Question

我想用几个优化器测试一个tensorflow分类器。使用此代码：

optimizers = [
tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy),
tf.train.AdadeltaOptimizer(learning_rate).minimize(cross_entropy),
tf.train.AdagradOptimizer(learning_rate).minimize(cross_entropy),
tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy),
tf.train.FtrlOptimizer(learning_rate).minimize(cross_entropy),
tf.train.ProximalGradientDescentOptimizer(learning_rate).minimize(cross_entropy),
tf.train.ProximalAdagradOptimizer(learning_rate).minimize(cross_entropy),
tf.train.RMSPropOptimizer(learning_rate).minimize(cross_entropy)]

for optimizer in optimizers:
    print(optimizer)

我收到了这个错误：

TypeError： init （）缺少1个必需的位置参数：＆＃39; name＆＃39;

请帮助。

Answer 1

在tensorflow.org上的MNIST tutorial之后，将其与您的优化器数组相结合，我可以获得所有准确率。您收到的错误消息似乎来自不同的地方。

<强>代码：

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y), reduction_indices=[1]))
learning_rate = 0.5

optimizers = [
    tf.train.GradientDescentOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.AdadeltaOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.AdagradOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.FtrlOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.ProximalGradientDescentOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.ProximalAdagradOptimizer(learning_rate).minimize(cross_entropy),
    tf.train.RMSPropOptimizer(learning_rate).minimize(cross_entropy)]

for optimizer in optimizers:
    sess = tf.InteractiveSession()
    tf.global_variables_initializer().run()
    for _ in range(1000):
      batch_xs, batch_ys = mnist.train.next_batch(100)
      sess.run(optimizer, feed_dict={x: batch_xs, y_: batch_ys})
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels}))

<强>输出： 0.9157 0.8832 0.9169 0.098 0.917 0.9149 0.917 0.098

优化器数组

1 个答案: