从 TensorFlow 变量中提取值

Question

我是 Python 和 Tensorflow 的新手，在训练阶段后从我的 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])

但是这个抛出错误Attempting to use uninitialized value weights1_1

这是因为我的变量在字典类型“hidden_​​1_layer”中吗？

我对 Python 和 Tensorflow 数据类型还不熟悉，所以我一头雾水！

Answer 1

使用以下代码：

tensor_1 = tf.get_default_graph().get_tensor_by_name("weights1:0")
tesnor_2 = tf.get_default_graph().get_tensor_by_name("biases1:0")
sess = tf.Session()
np_arrays = sess.run([tensor_1, tensor_2])

还有其他方法可以存储变量以供以后使用或分析。请说明您提取权重和偏差的目的。如果需要进一步讨论，请进一步评论。

Answer 2

当你写作时

w = tf.get_variable('weights1',shape=[784,50])
b = tf.get_variable('biases1',shape=[50,])

您正在定义 2 个新变量：

1. weights1 变为 weights1_1
2. biases1 变为 biases1_1

因为图中已经存在名为weights1和biases1的变量，所以tensorflow为你添加了_<counter>后缀，以避免命名冲突。

如果您想创建对现有变量的引用，您必须熟悉variable scope 的概念。

简而言之，您必须明确表示要重用某个变量，您可以使用 [tf.variable_scope]2 及其重用参数来做到这一点。

scope_name =  "" #default scope
with tf.variable_scope(scope_name, reuse=True):
    w = tf.get_variable('weights1',shape=[784,50])
    b = tf.get_variable('biases1',shape=[50,])

Answer 3

要让它训练值，你也可以这样做，自定义回调方法！

class custom_callback(tf.keras.callbacks.Callback): 
    tf.summary.create_file_writer(val_dir)      
    
    def _val_writer(self):
        if 'val' not in self._writers:
            self._writers['val'] = tf.summary.create_file_writer(val_dir)
        return self._writers['val']
    
    def on_epoch_end(self, epoch, logs={}):
        print('weights: ' + str(self.model.get_weights()))
        
        if self.model.optimizer and hasattr(self.model.optimizer, 'iterations'):
            with tf.summary.record_if(True): # self._val_writer.as_default():
                step = ''
                for name, value in logs.items():
                    tf.summary.scalar(
                    'evaluation_' + name + '_vs_iterations',
                    value,
                    step=self.model.optimizer.iterations.read_value(),
                    )           
        if(logs['accuracy'] == None) : pass
        else:
            if(logs['accuracy']> 0.90):
                self.model.stop_training = True
    
        with tf.compat.v1.variable_scope('Value', reuse=tf.compat.v1.AUTO_REUSE):                   
            w1 = tf.compat.v1.get_variable('w2', shape=[256])
            b1 = tf.compat.v1.get_variable('b2', shape=[256,])
            
            print('w1:' + str(w1))
            print('b1:' + str(b1))

custom_callback = custom_callback()

history = model_highscores.fit(batched_features, epochs=99 ,validation_data=(dataset.shuffle(len(list_image))), callbacks=[custom_callback])