Python3.7.1环境下TensorFlow2.0是否内置Keras及安装兼容性咨询

开发者阿泽

2026-5-13

Answers to Your TensorFlow 2.0 & Keras Questions

Great questions— let's tackle each one clearly:

Q1: Do I need to install Keras separately after installing TensorFlow 2.0 in a Python 3.7.1 environment?

Nope, you don't need to bother with a separate Keras install. Starting with TensorFlow 2.0, Keras is fully integrated into the TensorFlow ecosystem as the tf.keras module. This isn't a disconnected fork—it's the official, actively maintained Keras implementation that's tightly linked to TensorFlow's core features.

Using tf.keras gives you seamless access to TensorFlow-specific tools (like TF Data pipelines, SavedModel format, and distributed training) while avoiding version conflicts that can pop up if you install standalone Keras alongside TF2.0. Just import it directly in your code with from tensorflow import keras (or import tensorflow.keras as keras) and you're ready to build models.

Q2: Is it possible to install TensorFlow 2.0 on Ubuntu 16.04 64-bit with Python 3.7.1?

Absolutely, this setup is fully supported! Ubuntu 16.04 64-bit is a valid platform for TensorFlow 2.0, and Python 3.7.1 falls within the compatible Python version range for early TF2.0 releases.

Before installing, make sure your pip is up to date—older versions can cause headaches with dependency resolution:

python3.7 -m pip install --upgrade pip

CPU-only installation

Run this command to install the CPU version of TensorFlow 2.0:

python3.7 -m pip install tensorflow==2.0.0

GPU-enabled installation

If you want GPU acceleration, you'll first need to set up NVIDIA CUDA 10.0 and cuDNN 7.4+ (these are the compatible versions for TF2.0 on Ubuntu 16.04). Once those are configured correctly, install the GPU variant:

python3.7 -m pip install tensorflow-gpu==2.0.0

A quick heads-up: If you see dependency warnings during installation, you might need to install additional system packages (like libcupti-dev for GPU profiling) via sudo apt-get install libcupti-dev to resolve them.