
+            nn.Conv2d(num_input_channels, 6, 5, bias=bias),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+
+            nn.Conv2d(6, 16, 5, bias=bias),
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2),
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(16 * int((int((window_size-4)/2)-4)/2)**2, 120, bias=bias),
+            nn.ReLU(),
+            nn.Linear(120, 84, bias=bias),
+            nn.ReLU(),
+            nn.Linear(84, num_classes, bias=bias),
+        )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+
+class VGG7(nn.Module):
+
+    def __init__(self, num_input_channels=3, num_classes=10, window_size=32, init_weights=True, bias=True):
+        super(VGG7, self).__init__()
+        self.bias = bias
+        self.window_size = window_size
+        self.features = nn.Sequential(
+            nn.Conv2d(num_input_channels, 128, kernel_size=3, padding=1, bias=bias), # 128C3
+            nn.ReLU(),
+            nn.Conv2d(128, 128, kernel_size=3, padding=1, bias=bias), # 128C3
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),       # MP2
+
+            nn.Conv2d(128, 256, kernel_size=3, padding=1, bias=bias), # 256C3
+            nn.ReLU(),
+            nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=bias), # 256C3
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),       # MP2
+
+            nn.Conv2d(256, 512, kernel_size=3, padding=1, bias=bias), # 512C3
+            nn.ReLU(),
+            nn.Conv2d(512, 512, kernel_size=3, padding=1, bias=bias), # 512C3
+            nn.ReLU(),
+            nn.MaxPool2d(kernel_size=2, stride=2),       # MP2
+        )
+        self.classifier = nn.Sequential(
+            nn.Linear(512 * int(window_size/8)**2, 1024, bias=bias),
+            nn.ReLU(),
+            #  nn.Dropout(),
+            nn.Linear(1024, num_classes, bias=bias),
+        )
+        if init_weights:
+            self._initialize_weights()
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+    def _initialize_weights(self):
+        '''VGG initialization fromtorchvision/models/vgg.py
+        '''
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
+                m.weight.data.normal_(0, math.sqrt(2. / n))
+                if m.bias is not None:
+                    m.bias.data.zero_()
+            elif isinstance(m, nn.BatchNorm2d):
+                m.weight.data.fill_(1)
+                m.bias.data.zero_()
+            elif isinstance(m, nn.Linear):
+                m.weight.data.normal_(0, 0.01)
+                m.bias.data.zero_()
+
+
+# ----------------------------------------------
+# ResNet
+# ----------------------------------------------
+def conv3x3(in_planes, out_planes, stride=1):
+    """3x3 convolution with padding"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)
+
+def conv1x1(in_planes, out_planes, stride=1):
+    """1x1 convolution"""
+    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
+
+
+class BasicBlockLin(nn.Module):
+    expansion = 1
+
+    def __init__(self, inplanes, planes, stride=1, downsample=None):
+        super(BasicBlockLin, self).__init__()
+        self.conv1 = conv3x3(inplanes, planes, stride)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = conv3x3(planes, planes)
+        self.bn2 = nn.BatchNorm2d(planes)
+        self.downsample = downsample
+        self.stride = stride
+
+        self.has_codes = True
+
+    def forward(self, x):
+        residual = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            residual = self.downsample(x)
+
+        out += residual
+        #out = self.relu(out)
+        return out
+
+
+class ResNet(nn.Module):
+
+    def __init__(self, layers, num_classes=1000):
+        super(ResNet, self).__init__()
+        self.inplanes = 64
+        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.relu = nn.ReLU()
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+        self.layer1 = self._make_layer(64, layers[0])
+        self.layer2 = self._make_layer(128, layers[1], stride=2)
+        self.layer3 = self._make_layer(256, layers[2], stride=2)
+        self.layer4 = self._make_layer(512, layers[3], stride=2)
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.classifier = nn.Sequential(
+            nn.Linear(512, num_classes)
+        )
+
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+
+        self.features = nn.Sequential(
+            self.conv1,
+            self.bn1,
+            self.relu,
+            self.maxpool,
+
+            *self.layer1,
+            *self.layer2,
+            *self.layer3,
+            *self.layer4,
+
+            self.avgpool
+        )
+
+    def _make_layer(self, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.inplanes != planes:
+            downsample = nn.Sequential(
+                conv1x1(self.inplanes, planes, stride),
+                nn.BatchNorm2d(planes),
+            )
+
+        layers = []
+        layers.append(BasicBlockLin(self.inplanes, planes, stride, downsample))
+        layers.append(nn.ReLU())
+        self.inplanes = planes
+        for _ in range(1, blocks):
+            layers.append(BasicBlockLin(self.inplanes, planes))
+            layers.append(nn.ReLU())
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        x = self.classifier(x)
+        return x
+
+def resnet18(**kwargs):
+    "Constructs a ResNet-18 model."
+    return ResNet([2, 2, 2, 2], **kwargs)
+
+def resnet34(**kwargs):
+    "Constructs a ResNet-34 model."
+    return ResNet([3, 4, 6, 3], **kwargs)
diff --git a/CNN_DEMO/postHPexps/README.md b/CNN_DEMO/postHPexps/README.md
new file mode 100644
index 0000000..d4b0bb5
--- /dev/null
+++ b/CNN_DEMO/postHPexps/README.md
@@ -0,0 +1,5 @@
+This folder is "POST HYPER-PARAMETER SEARCH EXPERIMENTS"
+
+In other words, algorithm hyperparameters are now fixed for each method.
+
+Load hyperparameters from the appropriate folder.
diff --git a/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/TESTING.png b/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/TESTING.png
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diff --git a/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/getTestRez.sh b/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/getTestRez.sh
new file mode 100755
index 0000000..d1106a5
--- /dev/null
+++ b/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/getTestRez.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+
+savePath='./testResults/'
+gpuID=1
+
+max_epochs=10
+numTrials=100
+
+adamArgs='adam.pt'
+sgdArgs='sgd.pt'
+am11Args='amAdam1_me1.pt'
+
+for ((trial=1; trial<=numTrials; trial++)); do
+  # Get trial ID
+  trialNum=$trial #$((gpuID*numTrials + trial))
+  trialID='trial'$trialNum
+
+  saveName=$savePath'amAdam11_'$trialID
+  CUDA_VISIBLE_DEVICES=$gpuID python3 ../../train_cnn_14jan.py --seed=$trialNum --epochs=$max_epochs --loadBestHyperparameters=$am11Args --first-epoch-log --save-filename=$saveName
+
+  # SGD
+  saveName=$savePath'sgd_'$trialID
+  CUDA_VISIBLE_DEVICES=$gpuID python3 ../../train_cnn_14jan.py --seed=$trialNum --epochs=$max_epochs --loadBestHyperparameters=$sgdArgs --first-epoch-log --save-filename=$saveName
+
+  # Adam
+  saveName=$savePath'adam_'$trialID
+  CUDA_VISIBLE_DEVICES=$gpuID python3 ../../train_cnn_14jan.py --seed=$trialNum --epochs=$max_epochs --loadBestHyperparameters=$adamArgs --first-epoch-log --save-filename=$saveName
+done
+
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diff --git a/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/testResults/ims/testRezViz_19jan.py b/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/testResults/ims/testRezViz_19jan.py
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--- /dev/null
+++ b/CNN_DEMO/postHPexps/lenet/lenet_fashionMnist/testResults/ims/testRezViz_19jan.py
@@ -0,0 +1,466 @@
+"""
+Creates visualizations for RNN test results.
+
+@author Benjamin Cowen
+@date 19 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--reduced', type=bool, default=False, metavar='True',
+           help='Set to true if you only want SGD, Adam, and AltMin-Adam.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+  if not os.path.exists(savePath):
+    os.mkdir(savePath)
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"amAdam1_me1":[], 
+          "sgd":[], "adam":[]
+         }
+
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    # If SGD, check whether it's Nesterov.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    elif algBase=='altmin':
+      nit = str(DD.args['n_iter_codes'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Append results.
+    if args.reduced:
+      if not (expID=='sgd' or expID=='adam' or expID=='amAdam5_me1'):
+        continue
+    allSh[expID].append(ddict()._load(filepath))
+
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+# This better be the same for all DD anyway :)
+############################################
+# Print all single-exp. figures -----------------------
+avgRez  = {}
+numE0btch = 469   # Number of elements in 'first_epoch'
+numEpochs = 10   # Number of elements in 'te' and 'tr'
+
+for expID, rez in allSh.items():
+  if len(rez)==0:
+    continue
+  # Initialize each Exp ID.
+  avgRez[expID] = {'tr':[], 'te':[]}
+  numTrials = len(rez)
+
+  # (0.) Collect the Training/Testing histories.
+  # Compute the mean and std dev across trials.
+  allR0 = None
+  for trial in range(numTrials):
+    # Shelf for current trial.
+    tShelf = rez[trial]
+
+    # CHECK VALIDITY:-----------------
+    # If no 'perf' field, then performance wasn't yet recorded.
+    if hasattr(tShelf,'perf'):
+      expData = tShelf.perf
+      r0  = expData['first_epoch']
+      rTr = expData['tr']
+      rTe = expData['te']
+    else:
+      continue
+
+    # If the number of recorded epochs << number allowed epochs, it's in-progress.
+    # If the mean training accurac < 14%, consider this a FAILED attempt at training.
+    if len(rTe)<numEpochs or np.mean(rTr)<0.14 or len(r0)==0:
+      continue
+
+    # Finally, might need to remove FAILED attempts.
+    #----------------------------------
+
+    # If it checks out, extract the data.
+    r0  = th.Tensor(r0).view(numE0btch, 1)
+    rTr = th.Tensor(rTr).view(numEpochs, 1)
+    rTe = th.Tensor(rTe).view(numEpochs, 1)
+
+    # Append into a numpy array so we can get mean/std later.
+    if allR0 is None:
+      allR0 = r0
+      allTr = rTr
+      allTe = rTe
+    else:
+      allR0 = th.cat([allR0, r0], 1)
+      allTr = th.cat([allTr, rTr], 1)
+      allTe = th.cat([allTe, rTe], 1)
+    # End of loop through the trials.
+  # If no trials were valid, skip!
+  if allR0 is None:
+    continue
+
+#  if expID=='nest':
+#    1/0
+
+  # Need this for later...
+  # (1.) Else, compute mean and std devs.
+  # Update the number of trials actually used in this analysis.
+  numTrials = allR0.size(1)
+
+  # There shouldn't be any zero entries anymore...
+  meansR0 = allR0.mean(1).numpy()
+  meansTr = allTr.mean(1).numpy()
+  meansTe = allTe.mean(1).numpy()
+  if numTrials>0:
+    stdsR0 = allR0.std(1).numpy()
+    stdsTr = allTr.std(1).numpy()
+    stdsTe = allTe.std(1).numpy()
+  else:
+    stdsR0 = th.zeros(meansR0.size()).numpy()
+    stdsTr = th.zeros(meansTr.size()).numpy()
+    stdsTe = th.zeros(meansTe.size()).numpy()    
+
+
+  ###############
+  # For epoch-0 results to be tacked on..
+  x_axis = np.array(range(1, len(meansTr)+1))
+#  meansTr = np.insert(meansTr, 0, meansR0[0])
+#  meansTe = np.insert(meansTe, 0, meansR0[0])
+#  stdsTr  = np.insert(stdsTr,  0, stdsR0[0])
+#  stdsTe  = np.insert(stdsTe,  0, stdsR0[0])
+#  x_axis = np.array(range(0, len(meansTr)))
+
+  # Finally divide by the sqrt number of samples:
+  
+  stdsR0  /= np.sqrt(numTrials)
+  stdsTr  /= np.sqrt(numTrials)
+  stdsTe  /= np.sqrt(numTrials)
+  
+  ## (2.) If requested, plot the results for each parameter combination.
+  if not args.onlyBest:
+    # (2.a) Extract information about this setting for titles etc.
+    # These should NOT change for rez[i], i=/=0.
+    LR      = str(rez[0].args['lr_weights'])
+    # Data type (permuted vs sequential mnist)
+    if 'data_type' in rez[0].args:           #This arg was added late so may not exist.
+      dataType = rez[0].args['data_type']
+    else:
+      dataType = 'seq'
+    # Alg name.
+    algName = rez[0].args['opt_method']
+    if algName=='altmin':
+      algName += str(rez[0].args['n_iter_weights'])
+
+    TITLE  = rez[0].args['model']+'; trained with '+expID
+
+    plt.figure(1)
+    plt.clf()
+    plt.errorbar(x_axis, meansTr, stdsTr, marker = 'o', label='Training')
+    plt.errorbar(x_axis, meansTe, stdsTe, marker = '^', label='Testing')
+    plt.legend()
+    plt.xlabel('Epochs')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE)
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'allEpoch.png')
+
+    x_axis0 = np.array(range(0, len(meansR0)))
+    plt.figure(2)
+    plt.clf()
+    plt.errorbar(x_axis0, meansR0, stdsR0, marker = '.', label='Testing')
+    plt.xlabel('Minibatches')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE+'\n(Epoch 0 Minibatches)')
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'minibatchE0.png')
+
+  ## (3.) Finally, record the avg for a big plot at the end.
+  # For Plotting
+  avgRez[expID]['xax']     = x_axis
+  avgRez[expID]['xax0']    = x_axis0
+  avgRez[expID]['meansTr'] = meansTr
+  avgRez[expID]['meansTe'] = meansTe
+  avgRez[expID]['meansR0'] = meansR0
+  avgRez[expID]['stdsTr']  = stdsTr
+  avgRez[expID]['stdsTe']  = stdsTe
+  avgRez[expID]['stdsR0']  = stdsR0
+  avgRez[expID]['numTr']   = numTrials
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',
+           "amAdam1_me1":'r'
+          }
+markers  = {"sgd":'o', "adam":'P',
+           "amAdam1_me1":'^'
+           }
+names  = {"sgd":'SGD', "adam":'Adam',
+          "amAdam1_me1":'AM-Adam'}
+models={'lenet':'LeNet', 'vgg7':'VGG7'}
+datasets={'mnist':'MNIST', 'fashion_mnist':'Fashion-MNIST'}
+#colors = ['b','g','r','c','m','y','k']
+modelName = models[rez[0].args['model']]
+datName=datasets[rez[0].args['dataset']]
+resUsableTitle = datName+', '+modelName
+xtraAx1 = 0.1
+xtraAx2 = 1
+trmark='.'
+temark='^'
+xtras={'adam':1.001, 'amAdam1_me1':1, 'sgd':0.999}
+                        #mnistAll  #fMnistAll   
+for k,lims in enumerate([[0.9, 0.99],[0.675,0.91]]):
+  plt.figure(1)
+  plt.clf()
+  # Plot all results.
+  for expID, rez in avgRez.items():
+    if not ('meansTr' in rez):
+      continue
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+    yvals =rez['meansTe']
+    plt.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                    marker = temark, color=colors[expID],
+                    markersize = 4, linewidth = 1,
+                    label=names[expID]+' (N={})'.format(rez['numTr']))
+    # Plot the final accuracy to the right of the axes.
