Structure
import tensorflow as tf import numpy as np import tensorflow.compat.v1 as tf tf.disable_v2_behavior()# 由于部分代码含有版本不同,此将v2版本禁用使用v1版本
# create data x_data = np.random.rand(100).astype(np.float32) y_data = x_data*0.1 + 0.3
#create tensorflow structure start Weights = tf.Variable(tf.random.uniform([1], -1.0, 1.0)) #tf.random_uniform随机均匀分布,一维,初始值为-1到1 biases = tf.Variable(tf.zeros([1])) y = Weights*x_data + biases
#compute the loss loss = tf.reduce_mean(tf.square(y-y_data))
#反向传递误差的工作就教给optimizer #使用的误差传递方法是梯度下降法: Gradient Descent optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(loss)
#初始化所有之前定义的Variable init = tf.compat.v1.global_variables_initializer()
sess = tf.compat.v1.Session() sess.run(init) # Very important for step in range(1000): sess.run(train) if step % 20 == 0: print(step, sess.run(Weights), sess.run(biases))