在 MNIST 模型、Python TensorFlow 上测试图像

Question

我最近开始同时学习python和tensorflow，我目前正在研究MNIST，这里是代码，MNIST数据集的模型训练和测试已经完成，我的下一个任务是从计算机获取图像，将其导入我的在我训练有素的模型上编程和测试该图像。所以我有 2 个问题

1. 如何保存我的模型，这样我就不必一次又一次地运行它？

2. 如何在此模型上导入和测试图像，以便模型可以预测这是哪个数字

   import tensorflow as tf`
   from tensorflow.examples.tutorials.mnist import input_data
   
   mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
   n_nodes_hl1 = 500
   n_nodes_hl2 = 500
   n_nodes_hl3 = 500
   n_classes = 10
   batch_size = 100
   x = tf.placeholder('float', [None, 784])
   y = tf.placeholder('float')
   
   def neural_network_model(data):
       hidden_1_layer = {'weights': tf.Variable(tf.random_normal([784, n_nodes_hl1])),
                         'biases': tf.Variable(tf.random_normal([n_nodes_hl1]))}
   hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2])),
                     'biases': tf.Variable(tf.random_normal([n_nodes_hl2]))}
   
   hidden_3_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_nodes_hl3])),
                     'biases': tf.Variable(tf.random_normal([n_nodes_hl3]))}
   
   output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl3, n_classes])),
                   'biases': tf.Variable(tf.random_normal([n_classes])), }
   
   l1 = tf.add(
       tf.matmul(
           data,
           hidden_1_layer['weights']),
       hidden_1_layer['biases'])
   l1 = tf.nn.relu(l1)
   
   l2 = tf.add(
       tf.matmul(
           l1,
           hidden_2_layer['weights']),
       hidden_2_layer['biases'])
   l2 = tf.nn.relu(l2)
   
   l3 = tf.add(
       tf.matmul(
           l2,
           hidden_3_layer['weights']),
       hidden_3_layer['biases'])
   l3 = tf.nn.relu(l3)
   
   output = tf.matmul(l3, output_layer['weights']) + output_layer['biases']
   
   return output
   
   
   def train_neural_network(x):
       prediction = neural_network_model(x)
       cost = tf.reduce_mean(
           tf.nn.softmax_cross_entropy_with_logits(
               logits=prediction, labels=y))
       optimizer = tf.train.AdamOptimizer().minimize(cost)
   hm_epochs = 10
   with tf.Session() as sess:
       sess.run(tf.global_variables_initializer())
   
       for epoch in range(hm_epochs):
           epoch_loss = 0
           for _ in range(int(mnist.train.num_examples / batch_size)):
               epoch_x, epoch_y = mnist.train.next_batch(batch_size)
               _, c = sess.run([optimizer, cost], feed_dict={
                               x: epoch_x, y: epoch_y})
               epoch_loss += c
   
           print(
               'Epoch',
               epoch,
               'completed out of',
               hm_epochs,
               'loss:',
               epoch_loss)
   
       correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
   
       accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
       print('Accuracy:', accuracy.eval(
           {x: mnist.test.images, y: mnist.test.labels}))
   
   train_neural_network(x)
   

Answer 1

你的模型有一个输出张量prediction。如果您只提供图像，它应该包含 10 个数字。最高数字的索引是预测（您已经使用 tf.argmax(prediction, 1) 来执行此操作）。

要获得预测，您可以这样做

sess.run(prediction, feed_dict={x: <numpy array or tensor containing the 784 floats representing your image>})`