Utilizar função de ativação e usar o stochastic GD

ann_parameters.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Fri Apr  5 21:27:07 2019
+
+@author: bruna
+"""
+
+# 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
+
+# Initialising the ANN
+classifier = Sequential()
+
+# Adding the input layer and the first hidden layer
+classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'linear', input_dim = 11))
+
+# Adding the second hidden layer
+classifier.add(Dense(units = 10, kernel_initializer = 'uniform', activation = 'relu'))
+
+# Adding the output layer
+classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))
+
+# Compiling the ANN
+classifier.compile(optimizer = 'sgd', loss = 'binary_crossentropy', metrics = ['accuracy'])
+
+# Fitting the ANN to the Training set
+classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)
+
+# 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)
+
+# Making the Confusion Matrix
+from sklearn.metrics import confusion_matrix
+cm = confusion_matrix(y_test, y_pred)