# -*- coding: utf-8 -*-
"""通過卷積神經網路進行**
@author: elijah
"""#引入資料集
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
mnist = input_data.read_data_sets("mnist_data/",one_hot=true)
#x為訓練影象的佔位符，y為訓練影象標籤的佔位符
x = tf.placeholder(tf.float32, [none, 784])
y_ = tf.placeholder(tf.float32, [none, 10])
#將單張還原為28*28的矩陣
x_image = tf.reshape(x, [-1, 28, 28, 1])
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')
#第一層卷積
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)
#第二層卷積
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)
#兩層卷積層之間的全連線層
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)
#將h_fc1_drop轉換為10個類別的打分
w_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y_conv = tf.matmul(h_fc1_drop, w_fc2) + b_fc2
#計算交叉熵
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)
#定義測試的準確率
correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
#建立session，對物件進行初始化
sess = tf.interactivesession()
sess.run(tf.global_variables_initializer())
#訓練20000步
for i in range(20000):
batch = mnist.train.next_batch(50)
# 每100步驗證一次準確率
if i % 100 == 0:
train_accuracy = accuracy.eval(feed_dict=)
print("step %d, training accuracy %g" % (i, train_accuracy))
train_step.run(feed_dict=)
#在測試集上的準確率
print("test accuracy &g" % accuracy.eval(feed_dict=))
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