def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
"""Performs the max pooling on the input.
Args:
value: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
type `tf.float32`.
ksize: A list of ints that has length >= 4. The size of the window for
each dimension of the input tensor.
strides: A list of ints that has length >= 4. The stride of the sliding
window for each dimension of the input tensor.
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
data_format: A string. 'NHWC' and 'NCHW' are supported.
name: Optional name for the operation.
Returns:
A `Tensor` with type `tf.float32`. The max pooled output tensor.
"""
with ops.op_scope([value], name, "MaxPool") as name:
value = ops.convert_to_tensor(value, name="input")
return gen_nn_ops._max_pool(value,
ksize=ksize,
strides=strides,
padding=padding,
data_format=data_format,
name=name)
def max_pool(value, ksize, strides, padding, data_format="NHWC", name=None):
"""Performs the max pooling on the input.
Args:
value: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
type `tf.float32`.
ksize: A list of ints that has length >= 4. The size of the window for
each dimension of the input tensor.
strides: A list of ints that has length >= 4. The stride of the sliding
window for each dimension of the input tensor.
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
See the [comment here](https://www.tensorflow.org/api_docs/python/nn.html#convolution)
data_format: A string. 'NHWC' and 'NCHW' are supported.
name: Optional name for the operation.
Returns:
A `Tensor` with type `tf.float32`. The max pooled output tensor.
"""
with ops.op_scope([value], name, "MaxPool") as name:
value = ops.convert_to_tensor(value, name="input")
return gen_nn_ops._max_pool(value,
ksize=ksize,
strides=strides,
padding=padding,
data_format=data_format,
name=name)
def max_pool(value, ksize, strides, padding, name=None):
"""Performs the max pooling on the input.
Args:
value: A 4-D `Tensor` with shape `[batch, height, width, channels]` and
type `float32`, `float64`, `qint8`, `quint8`, `qint32`.
ksize: A list of ints that has length >= 4. The size of the window for
each dimension of the input tensor.
strides: A list of ints that has length >= 4. The stride of the sliding
window for each dimension of the input tensor.
padding: A string, either `'VALID'` or `'SAME'`. The padding algorithm.
name: Optional name for the operation.
Returns:
A `Tensor` with the same type as `value`. The max pooled output tensor.
"""
with ops.op_scope([value], name, "MaxPool") as name:
value = ops.convert_to_tensor(value, name="input")
return gen_nn_ops._max_pool(value, ksize=ksize, strides=strides, padding=padding, name=name)