+    plt.annotate('%0.3f' % yvals[-1], xy=(1,yvals[-1]*xtras[expID]), xytext=(8,0),
+                  xycoords=('axes fraction', 'data'), textcoords='offset points')
+  # Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+  plt.xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+  plt.ylim(lims)
+  plt.xlabel('Epochs')
+  plt.ylabel('Accuracy')
+  plt.title(resUsableTitle)
+  plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+  plt.savefig(savePath+'bestEpochs_19jan-'+modelName+datName+'lim'+str(k)+'.png', bbox_inches='tight')
+###########################################
+## Now do epoch-0.
+xtras={'adam':1.02, 'amAdam1_me1':1, 'sgd':0.999}
+                        #fmnist0   #mnist0  
+for i,xlims in enumerate([None, [1,50]]):
+  for k,lims in enumerate([[0.1,0.85],[0.1,1]]):
+    plt.figure(2)
+    plt.clf()
+
+    if xlims is None:
+      END=max(rez['xax0'])
+    else:
+      END=xlims[1]
+    # Plot all results.
+    for expID, rez in avgRez.items():
+      if not ('meansR0' in rez):
+        continue
+      yvals = rez['meansR0']
+      plt.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                      marker = markers[expID], color=colors[expID],
+                      markersize = 4, linewidth = 1,
+                      label=names[expID]+' (N={})'.format(rez['numTr']))
+      # Plot the final accuracy to the right of the axes.
+      plt.annotate('%0.3f' % yvals[END], xy=(1,yvals[END]*xtras[expID]), xytext=(8,0),
+                    xycoords=('axes fraction', 'data'), textcoords='offset points')
+    # Now plot a marker designating when TESTING phase began.
+  #  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+    if xlims is None:
+      plt.xlim([min(rez['xax0'])-xtraAx2, END+xtraAx2])
+    else:
+      plt.xlim(xlims)
+    plt.ylim(lims)
+    plt.xlabel('Minibatches')
+    plt.ylabel('Accuracy')
+    plt.title('Epoch 0 Test Accuracy\n'
+               + resUsableTitle)
+    plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+    plt.savefig(savePath+'bestMiniBatches_19jan-'+modelName+datName+'lim'+str(i)+str(k)+'.png', bbox_inches='tight')
+
+
+
+
+
+
+
+######################################
+## Combined plots.
+
+xtras={'adam':1.001, 'amAdam1_me1':1, 'sgd':0.999}
+if datName=='mnist':
+  ylim1 = [0.9, 0.99]
+  ylim2 = [0.1,1]
+else:
+  ylim1 = [0.675,0.91]
+  ylim2 = [0.1,0.85]
+
+fig = plt.figure(23)
+fig.clf()
+ax1 = fig.add_subplot(111)
+
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansTr' in rez):
+    continue
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+  yvals =rez['meansTe']
+  ax1.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                  marker = temark, color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  ax1.annotate('%0.3f' % yvals[-1], xy=(1,yvals[-1]*xtras[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now set axis  limits etc.
+ax1.set_xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+ax1.set_ylim(ylim1)
+ax1.set_xlabel('Epochs')
+ax1.set_ylabel('Accuracy')
+ax1.set_title(resUsableTitle)
+ax1.legend(loc='lower left')#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+
+###########################################
+## Now do epoch-0 inside the above axis...
+xtras={'adam':1.02, 'amAdam1_me1':1, 'sgd':0.999}
+
+ax2=plt.axes([.45, .11, .45, .4])
+
+for expID, rez in avgRez.items():
+  if not ('meansR0' in rez):
+    continue
+  yvals = rez['meansR0']
+  ax2.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                  marker = markers[expID], color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  ax2.annotate('%0.3f' % yvals[END], xy=(1,yvals[END]*xtras[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+ax2.set_xlim([min(rez['xax0'])-xtraAx2, END+xtraAx2])
+ax2.set_xticks([])
+ax2.set_ylim(ylim2)
+#ax2.set_xlabel('Minibatches')
+ax2.annotate('Epoch 0 Minibatches', xy=(12,0.79))
+#ax2.ylabel('Accuracy')
+#ax2.title('Epoch 0 Test Accuracy\n' + resUsableTitle)
+#ax2.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+
+plt.savefig('./TESTING.png', bbox_inches='tight')
+plt.show()
+
+
+
+
+#########################
+# For computing nonzero std. devs.
+#  # If there were actually multiple trials, compute standard deviations
+#  #   in accuracies using NONZERO entries only.
+#  if numTrials>1:
+#    for epoch, tnsr in enumerate(allTr):
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+#      if np.isnan(stdtr):
+#        stdtr=np.array([0])
+#      # Finally...
+#      stdsTr[epoch] = stdtr
+#      ##############################
+#      # Now do Testing.
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdte):
+#        stdte=np.array([0])
+#      # Finally...
+#      stdsTe[epoch] = stdte
+#    ##############################
+#    # Finally, do Epoch-0 results..
+#    # This is my stupid way of doing ONLY NONZERO std dev's...
+#    for minibatch, tnsr in enumerate(allR0):
+#      stdE0  = allTe[minibatch][ allTe[minibatch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdE0):
+#        stdE0=np.array([0])
+#      # Finally...
+#      stdE0[epoch] = stdte
+
+
+
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diff --git a/CNN_DEMO/postHPexps/lenet/lenet_mnist/getTestRez.sh b/CNN_DEMO/postHPexps/lenet/lenet_mnist/getTestRez.sh
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--- /dev/null
+++ b/CNN_DEMO/postHPexps/lenet/lenet_mnist/getTestRez.sh
@@ -0,0 +1,29 @@
+#!/bin/bash
+
+savePath='./testResults/'
+gpuID=0
+
+max_epochs=10
+numTrials=100
+
+adamArgs='adam.pt'
+sgdArgs='sgd.pt'
+am11Args='amAdam1_me1.pt'
+
+for ((trial=1; trial<=numTrials; trial++)); do
+  # Get trial ID
+  trialNum=$trial #$((gpuID*numTrials + trial))
+  trialID='trial'$trialNum
+
+  saveName=$savePath'amAdam11_'$trialID
+  CUDA_VISIBLE_DEVICES=$gpuID python3 ../../train_cnn_14jan.py --seed=$trialNum --epochs=$max_epochs --loadBestHyperparameters=$am11Args --first-epoch-log --save-filename=$saveName
+
+  # SGD
+  saveName=$savePath'sgd_'$trialID
+  CUDA_VISIBLE_DEVICES=$gpuID python3 ../../train_cnn_14jan.py --seed=$trialNum --epochs=$max_epochs --loadBestHyperparameters=$sgdArgs --first-epoch-log --save-filename=$saveName
+
+  # Adam
+  saveName=$savePath'adam_'$trialID
+  CUDA_VISIBLE_DEVICES=$gpuID python3 ../../train_cnn_14jan.py --seed=$trialNum --epochs=$max_epochs --loadBestHyperparameters=$adamArgs --first-epoch-log --save-filename=$saveName
+done
+
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diff --git a/CNN_DEMO/postHPexps/lenet/lenet_mnist/testResults/ims/stdDevErrBar/testRezViz_19jan.py b/CNN_DEMO/postHPexps/lenet/lenet_mnist/testResults/ims/stdDevErrBar/testRezViz_19jan.py
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--- /dev/null
+++ b/CNN_DEMO/postHPexps/lenet/lenet_mnist/testResults/ims/stdDevErrBar/testRezViz_19jan.py
@@ -0,0 +1,391 @@
+"""
+Creates visualizations for RNN test results.
+
+@author Benjamin Cowen
+@date 19 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--reduced', type=bool, default=False, metavar='True',
+           help='Set to true if you only want SGD, Adam, and AltMin-Adam.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+  if not os.path.exists(savePath):
+    os.mkdir(savePath)
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"amAdam1_me1":[], 
+          "sgd":[], "adam":[]
+         }
+
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    # If SGD, check whether it's Nesterov.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    elif algBase=='altmin':
+      nit = str(DD.args['n_iter_codes'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Append results.
+    if args.reduced:
+      if not (expID=='sgd' or expID=='adam' or expID=='amAdam5_me1'):
+        continue
+    allSh[expID].append(ddict()._load(filepath))
+
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+# This better be the same for all DD anyway :)
+############################################
+# Print all single-exp. figures -----------------------
+avgRez  = {}
+numE0btch = 469   # Number of elements in 'first_epoch'
+numEpochs = 10   # Number of elements in 'te' and 'tr'
+
+for expID, rez in allSh.items():
+  if len(rez)==0:
+    continue
+  # Initialize each Exp ID.
+  avgRez[expID] = {'tr':[], 'te':[]}
+  numTrials = len(rez)
+
+  # (0.) Collect the Training/Testing histories.
+  # Compute the mean and std dev across trials.
+  allR0 = None
+  for trial in range(numTrials):
+    # Shelf for current trial.
+    tShelf = rez[trial]
+
+    # CHECK VALIDITY:-----------------
+    # If no 'perf' field, then performance wasn't yet recorded.
+    if hasattr(tShelf,'perf'):
+      expData = tShelf.perf
+      r0  = expData['first_epoch']
+      rTr = expData['tr']
+      rTe = expData['te']
+    else:
+      continue
+
+    # If the number of recorded epochs << number allowed epochs, it's in-progress.
+    # If the mean training accurac < 14%, consider this a FAILED attempt at training.
+    if len(rTe)<numEpochs or np.mean(rTr)<0.14 or len(r0)==0:
+      continue
+
+    # Finally, might need to remove FAILED attempts.
+    #----------------------------------
+
+    # If it checks out, extract the data.
+    r0  = th.Tensor(r0).view(numE0btch, 1)
+    rTr = th.Tensor(rTr).view(numEpochs, 1)
+    rTe = th.Tensor(rTe).view(numEpochs, 1)
+
+    # Append into a numpy array so we can get mean/std later.
+    if allR0 is None:
+      allR0 = r0
+      allTr = rTr
+      allTe = rTe
+    else:
+      allR0 = th.cat([allR0, r0], 1)
+      allTr = th.cat([allTr, rTr], 1)
+      allTe = th.cat([allTe, rTe], 1)
+    # End of loop through the trials.
+  # If no trials were valid, skip!
+  if allR0 is None:
+    continue
+
+#  if expID=='nest':
+#    1/0
+
+  # Need this for later...
+  # (1.) Else, compute mean and std devs.
+  # Update the number of trials actually used in this analysis.
+  numTrials = allR0.size(1)
+
+  # There shouldn't be any zero entries anymore...
+  meansR0 = allR0.mean(1).numpy()
+  meansTr = allTr.mean(1).numpy()
+  meansTe = allTe.mean(1).numpy()
+  if numTrials>0:
+    stdsR0 = allR0.std(1).numpy()
+    stdsTr = allTr.std(1).numpy()
+    stdsTe = allTe.std(1).numpy()
+  else:
+    stdsR0 = th.zeros(meansR0.size()).numpy()
+    stdsTr = th.zeros(meansTr.size()).numpy()
+    stdsTe = th.zeros(meansTe.size()).numpy()    
+
+
+  ###############
+  # For epoch-0 results to be tacked on..
+  x_axis = np.array(range(1, len(meansTr)+1))
+#  meansTr = np.insert(meansTr, 0, meansR0[0])
+#  meansTe = np.insert(meansTe, 0, meansR0[0])
+#  stdsTr  = np.insert(stdsTr,  0, stdsR0[0])
+#  stdsTe  = np.insert(stdsTe,  0, stdsR0[0])
+#  x_axis = np.array(range(0, len(meansTr)))
+
+  # Finally divide by the sqrt number of samples:
+  
+#  stdsR0  /= np.sqrt(numTrials)
+#  stdsTr  /= np.sqrt(numTrials)
+#  stdsTe  /= np.sqrt(numTrials)
+  
+  ## (2.) If requested, plot the results for each parameter combination.
+  if not args.onlyBest:
+    # (2.a) Extract information about this setting for titles etc.
+    # These should NOT change for rez[i], i=/=0.
+    LR      = str(rez[0].args['lr_weights'])
+    # Data type (permuted vs sequential mnist)
+    if 'data_type' in rez[0].args:           #This arg was added late so may not exist.
+      dataType = rez[0].args['data_type']
+    else:
+      dataType = 'seq'
+    # Alg name.
+    algName = rez[0].args['opt_method']
+    if algName=='altmin':
+      algName += str(rez[0].args['n_iter_weights'])
+
+    TITLE  = rez[0].args['model']+'; trained with '+expID
+
+    plt.figure(1)
+    plt.clf()
+    plt.errorbar(x_axis, meansTr, stdsTr, marker = 'o', label='Training')
+    plt.errorbar(x_axis, meansTe, stdsTe, marker = '^', label='Testing')
+    plt.legend()
+    plt.xlabel('Epochs')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE)
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'allEpoch.png')
+
+    x_axis0 = np.array(range(0, len(meansR0)))
+    plt.figure(2)
+    plt.clf()
+    plt.errorbar(x_axis0, meansR0, stdsR0, marker = '.', label='Testing')
+    plt.xlabel('Minibatches')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE+'\n(Epoch 0 Minibatches)')
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'minibatchE0.png')
+
+  ## (3.) Finally, record the avg for a big plot at the end.
+  # For Plotting
+  avgRez[expID]['xax']     = x_axis
+  avgRez[expID]['xax0']    = x_axis0
+  avgRez[expID]['meansTr'] = meansTr
+  avgRez[expID]['meansTe'] = meansTe
+  avgRez[expID]['meansR0'] = meansR0
+  avgRez[expID]['stdsTr']  = stdsTr
+  avgRez[expID]['stdsTe']  = stdsTe
+  avgRez[expID]['stdsR0']  = stdsR0
+  avgRez[expID]['numTr']   = numTrials
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',
+           "amAdam1_me1":'r'
+          }
+markers  = {"sgd":'o', "adam":'P',
+           "amAdam1_me1":'^'
+           }
+names  = {"sgd":'SGD', "adam":'Adam',
+          "amAdam1_me1":'AM-Adam'}
+models={'lenet':'LeNet', 'vgg7':'VGG7'}
+datasets={'mnist':'MNIST', 'fashion_mnist':'Fashion-MNIST'}
+#colors = ['b','g','r','c','m','y','k']
+modelName = models[rez[0].args['model']]
+datName=datasets[rez[0].args['dataset']]
+resUsableTitle = modelName + ' trained on '+datName
+xtraAx1 = 0.1
+xtraAx2 = 1
+trmark='.'
+temark='^'
+xtras={'adam':1.001, 'amAdam1_me1':1, 'sgd':0.999}
+                        #mnistAll  #fMnistAll   
+for k,lims in enumerate([[0.9, 0.99],[0.675,0.91]]):
+  plt.figure(1)
+  plt.clf()
+  # Plot all results.
+  for expID, rez in avgRez.items():
+    if not ('meansTr' in rez):
+      continue
+    if lims[0]>.7:
+      mmm=' (zoomed)'
+    else:
+      mmm=''
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+    yvals =rez['meansTe']
+    plt.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                    marker = temark, color=colors[expID],
+                    markersize = 4, linewidth = 1,
+                    label=names[expID]+' (N={})'.format(rez['numTr']))
+    # Plot the final accuracy to the right of the axes.
