10 examples of 'maxpool2d keras' in Python

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143def maxpool (x):
144 return MaxPooling2D(pool_size=(2, 2), strides=None, padding="valid")(x)
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90def max_pool2d(name,input,ksize=(1,2,2,1),strides=(1,2,2,1)):
91 # the standard pooling
92 return
93 tf.nn.max_pool(input, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
91def _max_pool(layer_name, inputs, paddings, strides, ksize=[2, 2]):
92 ksize = [1, ksize[0], ksize[1], 1]
93 strides = [1, strides[0], strides[1], 1]
94 p_h, p_w = paddings[0], paddings[1]
95 paddings = [[0, 0], [p_h, p_h], [p_w, p_w], [0, 0]]
96
97 with tf.variable_scope(layer_name):
98 x = tf.pad(inputs, paddings=paddings)
99 max_pool_ = tf.nn.max_pool(value=x, ksize=ksize, strides=strides, padding='VALID', name='max_pool')
100 return max_pool_
25def max_pool_layer3d(x, kernel_size=(2, 2, 2), strides=(2, 2, 2), padding="SAME"):
26 '''
27 3D max pooling layer with 2x2x2 pooling as default
28 '''
29
30 kernel_size_aug = [1, kernel_size[0], kernel_size[1], kernel_size[2], 1]
31 strides_aug = [1, strides[0], strides[1], strides[2], 1]
32
33 op = tf.nn.max_pool3d(x, ksize=kernel_size_aug, strides=strides_aug, padding=padding)
34
35 return op
15def maxPool(input, stride=2, kernel=2, padding='SAME', name='pool'):
16 return tf.nn.max_pool(input, ksize=[1, kernel, kernel, 1], strides=[1, stride, stride, 1], padding='SAME', name=name)
158def _max_pool_layer(self, x, pool_size, stride):
159 with tf.name_scope('max_pool') as name_scope:
160 x = tf.layers.max_pooling2d(
161 x, pool_size, stride, 'SAME', data_format=self._data_format)
162 tf.logging.info('image after unit %s: %s', name_scope, x.get_shape())
163 return x
274def _pooling_function(self, inputs, pool_size, strides,
275 border_mode, dim_ordering):
276 output = K.pool2d(inputs, pool_size, strides,
277 border_mode, dim_ordering, pool_mode='avg')
278 return output
217def max_pool(*args, **kwargs):
218 return backend()["max_pool"](*args, **kwargs)
52def maxpool(x):
53 return tf.nn.max_pool(x, ksize=[1, 3, 3, 1],
54 strides=[1, 2, 2, 1], padding='SAME')
89def maxpool(input, output_size, params, flatten=False):
90 shape = tf.concat([tf.shape(input)[:-1], [output_size, params.maxnum]],
91 axis=0)
92 value = tf.reshape(input, shape)
93 output = tf.reduce_max(value, -1)
94 weight_ratio = wr.weight_ratio_maxpool(input, output, params.maxnum,
95 flatten=flatten)
96 return {"output": output, "weight_ratio": weight_ratio}

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