在MNIST模型，Python TensorFlow上測試圖像

Question

我最近開始同時學習python和tensorflow，我目前在MNIST上工作，這是對MNIST數據集的代碼，模型訓練和測試已完成，我的下一個任務是從計算機拍攝圖像，將其導入我的程序中並進行測試我訓練有素的模型上的那個圖像。所以我有兩個問題

1. 如何保存模型，而不必一次又一次運行它？

2. 如何在此模型上導入圖像並對其進行測試，以便模型可以預測此數字

    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

您的模型具有輸出張量prediction 。 如果僅提供圖像，則該圖像應包含10個數字。 編號最高的索引是預測（您已經在使用tf.argmax（prediction，1）進行此操作）。

要獲得預測，您可以

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