classifier = Sequential()

# Add the input layer and the first hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'he_uniform',activation='relu',input_dim = 10))

# Add the second hidden layer
classifier.add(Dense(units = 6, kernel_initializer = 'he_uniform',activation='relu'))

# Add the output layer
classifier.add(Dense(units = 1, kernel_initializer = 'glorot_uniform', activation = 'sigmoid'))

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

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

# list all data in history
classifier.summary()
#total params are total number of weights and biases

"""### **5. Model Evaluation**"""

# Predict the Test set results
Y_pred = classifier.predict(X_test)
Y_pred

Y_pred = (Y_pred > 0.5)

Y_pred

# Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, Y_pred)
print(cm)

# Calculate the Accuracy
from sklearn.metrics import accuracy_score
score=accuracy_score(Y_pred,y_test)

print(score)