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.
model = keras.Sequential([
	keras.layers.Dense(NUMBER_OF_HIDDEN1, activation="sigmoid",
			input_shape=(NUMBER_OF_INPUT_FEATURES,)),
	keras.layers.Dense(NUMBER_OF_HIDDEN2, activation="sigmoid"),
	keras.layers.Dense(NUMBER_OF_CLASSES, activation="softmax")
])

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

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