TensorFlow如何实现反向传播

 # 反向传播 #---------------------------------- # # 以下Python函数主要是展示回归和分类模型的反向传播 import matplotlib.pyplot as plt import numpy as np import tensorflow as tf from tensorflow.python.framework import ops ops.reset_default_graph() # 创建计算图会话 sess = tf.Session() # 回归算法的例子: # We will create sample data as follows: # x-data: 100 random samples from a normal ~ N(1, 0.1) # target: 100 values of the value 10. # We will fit the model: # x-data * A = target # Theoretically, A = 10. # 生成数据，创建占位符和变量A x_vals = np.random.normal(1, 0.1, 100) y_vals = np.repeat(10., 100) x_data = tf.placeholder(shape=[1], dtype=tf.float32) y_target = tf.placeholder(shape=[1], dtype=tf.float32) # Create variable (one model parameter = A) A = tf.Variable(tf.random_normal(shape=[1])) # 增加乘法操作 my_output = tf.multiply(x_data, A) # 增加L2正则损失函数 loss = tf.square(my_output - y_target) # 在运行优化器之前，需要初始化变量 init = tf.global_variables_initializer() sess.run(init) # 声明变量的优化器 my_opt = tf.train.GradientDescentOptimizer(0.02) train_step = my_opt.minimize(loss) # 训练算法 for i in range(100): rand_index = np.random.choice(100) rand_x = [x_vals[rand_index]] rand_y = [y_vals[rand_index]] sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) if (i+1)%25==0: print('Step #' + str(i+1) + ' A = ' + str(sess.run(A))) print('Loss = ' + str(sess.run(loss, feed_dict={x_data: rand_x, y_target: rand_y}))) # 分类算法例子 # We will create sample data as follows: # x-data: sample 50 random values from a normal = N(-1, 1) #     + sample 50 random values from a normal = N(1, 1) # target: 50 values of 0 + 50 values of 1. #     These are essentially 100 values of the corresponding output index # We will fit the binary classification model: # If sigmoid(x+A) <0.5 -> 0 else 1 # Theoretically, A should be -(mean1 + mean2)/2 # 重置计算图 ops.reset_default_graph() # Create graph sess = tf.Session() # 生成数据 x_vals = np.concatenate((np.random.normal(-1, 1, 50), np.random.normal(3, 1, 50))) y_vals = np.concatenate((np.repeat(0., 50), np.repeat(1., 50))) x_data = tf.placeholder(shape=[1], dtype=tf.float32) y_target = tf.placeholder(shape=[1], dtype=tf.float32) # 偏差变量A (one model parameter = A) A = tf.Variable(tf.random_normal(mean=10, shape=[1])) # 增加转换操作 # Want to create the operstion sigmoid(x + A) # Note, the sigmoid() part is in the loss function my_output = tf.add(x_data, A) # 由于指定的损失函数期望批量数据增加一个批量数的维度 # 这里使用expand_dims（）函数增加维度 my_output_expanded = tf.expand_dims(my_output, 0) y_target_expanded = tf.expand_dims(y_target, 0) # 初始化变量A init = tf.global_variables_initializer() sess.run(init) # 声明损失函数 交叉熵(cross entropy) xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output_expanded, labels=y_target_expanded) # 增加一个优化器函数 让TensorFlow知道如何更新和偏差变量 my_opt = tf.train.GradientDescentOptimizer(0.05) train_step = my_opt.minimize(xentropy) # 迭代 for i in range(1400): rand_index = np.random.choice(100) rand_x = [x_vals[rand_index]] rand_y = [y_vals[rand_index]] sess.run(train_step, feed_dict={x_data: rand_x, y_target: rand_y}) if (i+1)%200==0: print('Step #' + str(i+1) + ' A = ' + str(sess.run(A))) print('Loss = ' + str(sess.run(xentropy, feed_dict={x_data: rand_x, y_target: rand_y}))) # 评估预测 predictions = [] for i in range(len(x_vals)): x_val = [x_vals[i]] prediction = sess.run(tf.round(tf.sigmoid(my_output)), feed_dict={x_data: x_val}) predictions.append(prediction[0]) accuracy = sum(x==y for x,y in zip(predictions, y_vals))/100. print('最终精确度 = ' + str(np.round(accuracy, 2))) 

 Step #25 A = [ 6.12853956] Loss = [ 16.45088196] Step #50 A = [ 8.55680943] Loss = [ 2.18415046] Step #75 A = [ 9.50547695] Loss = [ 5.29813051] Step #100 A = [ 9.89214897] Loss = [ 0.34628963] Step #200 A = [ 3.84576249] Loss = [[ 0.00083012]] Step #400 A = [ 0.42345378] Loss = [[ 0.01165466]] Step #600 A = [-0.35141727] Loss = [[ 0.05375391]] Step #800 A = [-0.74206048] Loss = [[ 0.05468176]] Step #1000 A = [-0.89036471] Loss = [[ 0.19636908]] Step #1200 A = [-0.90850282] Loss = [[ 0.00608062]] Step #1400 A = [-1.09374011] Loss = [[ 0.11037558]] 最终精确度 = 1.0