Deep Learning Quick Reference

Putting it all together

Now let's look at the entire network, now that we understand the parts. The network is shown in the following code for your reference:

def build_network(vocab_size, embedding_dim, sequence_length):    input = Input(shape=(sequence_length,), name="Input")    embedding = Embedding(input_dim=vocab_size,         output_dim=embedding_dim, input_length=sequence_length,          name="embedding")(input)    lstm1 = LSTM(10, activation='tanh', return_sequences=False,       dropout=0.2, recurrent_dropout=0.2, name='lstm1')(embedding)    output = Dense(1, activation='sigmoid', name='sigmoid')(lstm1)    model = Model(inputs=input, outputs=output)    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])    return model

As we have with other ...