mlp_design.py

import keras
model = Sequential()

# Input (max_words) --(W1)-> Hidden Layer (512)
model.add(Dense(512, input_shape=(max_words,))) 
model.add(Activation('tanh'))
model.add(Dropout(0.5))

# Hidden Layer (512) --(W2)--> Output Layer (num_classes)
model.add(Dense(num_classes)) 
model.add(Activation('softmax'))

# Add optimization method, loss function, and optimization value
model.compile(loss='categorical_crossentropy', 
              optimizer='adam', metrics=['accuracy'])

# "Fit model" (train model), using training data (80% of data)
model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs)

# Evaluate the trained model, using the test data (20% of data)
score = model.evaluate(x_test, y_test, batch_size=batch_size)