import tensorflow as tf import numpy as np x_data = np.float32(np.random.rand(2, 100)) y_data = np.dot([0.100, 0.200], x_data) + 0.300 b = tf.Variable(tf.zeros([1])) W = tf.Variable(tf.random_uniform([1, 2], -1.0, 1.0)) y = tf.matmul(W, x_data) + b loss = tf.reduce_mean(tf.square(y - y_data)) optimizer = tf.train.GradientDescentOptimizer(0.5) train = optimizer.minimize(loss) init = tf.initialize_all_variables() sess = tf.Session() sess.run(init) for step in xrange(0, 201): sess.run(train) if step % 20 == 0: print step, sess.run(W), sess.run(b)
时间: 2024-10-21 06:54:07