TensorFlow MNIST 求助

#! /usr/bin/env python # -*- coding: utf-8 -*- import time import input_data import tensorflow as tf 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') # 计算开始时间 start = time.clock() # MNIST 数据输入 mnist = input_data.read_data_sets("MNIST_data/", one_hot=True) x = tf.placeholder(tf.float32, [None, 784]) # 图像输入向量 W = tf.Variable(tf.zeros([784, 10])) # 权重，初始化值为全零 b = tf.Variable(tf.zeros([10])) # 偏置，初始化值为全零 # 第一层卷积，由一个卷积接一个 maxpooling 完成，卷积在每个 # 5x5 的 patch 中算出 32 个特征。 # 卷积的权重张量形状是[5, 5, 1, 32]，前两个维度是 patch 的大小， # 接着是输入的通道数目，最后是输出的通道数目。 # 而对于每一个输出通道都有一个对应的偏置量。 W_conv1 = weight_variable([5, 5, 1, 32]) b_conv1 = bias_variable([32]) x_image = tf.reshape(x, [-1, 28, 28, 1]) # 最后一维代表通道数目，如果是 rgb 则为 3 # x_image 权重向量卷积，加上偏置项，之后应用 ReLU 函数，之后进行 max_polling h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) h_pool1 = max_pool_2x2(h_conv1) # 实现第二层卷积 # 每个 5x5 的 patch 会得到 64 个特征 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) # 密集连接层 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) # Dropout， 用来防止过拟合 #加在输出层之前，训练过程中开启 dropout，测试过程中关闭 keep_prob = tf.placeholder("float") h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) # 输出层, 添加 softmax 层 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) # 训练和评估模型 y_ = tf.placeholder("float", [None, 10]) cross_entropy = -tf.reduce_sum(y_ * tf.log(y_conv)) # 计算交叉熵 # 使用 adam 优化器来以 0.0001 的学习率来进行微调 train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) # 判断预测标签和实际标签是否匹配 correct_prediction = tf.equal(tf.argmax(y_conv, 1), tf.argmax(y_, 1)) accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) # 启动创建的模型，并初始化变量 saver = tf.train.Saver() # 声明 tf.train.Saver 类用于保存模型 write_version=tf.train.SaverDef.V1 sess = tf.Session() sess.run(tf.initialize_all_variables()) # 开始训练模型，循环训练 20000 次 for i in range(20000): batch = mnist.train.next_batch(100) # batch 大小设置为 50 if i % 1000 == 0: train_accuracy = accuracy.eval(session=sess, feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0}) print("step %d, train_accuracy %g" % (i, train_accuracy)) # 神经元输出保持不变的概率 keep_prob 为 0.5 train_step.run(session=sess, feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) # 神经元输出保持不变的概率 keep_prob 为 1，即不变，永远保持输出 print("test accuracy %g" % accuracy.eval(session=sess, feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) # 计算程序结束时间 end = time.clock() saver_path = saver.save(sess, "save/model.ckpt") # 将模型保存到 save/model.ckpt 文件 print("Model saved in file:", saver_path) print("running time is %g s" % (end - start)) 

上面的代码将训练的结果进行保存

下面的代码使用训练结果进行识别

#! /usr/bin/env python # -*- coding: utf-8 -*- """""" from PIL import Image from numpy import * import tensorflow as tf import sys if len(sys.argv) < 2: print('argv must at least 2. you give '+str(len(sys.argv))) sys.exit() filename = sys.argv[1] im = Image.open(filename) img = array(im.resize((28, 28), Image.ANTIALIAS).convert("L")) data = img.reshape([1, 784]) x = tf.placeholder(tf.float32, [None, 784]) W = tf.Variable(tf.zeros([784, 10])) b = tf.Variable(tf.zeros([10])) y = tf.nn.softmax(tf.matmul(x, W) + b) saver = tf.train.Saver() init_op = tf.initialize_all_variables() with tf.Session() as sess: sess.run(init_op) save_path = "./save/model.ckpt" saver.restore(sess, save_path) predictiOns= sess.run(y, feed_dict={x: data}) print(predictions[0]) 

使用的图片是张黑白的 数字 4

结果是：[ 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1]

这结果不对啊，起码也应该是：[ 0.1 0.1 0.1 0.1 1.0 0.1 0.1 0.1 0.1 0.1]

求大神帮忙！！！！！