def testCreateMaxPooling1DChannelsFirst(self):
width = 7
images = random_ops.random_uniform((5, 4, width))
layer = pooling_layers.MaxPooling1D(
2, strides=2, data_format='channels_first')
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 4, 3])
def testCreateMaxPooling1D(self):
width = 7
channels = 3
images = random_ops.random_uniform((5, width, channels))
layer = pooling_layers.MaxPooling1D(2, strides=2)
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(),
[5, width // 2, channels])
def max_pool1d(inputs,
kernel_size,
stride=2,
padding='VALID',
data_format=DATA_FORMAT_NHWC,
outputs_collections=None,
scope=None):
"""Adds a 1D Max Pooling op."""
if data_format not in (DATA_FORMAT_NCHW, DATA_FORMAT_NHWC):
raise ValueError('data_format has to be either NCHW or NHWC.')
with ops.name_scope(scope, 'MaxPool1D', [inputs]) as sc:
inputs = ops.convert_to_tensor(inputs)
df = ('channels_first' if data_format and data_format.startswith('NC')
else 'channels_last')
layer = pooling_layers.MaxPooling1D(pool_size=kernel_size,
strides=stride,
padding=padding,
data_format=df,
_scope=sc)
outputs = layer.apply(inputs)
return utils.collect_named_outputs(outputs_collections, sc, outputs)
def testCreateMaxPooling1D(self):
width = 7
images = tf.random_uniform((5, width, 4))
layer = pooling_layers.MaxPooling1D(2, strides=2)
output = layer.apply(images)
self.assertListEqual(output.get_shape().as_list(), [5, 3, 4])
