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Dies ist der Code von senddex tutorials: Wie werden die Daten vom MNIST-Set in den Platzhalter x übertragen.MNIST: x ist nur ein Platzhalter, Wie gehen Daten von MNIST in den Platzhalter x?
Bitte helfen Sie mir, wenn ich bedenke, ich bin nur ein Anfänger in Tensorflow, wenn es etwas mit dem Platzhalter zu tun hat, dann bitte erklären.
Vielen Dank im Voraus!
"""
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)