from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
import numpy as np

NUMBER_OF_DATA_POINTS = 100   # number of training examples
NUMBER_OF_INPUT_FEATURES = 10   # e.g. number of MFCC coefficients
NUMBER_OF_HIDDEN1 = 30
NUMBER_OF_HIDDEN2 = 20

input_data = np.zeros(shape=(NUMBER_OF_DATA_POINTS, NUMBER_OF_INPUT_FEATURES))
output_data = np.zeros(shape=(NUMBER_OF_DATA_POINTS,))

# Create a model with two hidden layers.
model = Sequential([
	Dense(NUMBER_OF_HIDDEN1, activation="sigmoid",
			input_shape=(NUMBER_OF_INPUT_FEATURES,)),
	Dense(NUMBER_OF_HIDDEN2, activation="sigmoid"),
	Dense(1, activation="sigmoid")
])

model.compile(optimizer='adam',
		loss='binary_crossentropy',
		metrics=['accuracy'])

model.fit(input_data, output_data, epochs=10, batch_size=32)