Python实现的TensorFlow入门案例

import tensorflow as tf import numpy as np # 1 collect data x_data = np.float32(np.random.rand(200,2)); y_data = np.matmul(x_data,[[5.2],[9.6]])+3.4; # 2 Ceate model W = tf.Variable(tf.random_uniform([2,1],-1,1)); b = tf.Variable(tf.zeros([1])); y_ = tf.matmul(x_data,W)+b; # 3 loss function loss = tf.reduce_mean(tf.square(y_-y_data)); optimizer = tf.train.GradientDescentOptimizer(0.5); train = optimizer.minimize(loss); # 4 Initialzer init = tf.initialize_all_variables(); sess = tf.Session(config = tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)); sess.run(init); # 5 Train for step in range(0,201): sess.run(train); if step%10 == 0: print(step,np.transpose(sess.run(W)),sess.run(b)); # 6 Output sess.close();