import?tensorflow?as?tf
from?tensorflow.examples.tutorials.mnist?import?input_data
#?一些包括網絡結構在內的超參數，全局變量
INPUT_NODE=784
OUTPUT_NODE=10
IMAGE_SIZE=28
NUM_CHANNELS=1
NUM_LABELS=10

CONV1_DEEP=32
CONV1_SIZE=5

CONV2_DEEP=64
CONV2_SIZE=5

FC_SIZE=512

BATCH_SIZE=20
LEARNING_RATE_base=0.06
LEARNING_RATE_DECAY=0.999
REGULARIZATION_RATE=0.0001
TRAINING_STEPS=20
MOVING_AVERAGE_DECAY=0.99

#?前向傳播的計算過程
def?inference（input_tensortrainavg_class=Noneregularizer=Nonereuse=False）:
????with?tf.variable_scope（‘layer1_conv1‘reuse=reuse）:
????????conv1_weights=tf.get_variable（
????????????‘weight‘[CONV1_SIZECONV1_SIZENUM_CHANNELSCONV1_DEEP]
????????????initializer=tf.truncated_normal_initializer（stddev=0.1））
????????conv1_biases=tf.get_variable（
????????????‘bias‘[CONV1_DEEP]initializer=tf.constant_initializer（0.0））
????????if?avg_class==None:
????????????conv1=tf.nn.conv2d（
????????????????input_tensorconv1_weightsstrides=[1221]padding=‘SAME‘）
????????????relu1=tf.nn.relu（tf.nn.bias_add（conv1conv1_biases））
????????else:
????????????conv1=tf.nn.conv2d（
????????????????input_tensoravg_class.average（conv1_weights）strides=[1221]padding=‘SAME‘）
????????????relu1=tf.nn.relu（tf.nn.bias_add（conv1avg_class.average（conv1_biases）））

????with?tf.name_scope（‘layer2_pool1‘）:
????????pool1=tf.nn.max_pool（
????????????relu1ksize=[1221]strides=[1221]padding=‘SAME‘）

????with?tf.variable_scope（‘layer3_conv2‘reuse=reuse）:
????????conv2_weights=tf.get_variable（
????????????‘weight‘[CONV2_SIZECONV2_SIZECONV1_DEEPCONV2_DEEP]
????????????initializer=tf.truncated_normal_initializer（stddev=0.1））
????????conv2_baises=tf.get_variable（
????????????‘bias‘[CONV2_DEEP]initializer=tf.constant_initializer（0.0））
????????if?avg_class==None:
????????????conv2=tf.nn.conv2d（
????????????????relu1conv2_weightsstrides=[1221]padding=‘SAME‘）
????????????relu2=tf.nn.relu（tf.nn.bias_add（conv2conv2_baises））
????????else:
????????????conv2=tf.nn.conv2d（
???????????????relu1avg_class.average（conv2_weights）strides=[1221]padding=‘SAME‘）
????????????relu2=tf.nn.relu（tf.nn.bias_add（conv2avg_class.average（conv2_baises）））

????with?tf.name_scope（‘layer4-pool2‘）:
????????pool2=tf.nn.max_pool（
????????????relu2ksize=[1221]strides=[1221]padding=‘SAME‘）
????
????pool_shape=pool2.get_shape（）.as_list（）
????nodes=pool_shape[1]*pool_shape[2]*pool_shape[3]
????reshaped=tf.reshape（pool2[-1nodes]）
????#?print（type（reshaped））

????with?tf.variable_scope（‘layer5_fc1‘reuse=reuse）:
????????fc1_weights=tf.get_variable（
????????????‘weight‘[nodesFC_SIZE]
????????????initializer=tf.truncated_normal_initializer（stddev=0.1））
????????if?regularizer!=None:
????????????tf.add_to_collection（‘losses‘regularizer（fc1_weights））
????????fc1_biases=tf.get_variable（
????????????‘bias‘[FC_SIZE]initializer=tf.constant_initializer（0.1））
????????if?avg_class==None:
????????????fc1=tf.nn.relu（tf.matmu