一、tensorflow 学习笔记
#!/bin/bash # -*-coding=utf-8-*- import matplotlib.pyplot as plt import tensorflow as tf import numpy as np def add_layer(inputs, in_size, out_size, activation_function=None): Weights = tf.Variable(tf.random_normal([in_size, out_size])) biases = tf.Variable(tf.zeros([1, out_size]) + 0.1) Wx_plus_b = tf.matmul(inputs, Weights) + biases if activation_function is None: outputs = Wx_plus_b else: outputs = activation_function(Wx_plus_b) return outputs x_data = np.linspace(-1, 1, 300)[:, np.newaxis] noise = np.random.normal(0, 0.05, x_data.shape) y_data = np.square(x_data) - 0.5 + noise xs = tf.placeholder(tf.float32, [None, 1]) ys = tf.placeholder(tf.float32, [None, 1]) l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu) prediction = add_layer(l1, 10, 1, activation_function=None) loss = tf.reduce_mean(tf.reduce_sum(tf.square(y_data - prediction), reduction_indices=[1])) train_step = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # 1行1列第一块 ax.scatter(x_data, y_data) plt.ion() plt.show() for i in range(0, 10000): sess.run(train_step, feed_dict={xs: x_data, ys: y_data}) if i % 50 == 0: try: ax.lines.remove(lines[0]) except Exception: pass # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data})) prediction_value = sess.run(prediction, feed_dict={xs: x_data}) lines = ax.plot(x_data, prediction_value, 'r-', lw=5) plt.pause(0.1) 两只橙 认证博客专家 TensorFlow NLP 神经网络 全球AI挑战赛百强选手，曾任职于腾讯微信事业部，魅族flyme事业部，现任中国平安AI研发工程师。《深度学习500问》作译者，博客专家及签约讲师，指弹吉他爱好者，简书专栏作家。