+    plt.annotate('%0.3f' % yvals.max(), xy=(1,yvals.max()*xtras[expID]), xytext=(8,0),
+                  xycoords=('axes fraction', 'data'), textcoords='offset points')
+  # Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+  plt.xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+  plt.ylim(lims)
+  plt.xlabel('Epochs')
+  plt.ylabel('Accuracy')
+  plt.title('Test Accuracy \n'
+             + resUsableTitle+mmm)
+  plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+  plt.savefig(savePath+'bestEpochs_19jan-'+modelName+datName+'lim'+str(k)+'.png', bbox_inches='tight')
+###########################################
+## Now do epoch-0.
+xtras={'adam':1.02, 'amAdam1_me1':1, 'sgd':0.999}
+                        #fmnist0   #mnist0  
+for k,lims in enumerate([[0.1,0.85],[0.1,1]]):
+  plt.figure(2)
+  plt.clf()
+  # Plot all results.
+  for expID, rez in avgRez.items():
+    if not ('meansR0' in rez):
+      continue
+    yvals = rez['meansR0']
+    plt.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                    marker = markers[expID], color=colors[expID],
+                    markersize = 4, linewidth = 1,
+                    label=names[expID]+' (N={})'.format(rez['numTr']))
+    # Plot the final accuracy to the right of the axes.
+    plt.annotate('%0.3f' % yvals.max(), xy=(1,yvals.max()*xtras[expID]), xytext=(8,0),
+                  xycoords=('axes fraction', 'data'), textcoords='offset points')
+  # Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+  plt.xlim([min(rez['xax0'])-xtraAx2, max(rez['xax0'])+xtraAx2])
+  plt.ylim(lims)
+  plt.xlabel('Minibatches')
+  plt.ylabel('Accuracy')
+  plt.title('Epoch 0 Test Accuracy\n'
+             + resUsableTitle)
+  plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+  plt.savefig(savePath+'bestMiniBatches_19jan-'+modelName+datName+'lim'+str(k)+'.png', bbox_inches='tight')
+
+
+
+
+
+
+
+#########################
+# For computing nonzero std. devs.
+#  # If there were actually multiple trials, compute standard deviations
+#  #   in accuracies using NONZERO entries only.
+#  if numTrials>1:
+#    for epoch, tnsr in enumerate(allTr):
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+#      if np.isnan(stdtr):
+#        stdtr=np.array([0])
+#      # Finally...
+#      stdsTr[epoch] = stdtr
+#      ##############################
+#      # Now do Testing.
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdte):
+#        stdte=np.array([0])
+#      # Finally...
+#      stdsTe[epoch] = stdte
+#    ##############################
+#    # Finally, do Epoch-0 results..
+#    # This is my stupid way of doing ONLY NONZERO std dev's...
+#    for minibatch, tnsr in enumerate(allR0):
+#      stdE0  = allTe[minibatch][ allTe[minibatch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdE0):
+#        stdE0=np.array([0])
+#      # Finally...
+#      stdE0[epoch] = stdte
+
+
+
diff --git a/CNN_DEMO/postHPexps/lenet/lenet_mnist/testResults/ims/testRezViz_19jan.py b/CNN_DEMO/postHPexps/lenet/lenet_mnist/testResults/ims/testRezViz_19jan.py
new file mode 100644
index 0000000..3b5cda0
--- /dev/null
+++ b/CNN_DEMO/postHPexps/lenet/lenet_mnist/testResults/ims/testRezViz_19jan.py
@@ -0,0 +1,507 @@
+"""
+Creates visualizations for RNN test results.
+
+@author Benjamin Cowen
+@date 19 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--reduced', type=bool, default=False, metavar='True',
+           help='Set to true if you only want SGD, Adam, and AltMin-Adam.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+  if not os.path.exists(savePath):
+    os.mkdir(savePath)
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"amAdam1_me1":[], 
+          "amAdam5_me1":[], 
+          "sgd":[], "adam":[]
+         }
+
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    # If SGD, check whether it's Nesterov.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    elif algBase=='altmin':
+      nit = str(DD.args['n_iter_codes'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Append results.
+    if args.reduced:
+      if not (expID=='sgd' or expID=='adam' or expID=='amAdam5_me1'):
+        continue
+    allSh[expID].append(ddict()._load(filepath))
+
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+# This better be the same for all DD anyway :)
+############################################
+# Print all single-exp. figures -----------------------
+avgRez  = {}
+numE0btch = 469 #59#300  # Number of elements in 'first_epoch'
+numEpochs = 10   # Number of elements in 'te' and 'tr'
+
+for expID, rez in allSh.items():
+  if len(rez)==0:
+    continue
+  # Initialize each Exp ID.
+  avgRez[expID] = {'tr':[], 'te':[]}
+  numTrials = len(rez)
+
+  # (0.) Collect the Training/Testing histories.
+  # Compute the mean and std dev across trials.
+  allR0 = None
+  for trial in range(numTrials):
+    # Shelf for current trial.
+    tShelf = rez[trial]
+
+    # CHECK VALIDITY:-----------------
+    # If no 'perf' field, then performance wasn't yet recorded.
+    if hasattr(tShelf,'perf'):
+      expData = tShelf.perf
+      r0  = expData['first_epoch']
+      rTr = expData['tr']
+      rTe = expData['te']
+    else:
+      continue
+
+    # If the number of recorded epochs << number allowed epochs, it's in-progress.
+    # If the mean training accurac < 14%, consider this a FAILED attempt at training.
+    if len(rTe)<numEpochs or np.mean(r0)<0.14 or np.mean(rTr)<0.14 or len(r0)==0:
+      continue
+
+    # Crazy occasional results for SGD here:
+    if 'dataset' in tShelf.args:
+      if tShelf.args['dataset']=='mnist':
+        if rTe[7]<0.98:
+          continue
+    # Finally, might need to remove FAILED attempts.
+    #----------------------------------
+
+    # If it checks out, extract the data.
+    r0  = th.Tensor(r0).view(numE0btch, 1)
+    rTr = th.Tensor(rTr).view(numEpochs, 1)
+    rTe = th.Tensor(rTe).view(numEpochs, 1)
+
+    # Append into a numpy array so we can get mean/std later.
+    if allR0 is None:
+      allR0 = r0
+      allTr = rTr
+      allTe = rTe
+    else:
+      allR0 = th.cat([allR0, r0], 1)
+      allTr = th.cat([allTr, rTr], 1)
+      allTe = th.cat([allTe, rTe], 1)
+    # End of loop through the trials.
+  # If no trials were valid, skip!
+  if allR0 is None:
+    continue
+
+#  if expID.startswith('sgd'):
+#    1/0
+
+  # Need this for later...
+  # (1.) Else, compute mean and std devs.
+  # Update the number of trials actually used in this analysis.
+  numTrials = allR0.size(1)
+
+  # There shouldn't be any zero entries anymore...
+  meansR0 = allR0.mean(1).numpy()
+  meansTr = allTr.mean(1).numpy()
+  meansTe = allTe.mean(1).numpy()
+  if numTrials>0:
+    stdsR0 = allR0.std(1).numpy()
+    stdsTr = allTr.std(1).numpy()
+    stdsTe = allTe.std(1).numpy()
+  else:
+    stdsR0 = th.zeros(meansR0.size()).numpy()
+    stdsTr = th.zeros(meansTr.size()).numpy()
+    stdsTe = th.zeros(meansTe.size()).numpy()    
+
+
+  ###############
+  # For epoch-0 results to be tacked on..
+  x_axis = np.array(range(1, len(meansTr)+1))
+#  meansTr = np.insert(meansTr, 0, meansR0[0])
+#  meansTe = np.insert(meansTe, 0, meansR0[0])
+#  stdsTr  = np.insert(stdsTr,  0, stdsR0[0])
+#  stdsTe  = np.insert(stdsTe,  0, stdsR0[0])
+#  x_axis = np.array(range(0, len(meansTr)))
+
+  # Finally divide by the sqrt number of samples:
+  
+  stdsR0  /= np.sqrt(numTrials)
+  stdsTr  /= np.sqrt(numTrials)
+  stdsTe  /= np.sqrt(numTrials)
+  
+  ## (2.) If requested, plot the results for each parameter combination.
+  if not args.onlyBest:
+    # (2.a) Extract information about this setting for titles etc.
+    # These should NOT change for rez[i], i=/=0.
+    LR      = str(rez[0].args['lr_weights'])
+    # Data type (permuted vs sequential mnist)
+    if 'data_type' in rez[0].args:           #This arg was added late so may not exist.
+      dataType = rez[0].args['data_type']
+    else:
+      dataType = 'seq'
+    # Alg name.
+    algName = rez[0].args['opt_method']
+    if algName=='altmin':
+      algName += str(rez[0].args['n_iter_weights'])
+    if 'rnnHidSize' in rez[0].args:
+      modelName='RNN 1-{}'.format(rez[0].args['rnnHidSize'])
+    else:
+      modelName=rez[0].args['model']
+    TITLE  = modelName +'; trained with '+expID
+
+    plt.figure(1)
+    plt.clf()
+    plt.errorbar(x_axis, meansTr, stdsTr, marker = 'o', label='Training')
+    plt.errorbar(x_axis, meansTe, stdsTe, marker = '^', label='Testing')
+    plt.legend()
+    plt.xlabel('Epochs')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE)
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'allEpoch.png')
+
+    x_axis0 = np.array(range(0, len(meansR0)))
+    plt.figure(2)
+    plt.clf()
+    plt.errorbar(x_axis0, meansR0, stdsR0, marker = '.', label='Testing')
+    plt.xlabel('Minibatches')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE+'\n(Epoch 0 Minibatches)')
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'minibatchE0.png')
+
+  ## (3.) Finally, record the avg for a big plot at the end.
+  # For Plotting
+  avgRez[expID]['xax']     = x_axis
+  avgRez[expID]['xax0']    = x_axis0
+  avgRez[expID]['meansTr'] = meansTr
+  avgRez[expID]['meansTe'] = meansTe
+  avgRez[expID]['meansR0'] = meansR0
+  avgRez[expID]['stdsTr']  = stdsTr
+  avgRez[expID]['stdsTe']  = stdsTe
+  avgRez[expID]['stdsR0']  = stdsR0
+  avgRez[expID]['numTr']   = numTrials
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',
+           "amAdam1_me1":'r',
+           "amAdam5_me1":'r'
+          }
+markers  = {"sgd":'o', "adam":'P',
+           "amAdam1_me1":'^',
+           "amAdam5_me1":'^'
+           }
+names  = {"sgd":'SGD', "adam":'Adam',
+          "amAdam1_me1":'AM-Adam',
+           "amAdam5_me1":'AM-Adam'}
+models={'lenet':'LeNet', 'vgg7':'VGG7'}
+datasets={'mnist':'MNIST', 'fashion_mnist':'Fashion-MNIST'}
+#colors = ['b','g','r','c','m','y','k']
+if 'rnnHidSize' in rez[0].args:
+  modelName='RNN 1-{}'.format(rez[0].args['rnnHidSize'])
+  datName='Sequential MNIST'
+else:
+  modelName=models[rez[0].args['model']]
+  datName=datasets[rez[0].args['dataset']]
+resUsableTitle = datName+', '+modelName
+
+
+if datName=='MNIST':
+  ylim1 = [0.96, 0.99]
+  ylim2 = [0.1,1]
+  forget='amAdam5_me1'
+  numMini=None
+elif datName=='Fashion-MNIST':
+  ylim1 = [0.78,0.9]
+  ylim2 = [0.1,0.8]
+  forget='amAdam1_me1'
+  numMini=150
+elif datName=='Sequential MNIST':
+  if modelName[-2:]=='50':
+    ylim1 = [0.3,0.965]
+    ylim2 = [0.1,1]
+  elif modelName[-2:]=='15':
+    ylim1 = [0.775,0.93]
+    ylim2 = [0.1,0.96]
+    if numEpochs==50:
+      ylim1=[0.775, 0.96]
+  forget=''
+  numMini=None
+
+epoch0Display=0.9
+if datName=='Sequential MNIST':
+  xtras1={'adam':1.001, 'amAdam1_me1':1, 'amAdam5_me1':1, 'sgd':0.99}
+  xtras2={'adam':1.03, 'amAdam1_me1':1,'amAdam5_me1':1, 'sgd':0.999}
+else:
+  if rez[0].args['batch_size']==200:
+    if datName=='Fashion-MNIST':
+      zzz=0.999
+      xtras1={'adam':1.005, 'amAdam1_me1':zzz, 'amAdam5_me1':zzz, 'sgd':1}
+      xtras2={'adam':1.06, 'amAdam1_me1':zzz,'amAdam5_me1':zzz, 'sgd':1}
+      epoch0Display = 0.8
+    else:
+      zzz=0.999
+      xtras1={'adam':1.001, 'amAdam1_me1':zzz, 'amAdam5_me1':zzz, 'sgd':1}
+      xtras2={'adam':1.06, 'amAdam1_me1':0.95,'amAdam5_me1':0.95, 'sgd':1}
+      epoch0Display = 1
+  else:
+    xtras1={'adam':1.0001, 'amAdam1_me1':1, 'amAdam5_me1':1, 'sgd':0.99}
+    xtras2={'adam':1.06, 'amAdam1_me1':1,'amAdam5_me1':1, 'sgd':0.999}
+
+xtraAx1 = 0.1
+xtraAx2 = 1
+trmark='.'
+temark='^'
+                        #mnistAll  #fMnistAll   
+plt.figure(1)
+plt.clf()
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansTr' in rez) or expID==forget:
+    continue
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+  yvals =rez['meansTe']
+  plt.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                  marker = temark, color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  plt.annotate('%0.3f' % yvals[-1], xy=(1,yvals[-1]*xtras1[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+plt.xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+plt.ylim(ylim1)
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.title(resUsableTitle)
+plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+plt.savefig(savePath+'bestEpochs_22jan-'+modelName+datName+'.png', bbox_inches='tight')
+###########################################
+## Now do epoch-0.
+plt.figure(2)
+plt.clf()
+
+END=max(rez['xax0'])
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansR0' in rez) or expID==forget:
+    continue
+  yvals = rez['meansR0']
+  plt.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                  marker = markers[expID], color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  plt.annotate('%0.3f' % yvals[END], xy=(1,yvals[END]*xtras2[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+plt.xlim([min(rez['xax0'])-xtraAx2, END+xtraAx2])
+plt.ylim(ylim2)
+plt.xlabel('Minibatches')
+plt.ylabel('Accuracy')
+plt.title(resUsableTitle)
+plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+plt.savefig(savePath+'bestMiniBatches_22jan-'+modelName+datName+'.png', bbox_inches='tight')
+
+
+
+
+
+######################################
+## Combined plots.
+
+fig = plt.figure(23)
+fig.clf()
+ax1 = fig.add_subplot(111)
+
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansTr' in rez) or expID==forget:
+    continue
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+  yvals = rez['meansTe']
+  ax1.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                  marker = temark, color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  ax1.annotate('%0.3f' % yvals[-1], xy=(1,yvals[-1]*xtras1[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now set axis  limits etc.
+ax1.set_xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+ax1.set_ylim(ylim1)
+ax1.set_xlabel('Epochs')
+ax1.set_ylabel('Accuracy')
+ax1.set_title(resUsableTitle)
+ax1.legend(loc=(.05,.0),fontsize='small')
+#loc=(0.5,0.65))
+#'best')#'lower left')
+#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+
+###########################################
+## Now do epoch-0 inside the above axis...
