我是Python和Tensorflow的新手,在训练阶段后,我在从NN获取值时遇到了一些困难。
import tensorflow as tf
import numpy as np
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot = True)
n_nodes_hl1 = 50
n_nodes_hl2 = 50
n_classes = 10
batch_size = 128
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]),name='weights1'),
'biases': tf.Variable(tf.random_normal([n_nodes_hl1]),name='biases1')}
hidden_2_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl1, n_nodes_hl2]),name='weights2'),
'biases': tf.Variable(tf.random_normal([n_nodes_hl2]),name='biases2')}
output_layer = {'weights': tf.Variable(tf.random_normal([n_nodes_hl2, n_classes]),name='weights3'),
'biases': tf.Variable(tf.random_normal([n_classes]),name='biases3')}
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)
output = tf.add(tf.matmul(l2, output_layer['weights']) , output_layer['biases'])
return output
def train_neural_network(x):
prediction = neural_network_model(x)
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=prediction,labels=y))
optimizer = tf.train.AdamOptimizer().minimize(cost)
hm_epochs = 100
init = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer() )
with tf.Session() as sess:
sess.run(init)
for epoch in range(hm_epochs):
epoch_loss = 0
for _ in range(int(mnist.train.num_examples / batch_size)) :
ep_x, ep_y = mnist.train.next_batch(batch_size)
_, c = sess.run([optimizer, cost], feed_dict = {x: ep_x, y: ep_y})
epoch_loss += c
print('Epoch', epoch+1, '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)
我尝试使用以下方法从第1层提取权重:
w = tf.get_variable('weights1',shape=[784,50])
b = tf.get_variable('biases1',shape=[50,])
myWeights, myBiases = sess.run([w,b])
但是这个投掷误差Attempting to use uninitialized value weights1_1
这是因为我的变量在dict类型“hidden_1_layer”中吗?
我对Python和Tensorflow数据类型还不是很熟悉,所以我完全搞不懂!
3条答案
按热度按时间u0njafvf1#
使用以下代码:
还有其他方法可以存储变量以供以后使用或分析。请指定提取权重和偏差的目的。如果需要进一步讨论,请进一步注解。
2g32fytz2#
当你写作的时候
您正在定义2个新变量:
weights1
变为weights1_1
biases1
变为biases1_1
因为变量名
weights1
和biases1
在图中已经存在,所以tensorflow为您添加了后缀_<counter>
,以避免命名冲突。如果你想创建一个已经存在的变量的引用,你必须熟悉variable scope的概念。
简而言之,必须明确表示要重用某个变量,可以使用
[tf.variable_scope
] 2及其reuse参数来实现这一点。hyrbngr73#
要训练它的值,你也可以这样做,自定义回调方法!