知乎问题代码
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# -*- coding: utf-8 -*- """ Created on Sat May 19 18:44:40 2018 @author: John Kwok """ # import import numpy as np import tensorflow as tf import GetDataUtil # 数据读取及预处理 ‘‘‘ 定义超参 ‘‘‘ BATCH_SIZE = 128 # 批大小 EPOCH = 5 # 训练EPOCH次数 HIDDEN_UNIT = 512 KERNEL_SIZE = 3 # 通用卷集核大小 COVN_1_CHANNELS = 128 # 第一层卷积层的输出chennel个数 COVN_2_CHANNELS = 64 # 第二层卷积层的输出chennel个数 INPUT_CHENNELS = 1 # 输入图片的chennels个数 SCNN_KERNEL_LENGTH = 9 # SCNN 卷集核的宽度 PAD = SCNN_KERNEL_LENGTH - 1 # 切片后需要pad的个数 # 切片需要后需要PAD PADDING = [[0, 0], [0,0], [int(PAD / 2),PAD - int(PAD/2)], [0,0]] ‘‘‘ 数据读取 ‘‘‘ #X_train_origin, X_test_origin, y_train, y_test = GetDataUtil.getTrainTestSet(dataPath = "../DataSet_NPSave/JustifiedData.npy",test_size = 0.1) X_train_origin, X_test_origin, y_train, y_test = GetDataUtil.getTrainTestSet(dataPath = "../DataSet_NPSave/RandomCrop_NPAWF_Noise_orgin_ACC_005_10000.npy",test_size = 0.1) ‘‘‘2. Data preprocessing‘‘‘ def DataPreprocess(data): print("Data Preprocessing,Please wait...") data[:,:3,:] = (data[:,:3,:] - np.mean(data[:,:3,:]))/np.std(data[:,:3,:]) data[:,3:6,:] = (data[:,3:6,:] - np.mean(data[:,3:6,:]))/np.std(data[:,3:6,:]) # 特征构造 sin = np.sin(data * np.pi / 2) cos = np.cos(data * np.pi / 2) X_2 = np.power(data,2) X_3 = np.power(data,3) ACC_All = np.sqrt((np.power(data[:,0,:],2)+ np.power(data[:,1,:],2)+ np.power(data[:,2,:],2))/3)[:,np.newaxis,:] Ay_Gz = (data[:,1,:] * data[:,5,:])[:,np.newaxis,:] Ay_2_Gz = (np.power(data[:,1,:],2) * data[:,5,:])[:,np.newaxis,:] Ay_Gz_2 = (np.power(data[:,5,:],2) * data[:,1,:])[:,np.newaxis,:] Ax_Gy = (data[:,0,:] * data[:,4,:])[:,np.newaxis,:] Ax_2_Gy = (np.power(data[:,0,:],2) * data[:,4,:])[:,np.newaxis,:] Ax_Gy_2 = (np.power(data[:,4,:],2) * data[:,0,:])[:,np.newaxis,:] Ax_Ay_Az = (data[:,0,:]*data[:,1,:]*data[:,2,:])[:,np.newaxis,:] newData = np.concatenate((data,sin,cos,X_3,X_2,ACC_All,Ay_Gz,Ay_2_Gz,Ay_Gz_2,Ax_Gy, Ax_2_Gy,Ax_Gy_2,Ax_Ay_Az),axis = 1) # data *= 255 print(np.min(data)) print(np.max(data)) print("Finished!") return newData # X_train = DataPreprocess(X_train_origin) X_test = DataPreprocess(X_test_origin) # # data = X_train[:,:,:,np.newaxis] label = y_train #data = np.random.randn(1000,38,300,1) # NHWC #label = np.random.randint(5,size = 1000) print(data.shape) print(label.shape) ‘‘‘ 声明待训练参数 ‘‘‘ # regularizers 也可以尝试0.01 weights = { ‘w_conv1‘:tf.get_variable(name = ‘w_conv1‘, shape = [2, 2, INPUT_CHENNELS, COVN_1_CHANNELS], initializer = tf.truncated_normal_initializer(0.0001), regularizer = tf.keras.regularizers.l2(l=0.1)), ‘w_conv2‘:tf.get_variable(name = ‘w_conv2‘, shape = [KERNEL_SIZE, KERNEL_SIZE, COVN_1_CHANNELS, COVN_2_CHANNELS], initializer = tf.truncated_normal_initializer(0.001), regularizer = tf.keras.regularizers.l2(l=0.1)) } biases = { ‘b_conv1‘: tf.get_variable(name = ‘b_conv1‘, shape= [COVN_1_CHANNELS], initializer = tf.zeros_initializer(), regularizer = tf.keras.regularizers.l2(l=0.01)), ‘b_conv2‘: tf.Variable(tf.zeros(COVN_2_CHANNELS), name = "b_conv2") } tf.summary.histogram(‘w_conv1‘,weights[‘w_conv1‘]) tf.summary.histogram(‘w_conv2‘,weights[‘w_conv2‘]) tf.summary.histogram(‘b_conv1‘,biases[‘b_conv1‘]) tf.summary.histogram(‘b_conv2‘,biases[‘b_conv2‘]) # 定义网络 x = tf.placeholder(tf.float32,shape = (None,data.shape[1],data.shape[2],data.shape[3])) y = tf.placeholder(tf.int64,shape = (None,1)) onehot_labels = tf.reshape(tf.one_hot(y,depth = 5),shape=(-1,5)) # 第一个卷积层 with tf.name_scope(‘covn_1‘): conv_out_1 = tf.nn.conv2d(input = x, filter = weights[‘w_conv1‘], strides = [1, 1, 1, 1], padding = "SAME", use_cudnn_on_gpu = True, data_format = ‘NHWC‘, dilations = [1, 1, 1, 1], name = ‘conv_out‘) relu_out_1 = tf.nn.relu(tf.nn.bias_add(conv_out_1,biases[‘b_conv1‘]), name = ‘relu_out‘) pooling_out_1 = tf.nn.max_pool(relu_out_1, ksize = [1,3,3,1], strides = [1,1,1,1], padding = "SAME", data_format=‘NHWC‘, name = ‘pooling_out‘) # 第二个卷积层 with tf.name_scope(‘covn_2‘): conv_out_2 = tf.nn.conv2d(input = pooling_out_1, filter = weights[‘w_conv2‘], strides = [1, 1, 1, 1], padding = "SAME", use_cudnn_on_gpu = True, data_format = ‘NHWC‘, dilations = [1, 1, 1, 1], name = ‘conv_out‘) relu_out_2 = tf.nn.relu(tf.nn.bias_add(conv_out_2,biases[‘b_conv2‘]), name = ‘relu_out‘) pooling_out_2 = tf.nn.max_pool(relu_out_2, ksize = [1,3,3,1], strides = [1,1,1,1], padding = "SAME", data_format=‘NHWC‘, name = ‘pooling_out‘) with tf.name_scope(‘output‘): fc_input = tf.layers.flatten(pooling_out_2,name=‘flatten‘) fc1_out = tf.layers.dense(fc_input, 1024, activation=None, use_bias=True, kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer(), kernel_regularizer=tf.keras.regularizers.l2(l=0.01), bias_regularizer=tf.keras.regularizers.l2(l=0.01), name=‘fc_1‘) fc2_out = tf.layers.dense(fc1_out, 512, activation=None, use_bias=True, kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer(), kernel_regularizer=tf.keras.regularizers.l2(l=0.01), bias_regularizer=tf.keras.regularizers.l2(l=0.01), name=‘fc_2‘) fc3_out = tf.layers.dense(fc2_out, 256, activation=None, use_bias=True, kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer(), kernel_regularizer=tf.keras.regularizers.l2(l=0.01), bias_regularizer=tf.keras.regularizers.l2(l=0.01), name=‘fc_3‘) logits = tf.layers.dense(fc3_out, 5, activation=None, use_bias=True, kernel_initializer=tf.truncated_normal_initializer(0.01), bias_initializer=tf.zeros_initializer(), kernel_regularizer=tf.keras.regularizers.l2(l=0.01), bias_regularizer=tf.keras.regularizers.l2(l=0.01), name=‘fc_output‘) loss = tf.reduce_sum(tf.nn.softmax_cross_entropy_with_logits_v2(labels = onehot_labels, logits = logits),name = ‘loss‘) optimizer = tf.train.AdamOptimizer(learning_rate = 0.001,name = ‘adam‘) train_op = optimizer.minimize(loss,name = ‘train_op‘) correct_pred = tf.equal(tf.argmax(logits,1),tf.argmax(onehot_labels,1),name = ‘correct_pred‘) c = tf.cast(correct_pred,tf.float32) accuracy = tf.reduce_mean(c,name = ‘accuracy‘) init_op = tf.global_variables_initializer() tf.summary.scalar(‘loss‘,loss) tf.summary.scalar(‘accuracy‘,accuracy) from sklearn.utils import shuffle with tf.Session() as sess: writer = tf.summary.FileWriter(‘./log/scnn/‘) writer.add_graph(sess.graph) merge_all = tf.summary.merge_all() sess.run(init_op) sess.graph.finalize() step = 0 for epoch in range(EPOCH): X,Label = shuffle(data,label,random_state=None) idx = 0 while idx < X.shape[0]: if(idx+BATCH_SIZE>X.shape[0]): x_batch = X[idx:] y_batch = Label[idx:].reshape(-1,1) else: x_batch = X[idx:idx+BATCH_SIZE] y_batch = Label[idx:idx+BATCH_SIZE].reshape(-1,1) print("Step:"+str(step)) pre, _, log = sess.run([accuracy,train_op,merge_all],feed_dict = {x:x_batch,y:y_batch}) print(pre) writer.add_summary(log,step) idx += BATCH_SIZE step += 1 l,acc = sess.run([loss,accuracy],feed_dict = {x:x_batch,y:y_batch}) print("Epoch " + str(epoch+1) + ", Minibatch Loss= " + "{:.4f}".format(l) + ", Training Accuracy= " + "{:.3f}".format(acc)) print("Optimization Finished!")
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