+ax2=plt.axes([.45, .2, .45, .4])
+
+if numMini is not None:
+  END=numMini
+else:
+  END=len(rez['meansR0'])-1
+for expID, rez in avgRez.items():
+  if not ('meansR0' in rez) or expID==forget:
+    continue
+  yvals = rez['meansR0']
+  ax2.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                  marker = markers[expID], color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  ax2.annotate('%0.3f' % yvals[END], xy=(1,yvals[END]*xtras2[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+ax2.set_xlim([min(rez['xax0'])-xtraAx2, END+xtraAx2])
+#ax2.set_xticks([])
+ax2.set_ylim(ylim2)
+ax2.set_xlabel('Minibatches')
+ax2.annotate('1st Epoch', xy=(12,epoch0Display), fontweight='bold')
+#ax2.ylabel('Accuracy')
+#ax2.title('Epoch 0 Test Accuracy\n' + resUsableTitle)
+#ax2.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+
+plt.savefig(savePath+'combined'+datName+'_'+modelName+'.png', bbox_inches='tight')
+plt.show()
+
+
+
+
+#########################
+# For computing nonzero std. devs.
+#  # If there were actually multiple trials, compute standard deviations
+#  #   in accuracies using NONZERO entries only.
+#  if numTrials>1:
+#    for epoch, tnsr in enumerate(allTr):
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+#      if np.isnan(stdtr):
+#        stdtr=np.array([0])
+#      # Finally...
+#      stdsTr[epoch] = stdtr
+#      ##############################
+#      # Now do Testing.
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdte):
+#        stdte=np.array([0])
+#      # Finally...
+#      stdsTe[epoch] = stdte
+#    ##############################
+#    # Finally, do Epoch-0 results..
+#    # This is my stupid way of doing ONLY NONZERO std dev's...
+#    for minibatch, tnsr in enumerate(allR0):
+#      stdE0  = allTe[minibatch][ allTe[minibatch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdE0):
+#        stdE0=np.array([0])
+#      # Finally...
+#      stdE0[epoch] = stdte
+
+
+
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diff --git a/CNN_DEMO/printHPOrez_cnn.py b/CNN_DEMO/printHPOrez_cnn.py
new file mode 100644
index 0000000..2a8530a
--- /dev/null
+++ b/CNN_DEMO/printHPOrez_cnn.py
@@ -0,0 +1,49 @@
+"""
+Loads saved .pt's and prints best hyperparameters.
+@author Benjamin Cowen
+@date 19 January 2019
+"""
+from utils import ddict
+import numpy as np
+import glob
+import torch
+import argparse
+parser = argparse.ArgumentParser(description='Load and print hyperparameters.')
+parser.add_argument('--loadPath', default='./',
+                    help='Path to directory w/data.')
+
+args = parser.parse_args()
+
+argList2Print={'all':['opt_method',
+                      'batch_size',
+                      'dataset',
+                      'model',
+                      'lr_weights'
+                      ],
+            'altmin':[
+                     # Algorithm
+                     'mini_epochs',
+                     'n_iter_codes',
+                     'n_iter_weights',
+                     'lr_codes',
+                     # Model Parameters
+                     'lambda_w_muFact',
+                     'lambda_c_muFact',
+                     'min_mu',
+                     'max_mu',
+                     'd_mu',
+                     'mu_update_freq',
+                     'mult_mu'
+                    ],
+              'sgd':['lambda_w'],
+             'adam':['lambda_w']
+            }
+for filepath in glob.iglob(args.loadPath + '*.pt'):
+  D = ddict()._load(filepath).args
+
+  print('============= {} ================='.format(D['algorithm']))
+  for argName in argList2Print['all']:
+    print('{} = {}'.format(argName, D[argName]))
+  for argName in argList2Print[D['opt_method']]:
+    print('{} = {}'.format(argName, D[argName]))
+
diff --git a/CNN_DEMO/testRezViz_19jan.py b/CNN_DEMO/testRezViz_19jan.py
new file mode 100644
index 0000000..3b5cda0
--- /dev/null
+++ b/CNN_DEMO/testRezViz_19jan.py
@@ -0,0 +1,507 @@
+"""
+Creates visualizations for RNN test results.
+
+@author Benjamin Cowen
+@date 19 Jan 2019
+"""
+
+############################################
+# Imports ----------------------------------
+
+# Loading
+from utils import ddict, load_dataset
+import glob
+import torch as th 
+
+# Plotting and Saving
+# The following two lines are for the remote branch.
+import matplotlib
+matplotlib.use('Agg')
+import matplotlib.pyplot as plt
+import os
+from shutil import copyfile
+import numpy as np
+import argparse
+
+
+p = argparse.ArgumentParser(description='Visualize hyperparameter grid search results.')
+p.add_argument('--loadPaths', type=str, default=None, metavar='path1/a/, path2/b/',
+           help='A list of strings/paths to results.')
+p.add_argument('--loadPath', type=str, default=None, metavar='path/to/data/',
+           help='Path to directory containing data to plot.')
+p.add_argument('--savePath', type=str, default=None, metavar='path/to/savedIms/',
+           help='Path to directory containing images saved here.')
+p.add_argument('--onlyBest', type=bool, default=False, metavar='True',
+           help='Set to true if you DONT want all the imgs (only best params).')
+p.add_argument('--reduced', type=bool, default=False, metavar='True',
+           help='Set to true if you only want SGD, Adam, and AltMin-Adam.')
+p.add_argument('--epoch0-accs', type=str, default=None, metavar='path/to/epoch-0-accuracies/',
+          help='Path to directory containing the epoch-0 accuracies... (avg result for all methods)')
+args = p.parse_args()
+
+# Check arguments.
+# Assumes loadPaths or loadPath is valid...
+if args.loadPaths is None:
+  # Load and Save Paths
+  loadPaths = [args.loadPath]
+else:
+  loadPaths = args.loadPaths.strip('[]').split(',')
+
+# For saving images
+if args.savePath is None:
+  savePath = loadPaths[0] + "ims/"
+  if not os.path.exists(savePath):
+    os.mkdir(savePath)
+print('*********************************************************')
+print('Saving images to '+savePath)
+print('*********************************************************')
+
+############################################
+# Make a copy of this file in the savePath!
+here = os.path.realpath(__file__)
+there = savePath + (__file__).replace('../','')
+copyfile(here,there)
+
+############################################
+# Decide tree structure here... (TODO way to automate this?)
+# Stands for "all shelves"
+allSh  = {"amAdam1_me1":[], 
+          "amAdam5_me1":[], 
+          "sgd":[], "adam":[]
+         }
+
+
+# Loop thru given loadpaths.
+for k,loadPath in enumerate(loadPaths):
+  print('Loading files from: ' + loadPath)
+  for filepath in glob.iglob(loadPath + '*.pt'):
+    fileName = filepath.replace(loadPath,'')
+
+    # Go ahead and load it.
+    DD = ddict()._load(filepath)
+    algBase = DD.args['opt_method']
+
+    # If SGD, check whether it's Nesterov.
+    if algBase=='sgd':
+      expID = 'sgd'
+    elif algBase=='adam':
+      expID='adam'
+    elif algBase=='altmin':
+      nit = str(DD.args['n_iter_codes'])
+      me  = str(DD.args['mini_epochs'])
+      expID='amAdam' + nit + '_me' + me
+    else:
+      raise Exception("Unrecognized optimization method "+algBase+" (only 'altmin','sgd','adam')")
+
+    # Append results.
+    if args.reduced:
+      if not (expID=='sgd' or expID=='adam' or expID=='amAdam5_me1'):
+        continue
+    allSh[expID].append(ddict()._load(filepath))
+
+
+# Compute the average accuracies for epoch 0 (ie, using initializations only)
+epoch0_meanTr = None
+if args.epoch0_accs is not None:
+  epoch0_meanTr = []
+  epoch0_meanTe = []
+  # Compute means and std devs of the initializations.
+  for filepath in glob.iglob(args.epoch0_accs + '*.pt'):
+    DD = ddict()._load(filepath)
+    epoch0_meanTr += DD.perf['tr']
+    epoch0_meanTe += DD.perf['te']
+
+  # Finally average.
+  epoch0_stdTr  = th.Tensor( epoch0_meanTr ).std()
+  epoch0_meanTr = th.Tensor( epoch0_meanTr ).mean()
+  epoch0_stdTe  = th.Tensor( epoch0_meanTe ).std()
+  epoch0_meanTe = th.Tensor( epoch0_meanTe ).mean()
+  
+
+# This better be the same for all DD anyway :)
+############################################
+# Print all single-exp. figures -----------------------
+avgRez  = {}
+numE0btch = 469 #59#300  # Number of elements in 'first_epoch'
+numEpochs = 10   # Number of elements in 'te' and 'tr'
+
+for expID, rez in allSh.items():
+  if len(rez)==0:
+    continue
+  # Initialize each Exp ID.
+  avgRez[expID] = {'tr':[], 'te':[]}
+  numTrials = len(rez)
+
+  # (0.) Collect the Training/Testing histories.
+  # Compute the mean and std dev across trials.
+  allR0 = None
+  for trial in range(numTrials):
+    # Shelf for current trial.
+    tShelf = rez[trial]
+
+    # CHECK VALIDITY:-----------------
+    # If no 'perf' field, then performance wasn't yet recorded.
+    if hasattr(tShelf,'perf'):
+      expData = tShelf.perf
+      r0  = expData['first_epoch']
+      rTr = expData['tr']
+      rTe = expData['te']
+    else:
+      continue
+
+    # If the number of recorded epochs << number allowed epochs, it's in-progress.
+    # If the mean training accurac < 14%, consider this a FAILED attempt at training.
+    if len(rTe)<numEpochs or np.mean(r0)<0.14 or np.mean(rTr)<0.14 or len(r0)==0:
+      continue
+
+    # Crazy occasional results for SGD here:
+    if 'dataset' in tShelf.args:
+      if tShelf.args['dataset']=='mnist':
+        if rTe[7]<0.98:
+          continue
+    # Finally, might need to remove FAILED attempts.
+    #----------------------------------
+
+    # If it checks out, extract the data.
+    r0  = th.Tensor(r0).view(numE0btch, 1)
+    rTr = th.Tensor(rTr).view(numEpochs, 1)
+    rTe = th.Tensor(rTe).view(numEpochs, 1)
+
+    # Append into a numpy array so we can get mean/std later.
+    if allR0 is None:
+      allR0 = r0
+      allTr = rTr
+      allTe = rTe
+    else:
+      allR0 = th.cat([allR0, r0], 1)
+      allTr = th.cat([allTr, rTr], 1)
+      allTe = th.cat([allTe, rTe], 1)
+    # End of loop through the trials.
+  # If no trials were valid, skip!
+  if allR0 is None:
+    continue
+
+#  if expID.startswith('sgd'):
+#    1/0
+
+  # Need this for later...
+  # (1.) Else, compute mean and std devs.
+  # Update the number of trials actually used in this analysis.
+  numTrials = allR0.size(1)
+
+  # There shouldn't be any zero entries anymore...
+  meansR0 = allR0.mean(1).numpy()
+  meansTr = allTr.mean(1).numpy()
+  meansTe = allTe.mean(1).numpy()
+  if numTrials>0:
+    stdsR0 = allR0.std(1).numpy()
+    stdsTr = allTr.std(1).numpy()
+    stdsTe = allTe.std(1).numpy()
+  else:
+    stdsR0 = th.zeros(meansR0.size()).numpy()
+    stdsTr = th.zeros(meansTr.size()).numpy()
+    stdsTe = th.zeros(meansTe.size()).numpy()    
+
+
+  ###############
+  # For epoch-0 results to be tacked on..
+  x_axis = np.array(range(1, len(meansTr)+1))
+#  meansTr = np.insert(meansTr, 0, meansR0[0])
+#  meansTe = np.insert(meansTe, 0, meansR0[0])
+#  stdsTr  = np.insert(stdsTr,  0, stdsR0[0])
+#  stdsTe  = np.insert(stdsTe,  0, stdsR0[0])
+#  x_axis = np.array(range(0, len(meansTr)))
+
+  # Finally divide by the sqrt number of samples:
+  
+  stdsR0  /= np.sqrt(numTrials)
+  stdsTr  /= np.sqrt(numTrials)
+  stdsTe  /= np.sqrt(numTrials)
+  
+  ## (2.) If requested, plot the results for each parameter combination.
+  if not args.onlyBest:
+    # (2.a) Extract information about this setting for titles etc.
+    # These should NOT change for rez[i], i=/=0.
+    LR      = str(rez[0].args['lr_weights'])
+    # Data type (permuted vs sequential mnist)
+    if 'data_type' in rez[0].args:           #This arg was added late so may not exist.
+      dataType = rez[0].args['data_type']
+    else:
+      dataType = 'seq'
+    # Alg name.
+    algName = rez[0].args['opt_method']
+    if algName=='altmin':
+      algName += str(rez[0].args['n_iter_weights'])
+    if 'rnnHidSize' in rez[0].args:
+      modelName='RNN 1-{}'.format(rez[0].args['rnnHidSize'])
+    else:
+      modelName=rez[0].args['model']
+    TITLE  = modelName +'; trained with '+expID
+
+    plt.figure(1)
+    plt.clf()
+    plt.errorbar(x_axis, meansTr, stdsTr, marker = 'o', label='Training')
+    plt.errorbar(x_axis, meansTe, stdsTe, marker = '^', label='Testing')
+    plt.legend()
+    plt.xlabel('Epochs')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE)
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'allEpoch.png')
+
+    x_axis0 = np.array(range(0, len(meansR0)))
+    plt.figure(2)
+    plt.clf()
+    plt.errorbar(x_axis0, meansR0, stdsR0, marker = '.', label='Testing')
+    plt.xlabel('Minibatches')
+    plt.ylabel('Classification Accuracy')
+    plt.title(TITLE+'\n(Epoch 0 Minibatches)')
+  #  plt.ylim([0.875, 1.05])
+    plt.savefig(savePath + expID + 'minibatchE0.png')
+
+  ## (3.) Finally, record the avg for a big plot at the end.
+  # For Plotting
+  avgRez[expID]['xax']     = x_axis
+  avgRez[expID]['xax0']    = x_axis0
+  avgRez[expID]['meansTr'] = meansTr
+  avgRez[expID]['meansTe'] = meansTe
+  avgRez[expID]['meansR0'] = meansR0
+  avgRez[expID]['stdsTr']  = stdsTr
+  avgRez[expID]['stdsTe']  = stdsTe
+  avgRez[expID]['stdsR0']  = stdsR0
+  avgRez[expID]['numTr']   = numTrials
+
+
+######################################################
+# Now we combine results into a plost that compare the 
+#  methods using their best performing parameters.
+# (0.) Each method gets a unique color.
