我已经完成了senddex的教程。但是当我执行程序时,损失总是0.0。
Epoch 0 completed out of 10 loss: 0.0
Epoch 1 completed out of 10 loss: 0.0
Epoch 2 completed out of 10 loss: 0.0
Epoch 3 completed out of 10 loss: 0.0
Epoch 4 completed out of 10 loss: 0.0
Epoch 5 completed out of 10 loss: 0.0
Epoch 6 completed out of 10 loss: 0.0
Epoch 7 completed out of 10 loss: 0.0
Epoch 8 completed out of 10 loss: 0.0
Epoch 9 completed out of 10 loss: 0.0
Accuracy: 0.0
我找不到任何解决方案。
import numpy as np
import tensorflow as tf
old_v = tf.logging.get_verbosity()
tf.logging.set_verbosity(tf.logging.ERROR)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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)
# OLD VERSION:
# cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
# NEW:
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:
# OLD:
# sess.run(tf.initialize_all_variables())
# NEW:
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)
这是完整的代码。为了确保我没写错,我从网站复制了代码。
我没有错误,但是损失值没有增加甚至没有变化。
能请你帮我吗?
Elias
答案 0 :(得分:1)
答案 1 :(得分:1)
损失不为零。即使在粘贴到附加位置(epoch_loss += c
)的代码中,它也会为我打印累积的损失。
代码的Slighlty修改版本是这样。绘制损失
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
old_v = tf.logging.get_verbosity()
tf.logging.set_verbosity(tf.logging.ERROR)
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
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)
# OLD VERSION:
# cost = tf.reduce_mean( tf.nn.softmax_cross_entropy_with_logits(prediction,y) )
# NEW:
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
hm_epochs = 10
with tf.Session() as sess:
# OLD:
# sess.run(tf.initialize_all_variables())
# NEW:
sess.run(tf.global_variables_initializer())
epoch_loss = []
for epoch in range(hm_epochs):
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.append(c)
print('Epoch', epoch, 'completed out of', hm_epochs, 'loss:', c)
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}))
plt.subplot(1, 2, 1)
plt.plot(epoch_loss)
plt.title('Epoch Loss')
plt.show()
train_neural_network(x)