TensorFlow 전문가를위한 MNIST 낮은 정확도

나는 전문가를위한 tensorflow MNIST 튜토리얼을 따라 갔다. 자습서의 사본 인 아래 코드를 작성합니다. 그러나, 내 코드를 실행할 때 정확도는 92 %, 86 %입니다 ... 그것은 내 Mac에서 매우 빠르게 단 1-2 분 실행됩니다. 그리고 단계 증가, 정확성TensorFlow 전문가를위한 MNIST 낮은 정확도

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

그러나 경우에 나는 공식 코드 mnist_deep.py를 실행합니다. 매우 느리게 작동하고 출력은

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

잘 작동합니다. 나는 내 코드와 mnist_deep.py를 비교한다. 차이점은 함께 사용하는 것입니다. 내 코드가 왜 그렇게 나쁜가요? 왜 그들은 함께 사용해야합니까? 아래는 제 코드입니다.

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)

출처

2017-10-21 zhen

당신은 if i % 100 == 0: 블록 안에 당신의 train_step.run 통화를 보류했습니다.

출처

2017-10-30 17:01:41

TensorFlow 전문가를위한 MNIST 낮은 정확도

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