+colors  = {"sgd":'b', "adam":'k',
+           "amAdam1_me1":'r',
+           "amAdam5_me1":'r'
+          }
+markers  = {"sgd":'o', "adam":'P',
+           "amAdam1_me1":'^',
+           "amAdam5_me1":'^'
+           }
+names  = {"sgd":'SGD', "adam":'Adam',
+          "amAdam1_me1":'AM-Adam',
+           "amAdam5_me1":'AM-Adam'}
+models={'lenet':'LeNet', 'vgg7':'VGG7'}
+datasets={'mnist':'MNIST', 'fashion_mnist':'Fashion-MNIST'}
+#colors = ['b','g','r','c','m','y','k']
+if 'rnnHidSize' in rez[0].args:
+  modelName='RNN 1-{}'.format(rez[0].args['rnnHidSize'])
+  datName='Sequential MNIST'
+else:
+  modelName=models[rez[0].args['model']]
+  datName=datasets[rez[0].args['dataset']]
+resUsableTitle = datName+', '+modelName
+
+
+if datName=='MNIST':
+  ylim1 = [0.96, 0.99]
+  ylim2 = [0.1,1]
+  forget='amAdam5_me1'
+  numMini=None
+elif datName=='Fashion-MNIST':
+  ylim1 = [0.78,0.9]
+  ylim2 = [0.1,0.8]
+  forget='amAdam1_me1'
+  numMini=150
+elif datName=='Sequential MNIST':
+  if modelName[-2:]=='50':
+    ylim1 = [0.3,0.965]
+    ylim2 = [0.1,1]
+  elif modelName[-2:]=='15':
+    ylim1 = [0.775,0.93]
+    ylim2 = [0.1,0.96]
+    if numEpochs==50:
+      ylim1=[0.775, 0.96]
+  forget=''
+  numMini=None
+
+epoch0Display=0.9
+if datName=='Sequential MNIST':
+  xtras1={'adam':1.001, 'amAdam1_me1':1, 'amAdam5_me1':1, 'sgd':0.99}
+  xtras2={'adam':1.03, 'amAdam1_me1':1,'amAdam5_me1':1, 'sgd':0.999}
+else:
+  if rez[0].args['batch_size']==200:
+    if datName=='Fashion-MNIST':
+      zzz=0.999
+      xtras1={'adam':1.005, 'amAdam1_me1':zzz, 'amAdam5_me1':zzz, 'sgd':1}
+      xtras2={'adam':1.06, 'amAdam1_me1':zzz,'amAdam5_me1':zzz, 'sgd':1}
+      epoch0Display = 0.8
+    else:
+      zzz=0.999
+      xtras1={'adam':1.001, 'amAdam1_me1':zzz, 'amAdam5_me1':zzz, 'sgd':1}
+      xtras2={'adam':1.06, 'amAdam1_me1':0.95,'amAdam5_me1':0.95, 'sgd':1}
+      epoch0Display = 1
+  else:
+    xtras1={'adam':1.0001, 'amAdam1_me1':1, 'amAdam5_me1':1, 'sgd':0.99}
+    xtras2={'adam':1.06, 'amAdam1_me1':1,'amAdam5_me1':1, 'sgd':0.999}
+
+xtraAx1 = 0.1
+xtraAx2 = 1
+trmark='.'
+temark='^'
+                        #mnistAll  #fMnistAll   
+plt.figure(1)
+plt.clf()
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansTr' in rez) or expID==forget:
+    continue
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+  yvals =rez['meansTe']
+  plt.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                  marker = temark, color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  plt.annotate('%0.3f' % yvals[-1], xy=(1,yvals[-1]*xtras1[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+plt.xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+plt.ylim(ylim1)
+plt.xlabel('Epochs')
+plt.ylabel('Accuracy')
+plt.title(resUsableTitle)
+plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+plt.savefig(savePath+'bestEpochs_22jan-'+modelName+datName+'.png', bbox_inches='tight')
+###########################################
+## Now do epoch-0.
+plt.figure(2)
+plt.clf()
+
+END=max(rez['xax0'])
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansR0' in rez) or expID==forget:
+    continue
+  yvals = rez['meansR0']
+  plt.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                  marker = markers[expID], color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  plt.annotate('%0.3f' % yvals[END], xy=(1,yvals[END]*xtras2[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now plot a marker designating when TESTING phase began.
+#  plt.axvline(x=59.5, linewidth=2, label='Begin testing')
+plt.xlim([min(rez['xax0'])-xtraAx2, END+xtraAx2])
+plt.ylim(ylim2)
+plt.xlabel('Minibatches')
+plt.ylabel('Accuracy')
+plt.title(resUsableTitle)
+plt.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+plt.savefig(savePath+'bestMiniBatches_22jan-'+modelName+datName+'.png', bbox_inches='tight')
+
+
+
+
+
+######################################
+## Combined plots.
+
+fig = plt.figure(23)
+fig.clf()
+ax1 = fig.add_subplot(111)
+
+# Plot all results.
+for expID, rez in avgRez.items():
+  if not ('meansTr' in rez) or expID==forget:
+    continue
+#    plt.errorbar(rez['xax'], rez['meansTr'], rez['stdsTr'], 
+#                    marker = trmark, color=colors[expID],
+#                    markersize = 4, linewidth = 1,
+#                    label=names[expID]+' TR')
+  yvals = rez['meansTe']
+  ax1.errorbar(rez['xax'], yvals, rez['stdsTe'], 
+                  marker = temark, color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  ax1.annotate('%0.3f' % yvals[-1], xy=(1,yvals[-1]*xtras1[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+# Now set axis  limits etc.
+ax1.set_xlim([min(rez['xax'])-xtraAx1, max(rez['xax'])+xtraAx1])
+ax1.set_ylim(ylim1)
+ax1.set_xlabel('Epochs')
+ax1.set_ylabel('Accuracy')
+ax1.set_title(resUsableTitle)
+ax1.legend(loc=(.05,.0),fontsize='small')
+#loc=(0.5,0.65))
+#'best')#'lower left')
+#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+
+###########################################
+## Now do epoch-0 inside the above axis...
+ax2=plt.axes([.45, .2, .45, .4])
+
+if numMini is not None:
+  END=numMini
+else:
+  END=len(rez['meansR0'])-1
+for expID, rez in avgRez.items():
+  if not ('meansR0' in rez) or expID==forget:
+    continue
+  yvals = rez['meansR0']
+  ax2.errorbar(rez['xax0'], yvals, rez['stdsR0'], 
+                  marker = markers[expID], color=colors[expID],
+                  markersize = 4, linewidth = 1,
+                  label=names[expID]+' (N={})'.format(rez['numTr']))
+  # Plot the final accuracy to the right of the axes.
+  ax2.annotate('%0.3f' % yvals[END], xy=(1,yvals[END]*xtras2[expID]), xytext=(8,0),
+                xycoords=('axes fraction', 'data'), textcoords='offset points')
+ax2.set_xlim([min(rez['xax0'])-xtraAx2, END+xtraAx2])
+#ax2.set_xticks([])
+ax2.set_ylim(ylim2)
+ax2.set_xlabel('Minibatches')
+ax2.annotate('1st Epoch', xy=(12,epoch0Display), fontweight='bold')
+#ax2.ylabel('Accuracy')
+#ax2.title('Epoch 0 Test Accuracy\n' + resUsableTitle)
+#ax2.legend()#loc=9, bbox_to_anchor=(0.5, -0.1), ncol=3)
+
+plt.savefig(savePath+'combined'+datName+'_'+modelName+'.png', bbox_inches='tight')
+plt.show()
+
+
+
+
+#########################
+# For computing nonzero std. devs.
+#  # If there were actually multiple trials, compute standard deviations
+#  #   in accuracies using NONZERO entries only.
+#  if numTrials>1:
+#    for epoch, tnsr in enumerate(allTr):
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdtr  = tnsr[ tnsr.gt(0) ].std().numpy()
+#      if np.isnan(stdtr):
+#        stdtr=np.array([0])
+#      # Finally...
+#      stdsTr[epoch] = stdtr
+#      ##############################
+#      # Now do Testing.
+#      # This is my stupid way of doing ONLY NONZERO std dev's...
+#      stdte  = allTe[epoch][ allTe[epoch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdte):
+#        stdte=np.array([0])
+#      # Finally...
+#      stdsTe[epoch] = stdte
+#    ##############################
+#    # Finally, do Epoch-0 results..
+#    # This is my stupid way of doing ONLY NONZERO std dev's...
+#    for minibatch, tnsr in enumerate(allR0):
+#      stdE0  = allTe[minibatch][ allTe[minibatch].gt(0.1) ].std().numpy()
+#      if np.isnan(stdE0):
+#        stdE0=np.array([0])
+#      # Finally...
+#      stdE0[epoch] = stdte
+
+
+
diff --git a/CNN_DEMO/train_cnn_14jan.py b/CNN_DEMO/train_cnn_14jan.py
new file mode 100644
index 0000000..cc75d35
--- /dev/null
+++ b/CNN_DEMO/train_cnn_14jan.py
@@ -0,0 +1,365 @@
+"""
+@author Benjamin Cowen and Mattia Rigotti
+@date 23 Jan 2019
+"""
+from __future__ import print_function, division
+import argparse
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.optim as optim
+from torchvision import datasets, transforms
+import random as rand # needed for validation shuffle
+from altmin14jan import get_mods, get_codes
+from altmin14jan import update_codes, update_last_layer_, update_hidden_weights_adam_, set_mod_optimizers_
+from altmin14jan import post_processing_step
+
+from utils import ddict, load_dataset
+
+##########################################################################################
+##########################################################################################
+
+parser = argparse.ArgumentParser(description='AltMin on mnist')
+#.............................................................
+# New experimental option is to load best hyperparameters from saved thing.
+# Will use the Bunch class to make a "namespace" from this loaded dict, if requested.
+class Bunch(object):
+  def __init__(self, adict):
+    self.__dict__.update(adict)
+parser.add_argument('--loadBestHyperparameters', type=str, default=None,
+                    help='Path to file containing ddict() of desired arguments.')
+#.............................................................
+
+# Training settings
+parser.add_argument('--delta-batch-size', type=int, default=0, metavar='N',
+                    help='batch size increas after each epoch')
+parser.add_argument('--epochs', type=int, default=200, metavar='N',
+                    help='number of epochs to train (default: 10)')
+
+parser.add_argument('--no-cuda', action='store_true', default=False,
+                    help='disables CUDA training')
+parser.add_argument('--postprocessing-steps', type=int, default=0, metavar='N',
+                    help='number of Carreira-Perpinan post-processing steps after training')
+parser.add_argument('--dataset', default='mnist', metavar='D',
+                    help='name of dataset')
+parser.add_argument('--use-validation-size', type=int, default=-1, metavar='N',
+                    help='If <=0, uses test data. Otherwise, uses validation set of size N from training set to "test".')
+parser.add_argument('--data-augmentation', action='store_true', default=False,
+                    help='enables data augmentation')
+parser.add_argument('--model', default='LeNet', metavar='M',
+                    help='name of model')
+parser.add_argument('--log-frequency', type=int, default=4, metavar='N',
+                    help='how many times per epoch to log training status')
+parser.add_argument('--save-interval', type=int, default=1000, metavar='N',
+                    help='how many batches to wait before saving test performance (only relevant if one epoch is larger than that)')
+parser.add_argument('--first-epoch-log', action='store_true', default=False,
+                    help='whether or not it should test and log after every mini-batch in first epoch')
+# WE ARE NOT ALLOWING INDEPENDENT PARAMETER HERE:
+#parser.add_argument('--lr-out', type=float, default=0.001, metavar='LR',
+#                    help='learning rate for last layer weights updates')
+
+# Logistics:
+parser.add_argument('--seed', type=int, default=1, metavar='S',
+                    help='random seed (default: 1)')
+parser.add_argument('--save-filename', default='', metavar='F',
+                    help='name of file where results are saved')
+parser.add_argument('--opt-method', type=str, default='altmin',
+                    help='"altmin" or "adam" or "sgd"')
+
+# Things we loop over:
+parser.add_argument('--batch-size', type=int, default=128, metavar='N',
+                    help='input batch size for training')
+# CODE LEARNING PARAMS
+parser.add_argument('--lr-codes', type=float, default=0.5, metavar='LR',
+                    help='learning rate for codes updates')
+parser.add_argument('--n-iter-codes', type=int, default=1, metavar='N',
+                    help='number of internal iterations for codes optimization')
+# Sparsity:
+parser.add_argument('--lambda_c', type=float, default=0.0, metavar='L',
+                    help='codes sparsity')
+# WEIGHT LEARNING PARAMS
+parser.add_argument('--n-iter-weights', type=int, default=1, metavar='N',
+                    help='number of internal iterations in learning weights')
+parser.add_argument('--lr-weights', type=float, default=0.002, metavar='LR',
+                    help='learning rate for hidden weights updates')
+# Sparsity:
+parser.add_argument('--lambda_w', type=float, default=0.0, metavar='L',
+                    help='weight sparsity')
+
+# Newly added:
+######## MU:
+parser.add_argument('--min-mu', type=float, default=0.01, metavar='M',
+                    help='starting point for mu')
+parser.add_argument('--d-mu', type=float, default=0.001/600, metavar='M',
+                    help='increase in mu after every mini-batch')
+parser.add_argument('--mult-mu', type=float, default=1, metavar='M',
+                    help='multiplies into mu after every mini-batch')
+parser.add_argument('--mu-update-freq', type=int, default=1, metavar='M',
+                    help='Number of minibatches between \mu updates (or just at end of epoch if larger than number of minibatches/epoch).')
+parser.add_argument('--max-mu', type=float, default=10000, metavar='M',
+                    help='cap for mu (can\'t go higher)')
+
+######### MU-DEPENDENT SPARSITY:
+parser.add_argument('--lambda_c_muFact', type=float, default=0.0, metavar='L',
+                    help='Takes precedent over regular lambda_c. Sets lambda_c = L*mu.')
+parser.add_argument('--lambda_w_muFact', type=float, default=0.0, metavar='L',
+                    help='Takes precedent over regular lambda_w. Sets lambda_w = L*mu.')
+####### MINI-EPOCHS
+parser.add_argument('--mini-epochs', type=int, default=1, metavar='N',
+                    help='Alternate between code and weight subproblem N times per minibatch')
+######### LAYER-WISE DROPOUT!!
+
+parser.add_argument('--LW-dropout-perc', type=float, default=0,
+                    help='Percent chance of dropping out.')
+parser.add_argument('--LW-dropout-delay', type=int, default=1,
+                    help='Number of epochs to delay application of layer-wise dropout.')
+##########################################################################################
+##########################################################################################
+
+# Continuing...
+args_og = parser.parse_args()
+if args_og.loadBestHyperparameters is None:
+  args = args_og
+else:
+  # OR~~ load args from previously saved ddict(). SOME ARGS (below) 
+  # WILL BE OVERWRITTEN (ie to load "best hyperparameters" then 
+  # run diff number of epochs etc)
+  args = Bunch(ddict()._load(args_og.loadBestHyperparameters).args)
+  # Certain args will be changed from original:
+  # all the "Logistics" arguments
+  args.save_filename   = args_og.save_filename
+  args.seed            = args_og.seed
+  args.no_cuda         = args_og.no_cuda
+  args.log_frequency   = args_og.log_frequency
+  args.save_interval   = args_og.save_interval
+  args.first_epoch_log = args_og.first_epoch_log
+
+  # epochs
+  args.epochs          = args_og.epochs
+
+  # dataset (noisy test set)
+  args.use_validation_size = args_og.use_validation_size
+
+  # TODO Maybe change these as well at some point?...
+  # L1 ?
