Tensorfow mnist 识别验证

参考:https://blog.csdn.net/sparta_117/article/details/66965760

20000遍的训练,结果方面,1和4的图片识别成8,偶尔正确,其他数字只要不变形严重基本正确

训练采用参考文章的,验证自己修改了一下,下面就是验证代码

import tensorflow as tf
import matplotlib.pyplot as plt
import struct
import numpy as np

model_path =r'D:\MNIST_data\model.ckpt'

test_images_path = r'D:\MNIST_data\t10k-images.idx3-ubyte'
test_labels_path = r'D:\MNIST_data\t10k-labels.idx1-ubyte'

test_images = []  # =mnist.train.images
test_labels = []  # =mnist.train.labels

"""Load MNIST data from `path`"""
with open(test_labels_path, 'rb') as lbpath:
    magic, n = struct.unpack('>II', lbpath.read(8))
    test_labels = np.fromfile(lbpath, dtype=np.uint8)
with open(test_images_path, 'rb') as imgpath:
    magic, num, rows, cols = struct.unpack('>IIII', imgpath.read(16))
    test_images = np.fromfile(imgpath, dtype=np.uint8).reshape(len(test_labels), 784)


#图片颜色值255归一化
def imageprepare(im):
    # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.
    tva = [(255 - x) * 1.0 / 255.0 for x in im]  # 0被转成了1.0,颜色被归一化 normalize pixels to 0 and 1. 0

    # 把1.0转成0
    tvc = []
    for x in tva:
        if x == 1.0:
            tvc.append(0)
        else:
            tvc.append(x)
    return tvc

x = tf.placeholder(tf.float32, [None, 784])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))

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')   

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

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)

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)

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)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])

y_conv=tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)

init_op = tf.initialize_all_variables()

saver = tf.train.Saver()
with tf.Session() as sess:
    sess.run(init_op)
    saver.restore(sess, model_path)#这里使用了之前保存的模型参数
    print ("Model restored.")
    prediction = tf.argmax(y_conv, 1)
    for image in test_images:
        predint=prediction.eval(feed_dict={x: [imageprepare(image)],keep_prob: 1.0}, session=sess)
        plt.title('recognize result=> ' +str(predint[0]))
        plt.imshow(image.reshape([28, 28]))
        plt.show()

Tensorfow mnist 识别验证

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