""" 二值分类,TensorFlow 示例 """ import os import tensorflow as tf import numpy as np os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' s = tf.Session() # 从正态分布(N(-1, 1), N(3, 1)) 生成数据,同时生成目标标签,占位符和偏差变量 A 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) A = tf.Variable(tf.random_normal(mean=10, shape=[1])) # 增加转换操作 my_output = tf.add(x_data, A) # 增加一个批量维度 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() s.run(init) # 声明损失函数,交叉熵使用预测结果来表征样本标签 xentropy = tf.nn.sigmoid_cross_entropy_with_logits(labels=y_target_expanded, logits=my_output_expanded) # 定义一个优化器 my_opt = tf.train.GradientDescentOptimizer(0.05) train_step = my_opt.minimize(xentropy) # 迭代训练, 每 200 次打印出损失和变量 A 的返回值 for i in range(1400): rand_index = np.random.choice(100) rand_x = [x_vals[rand_index]] rand_y = [y_vals[rand_index]] s.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(s.run(A))) print('Loss = ' + str(s.run(xentropy, feed_dict={x_data: rand_x, y_target: rand_y})))
TensorFlow 机器学实战指南示例代码之 TensorFlow 实现反向传播(二)
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