def build(self, hp, inputs=None):
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
num_layers = self.num_layers or hp.Choice("num_layers", [1, 2, 3],
default=2)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
use_batchnorm = hp.Boolean("use_batchnorm", default=False)
if self.dropout is not None:
dropout = self.dropout
else:
dropout = hp.Choice("dropout", [0.0, 0.25, 0.5], default=0)
for i in range(num_layers):
units = hp.Choice(
"units_{i}".format(i=i),
[16, 32, 64, 128, 256, 512, 1024],
default=32,
)
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
output_node = layers.BatchNormalization()(output_node)
output_node = layers.ReLU()(output_node)
if dropout > 0:
output_node = layers.Dropout(dropout)(output_node)
return output_node
def build(self, hp, inputs=None):
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
use_batchnorm = self.use_batchnorm
if use_batchnorm is None:
use_batchnorm = hp.Boolean("use_batchnorm", default=False)
if self.dropout is not None:
dropout = self.dropout
else:
dropout = hp.Choice("dropout", [0.0, 0.25, 0.5], default=0)
for i in range(utils.add_to_hp(self.num_layers, hp)):
units = utils.add_to_hp(self.num_units, hp,
"units_{i}".format(i=i))
output_node = layers.Dense(units)(output_node)
if use_batchnorm:
output_node = layers.BatchNormalization()(output_node)
output_node = layers.ReLU()(output_node)
if dropout > 0:
output_node = layers.Dropout(dropout)(output_node)
return output_node
def build(self, hp, inputs=None):
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
dropout = self.dropout or hp.Choice("dropout", [0.0, 0.25, 0.5], default=0)
if dropout > 0:
output_node = layers.Dropout(dropout)(output_node)
output_node = reduction.Flatten().build(hp, output_node)
output_node = layers.Dense(self.shape[-1], 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 = layers.Dropout(dropout_rate)(output_node)
output_node = reduction.Flatten().build(hp, output_node)
output_node = layers.Dense(self.output_shape[-1],
name=self.name)(output_node)
return output_node
