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()