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Chapter 2
For now, there's no need to understand what the layers (that is, the lego bricks) are
doing internally, rather, just observe the non-linear network topology. Note also that
a module can call another module, as a function can call another function:
import tensorflow as tf
def build_model():
# variable-length sequence of integers
text_input_a = tf.keras.Input(shape=(None,), dtype='int32')
# variable-length sequence of integers
text_input_b = tf.keras.Input(shape=(None,), dtype='int32')
# Embedding for 1000 unique words mapped to 128-dimensional vectors
shared_embedding = tf.keras.layers.Embedding(1000, 128)
# We reuse the same layer to encode both inputs
encoded_input_a = shared_embedding(text_input_a)
encoded_input_b = shared_embedding(text_input_b)
# two logistic predictions at the end
prediction_a = tf.keras.layers.Dense(1, activation='sigmoid',\\
name='prediction_a')(encoded_input_a)
prediction_b = tf.keras.layers.Dense(1, activation='sigmoid',\\
name='prediction_b')(encoded_input_b)
# this model has 2 inputs, and 2 outputs
# in the middle we have a shared model
model = tf.keras.Model(inputs=[text_input_a, text_input_b],
outputs=[prediction_a, prediction_b])
tf.keras.utils.plot_model(model, to_file="shared_model.png")
build_model()
Note that, first, you create a layer, then you pass it an input; with tf.keras.layers.
Dense(1, activation='sigmoid', name='prediction_a')(encoded_input_a)
the two steps are combined in a single line of code.
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