def build(self, hp, inputs=None): input_node = nest.flatten(inputs)[0] output_node = input_node vectorizer = self.vectorizer or hp.Choice('vectorizer', ['sequence', 'ngram'], default='sequence') if vectorizer == 'ngram': output_node = preprocessing.TextToNgramVector().build(hp, output_node) output_node = basic.DenseBlock().build(hp, output_node) else: output_node = preprocessing.TextToIntSequence().build(hp, output_node) output_node = basic.Embedding( pretraining=self.pretraining).build(hp, output_node) output_node = basic.ConvBlock().build(hp, output_node) output_node = reduction.SpatialReduction().build(hp, output_node) output_node = basic.DenseBlock().build(hp, output_node) return output_node
def test_dense_block(): utils.block_basic_exam( basic.DenseBlock(), tf.keras.Input(shape=(32,), dtype=tf.float32), [ 'num_layers', 'use_batchnorm', ])
def test_dense_block(): input_shape = (32,) block = basic.DenseBlock() hp = kerastuner.HyperParameters() block.build(hp, ak.Input(shape=input_shape).build()) assert utils.name_in_hps('num_layers', hp) assert utils.name_in_hps('use_batchnorm', hp)
def build_body(self, hp, input_node): output_node = basic.DenseBlock().build(hp, input_node) return output_node