- import numpy as np
- import tensorflow as tf
- # 这里是为了演示 numpy 和 tf 的区别.
- np.random.seed(43)
- x_data = np.random.rand(100).astype(np.float32)
- y_data = x_data * 0.1 + 0.3
- # w = np.random.rand()
- # print(w)
- # print(y_data)
- # todo 2, tf 代码
- # 一, 构建模型图
- with tf.Graph().as_default():
- print(tf.get_default_graph())
- w = tf.Variable(initial_value=tf.random_uniform([1], -1.0, 1.0))
- b = tf.Variable(tf.zeros([1]))
- y_hat = x_data * w + b
- # print(y_hat)
- loss = tf.reduce_mean(tf.square(y_data - y_hat))
- print(loss)
- optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.3)
- train_opt = optimizer.minimize(loss)
- epochs = 201
- # 二, 执行会话.
- with tf.Session() as sess:
- sess.run(tf.global_variables_initializer())
- for e in range(epochs):
- _, train_loss = sess.run([train_opt, loss])
- if e % 30 == 0:
- print('Epoch:{} - Train Loss:{}'.format(e, train_loss))
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