from keras.callbacks import ModelCheckpoint
from keras.models import Sequential
from keras.layers import Dense, Activation, Flatten
from keras import optimizers

NN_model = Sequential()

# The Input Layer :
NN_model.add(Dense(64, kernel_initializer='random_uniform',input_dim = X_train.shape[1], activation='relu'))

# The Hidden Layers :
NN_model.add(Dense(128, kernel_initializer='random_uniform',activation='relu'))
NN_model.add(Dense(128, kernel_initializer='random_uniform',activation='relu'))
NN_model.add(Dense(128, kernel_initializer='random_uniform',activation='relu'))

# The Output Layer :
NN_model.add(Dense(1, kernel_initializer='random_uniform',activation='linear'))
adam = optimizers.Adam(lr=0.1, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.0, amsgrad=False)
# Compile the network :
NN_model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['mean_squared_error'], )
NN_model.summary()