【tensorflow】:tf.Variable() & tf.get_variable()

本文详细介绍了TensorFlow中tf.Variable与tf.get_variable的区别。tf.Variable在命名冲突时自动添加后缀解决冲突,而tf.get_variable则会在相同的variable_scope下抛出错误,提示变量已存在,通常用于实现变量复用。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

tf.Variable() 和 tf.get_variable()的区别

tf.Variable():

检测到命名冲突时,系统会自动处理,通俗的说就是在变量后面自动加“_index”。

import tensorflow as tf
w_1 = tf.Variable(0,name="w_1")
w_2 = tf.Variable(1,name="w_1")
print w_1.name
print w_2.name
#output
#w_1:0
#w_1_1:0
tf.get_variable():

检测到命名冲突时,系统会报错。这个机制保护了命名系统(这个语句要和tf.variable_scope结合使用),在同样的variable_scope下命名冲突时,系统认为这两个命名是指向同一个内存地址的。

import tensorflow as tf
w_1 = tf.get_variable(name="w_1",initializer=0)
w_2 = tf.get_variable(name="w_1",initializer=1)
#error imformation
#ValueError: Variable w_1 already exists, disallowed. Did
#you mean to set reuse=True in VarScope?
import tensorflow as tf
with tf.variable_scope("scope1"):
    w1 = tf.get_variable("w1", shape=[])
    w2 = tf.Variable(0.0, name="w2")
with tf.variable_scope("scope1", reuse=True):
    w1_p = tf.get_variable("w1", shape=[])
    w2_p = tf.Variable(1.0, name="w2")

print(w1 is w1_p, w2 is w2_p)
#输出
#True  False
tf.get_variable()结合reuse使用例子
import tensorflow as tf

with tf.variable_scope('v_scope',reuse=True) as scope1:
    Weights1 = tf.get_variable('Weights', shape=[2,3])
    bias1 = tf.get_variable('bias', shape=[3])

# 下面来共享上面已经定义好的变量
# note: 在下面的 scope 中的变量必须已经定义过了,才能设置 reuse=True,否则会报错
with tf.variable_scope('v_scope', reuse=True) as scope2:
    Weights2 = tf.get_variable('Weights')

# 下面来共享上面已经定义好的变量
# note: 在下面的 scope 中的变量必须已经定义过了,才能设置 reuse=True,否则会报错
with tf.variable_scope('v_scope', reuse=True) as scope2:
    Weights3 = tf.get_variable('Weights')

print (Weights1.name)
print (Weights2.name)
print (Weights3.name)


#### output:
v_scope/Weights:0
v_scope/Weights:0
v_scope/Weights:0

可以看到三个变量指向的是同一个变量.

注意1:

variable_scope必须是同一个名为‘v_scope’,否则起不到共享变量的作用,会报ValueError: Variable v_scope1/Weights does not exist, or was not created with tf.get_variable(). Did you mean to set reuse=None in VarScope?

注意2:

get_variable()变量必须已经定义过了,而且必须是通过get_variable()定义的,才能设置 reuse=True,否则会报错Variable v_scope/bias does not exist, or was not created with tf.get_variable()

reference

https://blog.youkuaiyun.com/u012436149/article/details/53696970
https://www.cnblogs.com/gczr/p/7614821.html

``` !mkdir -p ~/.keras/datasets !cp work/mnist.npz ~/.keras/datasets/ import warnings warnings.filterwarnings("ignore") from keras.datasets import mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() print(f"训练数据形状: {train_images.shape}") print(f"训练标签长度: {len(train_labels)}") print(f"测试数据形状: {test_images.shape}") print(f"测试标签长度: {len(test_labels)}") from keras import models from keras import layers # 构建神经网络模型 network = models.Sequential() network.add(layers.Dense(512, activation='relu', input_shape=(28 * 28,))) # 隐藏层:512个神经元,激活函数为ReLU network.add(layers.Dense(10, activation='softmax')) # 输出层:10个分类,激活函数为Softmax # 编译模型 network.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) # 数据预处理 train_images = train_images.reshape((60000, 28 * 28)) # 将图像展平成一维向量 train_images = train_images.astype('float32') / 255 # 归一化到[0,1] test_images = test_images.reshape((10000, 28 * 28)) test_images = test_images.astype('float32') / 255 # 标签编码 from keras.utils import to_categorical train_labels = to_categorical(train_labels) test_labels = to_categorical(test_labels) # 训练模型 network.fit(train_images, train_labels, epochs=5, batch_size=128) # 测试模型性能 test_loss, test_acc = network.evaluate(test_images, test_labels) print('Test accuracy:', test_acc)```W0402 08:09:22.415642 140410418362176 deprecation.py:323] From /opt/conda/lib/python3.6/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where W0402 08:09:22.484165 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:986: The name tf.assign_add is deprecated. Please use tf.compat.v1.assign_add instead. W0402 08:09:22.495126 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:973: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead. W0402 08:09:22.537523 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:2741: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead. W0402 08:09:22.546429 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead. W0402 08:09:22.548026 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:181: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead. W0402 08:09:22.566734 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:190: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead. W0402 08:09:22.567799 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:199: The name tf.is_variable_initialized is deprecated. Please use tf.compat.v1.is_variable_initialized instead. W0402 08:09:22.613820 140410418362176 module_wrapper.py:139] From /opt/conda/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py:206: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.
最新发布
04-03
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包

打赏作者

yuanCruise

你的鼓励将是我创作的最大动力

¥1 ¥2 ¥4 ¥6 ¥10 ¥20
扫码支付:¥1
获取中
扫码支付

您的余额不足,请更换扫码支付或充值

打赏作者

实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值