def test_feature_encoder_layer():
data = np.array([['a', 'ab'], ['b', 'bc'], ['a', 'bc']])
input_node = tf.keras.Input(shape=(2,), dtype=tf.string)
layer = layer_module.CategoricalEncoding([
layer_module.INT,
layer_module.INT,
])
hidden_node = layer(input_node)
output_node = tf.keras.layers.Dense(1, activation='sigmoid')(hidden_node)
model = tf.keras.Model(input_node, output_node)
model.compile(loss='binary_crossentropy', optimizer='adam')
dataset = tf.data.Dataset.zip((
(tf.data.Dataset.from_tensor_slices(data).batch(32),),
(tf.data.Dataset.from_tensor_slices(np.random.rand(3, 1)).batch(32),),
))
tuner.AutoTuner._adapt_model(model, dataset)
model.fit(data, np.random.rand(3, 1), epochs=1)
output = model.predict(data)
model2 = tf.keras.Model(input_node, hidden_node)
result = model2.predict(data)
assert result[0][0] == result[2][0]
assert result[0][0] != result[1][0]
assert result[0][1] != result[1][1]
assert result[0][1] != result[2][1]
assert output.dtype == np.dtype('float32')
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
encoding = []
for column_name in self.column_names:
column_type = self.column_types[column_name]
if column_type == adapters.CATEGORICAL:
# TODO: Search to use one-hot or int.
encoding.append(keras_layers.INT)
else:
encoding.append(keras_layers.NONE)
return keras_layers.CategoricalEncoding(encoding)(input_node)