diff --git a/lessonOne/imgClassifierWeb/__pycache__/cnnModel.cpython-36.pyc b/lessonOne/imgClassifierWeb/__pycache__/cnnModel.cpython-36.pyc index 00d570a..9a6c049 100644 Binary files a/lessonOne/imgClassifierWeb/__pycache__/cnnModel.cpython-36.pyc and b/lessonOne/imgClassifierWeb/__pycache__/cnnModel.cpython-36.pyc differ diff --git a/lessonOne/imgClassifierWeb/cnnModel.py b/lessonOne/imgClassifierWeb/cnnModel.py index 1196aaf..bfbb165 100644 --- a/lessonOne/imgClassifierWeb/cnnModel.py +++ b/lessonOne/imgClassifierWeb/cnnModel.py @@ -45,52 +45,85 @@ def create_conv_layer(input_data, filter_size, num_filters): conv_layer = tf.nn.conv2d(input=input_data, filter=filters, strides=[1, 1, 1, 1], - padding="VALID") + padding="SAME") print("Size of conv result : ", conv_layer.shape) return filters, conv_layer def create_CNN(input_data, num_classes, keep_prop): - filters1, conv_layer1 = create_conv_layer(input_data=input_data, filter_size=5, num_filters=4) + filters1, conv_layer1 = create_conv_layer(input_data=input_data, filter_size=3, num_filters=64) relu_layer1 = tf.nn.relu(conv_layer1) print("Size of relu1 result : ", relu_layer1.shape) max_pooling_layer1 = tf.nn.max_pool(value=relu_layer1, ksize=[1, 2, 2, 1], strides=[1, 1, 1, 1], - padding="VALID") + padding="SAME") print("Size of maxpool1 result : ", max_pooling_layer1.shape) - filters2, conv_layer2 = create_conv_layer(input_data=max_pooling_layer1, filter_size=7, num_filters=3) + filters2, conv_layer2 = create_conv_layer(input_data=max_pooling_layer1, filter_size=3, num_filters=64) relu_layer2 = tf.nn.relu(conv_layer2) print("Size of relu2 result : ", relu_layer2.shape) max_pooling_layer2 = tf.nn.max_pool(value=relu_layer2, ksize=[1, 2, 2, 1], - strides=[1, 1, 1, 1], - padding="VALID") + strides=[1, 2, 2, 1], + padding="SAME") print("Size of maxpool2 result : ", max_pooling_layer2.shape) - # Conv layer with 2 filters and a filter sisze of 5x5. - filters3, conv_layer3 = create_conv_layer(input_data=max_pooling_layer2, filter_size=5, num_filters=2) + + filters3, conv_layer3 = create_conv_layer(input_data=max_pooling_layer2, filter_size=3, num_filters=64) relu_layer3 = tf.nn.relu(conv_layer3) print("Size of relu3 result : ", relu_layer3.shape) max_pooling_layer3 = tf.nn.max_pool(value=relu_layer3, ksize=[1, 2, 2, 1], - strides=[1, 1, 1, 1], + strides=[1, 2, 2, 1], padding="VALID") print("Size of maxpool3 result : ", max_pooling_layer3.shape) - # Adding dropout layer before the fully connected layers to avoid overfitting. - flattened_layer = dropout_flatten_layer(previous_layer=max_pooling_layer3, keep_prop=keep_prop) - # First fully connected (FC) layer. It accepts the result of the dropout layer after being flattened (1D). + + filters4, conv_layer4 = create_conv_layer(input_data=max_pooling_layer3, filter_size=3, num_filters=128) + relu_layer4 = tf.nn.relu(conv_layer4) + print("Size of relu4 result : ", relu_layer4.shape) + max_pooling_layer4 = tf.nn.max_pool(value=relu_layer4, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding="SAME") + print("Size