[译] TF-api(1) tf.nn.max_pool

tf.nn.max_pool

Args:

value: A 4-D Tensor with shape [batch, height, width, channels] and type tf.float32.

value是一个4D的tensor，是我们对其进行maxpooling的对象，它的shape是[batch, height, width, channels]

ksize: A list of ints that has length >= 4. The size of the window for each dimension of the input tensor.

ksize表示对于输入的tensor的每一维的池化尺度，比如在height那一维是2的话，表示在height轴会以2作为池化尺度。因为往往不会对batch和channel作池化，所以值一般为1。即ksize=[1, kheight, kwidth, 1]。

strides: A list of ints that has length >= 4. The stride of the sliding window for each dimension of the input tensor.

strides表示步长， 意同conv2d里面的strides，每一维度都表示窗口的在对应维度上的滑动步长。

padding: A string, either 'VALID' or 'SAME'. The padding algorithm. See the @{tf.nn.convolution$comment here}

padding表示pooling的模式，意同conv2d里面的strides。SAME模式就是将滑动窗口与矩阵进行左对齐，然后向右滑动。一直滑到与矩阵最右边那一列不相交为止。

data_format: A string. ‘NHWC’ and ‘NCHW’ are supported.

数据格式，这个一般都是默认的，不需要改变，具体是什么作用暂时不清楚。

name: Optional name for the operation.

op名字

Returns: A Tensor with type tf.float32. The max pooled output tensor.

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举例：

import tensorflow as tfimport numpy as npdata = np.random.randint(1, 9, size=[1, 5, 5, 1])print(np.transpose(data, (0, 3, 1, 2)))input_ = tf.convert_to_tensor(data, dtype=tf.float32)res = tf.nn.max_pool(input_, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1],padding='SAME')with tf.Session() as sess:    tf.global_variables_initializer().run()    output = sess.run(res, feed_dict={input_: data})    output = np.array(output)    print(np.transpose(output, (0, 3, 1, 2)))