+  # n-iter-codes?
+
+
+
+
+
+
+algName=args.opt_method
+args.algorithm =algName + '_CNN'
+# Train all layers with the same learning rate for controllability!
+args.lr_out = args.lr_weights
+
+# Check cuda
+device = torch.device("cuda:0" if not args.no_cuda and torch.cuda.is_available() else "cpu")
+torch.manual_seed(args.seed)
+if device.type != 'cpu':
+    print('\033[93m'+'Using CUDA'+'\033[0m')
+    torch.cuda.manual_seed(args.seed)
+
+# Do not use multi-gpu!!
+multi_gpu, num_workers = False, 1
+if False:
+  if device.type != 'cpu' and torch.cuda.device_count() > 1:
+      print('\033[93m'+'Using ', torch.cuda.device_count(), 'GPUs'+'\033[0m')
+      multi_gpu = True
+      num_workers = torch.cuda.device_count()
+
+# Load dataset
+print('\n* Loading dataset {}'.format(args.dataset))
+if   args.use_validation_size>0:
+  dataName  = 'valid_'
+  data_seed=23
+  rand.seed(data_seed)
+else:
+  dataName  = ''
+if args.data_augmentation:
+    print('    data augmentation')
+train_loader, test_loader, n_inputs = load_dataset(dataName+args.dataset, batch_size=args.batch_size, conv_net=True, data_augmentation=args.data_augmentation, num_workers=num_workers, valid_size=args.use_validation_size)
+
+window_size = train_loader.dataset.train_data[0].shape[0]
+if len(train_loader.dataset.train_data[0].shape) == 3:
+    num_input_channels = train_loader.dataset.train_data[0].shape[2]
+else:
+    num_input_channels = 1
+
+if hasattr(train_loader, 'numSamples'):
+  numTrData = train_loader.numSamples
+  numTeData = test_loader.numSamples
+else:
+  numTrData = len(train_loader.dataset)
+  numTeData = len(test_loader.dataset)
+
+
+# Load model
+print('* Loading model {}'.format(args.model))
+if args.model.lower() == 'lenet':
+    from models import test, LeNet
+    model = LeNet(num_input_channels=num_input_channels, window_size=window_size, bias=True).to(device)
+elif args.model.lower() == 'vgg7':
+    from models import test, VGG7
+    model = VGG7(num_input_channels=num_input_channels, window_size=window_size, bias=True).to(device)
+elif args.model.lower() in ['resnet', 'resnet18']:
+    from models import test, resnet18
+    model = resnet18(num_classes=10).to(device)
+criterion = nn.CrossEntropyLoss()
+
+
+# Main
+if __name__ == "__main__":
+    # Save everything in SHELF
+    print('********************')
+    print('Saving shelf to:')
+    print(args.save_filename)
+    print('********************')
+    SH = ddict(args=args.__dict__)
+    if args.save_filename:
+        SH._save(args.save_filename, date=True)
+
+    # Store training and test performance after each training epoch
+    SH.perf = ddict(tr=[], te=[])
+
+    # Store test performance after each iteration in first epoch
+    SH.perf.first_epoch = []
+
+    # Store test performance after each args.save_interval iterations
+    SH.perf.te_vs_iterations = []
+
+    # Create model, and expose modules that has_codes
+    if algName=='altmin':
+      model = get_mods(model)
+      set_mod_optimizers_(model, optimizer = 'Adam',
+                     optimizer_params = {'lr':args.lr_weights}, data_parallel=multi_gpu)
+      model[-1].optimizer.param_groups[0]['lr'] = args.lr_weights
+
+    elif algName == 'adam':
+      optimizer = optim.Adam(model.parameters(), lr=args.lr_weights)
+    elif algName == 'sgd':
+      optimizer = optim.SGD(model.parameters(), lr=args.lr_weights)
+      scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=0.9)
+
+    # Initial mu and increment after every mini-batch
+    mu = args.min_mu
+    mu_max = args.max_mu
+
+    for epoch in range(1, args.epochs+1):
+
+        # Configure dropout parameters.
+        if args.LW_dropout_perc>0 and args.LW_dropout_delay<epoch:
+          useDropout = args.LW_dropout_perc
+        else:
+          useDropout = 0
+
+        batch_size = args.batch_size + args.delta_batch_size*(epoch-1)
+        print('\nEpoch {} of {}. mu = {:.2f}, batch_size = {}, algorithm = {}'.format(epoch, args.epochs, mu, batch_size, algName))
+
+        model.train()
+        for batch_idx, (data, targets) in enumerate(train_loader):
+            data, targets = data.to(device), targets.to(device)
+
+            if algName=='altmin':
+            #----------------------------------------------------------
+              # Set L1 weights according to \mu.
+              if args.lambda_c_muFact>0:
+                epoch_lam_c = args.lambda_c_muFact * mu
+              else:
+                epoch_lam_c = args.lambda_c
+              if args.lambda_w_muFact>0:
+                epoch_lam_w = args.lambda_w_muFact * mu
+              else:
+                epoch_lam_w = args.lambda_w
+  
+              # Now run alt-min algorithm.
+              for mini_epoch in range(args.mini_epochs):
+                ######## Forward: compute codes.
+                model.train()
+                with torch.no_grad():
+                    outputs, codes = get_codes(model, data)
+  
+                # Update codes w/weights fixed.
+                codes = update_codes(codes, model, targets, criterion, mu, lambda_c=epoch_lam_c, n_iter=args.n_iter_codes, lr=args.lr_codes)
+  
+                # Update weights w/codes fixed.
+                # Manually apply dropout for last layer:
+                if (torch.rand(1).item() > useDropout) or useDropout<=0.00001:
+                  update_last_layer_(model[-1], codes[-1], targets, criterion, n_iter=args.n_iter_weights)
+  
+                update_hidden_weights_adam_(model, data, codes, lambda_w=epoch_lam_w, n_iter=args.n_iter_weights, dropout_perc=useDropout)
+  
+                # Increment mu. (batch_idx init to 0)
+                if ((batch_idx+1) % args.mu_update_freq == 0) and (mu < mu_max):
+                    mu += args.d_mu
+                    mu *= args.mult_mu
+            #----------------------------------------------------------
+            elif algName in ['sgd','adam']:
+              optimizer.zero_grad()
+              outputs = model(data)
+              loss = criterion(outputs, targets)
+              loss.backward()
+              optimizer.step()
+            #----------------------------------------------------------
+
+            # Store all iterations of first epoch
+            if epoch == 1 and args.first_epoch_log:
+                SH.perf.first_epoch += [test(model, data_loader=test_loader, label=" - Test")]
+
+            # Outputs to terminal
+            if batch_idx % int(len(train_loader)/args.log_frequency) == 0:
+                loss = criterion(outputs, targets)
+                print(' Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                    epoch, batch_idx * len(data), numTrData,
+                    100. * batch_idx / len(train_loader), loss.item()))
+
+            # After every args.save_interval iterations compute and save test error
+            if batch_idx % args.save_interval == 0 and batch_idx > 0:
+                SH.perf.te_vs_iterations += [test(model, data_loader=test_loader, label=" - Test")]
+                if args.save_filename:
+                    SH._save()
+
+        # If the mu-increment-frequency is larger than the number of batches,
+        # here is a separate update for epoch-wise mu updating.
+        if (args.mu_update_freq > len(train_loader)) and (mu < mu_max):
+            mu += args.d_mu
+            mu *= args.mult_mu
+
+        # Print performances
+        SH.perf.tr += [test(model, data_loader=train_loader, label="Training")]
+        SH.perf.te += [test(model, data_loader=test_loader, label="Test")]
+
+        # Save data after every epoch
+        if args.save_filename:
+            SH._save()
+
+    # ----------------------------------------------------------------
+    # Post-processing step from Carreira-Perpinan (fit last layer):
+    # ----------------------------------------------------------------
+    if args.postprocessing_steps > 0:
+
+        print('\nPost-processing step:\n')
+
+        for epoch in range(1, args.postprocessing_steps+1):
+            for batch_idx, (data, targets) in enumerate(train_loader):
+                data, targets = data.to(device), targets.to(device)
+                post_processing_step(model, data, targets, criterion, args.lambda_w)
+
+                # Outputs to terminal
+                if batch_idx % args.log_interval == 0:
+                    print(' Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                        epoch, batch_idx * len(data), len(train_loader.dataset),
+                        100. * batch_idx / len(train_loader), loss.item()))
+
+        # Print performances
+        SH.perf.tr_final = test(model, data_loader=train_loader, label="  Training set after post-processing")
+        SH.perf.te_final = test(model, data_loader=test_loader, label="  Test set after post-processing    ")
+
+        # Save data after every epoch
+        if args.save_filename:
+            SH._save()
+
+    if args.save_model:
+      SH.perf.model = model
diff --git a/CNN_DEMO/utils/__init__.py b/CNN_DEMO/utils/__init__.py
new file mode 100644
index 0000000..cb26718
--- /dev/null
+++ b/CNN_DEMO/utils/__init__.py
@@ -0,0 +1,13 @@
+try:
+    from .utils import ddict
+    from .datasets import load_dataset
+    from .mnist_multitask import load_multitask_dataset
+
+except ImportError as e:
+    import sys
+    print('''Could not import submodules (exact error was: %s).''' % e, file=sys.stderr)
+
+
+__all__ = [
+    'ddict', 'load_dataset', 'load_multitask_dataset', 'Flatten'
+]
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diff --git a/CNN_DEMO/utils/datasets.py b/CNN_DEMO/utils/datasets.py
new file mode 100644
index 0000000..e61ea36
--- /dev/null
+++ b/CNN_DEMO/utils/datasets.py
@@ -0,0 +1,347 @@
+"""
+dataloaders
+
+@author Benjamin Cowen and Mattia Rigotti
+@date 20 December 2018
+"""
+import torch
+from torchvision import datasets, transforms
+from torch.utils.data.sampler import SubsetRandomSampler
+from random import shuffle
+
+def load_dataset(namedataset='mnist', batch_size=200, data_augmentation=False, conv_net=False, num_workers=1, noise_sigma=0.0, valid_size=-1):
+    '''data_augmentation: use data augmentation, if it is available for dataset
+       conv_net: set to `True` if the dataset is being used with a conv net (i.e. the inputs have to be 3d tensors and not flattened)
+    '''
+
+    # Load mnist dataset
+    if namedataset == 'mnist':
+
+        DIR_DATASET = '~/data/mnist'
+
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))]
+
+        if not conv_net:
+            transform_list.append(transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))))
+
+        transform = transforms.Compose(transform_list)
+
+        trainset = datasets.MNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        testset = datasets.MNIST(DIR_DATASET, train=False, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+    elif namedataset == 'valid_mnist':
+
+        if valid_size < 1:
+          raise ValueError('Validation requested with validation size = {}'.format(valid_size))
+
+        DIR_DATASET = '~/data/mnist'
+
+        # Define the transforms applied to every sample.
+        # Desired mean and Std Dev of data:
+        MEAN = 0.1307
+        STD  = 0.3081
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((MEAN,), (STD,))]
+        transform = transforms.Compose(transform_list)
+
+        # Now load training data and make two dataloaders (train/validation) for it.
+        allTrainData = datasets.MNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        num_train = len(allTrainData)
+        indices   = list(range(num_train))
+        # First shuffle the indices:
+        shuffle(indices)
+        # Assign sampled before split to train; after split to validation.
+        split     = num_train - valid_size
+        print('nTrain='+str(num_train))
+        print('validsize='+str(valid_size))
+        print('split='+str(split))
+        train_idx, valid_idx = indices[:split], indices[split:]
+        # Random samplers for the relevant indices only.
+        train_sampler = SubsetRandomSampler(train_idx)
+        valid_sampler = SubsetRandomSampler(valid_idx)
+
+        # Finally, instantiate the data loaders.
+        train_loader = torch.utils.data.DataLoader(allTrainData,
+                                                  sampler     = train_sampler,
+                                                  batch_size  = batch_size,
+                                                  num_workers = num_workers)
+        train_loader.numSamples=len(train_idx)
+        # THIS IS THE VALIDATION SET:
+        test_loader = torch.utils.data.DataLoader(allTrainData,
+                                                  sampler     = valid_sampler,
+                                                  batch_size  = batch_size,
+                                                  num_workers = num_workers)
+        test_loader.numSamples=len(valid_idx)
+
+        print('train size = '+str(train_loader.numSamples))
+        print('valid size = '+str(test_loader.numSamples))
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    # Load SEQUENTIAL mnist dataloader
+    # That is, vectorized and grayscaled to a 784 length vector.
+    elif namedataset == 'seq_mnist':
+
+        DIR_DATASET = '~/data/mnist'
+
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))]
+
+        # Vectorize
+        transform_list.append(transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))))
+
+        transform = transforms.Compose(transform_list)
+
+        trainset = datasets.MNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        testset = datasets.MNIST(DIR_DATASET, train=False, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    elif namedataset == 'noisy_seq_mnist':
+
+        DIR_DATASET = '~/data/mnist'
+
+        # Desired mean and Std Dev of data:
+        MEAN = 0.1307
+        STD  = 0.3081
+
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((MEAN,), (STD,))]
+
+        # Vectorize
+        transform_list.append(transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))))
+
+        transform = transforms.Compose(transform_list)
+
+        trainset = datasets.MNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        # Add Gaussian noise to the test set.
+        transform_list.append(transforms.Lambda(lambda x: x + MEAN
+                                                 + noise_sigma*STD*torch.randn(x.size())))
+
+        testset = datasets.MNIST(DIR_DATASET, train=False, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    elif namedataset == 'valid_seq_mnist':
+
+        if valid_size < 1:
+          raise ValueError('Validation requested with validation size = {}'.format(valid_size))
+
+        DIR_DATASET = '~/data/mnist'
+
+        # Define the transforms applied to every sample.
+        # Desired mean and Std Dev of data:
+        MEAN = 0.1307
+        STD  = 0.3081
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((MEAN,), (STD,))]
+        # Vectorize
+        transform_list.append(transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))))
+        transform = transforms.Compose(transform_list)
+
+        # Now load training data and make two dataloaders (train/validation) for it.
+        allTrainData = datasets.MNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        num_train = len(allTrainData)
+        indices   = list(range(num_train))
+        # First shuffle the indices:
+        shuffle(indices)
+        # Assign sampled before split to train; after split to validation.
+        split     = num_train - valid_size
+        print('nTrain='+str(num_train))
+        print('validsize='+str(valid_size))
+        print('split='+str(split))
+        train_idx, valid_idx = indices[:split], indices[split:]
+        # Random samplers for the relevant indices only.
+        train_sampler = SubsetRandomSampler(train_idx)
+        valid_sampler = SubsetRandomSampler(valid_idx)
+
+        # Finally, instantiate the data loaders.
