这是来自sentdex教程的代码：MNIST数据集的数据是如何传输到占位符x中的。

请帮助我，考虑到我只是TensorFlow的初学者，如果这与占位符有关，请解释一下。

提前感谢！

"""os.environ removes the warning"""import osos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'"""tensorflow starts below"""import tensorflow as tffrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("/tmp/data/",one_hot=True)# 10 classes , 0-9"""nodes for the hidden layers"""n_nodes_hl1 = 500n_nodes_hl2 = 500n_nodes_hl3 = 500n_classes = 10 # 0-9batch_size = 100"""placeholders"""x = tf.placeholder('float',[None,784]) # 784 is 28*28 ,i.e., the size of mnist imagesy = tf.placeholder('float')# y is the label of datadef neural_network_model(data):    # biases are added so that the some neurons get fired even when input_data is 0    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]))}    # (input_data * weights) + biases    l1 = tf.add(tf.matmul(data,hidden_1_layer['weights']) , hidden_1_layer['biases'])    l1 = tf.nn.relu(l1) # activation func    l2 = tf.add(tf.matmul(l1,hidden_2_layer['weights']) , hidden_2_layer['biases'])    l2 = tf.nn.relu(l2) # activation func    l3 = tf.add(tf.matmul(l2,hidden_3_layer['weights']) , hidden_3_layer['biases'])    l3 = tf.nn.relu(l3) # activation func    output = tf.matmul(l3,output_layer['weights']) + output_layer['biases']     return output# we now have modeled a neural networkdef train_neural_network(x):    prediction = neural_network_model(x)    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))    # softmax_cross_entropy_with_logits ==> for changing weights    # we wanna minimize the difference    # AdamOptimizer optionally has a learning_reate : 0.0001    optimizer = tf.train.AdamOptimizer().minimize(cost)    hm_epochs = 5 # cycles of feed forward + back    with tf.Session() as sess:        sess.run(tf.global_variables_initializer()) # replace it with global_variable_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')) # cast changes the data type of a tensor        print('Accuracy: ',accuracy.eval({x:mnist.test.images,y:mnist.test.labels}))if __name__ == "__main__":    train_neural_network(x)

回答：

要查看MNIST数据是如何传输到tf.placeholder()张量x和y中的，请关注这些行：

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})

数组epoch_x和epoch_y是一对（命名上有些令人困惑的）NumPy数组，分别包含来自MNIST训练数据集的batch_size大小的图像和标签批次。它们在for循环的每次迭代中会包含不同的批次。

feed_dict参数传递给sess.run()，告诉TensorFlow用epoch_x的值替换占位符x，用epoch_y的值替换占位符y。因此，TensorFlow将使用这些值来运行优化算法（在本例中是Adam）的一步操作。

请注意，MNIST数据也在这行被使用：

print('Accuracy: ', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

…但在这里，程序使用整个测试数据集来评估模型的准确性。