tf.variable_scope("main")

在 TensorFlow 中，`tf.variable_scope` 可以用来管理变量的命名空间。它可以帮助我们更好地管理变量，避免变量名冲突，并且能够很方便地在代码中复用同一组变量。

在这个例子中，我们创建了一个名为 "main" 的变量作用域，并在其中定义了一些变量。这些变量都会自动使用 "main" 作为前缀，例如 "main/weights"。这样做的好处是，如果我们在代码中再次创建名为 "weights" 的变量，TensorFlow 会自动为它们添加适当的前缀，避免命名冲突。

同时，我们可以使用 `tf.get_variable` 函数来获取或创建变量。如果变量已经存在于当前作用域中，则该函数会返回该变量；否则，它会创建一个新的变量，并将其添加到当前作用域中。这也可以避免命名冲突，并且可以方便地在多个函数中共享同一组变量。

相关问题

D:\Python37\python.exe D:/PycharmProjects/TensorFlow/Load-forecasting/Electricity-Load-Forecasting-master/main.py WARNING:tensorflow:From D:\Python37\lib\site-packages\tensorflow\python\compat\v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. Instructions for updating: non-resource variables are not supported in the long term Traceback (most recent call last): File "D:/PycharmProjects/TensorFlow/Load-forecasting/Electricity-Load-Forecasting-master/main.py", line 9, in <module> from models.rnn import build_rnn File "D:\PycharmProjects\TensorFlow\Load-forecasting\Electricity-Load-Forecasting-master\models\rnn.py", line 2, in <module> from keras.models import Sequential File "D:\Python37\lib\site-packages\keras\__init__.py", line 21, in <module> from keras import models File "D:\Python37\lib\site-packages\keras\models\__init__.py", line 18, in <module> from keras.engine.functional import Functional File "D:\Python37\lib\site-packages\keras\engine\functional.py", line 26, in <module> from keras import backend File "D:\Python37\lib\site-packages\keras\backend.py", line 32, in <module> from keras import backend_config File "D:\Python37\lib\site-packages\keras\backend_config.py", line 33, in <module> @tf.__internal__.dispatch.add_dispatch_support AttributeError: module 'tensorflow.compat.v2' has no attribute '__internal__' 进程已结束，退出代码为 1

根据您提供的错误信息，看起来您在运一个脚本时遇到了模块入错误以及 TensorFlow 的警告信息。

首先，警告信息是关于 `disable_resource_variables` 函数的使用被弃的提示。这个警告是由于 TensorFlow 2.x 版本中不再支持非资源变量的使用，而这个函数在将来的版本中将被移除。虽然这只是一个警告，但建议您在代码中尽量避免使用非资源变量。

然后，出现了一个模块导入错误。根据错误信息，问题出现在 `main.py` 文件的第9行，无法导入 `models.rnn` 模块。

这个问题可能是由于路径配置或缺少依赖库引起的。请确保以下几点：

1. 确保 `models.rnn` 模块位于正确的路径下，并且文件名和大小写与导入语句中的一致。

2. 检查您的环境是否已经安装了所需的依赖库。在这种情况下，您需要确保已经安装了 `keras` 和 `tensorflow` 库，并且它们是兼容的版本。

您可以尝试使用以下命令更新这两个库：

   pip install --upgrade tensorflow keras

如果已经安装了这些库，请确保它们是兼容的版本。某些库在不同版本之间可能存在不兼容的情况。

如果问题仍然存在，请提供更多关于脚本、项目结构和版本信息的细节，我将尽力提供更多帮助。

python tensorflow 实现VGG16

以下是使用TensorFlow实现VGG16的示例代码：

import tensorflow as tf
import numpy as np
import os

def load_weights(weights_file, sess):
    weights_dict = np.load(weights_file, encoding='bytes').item()
    for op_name in weights_dict:
        if op_name not in ['fc6', 'fc7', 'fc8']:
            with tf.variable_scope(op_name, reuse=True):
                for data in weights_dict[op_name]:
                    if len(data.shape) == 1:
                        var = tf.get_variable('biases', trainable=False)
                        sess.run(var.assign(data))
                    else:
                        var = tf.get_variable('weights', trainable=False)
                        sess.run(var.assign(data))

def VGG16(x):
    # conv1_1
    with tf.variable_scope("conv1_1") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 3, 64], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[64], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(x, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv1_1 = tf.nn.relu(out, name=scope.name)

    # conv1_2
    with tf.variable_scope("conv1_2") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 64, 64], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[64], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv1_1, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv1_2 = tf.nn.relu(out, name=scope.name)

    # pool1
    pool1 = tf.nn.max_pool(conv1_2, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")

    # conv2_1
    with tf.variable_scope("conv2_1") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 64, 128], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[128], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(pool1, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv2_1 = tf.nn.relu(out, name=scope.name)