+        train_loader = torch.utils.data.DataLoader(allTrainData,
+                                                  sampler     = train_sampler,
+                                                  batch_size  = batch_size,
+                                                  num_workers = num_workers)
+        train_loader.numSamples=len(train_idx)
+        # THIS IS THE VALIDATION SET:
+        test_loader = torch.utils.data.DataLoader(allTrainData,
+                                                  sampler     = valid_sampler,
+                                                  batch_size  = batch_size,
+                                                  num_workers = num_workers)
+        test_loader.numSamples=len(valid_idx)
+
+        print('train size = '+str(train_loader.numSamples))
+        print('valid size = '+str(test_loader.numSamples))
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    # Load mnist dataset
+    elif namedataset == 'fashion_mnist':
+
+        DIR_DATASET = '~/data/fashion_mnist'
+
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,))]
+
+        if not conv_net:
+            transform_list.append(transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))))
+
+        transform = transforms.Compose(transform_list)
+
+        trainset = datasets.FashionMNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        testset = datasets.FashionMNIST(DIR_DATASET, train=False, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    elif namedataset == 'valid_fashion_mnist':
+
+        if valid_size < 1:
+          raise ValueError('Validation requested with validation size = {}'.format(valid_size))
+
+        DIR_DATASET = '~/data/fashion_mnist'
+
+        # Define the transforms applied to every sample.
+        # Desired mean and Std Dev of data:
+        MEAN = 0.1307
+        STD  = 0.3081
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((MEAN,), (STD,))]
+
+        if not conv_net:
+            transform_list.append(transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))))
+
+        transform = transforms.Compose(transform_list)
+
+        # Now load training data and make two dataloaders (train/validation) for it.
+        allTrainData = datasets.FashionMNIST(DIR_DATASET, train=True, download=True, transform=transform)
+        num_train = len(allTrainData)
+        indices   = list(range(num_train))
+        # First shuffle the indices:
+        shuffle(indices)
+        # Assign sampled before split to train; after split to validation.
+        split     = num_train - valid_size
+        print('nTrain='+str(num_train))
+        print('validsize='+str(valid_size))
+        print('split='+str(split))
+        train_idx, valid_idx = indices[:split], indices[split:]
+        # Random samplers for the relevant indices only.
+        train_sampler = SubsetRandomSampler(train_idx)
+        valid_sampler = SubsetRandomSampler(valid_idx)
+
+        # Finally, instantiate the data loaders.
+        train_loader = torch.utils.data.DataLoader(allTrainData,
+                                                  sampler     = train_sampler,
+                                                  batch_size  = batch_size,
+                                                  num_workers = num_workers)
+        train_loader.numSamples=len(train_idx)
+        # THIS IS THE VALIDATION SET:
+        test_loader = torch.utils.data.DataLoader(allTrainData,
+                                                  sampler     = valid_sampler,
+                                                  batch_size  = batch_size,
+                                                  num_workers = num_workers)
+        test_loader.numSamples=len(valid_idx)
+
+        print('train size = '+str(train_loader.numSamples))
+        print('valid size = '+str(test_loader.numSamples))
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    # Load mnist_tf dataset (mnist with tensorflow validation split, i.e. remove
+    # first 5000 samples from training set for validation split)
+    elif namedataset == 'mnist_tf':
+
+        from .mnist_tf import MNIST_TF
+        DIR_DATASET = '~/data/mnist'
+
+        transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,)),
+            transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))),
+        ])
+
+        trainset = MNIST_TF(DIR_DATASET, train=True, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        testset = MNIST_TF(DIR_DATASET, train=False, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=True, num_workers=num_workers)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    # Load cifar10 (preprocessing from https://github.com/kuangliu/pytorch-cifar)
+    elif namedataset == 'cifar10':
+
+        DIR_DATASET = '~/data/cifar10'
+
+        transform_list = [
+            transforms.ToTensor(),
+            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]
+
+        if not conv_net:
+            transform_list.append(
+                transforms.Lambda(lambda x: x.view(x.size(0)*x.size(1)*x.size(2))))
+
+        transform_test = transforms.Compose(transform_list)
+
+        if data_augmentation:
+            transform_train_list = [
+                transforms.RandomCrop(32, padding=4),
+                transforms.RandomHorizontalFlip(),
+                transforms.ToTensor(),
+                transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010))]
+
+            if not conv_net:
+                transform_train_list.append(
+                    transforms.Lambda(lambda x: x.view(x.size(0)*x.size(1)*x.size(2))))
+
+            transform_train = transforms.Compose(transform_train_list)
+
+        else:
+            transform_train = transform_test
+
+        trainset = datasets.CIFAR10(DIR_DATASET, train=True, download=True, transform=transform_train)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers)
+
+        testset = datasets.CIFAR10(DIR_DATASET, train=False, download=True, transform=transform_test)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=num_workers)
+
+        classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
+        n_inputs = 3*32*32
+
+    # Load Higgs (first 10'000 samples of dataset are test, the rest are training)
+    elif namedataset == 'higgs':
+
+        from .higgs import HIGGS_LOADER
+        DIR_DATASET = '~/data/higgs'
+
+        train_loader = HIGGS_LOADER(DIR_DATASET, train=True, download=True, batch_size=batch_size, num_workers=num_workers)
+        test_loader = HIGGS_LOADER(DIR_DATASET, train=False, download=True, batch_size=batch_size, num_workers=num_workers)
+        n_inputs = train_loader.n_inputs
+
+    else:
+        raise ValueError('Dataset {} not recognized'.format(namedataset))
+
+    return train_loader, test_loader, n_inputs
diff --git a/CNN_DEMO/utils/higgs.py b/CNN_DEMO/utils/higgs.py
new file mode 100644
index 0000000..aaabd4a
--- /dev/null
+++ b/CNN_DEMO/utils/higgs.py
@@ -0,0 +1,184 @@
+from __future__ import print_function
+import torch.utils.data as data
+import sys
+import os
+import os.path
+import errno
+import torch
+import codecs
+from utils.utils import download_progress
+import itertools
+import pandas as pd
+
+
+class HIGGS_LOADER(object):
+    """HIGGS dataset
+    Args:
+        root (string): Root directory of dataset where ``processed/training.pt``
+            and  ``processed/test.pt`` exist.
+        train (bool, optional): If True, creates dataset from ``training.pt``,
+            otherwise from ``test.pt``.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+    """
+    url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/00280/HIGGS.csv.gz'
+
+    n_test = 500000     # (as in paper use last 500k samples for test)
+    test_file = 'test.csv'
+    n_train = 11000000-n_test
+    chunk_size = 100000 # chuck_size must be a divisor of n_test and n_train
+    n_inputs = 28
+    classes = [0.0, 1.0]
+
+    def __init__(self, root, train=True, transform=None, target_transform=None, download=False, batch_size=100):
+        self.root = os.path.expanduser(root)
+        self.filename = os.path.basename(self.url)
+        self.filepath = os.path.join(self.root, self.filename)
+        self.testfilepath = os.path.join(self.root, self.test_file)
+        self.transform = transform
+        self.target_transform = target_transform
+        self.train = train  # training set or test set
+        self.batch_size = batch_size
+
+        self.columns = ['y'] + list(range(self.n_inputs))
+
+        if download:
+            self.download()
+
+        if not (self._check_exists_raw() and self._check_exists_test()):
+            raise RuntimeError('Dataset not found.' +
+                               ' You can use download=True to download it')
+        # Quick and dirty, so that len(dataloader.dataset) works
+        class dl:
+            def __init__(self, n):
+                self.n = n
+            def __len__(self):
+                return self.n
+        if self.train:
+            self.dataset = dl(self.n_train)
+        else:
+            self.dataset = dl(self.n_test)
+
+    def __len__(self):
+        if self.train:
+            return int(self.n_train/self.batch_size)
+        else:
+            return int(self.n_test/self.batch_size)
+
+    def _check_exists_raw(self):
+        return os.path.exists(self.filepath)
+
+    def _check_exists_test(self):
+        # Check test set and raw data for training
+        return os.path.exists(os.path.join(self.root, self.test_file))
+
+    def download(self):
+        """Download the MNIST data if it doesn't exist in processed_folder already."""
+        if not self._check_exists_raw():
+
+            # download files
+            try:
+                os.makedirs(self.root)
+            except OSError as e:
+                if e.errno == errno.EEXIST:
+                    pass
+                else:
+                    raise
+
+            download_progress(self.url, self.root)
+            print('Done!')
+
+        # Make the test set if file does not exist
+        if not self._check_exists_test():
+            print('\nCreating HIGGS test dataset (this will take some time):')
+            iter_start = 0
+            iter_end = int((self.n_train+self.n_test)/self.chunk_size)
+            test_start = int(self.n_train/self.chunk_size)
+
+            test_data = pd.DataFrame()
+            for i, data_chunk in enumerate(itertools.islice(
+                pd.read_csv(self.filepath, compression='gzip', sep=',', chunksize=self.chunk_size, names=self.columns),
+                iter_start, iter_end)):
+
+                sys.stdout.write("  Processes %d of %d data chunks (%0.2f%%)\r" %
+                    (i+1, iter_end, (i+1)*100/iter_end))
+                if i >= iter_end-1:
+                    sys.stdout.write('\n')
+
+                if i >= test_start:
+                    test_data = pd.concat([test_data, data_chunk])
+
+            print('Writing test file'+self.testfilepath)
+            test_data.to_csv(self.testfilepath)
+            print('Done!')
+
+    def __iter__(self):
+        if self.train:
+            iter_start = 0
+            iter_end = int(self.n_train/self.chunk_size)
+            csv_file = self.filepath
+        else:
+            iter_start = 0
+            iter_end = int(self.n_test/self.chunk_size)
+            csv_file = self.testfilepath
+
+        self.data_iter = itertools.islice(
+            pd.read_csv(self.filepath, compression='gzip', sep=',', chunksize=self.chunk_size, names=self.columns),
+            iter_start, iter_end)
+
+        return _DataLoaderIter(self)
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        tmp = 'train' if self.train is True else 'test'
+        fmt_str += '    Split: {}\n'.format(tmp)
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+
+class _DataLoaderIter(object):
+    r"""Iterates once over the DataLoader's dataset"""
+
+    def __init__(self, loader):
+        self.data_iter = loader.data_iter
+        self.batch_size = loader.batch_size
+        self.n_inputs = loader.n_inputs
+        self.n_batches = len(loader)
+
+        self.data_chunk = next(self.data_iter)
+
+    def __next__(self):
+        if len(self.data_chunk) < self.batch_size:
+            try:
+                new_data_chunk = next(self.data_iter)
+                self.data_chunk = pd.concat([self.data_chunk, new_data_chunk])
+            except StopIteration:
+                raise StopIteration
+
+        while True:
+            inputs = torch.from_numpy(
+                self.data_chunk.head(self.batch_size)[list(range(self.n_inputs))].values).float()
+            targets  = torch.from_numpy(
+                self.data_chunk.head(self.batch_size)['y'].values).long()
+
+            self.data_chunk = self.data_chunk.iloc[self.batch_size:]
+
+            return inputs, targets
+
+    next = __next__ # Python 2 compatibility
+
+    def __len__(self):
+        return self.n_batches
+
+    def __iter__(self):
+        return self
diff --git a/CNN_DEMO/utils/mnist_multitask.py b/CNN_DEMO/utils/mnist_multitask.py
new file mode 100644
index 0000000..2513106
--- /dev/null
+++ b/CNN_DEMO/utils/mnist_multitask.py
@@ -0,0 +1,146 @@
+import torch
+from torchvision import datasets, transforms
+from PIL import Image
+
+
+def load_multitask_dataset(namedataset='mnist_rotate', n_samples=1000, rotation=90, batch_size=100):
+
+    if namedataset == 'mnist_rotate':
+
+        DIR_DATASET = '~/data/mnist'
+
+        transform = transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,)),
+            transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))),
+        ])
+
+        trainset = MNIST_ROTATE(DIR_DATASET, train=True, rotation=rotation, n_samples=n_samples, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)
+
+        testset = MNIST_ROTATE(DIR_DATASET, train=False, rotation=rotation, n_samples=n_samples, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    elif namedataset == 'mnist_permute':
+
+        DIR_DATASET = '~/data/mnist'
+
+        transform = transform=transforms.Compose([
+            transforms.ToTensor(),
+            transforms.Normalize((0.1307,), (0.3081,)),
+            transforms.Lambda(lambda x: x.view(x.size(1)*x.size(2))),
+        ])
+
+        trainset = MNIST_PERMUTE(DIR_DATASET, train=True, n_samples=n_samples, download=True, transform=transform)
+        train_loader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True)
+
+        # Make sure to use same permutation for traning and test set...
+        testset = MNIST_PERMUTE(DIR_DATASET, permutation=trainset.permutation, train=False, n_samples=n_samples, transform=transform)
+        test_loader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=True)
+
+        classes = tuple(range(10))
+        n_inputs = 784
+
+    else:
+        raise ValueError('Dataset {} not recognized'.format(namedataset))
+
+    return train_loader, test_loader, n_inputs
+
+
+class MNIST_ROTATE(datasets.MNIST):
+
+    def __init__(self, root, train=True, rotation=90, n_samples=1000, transform=None, target_transform=None, download=False):
+        super(MNIST_ROTATE, self).__init__(root=root, train=train, transform=transform,
+                                           target_transform=target_transform, download=download)
+        self.rotation = rotation
+
+        '''New torchvision convention:'''
+        if not hasattr(self, 'data'):
+            if self.train:
+                self.data = self.train_data
+                self.targets = self.train_labels
+            else:
+                self.data = self.test_data
+                self.targets = self.test_labels
+
+        self.data = self.data[:n_samples]
+        self.targets = self.targets[:n_samples]
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.targets[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = Image.fromarray(img.numpy(), mode='L')
+        img = img.rotate(self.rotation)
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+
+class MNIST_PERMUTE(datasets.MNIST):
+
+    def __init__(self, root, permutation=None, train=True, n_samples=1000, transform=None, target_transform=None, download=False):
+        super(MNIST_PERMUTE, self).__init__(root=root, train=train, transform=transform,
+                                           target_transform=target_transform, download=download)
+        '''New torchvision convention:'''
+        if not hasattr(self, 'data'):
+            if self.train:
+                self.data = self.train_data
+                self.targets = self.train_labels
+            else:
+                self.data = self.test_data
+                self.targets = self.test_labels
+
+        if permutation is None:
+            self.permutation = torch.randperm(self.data.numel()//len(self.data)).long().view(-1)
+        else:
+            self.permutation = permutation
+
+        size = self.data.size()
+        self.data = self.data.view(size[0], -1).index_select(1, self.permutation).view(size)
+
+        self.data = self.data[:n_samples]
+        self.targets = self.targets[:n_samples]
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.targets[index]
+
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = Image.fromarray(img.numpy(), mode='L')
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
diff --git a/CNN_DEMO/utils/mnist_tf.py b/CNN_DEMO/utils/mnist_tf.py
new file mode 100644
index 0000000..879e0d8
--- /dev/null
+++ b/CNN_DEMO/utils/mnist_tf.py
@@ -0,0 +1,187 @@
+from __future__ import print_function
+import torch.utils.data as data
+from PIL import Image
+import os
+import os.path
+import errno
+import numpy as np
+import torch
+import codecs
+from torchvision.datasets.utils import download_url
+
+
+class MNIST_TF(data.Dataset):
+    """`MNIST <http://yann.lecun.com/exdb/mnist/>`_ Dataset, with training and validation split as tensorflow
+        (i.e., the first 5000 samples of the standard training set are used as validation split)
+    Args:
+        root (string): Root directory of dataset where ``processed/training.pt``
+            and  ``processed/test.pt`` exist.
