def _instantiate_layers(self):
"""Instantiates all the convolutional modules used in the network."""
with self._enter_variable_scope():
self._layers = tuple(
conv.Conv2DTranspose(name="conv_2d_transpose_{}".format(i),
output_channels=self._output_channels[i],
output_shape=self._output_shapes[i],
kernel_shape=self._kernel_shapes[i],
stride=self._strides[i],
padding=self._paddings[i],
initializers=self._initializers,
partitioners=self._partitioners,
regularizers=self._regularizers,
use_bias=self._use_bias[i])
for i in xrange(self._num_layers))
def _instantiate_layers(self):
"""Instantiates all the convolutional modules used in the network."""
# See `ConvNet2D._instantiate_layers` for more information about why we are
# using `check_same_graph=False`.
with self._enter_variable_scope(check_same_graph=False):
self._layers = tuple(
conv.Conv2DTranspose(name="conv_2d_transpose_{}".format(i),
output_channels=self._output_channels[i],
output_shape=self._output_shapes[i],
kernel_shape=self._kernel_shapes[i],
stride=self._strides[i],
padding=self._paddings[i],
initializers=self._initializers,
partitioners=self._partitioners,
regularizers=self._regularizers,
data_format=self._data_format,
use_bias=self._use_bias[i])
for i in xrange(self._num_layers))