10 examples of 'maxpool2d keras' in Python

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def maxpool (x):
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    return MaxPooling2D(pool_size=(2, 2), strides=None, padding="valid")(x)

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def max_pool2d(name,input,ksize=(1,2,2,1),strides=(1,2,2,1)):
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    # the standard pooling
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    return
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    tf.nn.max_pool(input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')

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def _max_pool(layer_name, inputs, paddings, strides, ksize=[2, 2]):
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    ksize = [1, ksize[0], ksize[1], 1]
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    strides = [1, strides[0], strides[1], 1]
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    p_h, p_w = paddings[0], paddings[1]
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    paddings = [[0, 0], [p_h, p_h], [p_w, p_w], [0, 0]]
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    with tf.variable_scope(layer_name):
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        x = tf.pad(inputs, paddings=paddings)
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        max_pool_ = tf.nn.max_pool(value=x, ksize=ksize, strides=strides, padding='VALID', name='max_pool')
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        return max_pool_

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def max_pool_layer3d(x, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="SAME"):
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    '''
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    3D max pooling layer with 2x2x2 pooling as default
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    '''
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    kernel_size_aug = [1, kernel_size[0], kernel_size[1], kernel_size[2], 1]
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    strides_aug = [1, strides[0], strides[1], strides[2], 1]
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    op = tf.nn.max_pool3d(x, ksize=kernel_size_aug, strides=strides_aug, padding=padding)
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    return op

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def maxPool(input, stride=2, kernel=2, padding='SAME', name='pool'):
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	return tf.nn.max_pool(input, ksize=[1, kernel, kernel, 1], strides=[1, stride, stride, 1], padding='SAME', name=name)

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def _max_pool_layer(self, x, pool_size, stride):
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  with tf.name_scope('max_pool') as name_scope:
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    x = tf.layers.max_pooling2d(
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        x, pool_size, stride, 'SAME', data_format=self._data_format)
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  tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
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  return x

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def _pooling_function(self, inputs, pool_size, strides,
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                      border_mode, dim_ordering):
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    output = K.pool2d(inputs, pool_size, strides,
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                      border_mode, dim_ordering, pool_mode='avg')
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    return output

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def max_pool(*args, **kwargs):
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  return backend()["max_pool"](*args, **kwargs)

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def maxpool(x):
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  return tf.nn.max_pool(x, ksize=[1, 3, 3, 1],
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                        strides=[1, 2, 2, 1], padding='SAME')

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def maxpool(input, output_size, params, flatten=False):
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    shape = tf.concat([tf.shape(input)[:-1], [output_size, params.maxnum]],
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                      axis=0)
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    value = tf.reshape(input, shape)
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    output = tf.reduce_max(value, -1)
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    weight_ratio = wr.weight_ratio_maxpool(input, output, params.maxnum, 
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                                           flatten=flatten)
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    return {"output": output, "weight_ratio": weight_ratio}