modify the processor

Author: feiyuxiao

Code/Processor/ResNet/README.md

@@ -0,0 +1,9 @@
+# ReadMe
+该模块为整个回归系统的回归器。分为4个部分，分别是：数据处理、Resnet搭建、网络训练、模型预测
+模块的主要功能为：
+
+1. 从本地目录获取所有清洗好的数据，将数据按照Resnet的要求加载到内存。
+2. 对数据自动分为训练数据和预测数据，其中训练数据喂入Resnet进行模型训练。
+3. 对训练好的模型进行预测结果的输出。
+4. 支持断点续训，训练过程中模型会每10轮保存一次，可以自由跳转到已经保存好的模型进行继续训练或者模型输出
+5. 支持迁移学习。即利用已经训练好的模型训练新的场景，提高训练的效率和降低对样本数量的需求。

Code/ResNet/ResnetTHU.py renamed to Code/Processor/ResNet/ResnetTHU.py

@@ -1,171 +1,171 @@
-'''
-该模块为前向传播网路，定义网络结构
-'''
-import tensorflow as tf
-
-CONV_SIZE = 5#卷积核大小
-
-# 各层通道数
-CONV1_KERNEL_NUM = 64
-CONV2_KERNEL_NUM = 64
-CONV3_KERNEL_NUM = 64
-CONV4_KERNEL_NUM = 64
-CONV5_KERNEL_NUM = 64
-CONV6_KERNEL_NUM = 64
-CONV7_KERNEL_NUM = 64
-CONV8_KERNEL_NUM = 64
-CONV9_KERNEL_NUM = 64
-CONV10_KERNEL_NUM = 64
-CONV11_KERNEL_NUM = 64
-CONV12_KERNEL_NUM = 1
-
-# 生成卷积核，权重w
-def get_weight(shape, regularizer=None):
-    '''生成卷积核，卷积核形状shape,权重w由正态分布产生'''
-    w = tf.Variable(tf.truncated_normal(shape, stddev=0.01))
-    if regularizer != None:
-        tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
-    return w
-
-# 生成偏置项
-def get_bias(shape):
-    '''初始化0偏置项b'''
-    b = tf.Variable(tf.zeros(shape))
-    return b
-
-def conv2d(x,w):
-    '''
-    定义卷积操作
-    x:输入图片
-    w:卷积核
-    strides:移动步长，左右上下移动步长为1
-    padding:0填充操作，为了卷积操作后图片维度与原来相等
-    '''
-    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
-
-
-# 定义前向传播网络，按照文章的ResNet
-def forward(x, channel, regularizer=None):
-
-    # 第1个卷积层
-    conv1_w = get_weight([CONV_SIZE, CONV_SIZE, channel, CONV1_KERNEL_NUM], regularizer)
-    conv1_b = get_bias([CONV1_KERNEL_NUM])
-    conv1 = conv2d(x, conv1_w)
-    conv1_op = tf.nn.bias_add(conv1, conv1_b)
-
-    # 第1个激活
-    relu1 = tf.nn.relu(conv1_op)
-
-    # 第2个卷积层
-    conv2_w = get_weight([CONV_SIZE, CONV_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM], regularizer)
-    conv2_b = get_bias([CONV2_KERNEL_NUM])
-    conv2 = conv2d(relu1, conv2_w)
-    conv2_op = tf.nn.bias_add(conv2, conv2_b)
-
-    # 第2个激活
-    relu2 = tf.nn.relu(conv2_op)
-
-    # 第3个卷积层
-    conv3_w = get_weight([CONV_SIZE, CONV_SIZE, CONV2_KERNEL_NUM, CONV3_KERNEL_NUM], regularizer)
-    conv3_b = get_bias([CONV3_KERNEL_NUM])
-    conv3 = conv2d(relu2, conv3_w)
-    conv3_op = tf.nn.bias_add(conv3,conv3_b)
-
-    # 第1个add层
-    add1 = tf.add(conv3_op, conv1_op)
-
-    # 第3个激活层
-    relu3 = tf.nn.relu(add1)
-
-    # 第4个卷积层
-    conv4_w = get_weight([CONV_SIZE, CONV_SIZE, CONV3_KERNEL_NUM, CONV4_KERNEL_NUM], regularizer)
-    conv4_b = get_bias([CONV4_KERNEL_NUM])
-    conv4 = conv2d(relu3, conv4_w)
-    conv4_op = tf.nn.bias_add(conv4, conv4_b)
-
-    # 第4个激活层
-    relu4 = tf.nn.relu(conv4_op)
-
-    # 第5个卷积层
-    conv5_w = get_weight([CONV_SIZE, CONV_SIZE, CONV4_KERNEL_NUM, CONV5_KERNEL_NUM], regularizer)
-    conv5_b = get_bias([CONV5_KERNEL_NUM])
-    conv5 = conv2d(relu4, conv5_w)
-    conv5_op = tf.nn.bias_add(conv5, conv5_b)
-
-    # 第2个add层
-    add2 = tf.add(conv5_op, add1)
-
-    # 第5个激活层
-    relu5 = tf.nn.relu(add2)
-
-    # 第6个卷积层
-    conv6_w = get_weight([CONV_SIZE, CONV_SIZE, CONV5_KERNEL_NUM, CONV6_KERNEL_NUM], regularizer)
-    conv6_b = get_bias([CONV6_KERNEL_NUM])
-    conv6 = conv2d(relu5, conv6_w)
-    conv6_op = tf.nn.bias_add(conv6, conv6_b)
-
-    # 第6个激活层
-    relu6 = tf.nn.relu(conv6_op)
-
-    # 第7个卷积层
-    conv7_w = get_weight([CONV_SIZE, CONV_SIZE, CONV6_KERNEL_NUM, CONV7_KERNEL_NUM], regularizer)
-    conv7_b = get_bias([CONV7_KERNEL_NUM])
-    conv7 = conv2d(relu6, conv7_w)
-    conv7_op = tf.nn.bias_add(conv7, conv7_b)
-
-    # 第3个add层
-    add3 = tf.add(conv7_op, add2)
-
-    # 第7个激活层
-    relu7 = tf.nn.relu(add3)
-
-    # 第8个卷积层
-    conv8_w = get_weight([CONV_SIZE, CONV_SIZE, CONV7_KERNEL_NUM, CONV8_KERNEL_NUM], regularizer)
-    conv8_b = get_bias([CONV8_KERNEL_NUM])
-    conv8 = conv2d(relu7, conv8_w)
-    conv8_op = tf.nn.bias_add(conv8, conv8_b)
-
-    # 第8个激活层
-    relu8 = tf.nn.relu(conv8_op)
-
-    # 第9个卷积层
-    conv9_w = get_weight([CONV_SIZE, CONV_SIZE, CONV8_KERNEL_NUM, CONV9_KERNEL_NUM], regularizer)
-    conv9_b = get_bias([CONV9_KERNEL_NUM])
-    conv9 = conv2d(relu8, conv9_w)
-    conv9_op = tf.nn.bias_add(conv9, conv9_b)
-
-    # 第4个add层
-    add4 = tf.add(conv9_op, add3)
-
-    # 第9个激活层
-    relu9 = tf.nn.relu(add4)
-
-    # 第10个卷积层
-    conv10_w = get_weight([CONV_SIZE, CONV_SIZE, CONV9_KERNEL_NUM, CONV10_KERNEL_NUM], regularizer)
-    conv10_b = get_bias([CONV10_KERNEL_NUM])
-    conv10 = conv2d(relu9, conv10_w)
-    conv10_op = tf.nn.bias_add(conv10, conv10_b)
-
-    # 第10个激活层
-    relu10 = tf.nn.relu(conv10_op)
-
-    # 第11个卷积层
-    conv11_w = get_weight([CONV_SIZE, CONV_SIZE, CONV10_KERNEL_NUM, CONV11_KERNEL_NUM], regularizer)
-    conv11_b = get_bias([CONV11_KERNEL_NUM])
-    conv11 = conv2d(relu10, conv11_w)
-    conv11_op = tf.nn.bias_add(conv11, conv11_b)
-
-    # 第5个add层
-    add5 = tf.add(conv11_op, add4)
-
-    # 第11个激活层
-    relu11 = tf.nn.relu(add5)
-
-    # 第12个卷积层