    # conv2_2
    with tf.variable_scope("conv2_2") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 128, 128], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[128], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv2_1, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv2_2 = tf.nn.relu(out, name=scope.name)

    # pool2
    pool2 = tf.nn.max_pool(conv2_2, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")

    # conv3_1
    with tf.variable_scope("conv3_1") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 128, 256], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[256], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(pool2, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv3_1 = tf.nn.relu(out, name=scope.name)

    # conv3_2
    with tf.variable_scope("conv3_2") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 256, 256], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[256], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv3_1, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv3_2 = tf.nn.relu(out, name=scope.name)

    # conv3_3
    with tf.variable_scope("conv3_3") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 256, 256], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[256], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv3_2, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv3_3 = tf.nn.relu(out, name=scope.name)

    # pool3
    pool3 = tf.nn.max_pool(conv3_3, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")

    # conv4_1
    with tf.variable_scope("conv4_1") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 256, 512], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[512], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(pool3, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv4_1 = tf.nn.relu(out, name=scope.name)

    # conv4_2
    with tf.variable_scope("conv4_2") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 512, 512], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[512], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv4_1, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv4_2 = tf.nn.relu(out, name=scope.name)

    # conv4_3
    with tf.variable_scope("conv4_3") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 512, 512], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[512], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv4_2, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv4_3 = tf.nn.relu(out, name=scope.name)

    # pool4
    pool4 = tf.nn.max_pool(conv4_3, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")

    # conv5_1
    with tf.variable_scope("conv5_1") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 512, 512], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[512], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(pool4, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv5_1 = tf.nn.relu(out, name=scope.name)

    # conv5_2
    with tf.variable_scope("conv5_2") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 512, 512], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[512], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv5_1, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv5_2 = tf.nn.relu(out, name=scope.name)

    # conv5_3
    with tf.variable_scope("conv5_3") as scope:
        kernel = tf.get_variable("weights", shape=[3, 3, 512, 512], dtype=tf.float32, trainable=False)
        biases = tf.get_variable("biases", shape=[512], dtype=tf.float32, trainable=False)
        conv = tf.nn.conv2d(conv5_2, kernel, [1, 1, 1, 1], padding="SAME")
        out = tf.nn.bias_add(conv, biases)
        conv5_3 = tf.nn.relu(out, name=scope.name)

    # pool5
    pool5 = tf.nn.max_pool(conv5_3, [1, 2, 2, 1], [1, 2, 2, 1], padding="SAME")

    # fc6
    with tf.variable_scope("fc6") as scope:
        shape = int(np.prod(pool5.get_shape()[1:]))
        fc6w = tf.get_variable("weights", shape=[shape, 4096], dtype=tf.float32, trainable=False)
        fc6b = tf.get_variable("biases", shape=[4096], dtype=tf.float32, trainable=False)
        pool5_flat = tf.reshape(pool5, [-1, shape])
        fc6 = tf.nn.bias_add(tf.matmul(pool5_flat, fc6w), fc6b)
        fc6 = tf.nn.relu(fc6)

    # fc7
    with tf.variable_scope("fc7") as scope:
        fc7w = tf.get_variable("weights", shape=[4096, 4096], dtype=tf.float32, trainable=False)
        fc7b = tf.get_variable("biases", shape=[4096], dtype=tf.float32, trainable=False)
        fc7 = tf.nn.bias_add(tf.matmul(fc6, fc7w), fc7b)
        fc7 = tf.nn.relu(fc7)

    # fc8
    with tf.variable_scope("fc8") as scope:
        fc8w = tf.get_variable("weights", shape=[4096, 1000], dtype=tf.float32, trainable=False)
        fc8b = tf.get_variable("biases", shape=[1000], dtype=tf.float32, trainable=False)
        fc8 = tf.nn.bias_add(tf.matmul(fc7, fc8w), fc8b)

    return fc8

if __name__ == '__main__':
    img = tf.placeholder(tf.float32, [None, 224, 224, 3])
    with tf.Session() as sess:
        vgg16_npy_path = 'vgg16.npy'
        if os.path.isfile(vgg16_npy_path):
            print('Loading the weights of VGG16')
            load_weights(vgg16_npy_path, sess)
        else:
            print('Please download the pre-trained VGG16 weights')
        preds = VGG16(img)
        sess.run(tf.global_variables_initializer())
        img_data = np.zeros((10, 224, 224, 3))
        predictions = sess.run(preds, feed_dict={img: img_data})
        print(predictions.shape) # (10, 1000)

其中，`load_weights`函数用于从预训练模型文件中读取权重值，`VGG16`函数定义了VGG16的网络结构，`if __name__ == '__main__'`中的代码用于测试模型，生成一个10x1000的预测结果。需要注意的是，在运行代码之前，需要下载预训练模型的权重文件`vgg16.npy`，并将其放在代码所在目录下。