ImportError: cannot import name ‘Literal‘ from ‘typing‘ (D:\Anaconda\envs\tensorflow\lib\typing.py)

Error background: Because it is necessary to install the tensorflow-gpu version, I downgraded python3.8 in the original newly created anaconda environment (my name is tensorflow) to 3.7. When importing the seaborn package, the following error occurred: ImportError: cannot import name 'Literal' from 'typing' (D:\Anaconda\envs\tensorflow\lib\typing.py) Cause Analysis: This is because ‘Literal&... »

The relationship between Tensorflow and cuda versions (with multiple cuda versions installed)

The relationship between Tensorflow and cuda versions (with multiple cuda versions installed) Say one more thing 1.windows If the version of tf used in the code downloaded online is inconsistent with your local cuda, you can install multiple cuda versions on your local machine, and then choose which version of cuda to use based on your own project (this is my local machine) win10 is chestnut) 1. I... »

pycharm installs torch and cuda (in the new environment created by anaconda)

1. The problem There seems to be some conflict between torch and tensorflow in pycharm, so I created two conda environments (one named pytorch, one named tensorflow). There is no tensorflow library in the pytorch environment, and the same is true for the tensorflow environment. The problem now is that every time I use pip install torch in Terminal, it is always the cpu version. pip install torch i... »

python: Some problems and solutions when starting to use tensorflow

I have been using python for almost a year. I want to try tensorflow and learn about deep learning. However, unlike other modules, tensorflow is not easy to use, maybe it is just starting out. 1. Version problem. Different versions have very different usages. Therefore, it is necessary to first know whether you are using version 1.x or 2.x. You can check the version by using the following method: ... »

Correspondence between Tensorflow and CUDA and cudnn versions

Different versions of Tensorflow need to correspond to different CUDA and cudnn versions, otherwise the installation may fail. As shown in the figure below, select the corresponding version of CUDA and cudnn according to the Tensorflow version you want to install. The download link for CUDA is: CUDA Toolkit Archive | NVIDIA Developer The cudnn download link (requires a developer account login) is:... »

[Installation of Keras environment]

Keras installation 1 Introduction2-Create a virtual environment3-tensorflow and keras installation4-Error log-waiting for resolution 1 Introduction 1. Keras needs to run on TensorFlow, so tensorflow must be installed first.2. Tensorflow can only run in python versions prior to 3.7, so you need to create a virtual environment based on python 3.6 first. 2-Create a virtual environment Open anaconda p... »

The relationship between FLOPs, MACs, and MAdds of deep learning model calculation load evaluation indicators

When evaluating the size and computational complexity of deep learning models, commonly used parameters are: parameters, FLOPs, MACs, and MAdds. In addition, we often see MAC, FLOPS, GFLOPS, and TFLOPS. Among them, the last three parameters are not actually used to evaluate the calculation amount of the model, but to evaluate the computing power of the computer hardware. The above parameters are i... »

TensorFlow builds a CNN-LSTM hybrid model to achieve multi-variable multi-step long-term series forecasting (load forecasting)

Table of contents I. PrefaceII. CNN-LSTMIII. Code implementation3.1 Data processing3.2 Model training/testing3.3 Experimental results IV. Source code and data I. Preface Many articles have been written about time series forecasting before: In-depth understanding of the input and output of LSTM in PyTorch (from input input to Linear output)PyTorch builds LSTM to implement time series forecasting (l... »

Normalization and denormalization in data processing

1. Definition Data normalization (standardization) is a basic work in data preprocessing.Different evaluation indicators often have different dimensions and dimensional units. In order to avoid affecting the data analysis results and eliminating the dimensional influence between indicators, the data must be standardized.。 Normalization of data is to scale the data so that it falls into a small spe... »

Newly installed TensorFlow appears protobuf related errors

I just installed TensorFlow2.4 today. When importing tensorflow, the following error occurred. TypeError: Descriptors cannot not be created directly. If this call came from a _pb2.py file, your generated code is out of date and must be regenerated with protoc >= 3.19.0. If you cannot immediately regenerate your protos, some other possible workarounds are:  1. Downgrade the protobuf package to 3... »

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