TensorFlow MNIST專家低準確性

我遵循專家的tensorflow MNIST教程。我寫下如下所示的代碼，這是本教程的副本。但是，當我運行我的代碼時，其準確率僅爲92％，86％......它在我的Mac上運行速度僅爲1或2分鐘。而隨着步驟的增加，精度TensorFlow MNIST專家低準確性

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

但是當我運行官方的代碼mnist_deep.py。它工作非常緩慢，輸出是

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

它運作良好。我比較我的代碼和mnist_deep.py。唯一不同的是他們使用。爲什麼我的代碼工作如此糟糕？爲什麼他們應該使用？以下是我的代碼。

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)

來源

2017-10-21 zhen

你已經把if i % 100 == 0:塊中的通話train_step.run。

來源

2017-10-30 17:01:41

TensorFlow MNIST專家低準確性

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