+        train (bool, optional): If True, creates dataset from ``training.pt``,
+            otherwise from ``test.pt``.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+    """
+    urls = [
+        'http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
+        'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
+        'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
+        'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
+    ]
+    raw_folder = 'raw'
+    processed_folder = 'processed'
+    training_file = 'mnist_tf_training.pt'
+    test_file = 'mnist_tf_test.pt'
+    valid_file = 'mnist_tf_valid.pt'
+    classes = ['0 - zero', '1 - one', '2 - two', '3 - three', '4 - four',
+               '5 - five', '6 - six', '7 - seven', '8 - eight', '9 - nine']
+    class_to_idx = {_class: i for i, _class in enumerate(classes)}
+
+    def __init__(self, root, train=True, transform=None, target_transform=None, download=False):
+        self.root = os.path.expanduser(root)
+        self.transform = transform
+        self.target_transform = target_transform
+        self.train = train  # training set or test set
+
+        if download:
+            self.download()
+
+        if not self._check_exists():
+            raise RuntimeError('Dataset not found.' +
+                               ' You can use download=True to download it')
+
+        if self.train == True:
+            data_file = self.training_file
+        elif self.train == False:
+            data_file = self.test_file
+        else:
+            data_file = self.valid_file
+        self.data, self.targets = torch.load(os.path.join(self.root, self.processed_folder, data_file))
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.targets[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+        img = Image.fromarray(img.numpy(), mode='L')
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+    def _check_exists(self):
+        return os.path.exists(os.path.join(self.root, self.processed_folder, self.training_file)) and \
+            os.path.exists(os.path.join(self.root, self.processed_folder, self.test_file))
+
+    def download(self):
+        """Download the MNIST data if it doesn't exist in processed_folder already."""
+        import gzip
+
+        if self._check_exists():
+            return
+
+        # download files
+        try:
+            os.makedirs(os.path.join(self.root, self.raw_folder))
+            os.makedirs(os.path.join(self.root, self.processed_folder))
+        except OSError as e:
+            if e.errno == errno.EEXIST:
+                pass
+            else:
+                raise
+
+        for url in self.urls:
+            filename = url.rpartition('/')[2]
+            file_path = os.path.join(self.root, self.raw_folder, filename)
+            download_url(url, root=os.path.join(self.root, self.raw_folder),
+                         filename=filename, md5=None)
+            with open(file_path.replace('.gz', ''), 'wb') as out_f, \
+                    gzip.GzipFile(file_path) as zip_f:
+                out_f.write(zip_f.read())
+            os.unlink(file_path)
+
+        # process and save as torch files
+        print('Processing...')
+
+        training_set = (
+            read_image_file(os.path.join(self.root, self.raw_folder, 'train-images-idx3-ubyte')),
+            read_label_file(os.path.join(self.root, self.raw_folder, 'train-labels-idx1-ubyte'))
+        )
+        test_set = (
+            read_image_file(os.path.join(self.root, self.raw_folder, 't10k-images-idx3-ubyte')),
+            read_label_file(os.path.join(self.root, self.raw_folder, 't10k-labels-idx1-ubyte'))
+        )
+
+        # Split training set in validation and training set:
+        valid_set = (
+            training_set[0][:5000],
+            training_set[1][:5000]
+        )
+        training_set = (
+            training_set[0][5000:],
+            training_set[1][5000:]
+        )
+
+        with open(os.path.join(self.root, self.processed_folder, self.training_file), 'wb') as f:
+            torch.save(training_set, f)
+        with open(os.path.join(self.root, self.processed_folder, self.test_file), 'wb') as f:
+            torch.save(test_set, f)
+        with open(os.path.join(self.root, self.processed_folder, self.valid_file), 'wb') as f:
+            torch.save(valid_set, f)
+
+        print('Done!')
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        tmp = 'train' if self.train is True else 'test'
+        fmt_str += '    Split: {}\n'.format(tmp)
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+
+
+def get_int(b):
+    return int(codecs.encode(b, 'hex'), 16)
+
+
+def read_label_file(path):
+    with open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2049
+        length = get_int(data[4:8])
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=8)
+        return torch.from_numpy(parsed).view(length).long()
+
+
+def read_image_file(path):
+    with open(path, 'rb') as f:
+        data = f.read()
+        assert get_int(data[:4]) == 2051
+        length = get_int(data[4:8])
+        num_rows = get_int(data[8:12])
+        num_cols = get_int(data[12:16])
+        images = []
+        parsed = np.frombuffer(data, dtype=np.uint8, offset=16)
+        return torch.from_numpy(parsed).view(length, num_rows, num_cols)
diff --git a/CNN_DEMO/utils/nn.py b/CNN_DEMO/utils/nn.py
new file mode 100644
index 0000000..2383765
--- /dev/null
+++ b/CNN_DEMO/utils/nn.py
@@ -0,0 +1,69 @@
+import torch
+from torch import nn
+
+class LpNorm(nn.Module):
+    '''Lp-Normalization
+        Normalizes inputs by dividing by norm(inputs, p)
+    '''
+    def __init__(self, p=2, eps=1e-9):
+        super(LpNorm, self).__init__()
+        self.eps = eps
+        self.p = p
+
+    def forward(self, inputs):
+        norm = inputs.norm(self.p, -1, keepdim=True).add(self.eps)
+        return inputs.div(norm)
+
+    def extra_repr(self):
+        return '{p}'.format(**self.__dict__)
+
+class BatchCenter(nn.Module):
+    '''Mini-batch centering
+    computes the component-wise average across minibatch and substracts it
+    '''
+    def __init__(self, num_features, track_running_stats=True, use_running_stats=False):
+        super(BatchCenter, self).__init__()
+        self.num_features = num_features
+
+        self.track_running_stats = track_running_stats
+        self.use_running_stats = use_running_stats
+        if self.track_running_stats:
+            self.register_buffer('running_mean', torch.zeros(num_features))
+            self.register_buffer('num_batches_tracked', torch.tensor(0, dtype=torch.long))
+        else:
+            self.register_buffer('running_mean', None)
+            self.register_buffer('num_batches_tracked', None)
+
+    def reset_running_stats(self):
+        if self.track_running_stats:
+            self.running_mean.zero_()
+            self.num_batches_tracked.zero_()
+
+    def forward(self, inputs):
+        mean = inputs.mean(0)
+        if self.training and self.track_running_stats:
+            self.num_batches_tracked += 1
+            exp_avg_factor = 1/self.num_batches_tracked.float()
+            self.running_mean.mul_(1 - exp_avg_factor)
+            self.running_mean.add_(exp_avg_factor*mean)
+            if not self.use_running_stats:
+                return inputs - mean
+        return  inputs - self.running_mean
+
+    def extra_repr(self):
+        return '{num_features}, track_running_stats={track_running_stats}'.format(**self.__dict__)
+
+class Flatten(nn.Module):
+    r"""Reshapes the input tensor as a 2d tensor, where the size of the first (batch) dimension is preserved.
+
+    Inputs: input
+        - **input** (batch, num_dim1, num_dim1,...): tensor containing input features
+
+    Outputs: output
+        - **output'**: (batch, num_dim1*num_dim2*...): tensor containing the output
+    """
+    def __init__(self):
+        super(Flatten, self).__init__()
+
+    def forward(self, x):
+        return x.view(x.size(0), -1)
diff --git a/CNN_DEMO/utils/utils.py b/CNN_DEMO/utils/utils.py
new file mode 100644
index 0000000..e132084
--- /dev/null
+++ b/CNN_DEMO/utils/utils.py
@@ -0,0 +1,234 @@
+from itertools import product
+from collections import namedtuple, MutableMapping
+from random import randint
+import os
+import time
+import torch
+import copy
+import sys
+try:
+    from urllib.request import urlopen # For Python 3.0 and later
+except ImportError:
+    from urllib2 import urlopen # Fall back to Python 2's urllib
+
+
+class ddict(object):
+    '''
+    dd = ddict(lr=[0.1, 0.2], n_hiddens=[100, 500, 1000], n_layers=2)
+    # Save to shelf:
+    dd._save('my_file', date=False)
+    # Load ddict from file:
+    new_dd = ddict()._load('my_file')
+    '''
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+
+    def __add__(self, other):
+        if isinstance(other, type(self)):
+            sum_dct = copy.copy(self.__dict__)
+            for k,v in other.__dict__.items():
+                if k not in sum_dct:
+                    sum_dct[k] = v
+                else:
+                    if type(v) is list and type(sum_dct[k]) is list:
+                        sum_dct[k] = sum_dct[k] + v
+                    elif type(v) is not list and type(sum_dct[k]) is list:
+                        sum_dct[k] = sum_dct[k] + [v]
+                    elif type(v) is list and type(sum_dct[k]) is not list:
+                        sum_dct[k] = [sum_dct[k]] + v
+                    else:
+                        sum_dct[k] = [sum_dct[k]] + [v]
+            return ddict(**sum_dct)
+
+        elif isinstance(other, dict):
+            return self.__add__(ddict(**other))
+        else:
+            raise ValueError("ddict or dict is required")
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __repr__(self):
+        items = ("{}={!r}".format(k, self.__dict__[k]) for k in self._keys())
+        return "{}({})".format(type(self).__name__, ", ".join(items))
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
+    def __iter__(self):
+        return self.__dict__.__iter__()
+
+    def __len__(self):
+        return len(self.__dict__)
+
+    def __setitem__(self, key, value):
+        self.__dict__[key] = value
+
+    def __getitem__(self, key):
+        return self.__dict__[key]
+
+    def _keys(self):
+        return tuple(sorted([k for k in self.__dict__ if not k.startswith('_')]))
+
+    def _values(self):
+        return  tuple([self.__dict__[k] for k in self._keys()])
+
+    def _items(self):
+        return  tuple(zip(self._keys(), self._values()))
+
+    def _save(self, filename=None, date=True):
+        if filename is None:
+            if not hasattr(self, '_filename'): # First save
+                raise ValueError("filename must be provided the first time you call _save()")
+            else: # Already saved
+                torch.save(self, self._filename + '.pt')
+        else: # New filename
+            if date:
+                filename += '_'+time.strftime("%Y%m%d-%H:%M:%S")
+            # Check if filename does not already exist. If it does, change name.
+            while os.path.exists(filename + '.pt') and len(filename) < 100:
+                filename += str(randint(0,9))
+            self._filename = filename
+            torch.save(self, self._filename + '.pt')
+        return self
+
+    def _load(self, filename):
+        try:
+            self = torch.load(filename)
+        except FileNotFoundError:
+            self = torch.load(filename + '.pt')
+        return self
+
+    def _to_dict(self):
+        "Returns a dict (it's recursive)"
+        return_dict = {}
+        for k,v in self.__dict__.items():
+            if isinstance(v, type(self)):
+                return_dict[k] = v._to_dict()
+            else:
+                return_dict[k] = v
+        return return_dict
+
+    @staticmethod
+    def _flatten_dict(d, parent_key='', sep='_'):
+        "Recursively flattens nested dicts"
+        items = []
+        for k, v in d.items():
+            new_key = parent_key + sep + k if parent_key else k
+            if isinstance(v, MutableMapping):
+                items.extend(ddict._flatten_dict(v, new_key, sep=sep).items())
+            else:
+                items.append((new_key, v))
+        return dict(items)
+
+    def _flatten(self, parent_key='', sep='_'):
+        "Recursively flattens nested ddicts"
+        d = self._to_dict()
+        return ddict._flatten_dict(d)
+
+
+class Gridder(object):
+    '''
+    g = Gridder(**{
+        'lr': 0.1,
+        'n_hiddens': [100, 500, 1000],
+        'repetitions': list(range(5))})
+    h = Gridder(lr=[0.1, 0.2], n_hiddens=[100, 500, 1000], n_layers=2)
+    '''
+    def __init__(self, **kwargs):
+        self.__dict__.update(kwargs)
+        self.__grid__ = None
+        self.__tuple__ = None
+        self.__normalize__()
+
+    def __normalize__(self):
+        "Checks that all values are lists (and not for instance individual instances)"
+        for k in self.__keys__():
+            if type(self.__dict__[k]) is not list:
+                self.__dict__[k] = [self.__dict__[k]]
+
+    def __add__(self, other):
+        if isinstance(other, type(self)):
+            uniq_keys = set(self.__keys__() + other.__keys__())
+            sum_dct = {}
+            for k in uniq_keys:
+                vals = []
+                try:
+                    vals += self.__dict__[k]
+                except KeyError:
+                    pass
+                try:
+                    vals += other.__dict__[k]
+                except KeyError:
+                    pass
+                sum_dct[k] = list(set(vals))
+            return Gridder(**sum_dct)
+
+        elif isinstance(other, dict):
+            return self.__add__(Gridder(**other))
+        else:
+            raise ValueError("dict or Gridder is required")
+
+    def __radd__(self, other):
+        return self.__add__(other)
+
+    def __keys__(self):
+        keys = sorted([k for k in self.__dict__
+                       if not k.startswith('__') and not k.endswith('__')])
+        return keys
+
+    def __values__(self):
+        return [self.__dict__[k] for k in self.__keys__()]
+
+    def __repr__(self):
+        keys = self.__keys__()
+        items = ("{}={!r}".format(k, self.__dict__[k]) for k in keys)
+        return "{}({})".format(type(self).__name__, ", ".join(items))
+
+    def __eq__(self, other):
+        return self.__dict__ == other.__dict__
+
+    def __next__(self):
+        if self.__grid__ is None:
+            self.__grid__ = product(*self.__values__())
+            self.__tuple__ = namedtuple('params', self.__keys__())
+        return self.__tuple__(*next(self.__grid__))
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return len(self.__keys__())
+
+
+def download_progress(url, dest_folder):
+
+    print('Downloading ' + url)
+
+    with open(os.path.join(dest_folder, os.path.basename(url)), "wb") as local_file:
+
+        def chunk_report(bytes_so_far, chunk_size, total_size):
+            percent = float(bytes_so_far) / total_size
+            percent = round(percent*100, 2)
+            sys.stdout.write("  Downloaded %d of %d kB (%0.2f%%)\r" %
+                (bytes_so_far/1024, total_size/1024, percent))
+            if bytes_so_far >= total_size:
+                sys.stdout.write('\n')
+
+        def chunk_read(response, chunk_size=102400, report_hook=None):
+            total_size = response.info().get('Content-Length').strip()
+            total_size = int(total_size)
+            bytes_so_far = 0
+            while 1:
+                chunk = response.read(chunk_size)
+                local_file.write(chunk)
+
+                bytes_so_far += len(chunk)
+                if not chunk:
+                    break
+                if report_hook:
+                    report_hook(bytes_so_far, chunk_size, total_size)
+            return bytes_so_far
+
+        response = urlopen(url)
+        chunk_read(response, report_hook=chunk_report)
diff --git a/README.md b/README.md
index 037c9e6..84d6cc9 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,7 @@
-Beyond Backprop: Online Alternating Minimization with Auxiliary Variables
 
-Source code for the paper
+
+# [Beyond Backprop: Online Alternating Minimization with Auxiliary Variables](http://proceedings.mlr.press/v97/choromanska19a/choromanska19a.pdf)
+This repository hosts source code for the CNN experiments published in this ICML paper.
 
 @inproceedings{BeyondBackpropAM,