from tensorflow import keras
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_CLASSES = 2
NUMBER_OF_HIDDEN1 = 200
NUMBER_OF_HIDDEN2 = 50

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

# Create a model with two hidden layers.
input_layer = keras.Input(shape=(NUMBER_OF_INPUT_FEATURES,))
hidden_layer1 = keras.layers.Dense(NUMBER_OF_HIDDEN1,
		activation="sigmoid")(input_layer)
hidden_layer2 = keras.layers.Dense(NUMBER_OF_HIDDEN2,
		activation="sigmoid")(hidden_layer1)
output_layer = keras.layers.Dense(NUMBER_OF_CLASSES,
		activation="softmax")(hidden_layer2)
model = keras.models.Model(input_layer,output_layer)

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

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