conv1 = tf.layers.conv2d(x_image,
32, # output channel number
(3,3), # kernel size
padding = ‘same’,
activation = tf.nn.relu,
name = ‘conv1’)

pooling1 = tf.layers.max_pooling2d(conv1,
(2, 2), # kernel size
(2, 2), # stride
name = ‘pool1’)

inception_2a = inception_block(pooling1,
[16, 16, 16],
name = ‘inception_2a’)
inception_2b = inception_block(inception_2a,
[16, 16, 16],
name = ‘inception_2b’)

pooling2 = tf.layers.max_pooling2d(inception_2b,
(2, 2), # kernel size
(2, 2), # stride
name = ‘pool2’)

inception_3a = inception_block(pooling2,
[16, 16, 16],
name = ‘inception_3a’)
inception_3b = inception_block(inception_3a,
[16, 16, 16],
name = ‘inception_3b’)

pooling3 = tf.layers.max_pooling2d(inception_3b,
(2, 2), # kernel size
(2, 2), # stride
name = ‘pool3’)

flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)
老师Inception这节的整个过程各个操作后的shape您能解答一下吗