TensorFlow MNIST专家低准确性

我遵循专家的tensorflow MNIST教程。我写下如下所示的代码，这是本教程的副本。但是，当我运行我的代码时，其准确率仅为92％，86％......它在我的Mac上运行速度仅为1或2分钟。而随着步骤的增加，精度TensorFlow MNIST专家低准确性

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

但是当我运行官方的代码mnist_deep.py。它工作非常缓慢，输出是

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。唯一不同的是他们使用。为什么我的代码工作如此糟糕？为什么他们应该使用？以下是我的代码。

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)

来源

2017-10-21 zhen

你已经把if i % 100 == 0:块中的通话train_step.run。

来源

2017-10-30 17:01:41

TensorFlow MNIST专家低准确性

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