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

# Importing the dataset
X1 = np.random(10)
X2 = np.random(10)
y = np.random(10)



# 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)


import keras
from keras.models import Sequential
from keras.layers import Dense

def custom(y,y_pred):
	x=y-y_pred
	x=x*x
	
# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu', input_dim = 10))

# Adding the second hidden layer
classifier.add(Dense(output_dim = 6, init = 'uniform', activation = 'relu'))

# Adding the output layer
classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))

classifier.compile(optimizer = 'rmsprop', loss = 'mean_squared_error', metrics = ['accuracy'])

classifier.fit(X_train, y_train, batch_size = 10, nb_epoch = 100)


y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)