def testDeconvStride2MultiStep(self):
x1 = np.random.rand(5, 2, 1, 11)
a = common_layers.deconv_stride2_multistep(
tf.constant(x1, dtype=tf.float32), 4, 16)
self.evaluate(tf.global_variables_initializer())
actual = self.evaluate(a)
self.assertEqual(actual.shape, (5, 32, 1, 16))
def testDeconvStride2MultiStep(self):
x1 = np.random.rand(5, 2, 1, 11)
a = common_layers.deconv_stride2_multistep(
tf.constant(x1, dtype=tf.float32), 4, 16)
self.evaluate(tf.global_variables_initializer())
actual = self.evaluate(a)
self.assertEqual(actual.shape, (5, 32, 1, 16))
def top(self, body_output, _):
# TODO(lukaszkaiser): work on a better way to generate large images.
with tf.variable_scope(self.name):
decompressed_inputs = common_layers.deconv_stride2_multistep(
body_output,
self._model_hparams.compress_steps,
body_output.get_shape()[-1],
name="deconv")
return common_layers.conv(
decompressed_inputs, self._vocab_size, (1, 1), padding="SAME")
