from tensorflow.examples.tutorials.mnist import input_data
from network import network_mnist
import matplotlib.pyplot as plt
import numpy as np
size_input = 28*28
size_output = 10
model_path = './mnist_model/'
mnist = input_data.read_data_sets("../MNIST_data", one_hot=True)
x = tf.placeholder(tf.float32, [None, size_input]) #数据
y = tf.placeholder(tf.float32, [None, size_output]) #标签
y_pre = network_mnist(x, size_input, size_output) #预测值,预测标签
# compute the accuracy
correct_predictions = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
saver = tf.train.Saver()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, model_path)
y_pre_get, acc_test = sess.run([y_pre, accuracy], feed_dict={x: mnist.test.images, y: mnist.test.labels})
print('acc_test: %.4f%%'%(acc_test*100))
print('***********DONE***************')
fig, ax = plt.subplots(nrows=4, ncols=5,
from network import network_mnist
import matplotlib.pyplot as plt
import numpy as np
size_input = 28 * 28
size_output = 10
model_path = './mnist_model/'
mnist = input_data.read_data_sets("../MNIST_data", one_hot=True)
x = tf.placeholder(tf.float32, [None, size_input]) #数据
y = tf.placeholder(tf.float32, [None, size_output]) #标签
keep_prob_layer = tf.placeholder(tf.float32)
y_pre = network_mnist(x, size_input, size_output, keep_prob_layer) #预测值,预测标签
# compute the accuracy
correct_predictions = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
saver = tf.train.Saver()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
saver.restore(sess, model_path)
y_pre_get, acc_test = sess.run([y_pre, accuracy],
feed_dict={
x: mnist.test.images,
y: mnist.test.labels,
keep_prob_layer: 1
batch_size = 512
step_max = int(55000 / batch_size)
acc_stop = 0.99
model_path = './mnist_model/'
shutil.rmtree(model_path, ignore_errors=True)
os.mkdir(model_path)
mnist = input_data.read_data_sets("../MNIST_data", one_hot=True)
x = tf.placeholder(tf.float32, [None, size_input * size_input]) # 数据
y = tf.placeholder(tf.float32, [None, size_output]) # 标签
x_image = tf.reshape(x, [-1, size_input, size_input, c_in]) # 转换为图像的格式
y_pre = network_mnist(x_image, c_in, size_output) # 预测值,预测标签
cross_entropy = tf.reduce_mean(
tf.reduce_sum(-y * tf.log(y_pre), reduction_indices=[1]))
train = tf.train.GradientDescentOptimizer(0.08).minimize(cross_entropy)
# compute the accuracy
correct_predictions = tf.equal(tf.argmax(y_pre, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32))
saver = tf.train.Saver()
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
acc_max = -1
str_time = time.strftime('%H:%M:%S', time.localtime())
print('%s: begin' % (str_time))