From 2390178b6192d73b100c6b547d0b5d3546b85fe8 Mon Sep 17 00:00:00 2001 From: Benjamin Cowen Date: Tue, 14 May 2019 11:47:34 -0400 Subject: [PATCH 1/6] [benC] added CNN code and hyperparameter results --- CNN_DEMO/README.txt | 35 ++ .../__pycache__/altmin14jan.cpython-36.pyc | Bin 0 -> 12099 bytes CNN_DEMO/__pycache__/models.cpython-36.pyc | Bin 0 -> 8701 bytes CNN_DEMO/altmin14jan.py | 405 ++++++++++++++ CNN_DEMO/amAllTest.sh | 104 ++++ CNN_DEMO/cnnMnistViz19jan.py | 423 +++++++++++++++ CNN_DEMO/experiments/README.txt | 9 + .../all_lenetFashionMnist_18jan.sh | 88 +++ CNN_DEMO/experiments/am_lenetMnist_18jan.sh | 72 +++ .../adam_combo0trial1_20190118-16:37:44.pt | Bin 0 -> 1372 bytes .../adam_combo0trial1_20190118-17:04:04.pt | Bin 0 -> 1372 bytes .../adam_combo0trial1_20190118-17:51:29.pt | Bin 0 -> 1372 bytes .../adam_combo0trial1_20190118-18:52:01.pt | Bin 0 -> 1372 bytes .../adam_combo0trial2_20190118-19:52:57.pt | Bin 0 -> 1372 bytes .../adam_combo0trial2_20190118-20:54:51.pt | Bin 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+`./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. +(a) 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`. +(b) 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. + +(c) 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 which +(a) Load the best hyperparameters determined in Step (2a) and copied in Step (2b). +(b) Train corresponding network using entire training set. +(c) Evaluate on the test. +(d) Repeat for many random intializations. + +Step 4: VISUALIZATION. +From the `postHPexps` subfolder, use 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zuRJj2R*cg#ePN>C@P;OSBgaVqbY$&mBRjD<&7X}Ue~IiPfF|`)-V;!jU?l^1g{{!IQJx($@!-aJ5X!9J+VKhvlG5%k(wao`x$OZC0?Z#1!>8l$?^JqKNRe% z)LG|i7=u3~$;;nEl{&~SH4h@CrsS94+uT4r5rE2zjP((@%F4ddQPKL}A3B#E9@gNs0fH#KbBsffQoO>lVo zA(@XHG~=l1RTaB;H{YVK&3h=21K17qVz?@D}XADAt%ElNOlGs_0k{F*`A+MEiTAF`bo#o!44Ug`i gT0*v&a?T~Qa0.0: + 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/amAllTest.sh b/CNN_DEMO/amAllTest.sh new file mode 100755 index 0000000..af06401 --- /dev/null +++ b/CNN_DEMO/amAllTest.sh @@ -0,0 +1,104 @@ +#!/bin/bash +# Convolutional experiments for alt-min! +# All internal loops have just ONE iteration. + +# Which GPU to run it on? +gpuID=0 + +# Where to save results? +savePath='./test/' + +# These are fixed for all experiments. +max_epochs=2 +numTrials=2 +# It seems like I have to set this to get a preliminary result...? +save_interval=2000 + +# LOOPED HYPERPARAMETERS--------------------------- +# Increment mu by this amount after each minibatch. +# 1234567 1234 +muSchedules=(0.0000001 0.0001) + +# Code learning rates. +code_LRs=(0.01 1) + +# weight learning rates for batchsize 50 +weight_LRs=(0.00005 0.0005 0.005 0.05) + +# FIXED HYPERPARAMETERS----------------------------- +# Sparsity parameters. +sprs_codes=(0) +sp_weights=(0) + +# Subproblem iterations. +ncodeitrs=(5) +nweightitrs=(5) + +# batches/epoch = 58.59 +bszs=(4096) + +# RNN parameters +hidSizes=(20) +seqFacts=(1) + +# For Nesterov SGD subproblem +momentumVal=0.9 + +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 "${bszs[@]}"; do + for LR in "${weight_LRs[@]}"; do #active loop + for hidSz in "${hidSizes[@]}"; do + for seqFct in "${seqFacts[@]}"; do + for codeLR in "${code_LRs[@]}"; do #active loop + for d_mu in "${muSchedules[@]}"; do #active loop + for spCode in "${sprs_codes[@]}"; do + for wCode in "${sp_weights[@]}"; do + for codeIter in "${ncodeitrs[@]}"; do + for weightIter in "${nweightitrs[@]}"; do + + # Parameter combination ID. + let "(comboInc++)" + comboID='combo'$comboInc + + #------------------------------------------ + # Alt-Min-Adam + # Compute save name + savename=$savePath'amAdam_'$comboID$trialID + + # Run alt-min experiment! + CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_rnn_5jan.py --opt-method='altmin' --internalAlg='adam' --rnnHidSize=$hidSz --rnnOutSeqFact=$seqFct --lr-codes=$codeLR --lr-weights=$LR --n-iter-codes=$codeIter --n-iter-weights=$weightIter --lambda_c=$spCode --lambda_w=$wCode --batch-size=$BSZ --d-mu=$d_mu --save-filename=$savename --save-interval=$save_interval --seed=$trialNum --epochs=$max_epochs + + #------------------------------------------ + # Alt-Min-SGD + # Compute save name + savename=$savePath'amSgd_'$comboID$trialID + + # Run alt-min experiment! + CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_rnn_5jan.py --opt-method='altmin' --internalAlg='sgd' --rnnHidSize=$hidSz --rnnOutSeqFact=$seqFct --lr-codes=$codeLR --lr-weights=$LR --n-iter-codes=$codeIter --n-iter-weights=$weightIter --lambda_c=$spCode --lambda_w=$wCode --batch-size=$BSZ --d-mu=$d_mu --save-filename=$savename --save-interval=$save_interval --seed=$trialNum --epochs=$max_epochs + + #------------------------------------------ + # Alt-Min-Nesterov (that is SGD w/Nesterov Momentum) + # Compute save name + savename=$savePath'amNext_'$comboID$trialID + + # Run alt-min experiment! + CUDA_VISIBLE_DEVICES=$gpuID python3 ../train_rnn_5jan.py --opt-method='altmin' --internalAlg='sgd' --momentum=$momentumVal --rnnHidSize=$hidSz --rnnOutSeqFact=$seqFct --lr-codes=$codeLR --lr-weights=$LR --n-iter-codes=$codeIter --n-iter-weights=$weightIter --lambda_c=$spCode --lambda_w=$wCode --batch-size=$BSZ --d-mu=$d_mu --save-filename=$savename --save-interval=$save_interval --seed=$trialNum --epochs=$max_epochs + + done + done + done + done + done + done + done + done + done + done +done 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]s_WK|Hf!5#(yr;Y*alHHLYgZ2(9tChlY1x0Ecf2Y 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