def build(self, hp, inputs=None):
inputs = nest.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
kernel_size = self.kernel_size or hp.Choice('kernel_size', [3, 5, 7],
default=3)
num_blocks = self.num_blocks or hp.Choice('num_blocks', [1, 2, 3],
default=2)
num_layers = self.num_layers or hp.Choice('num_layers', [1, 2],
default=2)
separable = self.separable
if separable is None:
separable = hp.Boolean('separable', default=False)
if separable:
conv = utils.get_sep_conv(input_node.shape)
else:
conv = utils.get_conv(input_node.shape)
max_pooling = self.max_pooling
if max_pooling is None:
max_pooling = hp.Boolean('max_pooling', default=True)
pool = utils.get_max_pooling(input_node.shape)
if self.dropout_rate is not None:
dropout_rate = self.dropout_rate
else:
dropout_rate = hp.Choice('dropout_rate', [0.0, 0.25, 0.5],
default=0)
for i in range(num_blocks):
for j in range(num_layers):
output_node = conv(hp.Choice('filters_{i}_{j}'.format(i=i,
j=j),
[16, 32, 64, 128, 256, 512],
default=32),
kernel_size,
padding=self._get_padding(
kernel_size, output_node),
activation='relu')(output_node)
if max_pooling:
output_node = pool(kernel_size - 1,
padding=self._get_padding(
kernel_size - 1,
output_node))(output_node)
if dropout_rate > 0:
output_node = layers.Dropout(dropout_rate)(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
kernel_size = self.kernel_size or hp.Choice('kernel_size',
[3, 5, 7],
default=3)
num_blocks = self.num_blocks or hp.Choice('num_blocks',
[1, 2, 3],
default=2)
separable = self.separable
if separable is None:
separable = hp.Choice('separable', [True, False], default=False)
if separable:
conv = utils.get_sep_conv(input_node.shape)
else:
conv = utils.get_conv(input_node.shape)
pool = utils.get_max_pooling(input_node.shape)
for i in range(num_blocks):
output_node = conv(
hp.Choice('filters_{i}_1'.format(i=i),
[16, 32, 64],
default=32),
kernel_size,
padding=self._get_padding(kernel_size, output_node),
activation='relu')(output_node)
output_node = conv(
hp.Choice('filters_{i}_2'.format(i=i),
[16, 32, 64],
default=32),
kernel_size,
padding=self._get_padding(kernel_size, output_node),
activation='relu')(output_node)
output_node = pool(
kernel_size - 1,
padding=self._get_padding(kernel_size - 1, output_node))(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
separable = self.separable
if separable is None:
separable = hp.Choice('separable', [True, False], default=False)
if separable:
conv = utils.get_sep_conv(input_node.shape)
else:
conv = utils.get_conv(input_node.shape)
pool = utils.get_max_pooling(input_node.shape)
dropout = utils.get_dropout(input_node.shape)
kernel_size = hp.Choice('kernel_size', [3, 5, 7], default=3)
dropout_rate = hp.Choice('dropout_rate', [0, 0.25, 0.5], default=0.5)
for i in range(hp.Choice('num_blocks', [1, 2, 3], default=2)):
if dropout_rate > 0:
output_node = dropout(dropout_rate)(output_node)
output_node = conv(hp.Choice('filters_{i}_1'.format(i=i),
[16, 32, 64],
default=32),
kernel_size,
padding=self._get_padding(
kernel_size, output_node))(output_node)
output_node = conv(hp.Choice('filters_{i}_2'.format(i=i),
[16, 32, 64],
default=32),
kernel_size,
padding=self._get_padding(
kernel_size, output_node))(output_node)
output_node = pool(kernel_size - 1,
padding=self._get_padding(
kernel_size - 1, output_node))(output_node)
return output_node