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TensorFlow MNIST2

チュートリアルのサンプル(少し編集)

				

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os

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')

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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])

W_fc1 = weight_variable([7 * 7 * 64, 1024])
b_fc1 = bias_variable([1024])

W_conv1 = weight_variable([5, 5, 1, 32])
b_conv1 = bias_variable([32])

W_conv2 = weight_variable([5, 5, 32, 64])
b_conv2 = bias_variable([64])

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

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1)
h_pool1 = max_pool_2x2(h_conv1)

h_conv2 = tf.nn.relu(conv2d(h_pool1, W_conv2) + b_conv2)
h_pool2 = max_pool_2x2(h_conv2)

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))

# saverを作成
saver = tf.train.Saver()

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

     # 保存データ確認
    if tf.train.get_checkpoint_state('.'):
        try:
            # 保存データ読み込み
            saver.restore(sess, "./test2")
        except:
            print("保存と読み込みでsave_pathが異なる？")
    else:
        print("初回実行")

#    処理に時間が掛かるため
#    for i in range(20000):
    for i in range(201):
        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}))

    # データ保存
    saver.save(sess, './test2')
    

			

tf.truncated_normal

				

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os


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

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

W_conv1 = weight_variable([5, 5, 1, 32])

with tf.Session() as sess:
    
    sess.run(tf.global_variables_initializer())
    ary = sess.run(W_conv1)
    print(W_conv1)
    print(len(ary))
    print(len(ary[0]))
    print(len(ary[0][0]))
    print(len(ary[0][0][0]))
    print(ary)


			

tf.reshape

				
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import os


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


os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)

batch_xs, batch_ys = mnist.train.next_batch(100)

print(tf.reshape(batch_xs[0], [-1, 28, 28, 1]))
print(tf.reshape(batch_xs[0], [-1, 1, 28, 1]))
print(tf.reshape(batch_xs[0], [-1, 2, 28, 1]))