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")