def build(self, hp, inputs=None):
if self.identity:
return IdentityLayer(name=self.name)(inputs)
if self.num_classes:
expected = self.num_classes if self.num_classes > 2 else 1
if self.output_shape[-1] != expected:
raise ValueError('The data doesn\'t match the expected shape. '
'Expecting {} but got {}'.format(
expected, self.output_shape[-1]))
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
dropout_rate = self.dropout_rate or hp.Choice(
'dropout_rate', [0.0, 0.25, 0.5], default=0)
if dropout_rate > 0:
output_node = tf.keras.layers.Dropout(dropout_rate)(output_node)
output_node = block_module.Flatten().build(hp, output_node)
output_node = tf.keras.layers.Dense(self.output_shape[-1])(output_node)
if self.loss == 'binary_crossentropy':
output_node = Sigmoid(name=self.name)(output_node)
else:
output_node = tf.keras.layers.Softmax(name=self.name)(output_node)
return output_node
def build(self, hp, inputs=None):
if self.output_dim and self.output_shape[-1] != self.output_dim:
raise ValueError('The data doesn\'t match the output_dim. '
'Expecting {} but got {}'.format(
self.output_dim, self.output_shape[-1]))
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
dropout_rate = self.dropout_rate or hp.Choice(
'dropout_rate', [0.0, 0.25, 0.5], default=0)
if dropout_rate > 0:
output_node = tf.keras.layers.Dropout(dropout_rate)(output_node)
output_node = block.Flatten().build(hp, output_node)
output_node = tf.keras.layers.Dense(self.output_shape[-1])(output_node)
return output_node
def build(self, hp, inputs=None):
if self.num_classes and self.output_shape[-1] != self.num_classes:
raise ValueError('The data doesn\'t match the num_classes. '
'Expecting {} but got {}'.format(
self.num_classes, self.output_shape[-1]))
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
dropout_rate = self.dropout_rate or hp.Choice(
'dropout_rate', [0.0, 0.25, 0.5], default=0)
if dropout_rate > 0:
output_node = tf.keras.layers.Dropout(dropout_rate)(output_node)
output_node = block.Flatten().build(hp, output_node)
output_node = tf.keras.layers.Dense(self.output_shape[-1])(output_node)
if self.loss == 'binary_crossentropy':
output_node = tf.keras.activations.sigmoid(output_node)
else:
output_node = tf.keras.layers.Softmax()(output_node)
return output_node
