这是senddex教程中的代码：
来自MNIST集的数据如何传输到占位符x中。
请帮助我，考虑到我只是tensorflow的初学者，如果与占位符有关，请解释。
提前致谢！
"""
os.environ removes the warning
"""
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
"""
tensorflow starts below
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("/tmp/data/",one_hot=True)
# 10 classes , 0-9
"""
nodes for the hidden layers
"""
n_nodes_hl1 = 500
n_nodes_hl2 = 500
n_nodes_hl3 = 500
n_classes = 10 # 0-9
batch_size = 100
"""
placeholders
"""
x = tf.placeholder('float',[None,784]) # 784 is 28*28 ,i.e., the size of mnist images
y = tf.placeholder('float')
# y is the label of data
def 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 network
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))
# 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)
最佳答案
若要查看MNIST数据在哪里传输到tf.placeholder()张量x和y，请关注以下几行：
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NumPy数组（名称有些混淆，它们分别包含来自MNIST训练数据集的一批batch_size图像和标签）。在for循环的每次迭代中，它们将包含不同的批处理。feed_dict的sess.run()参数告诉TensorFlow将epoch_x的值替换为占位符x，将epoch_y的值替换为占位符y。因此TensorFlow将使用这些值来运行优化算法的一个步骤（在本例中为Adam）。请注意，MNIST数据也用于此行：
print('Accuracy: ', accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
...除了程序在这里使用整个测试数据集来评估模型的准确性。