来自于官方教程中的一个例子，使用梯度下降算法进行线性逼近
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import tensorflow as tf # Model parameters W = tf.Variable([.3], dtype=tf.float32) b = tf.Variable([-.3], dtype=tf.float32) # Model input and output x = tf.placeholder(tf.float32) linear_model = W*x + b y = tf.placeholder(tf.float32) # loss loss = tf.reduce_sum(tf.square(linear_model - y)) # sum of the squares # optimizer optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) # training data x_train = [1, 2, 3, 4] y_train = [0, -1, -2, -3] # training loop init = tf.global_variables_initializer() sess = tf.Session() sess.run(init) # reset values to wrong for i in range(1000): sess.run(train, {x: x_train, y: y_train}) # evaluate training accuracy curr_W, curr_b, curr_loss = sess.run([W, b, loss], {x: x_train, y: y_train}) print("W: %s b: %s loss: %s"%(curr_W, curr_b, curr_loss)) |
训练集
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x_train = [1, 2, 3, 4] y_train = [0, -1, -2, -3] |
使用梯度下降算法，步进0.01，求方差最小化
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optimizer = tf.train.GradientDescentOptimizer(0.01) train = optimizer.minimize(loss) |
结果
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W: [-0.9999969] b: [ 0.99999082] loss: 5.69997e-11 |
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