TensorFlow MNIST for Experts低精度

Question

我遵循了tensorflow MNIST专家教程。 我编写如下代码，该代码是本教程的副本。 但是，当我运行我的代码时，准确性仅为92％，86％...。它在我的Mac上仅运行1或2分钟即可非常快地运行。 随着步数的增加，准确性

step 0, training accuracy 0.08
step 100, training accuracy 0.1
step 200, training accuracy 0.16
step 300, training accuracy 0.22
step 400, training accuracy 0.1
step 500, training accuracy 0.18
step 600, training accuracy 0.26
step 700, training accuracy 0.16
step 800, training accuracy 0.24
...
step 19600, training accuracy 0.9
step 19700, training accuracy 0.82
step 19800, training accuracy 0.98
step 19900, training accuracy 0.86
test accuracy 0.9065

但是当我运行官方代码mnist_deep.py时 。 它工作非常缓慢，输出为

step 0, training accuracy 0.1
step 100, training accuracy 0.84
step 200, training accuracy 0.84
step 300, training accuracy 0.9
step 400, training accuracy 0.88
step 500, training accuracy 0.92
step 600, training accuracy 0.98
step 700, training accuracy 0.96
step 800, training accuracy 0.96
step 900, training accuracy 0.96
step 1000, training accuracy 0.96
step 1100, training accuracy 0.94
step 1200, training accuracy 0.96

它运作良好。 我比较我的代码和mnist_deep.py。 唯一的区别是它们与一起使用。 为什么我的代码如此糟糕？ 以及为什么要与？ 下面是我的代码。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

def weight_variable(shape):
    initial = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1, shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')

def max_pool_2x2(x):
    return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')

def main(_):
    mnist = input_data.read_data_sets("/MNIST_data/", one_hot=True)

    x = tf.placeholder(tf.float32, [None, 784])
    y_ = tf.placeholder(tf.float32, [None, 10])

    x_image = tf.reshape(x, [-1, 28, 28, 1])

    W_conv1 = weight_variable([5, 5, 1, 32])
    b_conv1 = bias_variable([32])
    h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
    h_pool1 = max_pool_2x2(h_conv1)

    W_conv2 = weight_variable([5, 5, 32, 64])
    b_conv2 = bias_variable([64])
    h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
    h_pool2 = max_pool_2x2(h_conv2)

    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])
    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

    cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
    train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
    correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(20000):
            batch = mnist.train.next_batch(50)
            if i % 100 == 0:
                train_accuracy = accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})
                print('step %d, training accuracy %g' % (i, train_accuracy))
                train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5})

        print('test accuracy %g' % accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))

if __name__ == '__main__':
    tf.app.run(main=main)

Answer 1

您已将train_step.run调用放入if i % 100 == 0:块内。