of maxpool4 result : ", max_pooling_layer4.shape) + + + + filters5, conv_layer5 = create_conv_layer(input_data=max_pooling_layer4, filter_size=3, num_filters=128) + relu_layer5 = tf.nn.relu(conv_layer5) + print("Size of relu5 result : ", relu_layer5.shape) + max_pooling_layer5 = tf.nn.max_pool(value=relu_layer5, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding="SAME") + print("Size of maxpool5 result : ", max_pooling_layer5.shape) + + + + filters6, conv_layer6 = create_conv_layer(input_data=max_pooling_layer5, filter_size=3, num_filters=128) + relu_layer6 = tf.nn.relu(conv_layer6) + print("Size of relu6 result : ", relu_layer6.shape) + max_pooling_layer6 = tf.nn.max_pool(value=relu_layer6, + ksize=[1, 2, 2, 1], + strides=[1, 2, 2, 1], + padding="SAME") + print("Size of maxpool6 result : ", max_pooling_layer6.shape) + + # 将输出维度降为一维，并增加dropout防止过拟合 + flattened_layer = dropout_flatten_layer(previous_layer=max_pooling_layer6, keep_prop=keep_prop) + + # 全连接网络+dropout fc_resultl = fc_layer(flattened_layer=flattened_layer, num_inputs=flattened_layer.get_shape()[1:].num_elements(), num_outputs=200) - # Second fully connected layer accepting the output of the previous fully connected layer. Number of outputs is equal to the number of dataset classes. + # 全连接网络+dropout，这里的网络输出的数量要和标注的数量一致 fc_result2 = fc_layer(flattened_layer=fc_resultl, num_inputs=fc_resultl.get_shape()[1:].num_elements(), num_outputs=num_classes) print("Fully connected layer results : ", fc_result2) - return fc_result2 # Returning the result of the last FC layer. + return fc_result2 def dropout_flatten_layer(previous_layer, keep_prop): diff --git a/lessonOne/imgClassifierWeb/config.ini b/lessonOne/imgClassifierWeb/config.ini index a0c48d2..54f91a4 100644 --- a/lessonOne/imgClassifierWeb/config.ini +++ b/lessonOne/imgClassifierWeb/config.ini @@ -14,11 +14,11 @@ num_channels = 3 num_files=5 images_per_file=10000 max_gradient_norm=5 +percent=1 [floats] learning_rate = 0.01 keeps=0.5 -learning_rate_decay_factor = 0.9 -percent=1 +learning_rate_decay_factor = 0.5 end_learning_rate=0.0 diff --git a/lessonOne/imgClassifierWeb/execute.py b/lessonOne/imgClassifierWeb/execute.py index 807c50d..c722db5 100644 --- a/lessonOne/imgClassifierWeb/execute.py +++ b/lessonOne/imgClassifierWeb/execute.py @@ -57,11 +57,8 @@ def create_model(session,forward_only): if ckpt and ckpt.model_checkpoint_path: print("Reading model parameters from %s" % ckpt.model_checkpoint_path) model.saver.restore(session, ckpt.model_checkpoint_path) - session.run(tf.global_variables_initializer()) graph = tf.get_default_graph() - return model,graph - else: print("Created model with fresh parameters.") session.run(tf.global_variables_initializer()) @@ -130,8 +127,8 @@ def train(): # 达到一个训练模型保存点后，将模型保存下来，并打印出这个保存点的平均准确率 if current_step % gConfig['steps_per_checkpoint'] == 0: - #如果超过三次预测正确率没有升高则改变学习率 - if len(previous_correct) > 2 and accuracy < min(previous_correct[-3:]): + #如果超过5次预测正确率没有升高则改变学习率 + if len(previous_correct) > 2 and accuracy < min(previous_correct[-5:]): sess.run(model.learning_rate_decay_op) previous_correct.append(accuracy) checkpoint_path = os.path.join(gConfig['working_directory'], "cnn.ckpt") diff --git a/lessonThree/Anti-Fraud-App/VAE.py b/lessonThree/Anti-Fraud-App/VAE.py index 7cba1b2..13b08e7 100644 --- a/lessonThree/Anti-Fraud-App/VAE.py +++ b/lessonThree/Anti-Fraud-App/VAE.py @@ -4,17 +4,14 @@ import matplotlib.pyplot as plt from tensorflow.examples.tutorials.mnist import input_data import getConfig +import sys + +import os gConfig={} gConfig=getConfig.get_config(config_file='config.ini') #定义训练数据的维度 seq_len=gConfig['seqlen'] -#class vaeModel(object): - - # def __init__(self, learning_rate,learning_rate_decay_factor): - # self.learning_rate=tf.Variable(float(learning_rate), trainable=False) - # self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor) - # self.global_step = tf.Variable(0, trainable=False) #读取数据 def read_data(source_file): @@ -76,6 +73,7 @@ def decoder(sampled_z, keep_prob): decoder_set = tf.reshape(y_out, shape=[-1, seq_len, 1]) return decoder_set #定义计算loss以及进行优化器优化的一系列tensor +global_step = tf.Variable(0, trainable=False) sampled, mn, sd = encoder(X_in, keep_prob) dec = decoder(sampled, keep_prob) unreshaped = tf.reshape(dec, [-1, seq_len*1]) @@ -83,20 +81,55 @@ def decoder(sampled_z, keep_prob): latent_loss = -0.5 * tf.reduce_sum(1.0 + 2.0 * sd - tf.square(mn) - tf.exp(2.0 * sd), 1) dst_loss=img_loss+latent_loss loss = tf.reduce_mean(img_loss + latent_loss) -optimizer = tf.train.AdamOptimizer(gConfig['learning_rate']).minimize(loss) +optimizer = tf.train.AdamOptimizer(gConfig['learning_rate']).minimize(loss,global_step=global_step) sess = tf.Session() sess.run(tf.global_variables_initializer()) - +saver = tf.train.Saver(tf.all_variables()) + + +def vae_train(): #开始vae的训练 -for i in range(gConfig['vae_steps']): - batch=dataset - sess.run(optimizer, feed_dict = {X_in: batch, Y: batch, keep_prob: 0.8}) - if not i % 200: + for i in range(gConfig['vae_steps']): + batch=dataset +#通过循环训练来通过优化器进行BP计算，进而更新参数使网络进行拟合，这里使用的是fullbatch模式 + sess.run(optimizer, feed_dict = {X_in: batch, Y: batch, keep_prob: 0.8}) +#根据checkpoint点对网络的收敛情况进行监测并保存下来模型 + if not i % 200: ls, d, i_ls, d_ls, mu, sampled_data = sess.run([loss, dec, img_loss, dst_loss, mn, sampled], feed_dict = {X_in: batch, Y: batch, keep_prob: 1.0}) - print(i, ls, np.mean(i_ls), np.mean(d_ls)) + checkpoint_path = os.path.join(gConfig['working_directory'], "vae.ckpt") + saver=tf.train.Saver() + saver.save(sess,checkpoint_path,global_step=global_step) +#最后将训练集的所有的数据的encoder数据保存下来，用于接下来的kmeans训练 + sampled_data = sess.run(sampled, feed_dict = {X_in: batch, Y: batch, keep_prob: 1.0}) + sampled_data=pd.DataFrame(sampled_data) + sampled_data.to_csv(gConfig['sampled_path']) + +#定义 +def vae_encoder(sess,input_data): + + + ckpt=tf.train.get_checkpoint_state(gConfig['working_directory']) + if ckpt and ckpt.model_checkpoint_path: + print("Reading model parameters from %s" % ckpt.model_checkpoint_path) + saver.restore(sess, ckpt.model_checkpoint_path) + graph = tf.get_default_graph() + + sampled_data = sess.run(sampled, feed_dict = {X_in: input_data, Y: input_data, keep_prob: 1.0}) + + return sampled_data + + +if __name__=='__main__': + if len(sys.argv) - 1: + gConfig = getConfig(sys.argv[1]) + else: + # get configuration from config.ini + gConfig = getConfig.get_config() + if gConfig['mode']=='train': + vae_train() + elif gConfig['mode']=='server': + print('Sever Usage:python3 app.py') + -#保存训练的encode的结果，就是要进行特征压缩后的特征 -sampled_data=pd.DataFrame(sampled_data) -sampled_data.to_csv(gConfig['sampled_path']) \ No newline at end of file diff --git a/lessonThree/Anti-Fraud-App/app.py b/lessonThree/Anti-Fraud-App/app.py index 67daacf..036e6ec 100644 --- a/lessonThree/Anti-Fraud-App/app.py +++ b/lessonThree/Anti-Fraud-App/app.py @@ -12,8 +12,8 @@ import hashlib import threading import execute +import VAE -# app = Flask(__name__) #路由注解，我们这里使用的是path的形式进行传参 #示例url:http://0.0.0.0:8088/predict/1/2/3/4/5/6/7/8/9/10/11/12 这里的1...12换成需要进行聚类的值就可以了 @@ -26,6 +26,7 @@ def predict(a,b,c,d,e,f,g,h,i,j,k,l): lines=range(k) lines=[int(i) for i in line] lines=[lines] + lines=VAE.vae_encoder(VAE.sess,lines) predict_result=execute.predicts(lines) return jsonify( { 'result of cluster': str(predict_result) } ) diff --git a/lessonThree/Anti-Fraud-App/config.ini b/lessonThree/Anti-Fraud-App/config.ini index f00bc62..15cf98e 100644 --- a/lessonThree/Anti-Fraud-App/config.ini +++ b/lessonThree/Anti-Fraud-App/config.ini @@ -9,7 +9,7 @@ steps=10 #VAE训练步数 vae_steps=300 #需要聚类的序列的长度,如果要将vae和clustering串起来用这个值要和enc_out_len保持一致 -seqlen=12 +seqlen=8 #vae encoder输出的中间向量的长度 encoutlen=8 @@ -25,7 +25,7 @@ keeps=0.5 [strings] mode = train working_directory=working_directory/ -input_file=train_data/train_data.csv +input_file=train_data/sampled_data.csv #这里model的文件路径，要根据kmeansMode中的文件夹的来设置 model_path=kmeansMode/1542258970 sampled_path=train_data/sampled_data.csv diff --git a/lessonThree/Anti-Fraud-App/readme.md b/lessonThree/Anti-Fraud-App/readme.md new file mode 100644 index 0000000..5370df3 --- /dev/null +++ b/lessonThree/Anti-Fraud-App/readme.md @@ -0,0 +1 @@ +最新的代码将VAE和kmeans进行了结合，如果需要使用的话，需要先执行python3 VAE.py 然后执行python3 execute.py 最后再执行python3.py \ No newline at end of file diff --git a/lessonThree/Anti-Fraud-App/train_data/sampled_data.csv b/lessonThree/Anti-Fraud-App/train_data/sampled_data.csv index 94d4ffb..ab66a7d 100644 --- a/lessonThree/Anti-Fraud-App/train_data/sampled_data.csv +++ b/lessonThree/Anti-Fraud-App/train_data/sampled_data.csv @@ -1,10 +1,1303 @@ ,0,1,2,3,4,5,6,7 -0,0.38341895,-1.7428886,0.29892516,0.14158645,0.043346316,0.52457184,-0.51498866,-0.0033487156 -1,0.2401895,-1.6238832,-0.03735471,-0.32811102,0.85901475,0.61071527,1.7249069,0.19866326 -2,-2.120377,-0.7846775,-0.44127777,-2.8433104,1.7282515,1.3949893,0.006724566,1.5689514 -3,-0.24871545,-0.117795594,0.46943024,0.5850498,-1.8775313,0.5547079,1.5917208,-0.013330079 -4,0.048848335,-1.1120182,-1.05481,1.7671635,-0.8837705,-1.0044992,-0.6401813,1.51355 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