mnist 필기체 식별
15248 단어 21개 항목
# coding:utf-8
# tensorflow.examples.tutorials.mnist 。 TensorFlow MNIST
from tensorflow.examples.tutorials.mnist import input_data
# MNIST_data/ MNIST 。 ，
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#
print(mnist.train.images.shape) # (55000, 784)
print(mnist.train.labels.shape) # (55000, 10)
#
print(mnist.validation.images.shape) # (5000, 784)
print(mnist.validation.labels.shape) # (5000, 10)
#
print(mnist.test.images.shape) # (10000, 784)
print(mnist.test.labels.shape) # (10000, 10)
# 0
print(mnist.train.images[0, :])배열을 그림save 로 저장pic.py
#coding: utf-8
from tensorflow.examples.tutorials.mnist import input_data
import scipy.misc
import os
# MNIST 。 。
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# MNIST_data/raw/
#
save_dir = 'MNIST_data/raw/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
# 20
# for i in range(20):
# ，mnist.train.images[i, :] i （ 0 ）
image_array = mnist.train.images[i, :]
# TensorFlow MNIST 784 ， 28x28 。
image_array = image_array.reshape(28, 28)
# mnist_train_0.jpg, mnist_train_1.jpg, ... ,mnist_train_19.jpg
filename = save_dir + 'mnist_train_%d.jpg' % i
# image_array
# scipy.misc.toimage ， save 。
scipy.misc.toimage(image_array, cmin=0.0, cmax=1.0).save(filename)
print('Please check: %s ' % save_dir)
one-hot의 label label을 표시합니다.py
# coding: utf-8
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
# mnist 。 。
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# 20 label
for i in range(20):
# one-hot ， (0, 1, 0, 0, 0, 0, 0, 0, 0, 0)
one_hot_label = mnist.train.labels[i, :]
# np.argmax label
# 1 1， 0
label = np.argmax(one_hot_label)
print('mnist_train_%d.jpg label: %d' % (i, label))
권적 신경 네트워크의 방식을 이용하여 훈련을 진행하다
# coding: utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
#
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')
if __name__ == '__main__':
#
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# x 、y_
x = tf.placeholder(tf.float32, [None, 784])
y_ = tf.placeholder(tf.float32, [None, 10])
# 784 28x28
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)
# ， 1024
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)
# Dropout，keep_prob ， 0.5， 1
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
# 1024 10 ， 10
W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2
# Softmax ， tf.nn.softmax_cross_entropy_with_logits
cross_entropy = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv))
# train_step
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))
# Session
sess = tf.InteractiveSession()
sess.run(tf.global_variables_initializer())
# 20000
for i in range(20000):
batch = mnist.train.next_batch(50)
# 100
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}))
기계 학습의 방식으로 훈련을 진행하다
# coding:utf-8
# tensorflow。
# import tensorflow as tf TensorFlow ， 。
import tensorflow as tf
# MNIST
from tensorflow.examples.tutorials.mnist import input_data
# ， MNIST
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
# x，x （placeholder），
x = tf.placeholder(tf.float32, [None, 784])
# W Softmax ， 784 10
# TensorFlow ， tf.Variable
W = tf.Variable(tf.zeros([784, 10]))
# b Softmax ， “ ”（bias）。
b = tf.Variable(tf.zeros([10]))
# y=softmax(Wx + b)，y
y = tf.nn.softmax(tf.matmul(x, W) + b)
# y_ ， 。
y_ = tf.placeholder(tf.float32, [None, 10])
# ， Tensor：y y_。
# y ，y_ ， y_
# y y_
# y, y_
cross_entropy = tf.reduce_mean(-tf.reduce_sum(y_ * tf.log(y)))
# ， （W b）
train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)
# Session。 Session train_step。
sess = tf.InteractiveSession()
# ， 。
tf.global_variables_initializer().run()
print('start training...')
# 1000
for _ in range(1000):
# mnist.train 100
# batch_xs (100, 784) ，batch_ys (100, 10)
# batch_xs, batch_ys x y_
batch_xs, batch_ys = mnist.train.next_batch(100)
# Session train_step，
sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
#
correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
# ， Tensor
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
# Session Tensor Tensor
#
print(sess.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels})) # 0.9185
좋은 웹페이지 즐겨찾기
