MNIST double layer CNN classification for command line CPU Version https://github.com/enakai00/jupyter_tfbook/blob/master/Chapter05/MNIST%20double%20layer%20CNN%20classification.ipynb Raw

double_layer_cnn_cpu.py

#!/usr/bin/env/python
# coding: utf-8

# **[CNN-01]** 必要なモジュールをインポートして、乱数のシードを設定します。

# In[1]:

import time
import tensorflow as tf
import numpy as np
from tensorflow.examples.tutorials.mnist import input_data

np.random.seed(20160704)
tf.set_random_seed(20160704)


# **[CNN-02]** MNISTのデータセットを用意します。

# In[2]:

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


# **[CNN-03]** 1段目の畳み込みフィルターとプーリング層を定義します。

# In[3]:

num_filters1 = 32

with tf.device("/cpu:0"):
    x = tf.placeholder(tf.float32, [None, 784])
    x_image = tf.reshape(x, [-1,28,28,1])
    
    W_conv1 = tf.Variable(tf.truncated_normal([5,5,1,num_filters1],
                                              stddev=0.1))
    h_conv1 = tf.nn.conv2d(x_image, W_conv1,
                           strides=[1,1,1,1], padding='SAME')
    
    b_conv1 = tf.Variable(tf.constant(0.1, shape=[num_filters1]))
    h_conv1_cutoff = tf.nn.relu(h_conv1 + b_conv1)
    
    h_pool1 = tf.nn.max_pool(h_conv1_cutoff, ksize=[1,2,2,1],
                             strides=[1,2,2,1], padding='SAME')
    
    
    # **[CNN-04]** 2段目の畳み込みフィルターとプーリング層を定義します。
    
    # In[4]:
    
    num_filters2 = 64
    
    W_conv2 = tf.Variable(
                tf.truncated_normal([5,5,num_filters1,num_filters2],
                                    stddev=0.1))
    h_conv2 = tf.nn.conv2d(h_pool1, W_conv2,
                           strides=[1,1,1,1], padding='SAME')
    
    b_conv2 = tf.Variable(tf.constant(0.1, shape=[num_filters2]))
    h_conv2_cutoff = tf.nn.relu(h_conv2 + b_conv2)
    
    h_pool2 = tf.nn.max_pool(h_conv2_cutoff, ksize=[1,2,2,1],
                             strides=[1,2,2,1], padding='SAME')
    
    
    # **[CNN-05]** 全結合層、ドロップアウト層、ソフトマックス関数を定義します。
    
    # In[5]:
    
    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*num_filters2])
    
    num_units1 = 7*7*num_filters2
    num_units2 = 1024
    
    w2 = tf.Variable(tf.truncated_normal([num_units1, num_units2]))
    b2 = tf.Variable(tf.constant(0.1, shape=[num_units2]))
    hidden2 = tf.nn.relu(tf.matmul(h_pool2_flat, w2) + b2)
    
    keep_prob = tf.placeholder(tf.float32)
    hidden2_drop = tf.nn.dropout(hidden2, keep_prob)
    
    w0 = tf.Variable(tf.zeros([num_units2, 10]))
    b0 = tf.Variable(tf.zeros([10]))
    p = tf.nn.softmax(tf.matmul(hidden2_drop, w0) + b0)
    
    # **[CNN-06]** 誤差関数 loss、トレーニングアルゴリズム train_step、正解率 accuracy を定義します。
    
    # In[6]:
    
    t = tf.placeholder(tf.float32, [None, 10])
    loss = -tf.reduce_sum(t * tf.log(p))
    train_step = tf.train.AdamOptimizer(0.0001).minimize(loss)
    correct_prediction = tf.equal(tf.argmax(p, 1), tf.argmax(t, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    
    
    with tf.Session() as sess:
        sess = tf.Session()
        sess.run(tf.initialize_all_variables())
        saver = tf.train.Saver()
        
        start_time = time.time()
        
        i = 0
        for _ in range(20000):
            i += 1
            batch_xs, batch_ts = mnist.train.next_batch(50)
            sess.run(train_step,
                     feed_dict={x:batch_xs, t:batch_ts, keep_prob:0.5})
            if i % 500 == 0:
                loss_vals, acc_vals = [], []
                for c in range(4):
                    start = len(mnist.test.labels) / 4 * c
                    end = len(mnist.test.labels) / 4 * (c+1)
                    loss_val, acc_val = sess.run([loss, accuracy],
                        feed_dict={x:mnist.test.images[start:end],
                                   t:mnist.test.labels[start:end],
                                   keep_prob:1.0})
                    loss_vals.append(loss_val)
                    acc_vals.append(acc_val)
                loss_val = np.sum(loss_vals)
                acc_val = np.mean(acc_vals)
                print ('Step: %d, Loss: %f, Accuracy: %f'
                       % (i, loss_val, acc_val))
                saver.save(sess, 'cnn_session', global_step=i)
        end_time = time.time()
        print("Run time %i" % (end_time - start_time))