-    conv12_w = get_weight([CONV_SIZE, CONV_SIZE, CONV11_KERNEL_NUM, CONV12_KERNEL_NUM], regularizer)
-    conv12_b = get_bias([CONV12_KERNEL_NUM])
-    conv12 = conv2d(relu11, conv12_w)
-    conv12_op = tf.nn.bias_add(conv12, conv12_b)
-
-    # 输出结果
-    return conv12_op
+'''
+该模块为前向传播网路，定义网络结构
+'''
+import tensorflow as tf
+
+CONV_SIZE = 5#卷积核大小
+
+# 各层通道数
+CONV1_KERNEL_NUM = 64
+CONV2_KERNEL_NUM = 64
+CONV3_KERNEL_NUM = 64
+CONV4_KERNEL_NUM = 64
+CONV5_KERNEL_NUM = 64
+CONV6_KERNEL_NUM = 64
+CONV7_KERNEL_NUM = 64
+CONV8_KERNEL_NUM = 64
+CONV9_KERNEL_NUM = 64
+CONV10_KERNEL_NUM = 64
+CONV11_KERNEL_NUM = 64
+CONV12_KERNEL_NUM = 1
+
+# 生成卷积核，权重w
+def get_weight(shape, regularizer=None):
+    '''生成卷积核，卷积核形状shape,权重w由正态分布产生'''
+    w = tf.Variable(tf.truncated_normal(shape, stddev=0.01))
+    if regularizer != None:
+        tf.add_to_collection('losses', tf.contrib.layers.l2_regularizer(regularizer)(w))
+    return w
+
+# 生成偏置项
+def get_bias(shape):
+    '''初始化0偏置项b'''
+    b = tf.Variable(tf.zeros(shape))
+    return b
+
+def conv2d(x,w):
+    '''
+    定义卷积操作
+    x:输入图片
+    w:卷积核
+    strides:移动步长，左右上下移动步长为1
+    padding:0填充操作，为了卷积操作后图片维度与原来相等
+    '''
+    return tf.nn.conv2d(x, w, strides=[1, 1, 1, 1], padding='SAME')
+
+
+# 定义前向传播网络，按照文章的ResNet
+def forward(x, channel, regularizer=None):
+
+    # 第1个卷积层
+    conv1_w = get_weight([CONV_SIZE, CONV_SIZE, channel, CONV1_KERNEL_NUM], regularizer)
+    conv1_b = get_bias([CONV1_KERNEL_NUM])
+    conv1 = conv2d(x, conv1_w)
+    conv1_op = tf.nn.bias_add(conv1, conv1_b)
+
+    # 第1个激活
+    relu1 = tf.nn.relu(conv1_op)
+
+    # 第2个卷积层
+    conv2_w = get_weight([CONV_SIZE, CONV_SIZE, CONV1_KERNEL_NUM, CONV2_KERNEL_NUM], regularizer)
+    conv2_b = get_bias([CONV2_KERNEL_NUM])
+    conv2 = conv2d(relu1, conv2_w)
+    conv2_op = tf.nn.bias_add(conv2, conv2_b)
+
+    # 第2个激活
+    relu2 = tf.nn.relu(conv2_op)
+
+    # 第3个卷积层
+    conv3_w = get_weight([CONV_SIZE, CONV_SIZE, CONV2_KERNEL_NUM, CONV3_KERNEL_NUM], regularizer)
+    conv3_b = get_bias([CONV3_KERNEL_NUM])
+    conv3 = conv2d(relu2, conv3_w)
+    conv3_op = tf.nn.bias_add(conv3,conv3_b)
+
+    # 第1个add层
+    add1 = tf.add(conv3_op, conv1_op)
+
+    # 第3个激活层
+    relu3 = tf.nn.relu(add1)
+
+    # 第4个卷积层
+    conv4_w = get_weight([CONV_SIZE, CONV_SIZE, CONV3_KERNEL_NUM, CONV4_KERNEL_NUM], regularizer)
+    conv4_b = get_bias([CONV4_KERNEL_NUM])
+    conv4 = conv2d(relu3, conv4_w)
+    conv4_op = tf.nn.bias_add(conv4, conv4_b)
+
+    # 第4个激活层
+    relu4 = tf.nn.relu(conv4_op)
+
+    # 第5个卷积层
+    conv5_w = get_weight([CONV_SIZE, CONV_SIZE, CONV4_KERNEL_NUM, CONV5_KERNEL_NUM], regularizer)
+    conv5_b = get_bias([CONV5_KERNEL_NUM])
+    conv5 = conv2d(relu4, conv5_w)
+    conv5_op = tf.nn.bias_add(conv5, conv5_b)
+
+    # 第2个add层
+    add2 = tf.add(conv5_op, add1)
+
+    # 第5个激活层
+    relu5 = tf.nn.relu(add2)
+
+    # 第6个卷积层
+    conv6_w = get_weight([CONV_SIZE, CONV_SIZE, CONV5_KERNEL_NUM, CONV6_KERNEL_NUM], regularizer)
+    conv6_b = get_bias([CONV6_KERNEL_NUM])
+    conv6 = conv2d(relu5, conv6_w)
+    conv6_op = tf.nn.bias_add(conv6, conv6_b)
+
+    # 第6个激活层
+    relu6 = tf.nn.relu(conv6_op)
+
+    # 第7个卷积层
+    conv7_w = get_weight([CONV_SIZE, CONV_SIZE, CONV6_KERNEL_NUM, CONV7_KERNEL_NUM], regularizer)
+    conv7_b = get_bias([CONV7_KERNEL_NUM])
+    conv7 = conv2d(relu6, conv7_w)
+    conv7_op = tf.nn.bias_add(conv7, conv7_b)
+
+    # 第3个add层
+    add3 = tf.add(conv7_op, add2)
+
+    # 第7个激活层
+    relu7 = tf.nn.relu(add3)
+
+    # 第8个卷积层
+    conv8_w = get_weight([CONV_SIZE, CONV_SIZE, CONV7_KERNEL_NUM, CONV8_KERNEL_NUM], regularizer)
+    conv8_b = get_bias([CONV8_KERNEL_NUM])
+    conv8 = conv2d(relu7, conv8_w)
+    conv8_op = tf.nn.bias_add(conv8, conv8_b)
+
+    # 第8个激活层
+    relu8 = tf.nn.relu(conv8_op)
+
+    # 第9个卷积层
+    conv9_w = get_weight([CONV_SIZE, CONV_SIZE, CONV8_KERNEL_NUM, CONV9_KERNEL_NUM], regularizer)
+    conv9_b = get_bias([CONV9_KERNEL_NUM])
+    conv9 = conv2d(relu8, conv9_w)
+    conv9_op = tf.nn.bias_add(conv9, conv9_b)
+
+    # 第4个add层
+    add4 = tf.add(conv9_op, add3)
+
+    # 第9个激活层
+    relu9 = tf.nn.relu(add4)
+
+    # 第10个卷积层
+    conv10_w = get_weight([CONV_SIZE, CONV_SIZE, CONV9_KERNEL_NUM, CONV10_KERNEL_NUM], regularizer)
+    conv10_b = get_bias([CONV10_KERNEL_NUM])
+    conv10 = conv2d(relu9, conv10_w)
+    conv10_op = tf.nn.bias_add(conv10, conv10_b)
+
+    # 第10个激活层
+    relu10 = tf.nn.relu(conv10_op)
+
+    # 第11个卷积层
+    conv11_w = get_weight([CONV_SIZE, CONV_SIZE, CONV10_KERNEL_NUM, CONV11_KERNEL_NUM], regularizer)
+    conv11_b = get_bias([CONV11_KERNEL_NUM])
+    conv11 = conv2d(relu10, conv11_w)
+    conv11_op = tf.nn.bias_add(conv11, conv11_b)
+
+    # 第5个add层
+    add5 = tf.add(conv11_op, add4)
+
+    # 第11个激活层
+    relu11 = tf.nn.relu(add5)
+
+    # 第12个卷积层
+    conv12_w = get_weight([CONV_SIZE, CONV_SIZE, CONV11_KERNEL_NUM, CONV12_KERNEL_NUM], regularizer)
+    conv12_b = get_bias([CONV12_KERNEL_NUM])
+    conv12 = conv2d(relu11, conv12_w)
+    conv12_op = tf.nn.bias_add(conv12, conv12_b)
+
+    # 输出结果
+    return conv12_op