# 反向傳播#----------------------------------## 以下Python函數主要是展示回歸和分類模型的反向傳播import matplotlib.pyplot as pltimport numpy as npimport tensorflow as tffrom tensorflow.python.framework import opsops.reset_default_graph()# 創建計算圖會話sess = tf.Session()# 回歸算法的例子:# 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.# 生成數據，創建占位符和變量Ax_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正則損失函數loss = tf.square(my_output - y_target)# 在運行優化器之前，需要初始化變量init = tf.global_variables_initializer()sess.run(init)# 聲明變量的優化器my_opt = tf.train.GradientDescentOptimizer(0.02)train_step = my_opt.minimize(loss)# 訓練算法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})))# 分類算法例子# 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# 重置計算圖ops.reset_default_graph()# Create graphsess = tf.Session()# 生成數據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)# 偏差變量A (one model parameter = A)A = tf.Variable(tf.random_normal(mean=10, shape=[1]))# 增加轉換操作# Want to create the operstion sigmoid(x + A)# Note, the sigmoid() part is in the loss functionmy_output = tf.add(x_data, A)# 由于指定的損失函數期望批量數據增加一個批量數的維度# 這里使用expand_dims（）函數增加維度my_output_expanded = tf.expand_dims(my_output, 0)y_target_expanded = tf.expand_dims(y_target, 0)# 初始化變量Ainit = tf.global_variables_initializer()sess.run(init)# 聲明損失函數 交叉熵(cross entropy)xentropy = tf.nn.sigmoid_cross_entropy_with_logits(logits=my_output_expanded, labels=y_target_expanded)# 增加一個優化器函數 讓TensorFlow知道如何更新和偏差變量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})))# 評估預測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('最終精確度 = ' + 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]]最終精確度 = 1.0