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[WIP] Brincando com MLP e LSTM #9

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34d49d6
Add new ann
Hiroshi18 Apr 3, 2019
b57270c
add drop and change param in layers
Hiroshi18 Apr 3, 2019
abb2a94
Add evaluation
Hiroshi18 Apr 13, 2019
fa3eec0
Add new evaluation
Hiroshi18 Apr 26, 2019
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21 changes: 12 additions & 9 deletions ann.py
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Expand Up @@ -44,24 +44,27 @@
# Part 2 - Now let's make the ANN!

# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dense, Dropout, Conv1D, Masking, Embedding, LSTM
from keras.initializers import RandomNormal

# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dense(256, input_dim=11, activation='relu'))

# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(0.5))

# Adding the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.add(Dense(256, activation='relu'))

classifier.add(Dropout(0.5))

classifier.add(Dense(1, activation='sigmoid'))

# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])

print(classifier.summary())

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 30)
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155 changes: 155 additions & 0 deletions eitlstm.py
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# Artificial Neural Network

# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git

# Installing Tensorflow
# pip install tensorflow

# Installing Keras
# pip install --upgrade keras

# Part 1 - Data Preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Part 2 - Now let's make the ANN!

# Importing the Keras libraries and packages
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout

# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

# Compiling the ANN
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 10)


# Before optimization loss: 0.3864 - acc: 0.8512

# Part 3 - Making predictions and evaluating the model

# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

# Predicting a single new observation
"""Predict if the customer with the following informations will leave the bank:
Geography: France
Credit Score: 600
Gender: Male
Age: 40
Tenure: 3
Balance: 60000
Number of Products: 2
Has Credit Card: Yes
Is Active Member: Yes
Estimated Salary: 50000"""
new_prediction = classifier.predict(sc.transform(np.array([[0.0, 0, 600, 1, 40, 3, 60000, 2, 1, 1, 50000]])))
new_prediction = (new_prediction > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

# Part 4 - Evaluating, Improving and Tuning the ANN

# Evaluating the ANN
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import cross_val_score
from keras.models import Sequential
from keras.layers import Dense
#from keras.layers import Dropout
def build_classifier():
classifier = Sequential()
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 10)
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
mean = accuracies.mean()
variance = accuracies.std()

# Improving the ANN
# Dropout Regularization to reduce overfitting if needed

# Tuning the ANN
from keras.wrappers.scikit_learn import KerasClassifier
from sklearn.model_selection import GridSearchCV
from keras.models import Sequential
from keras.layers import Dense

def build_classifier(optimizer):
classifier = Sequential()
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier

classifier = KerasClassifier(build_fn = build_classifier)

parameters = {'batch_size': [20,32],
'epochs': [20, 32],
'optimizer': ['adam', 'rmsprop']}

grid_search = GridSearchCV(estimator = classifier,
scoring = 'accuracy',
param_grid = parameters,
cv = 10)

grid_search = grid_search.fit(X_train, y_train)

best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_

# Best Accuracy: 0.861375
# Best parameter: {'batch_size': 30, 'epochs': 32, 'optimizer': 'adam'}
49 changes: 26 additions & 23 deletions evaluating_improving_tuning.py → eitmlp.py
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@@ -1,3 +1,4 @@

# Artificial Neural Network

# Installing Theano
Expand Down Expand Up @@ -53,21 +54,20 @@
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
# classifier.add(Dropout(rate = 0.1))

# Adding the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
# classifier.add(Dropout(rate = 0.1))

# Adding the output layer
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(0.5))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))

# Compiling the ANN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])

# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
classifier.fit(X_train, y_train, batch_size = 10, epochs = 10)


# Before optimization loss: 0.3864 - acc: 0.8512

# Part 3 - Making predictions and evaluating the model

Expand Down Expand Up @@ -104,12 +104,12 @@
#from keras.layers import Dropout
def build_classifier():
classifier = Sequential()
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.1))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.1))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
classifier.compile(optimizer = 'rmsprop', loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier
classifier = KerasClassifier(build_fn = build_classifier, batch_size = 10, epochs = 10)
accuracies = cross_val_score(estimator = classifier, X = X_train, y = y_train, cv = 10, n_jobs = -1)
Expand All @@ -127,18 +127,18 @@ def build_classifier():

def build_classifier(optimizer):
classifier = Sequential()
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.1))
classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.1))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 256, kernel_initializer = 'uniform', activation = 'relu'))
classifier.add(Dropout(rate = 0.5))
classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])
return classifier

classifier = KerasClassifier(build_fn = build_classifier)

parameters = {'batch_size': [25, 32],
'epochs': [10, 50],
parameters = {'batch_size': [10, 12],
'epochs': [5, 10],
'optimizer': ['adam', 'rmsprop']}

grid_search = GridSearchCV(estimator = classifier,
Expand All @@ -148,5 +148,8 @@ def build_classifier(optimizer):

grid_search = grid_search.fit(X_train, y_train)

best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_
best_parameters = grid_search.best_params_
best_accuracy = grid_search.best_score_

# Best Accuracy: 0.857875
# Best parameter: {'batch_size': 12, 'epochs': 10, 'optimizer': 'adam'}
83 changes: 83 additions & 0 deletions lstm.py
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# Artificial Neural Network

# Installing Theano
# pip install --upgrade --no-deps git+git://github.com/Theano/Theano.git

# Installing Tensorflow
# pip install tensorflow

# Installing Keras
# pip install --upgrade keras

# Part 1 - Data Preprocessing

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
labelencoder_X_1 = LabelEncoder()
X[:, 1] = labelencoder_X_1.fit_transform(X[:, 1])
labelencoder_X_2 = LabelEncoder()
X[:, 2] = labelencoder_X_2.fit_transform(X[:, 2])
onehotencoder = OneHotEncoder(categorical_features = [1])
X = onehotencoder.fit_transform(X).toarray()
X = X[:, 1:]

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Convert to 3d (not recommended)
X_test = np.reshape(X_test, X_test.shape + (1,))
X_train = np.reshape(X_train, X_train.shape + (1,))

# Part 2 - Now let's make the ANN!

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Dense, Dropout, Conv1D, Masking, Embedding, LSTM
from keras.initializers import RandomNormal

# Initialising the ANN

model = Sequential()

model.add(LSTM(128))

model.add(Dropout(0.5))

model.add(Dense(1, activation='sigmoid'))

# Compiling the ANN
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])

# Fitting the ANN to the Training set
model.fit(X_train, y_train, batch_size=16, epochs=10)
print(model.summary())


# Part 3 - Making predictions and evaluating the model

# Predicting the Test set results
y_pred = model.predict(X_test)
y_pred = (y_pred > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)