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| def inception_block(x,output_channel_for_each_path,name):
''' inception block implementation'''
'''
Args:
-x: 输入
-output_channel_for_each_path: 每个组的输出
-name: block名称
'''
with tf.variable_scope(name):
conv1_1 = tf.layers.conv2d(x,
output_channel_for_each_path[0],
(1,1),
strides = (1,1),
padding = 'same',
activation = tf.nn.relu,
name = 'conv1_1'
)
conv3_3 = tf.layers.conv2d(x,
output_channel_for_each_path[1],
(3,3),
strides = (1,1),
padding = 'same',
activation = tf.nn.relu,
name = 'conv3_3'
)
conv5_5 = tf.layers.conv2d(x,
output_channel_for_each_path[2],
(5,5),
strides = (1,1),
padding = 'same',
activation = tf.nn.relu,
name = 'conv5_5'
)
max_pooling = tf.layers.max_pooling2d(x,
(2,2),
(2,2),
name = 'max_pooling'
)
max_pooling_shape = max_pooling.get_shape().as_list()[1:]
input_shape = x.get_shape().as_list()[1:]
width_padding = (input_shape[0] - max_pooling_shape[0]) // 2
height_padding = (input_shape[1] - max_pooling_shape[1]) // 2
padded_pooling = tf.pad(max_pooling,
[
[0,0],
[width_padding,width_padding],
[height_padding,height_padding],
[0,0]
])
concat_layer = tf.concat([conv1_1,conv3_3,conv5_5,padded_pooling], axis = 3)
return concat_layer
x = tf.placeholder(tf.float32, [None, 3072])
y = tf.placeholder(tf.int64, [None])
x_image = tf.reshape(x, [-1, 3, 32, 32])
x_image = tf.transpose(x_image, perm=[0, 2, 3, 1])
conv1 = tf.layers.conv2d(x_image,32,(3,3),strides = (1,1),padding = 'same',activation = tf.nn.relu,name = 'conv1')
pooling1 = tf.layers.max_pooling2d(conv1,(2,2),(2,2),name = 'pooling1')
inception_block1a = inception_block(pooling1,[16,16,16],'inception1')
inception_block1b = inception_block(inception_block1a,[16,16,16],'inception2')
pooling2 = tf.layers.max_pooling2d(inception_block1b,(2,2),(2,2),name = 'pooling2')
inception_block2a = inception_block(pooling2,[16,16,16],'inception3')
inception_block2b = inception_block(inception_block2a,[16,16,16],'inception4')
pooling3 = tf.layers.max_pooling2d(inception_block2b,(2,2),(2,2),name = 'pooling3')
flatten = tf.layers.flatten(pooling3)
y_ = tf.layers.dense(flatten, 10)
loss = tf.losses.sparse_softmax_cross_entropy(labels=y, logits=y_)
predict = tf.math.argmax(y_, 1)
correct_prediction = tf.equal(predict, y)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float64))
with tf.name_scope('train_op'):
train_op = tf.train.GradientDescentOptimizer(0.01
).minimize(loss)
|
)
