def cnnKeras(training_data, training_labels, test_data, test_labels, n_dim):
print("Initiating CNN")
seed = 8
numpy.random.seed(seed)
model = Sequential()
model.add(Convolution2D(64, 1, 1, init='glorot_uniform', border_mode='valid',
input_shape=(16, 1, 1), activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 1)))
model.add(Convolution2D(32, 1, 1, init='glorot_uniform', activation='relu'))
model.add(MaxPooling2D(pool_size=(1, 1)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dense(1, activation='softmax'))
# Compile model
model.compile(loss='sparse_categorical_crossentropy',
optimizer='adam', metrics=['accuracy'])
model.fit(training_data, training_labels, validation_data=(
test_data, test_labels), nb_epoch=30, batch_size=8, verbose=2)
scores = model.evaluate(test_data, test_labels, verbose=1)
print("Baseline Error: %.2f%%" % (100 - scores[1] * 100))
# model.save('trained_CNN.h5')
return None