为了一个学校项目，我已经分析了下面的代码，但我想要添加一个功能：当神经网络完成训练后，我想给它一张来自MNIST的手写数字图像（比如数字8），让它尝试识别这个数字8。因为我是编程和机器学习的新手，尽管我非常喜欢并且想学更多，我无法自己弄清楚这样的代码应该是什么样的。有人可以帮助我吗？
代码是用Python编写的：
import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("MNIST_data", one_hot=True)learning_rate = 0.0001batch_size = 100update_step = 10layer_1_nodes = 500layer_2_nodes = 500layer_3_nodes = 500output_nodes = 10network_input = tf.placeholder(tf.float32, [None, 784])target_output = tf.placeholder(tf.float32, [None, output_nodes])layer_1 = tf.Variable(tf.random_normal([784, layer_1_nodes]))layer_1_bias = tf.Variable(tf.random_normal([layer_1_nodes]))layer_2 = tf.Variable(tf.random_normal([layer_1_nodes, layer_2_nodes]))layer_2_bias = tf.Variable(tf.random_normal([layer_2_nodes]))layer_3 = tf.Variable(tf.random_normal([layer_2_nodes, layer_3_nodes]))layer_3_bias = tf.Variable(tf.random_normal([layer_3_nodes]))out_layer = tf.Variable(tf.random_normal([layer_3_nodes, output_nodes]))out_layer_bias = tf.Variable(tf.random_normal([output_nodes]))l1_output = tf.nn.relu(tf.matmul(network_input, layer_1) + layer_1_bias)l2_output = tf.nn.relu(tf.matmul(l1_output, layer_2) + layer_2_bias)l3_output = tf.nn.relu(tf.matmul(l2_output, layer_3) + layer_3_bias)ntwk_output_1 = tf.matmul(l3_output, out_layer) + out_layer_biasntwk_output_2 = tf.nn.softmax(ntwk_output_1)cf = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=ntwk_output_1, labels=target_output))ts = tf.train.GradientDescentOptimizer(learning_rate).minimize(cf)cp = tf.equal(tf.argmax(ntwk_output_2, 1), tf.argmax(target_output, 1))acc = tf.reduce_mean(tf.cast(cp, tf.float32))with tf.Session() as sess:sess.run(tf.global_variables_initializer())num_epochs = 10for epoch in range(num_epochs): total_cost = 0 for _ in range(int(mnist.train.num_examples / batch_size)): batch_x, batch_y = mnist.train.next_batch(batch_size) t, c = sess.run([ts, cf], feed_dict={network_input: batch_x, target_output: batch_y}) total_cost += c print('Epoch', epoch, 'completed out of', num_epochs, 'loss:', total_cost)print('Accuracy:', acc.eval({network_input: mnist.test.images,target_output: mnist.test.labels}))回答：
with tf.Session() as sess: number_prediction = tf.argmax(ntwk_output_2 , 1) number_prediction = sess.run(number_prediction , feed_dict={network_input : yourImageNdArray } ) print("your prediction : ",number_prediction)你需要知道的：
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ntwk_output_2 是神经网络的输出，它给你10个概率——你用 tf.argmax 取最大的那个（tf.argmax 返回的不是最大值，而是它的位置）
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sess.run 负责运行你的 TensorFlow 图并评估第一个参数给定的张量
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你还需要在 feed_dict 中为网络提供你想要预测的图像
希望这对你有帮助！
