手写数字识别程序，模型和测试图片

from PIL import Image, ImageFilter import tensorflow as tf import matplotlib.pyplot as plt import time def imageprepare(): """ This function returns the pixel values. The imput is a png file location. """ file_name='C:/Users/mechrevo/Desktop/9.png'#导入自己的图片地址 #in terminal 'mogrify -format png *.jpg' convert jpg to png im = Image.open(file_name) plt.imshow(im) plt.show() im = im.convert('L') im.save("C:/Users/mechrevo/Desktop/sample.png") tv = list(im.getdata()) #get pixel values #normalize pixels to 0 and 1. 0 is pure white, 1 is pure black. tva = [ (255-x)*1.0/255.0 for x in tv] print(tva) return tva """ This function returns the predicted integer. The imput is the pixel values from the imageprepare() function. """ # Define the model (same as when creating the model file) result=imageprepare() x = tf.placeholder(tf.float32, [None, 784]) y_ = tf.placeholder(tf.float32, [None, 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("float") 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) cross_entropy = -tf.reduce_sum(y_*tf.log(y_conv)) 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, "float")) saver = tf.train.Saver() with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver.restore(sess, "H:/CSDN/SAVE/model.ckpt")#这里使用了之前保存的模型参数 #print ("Model restored.") prediction=tf.argmax(y_conv,1) predint=prediction.eval(feed_dict={x: [result],keep_prob: 1.0}, session=sess) print(h_conv2) print('识别结果:') print(predint[0])