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)