windows10安装Tensorflow-gpu 2.10.0_tensorflow2.10.0对应tensorflow intel版本

windows10安装Tensorflow-gpu 2.10.0

本文主要目的是 从0开始演示 在windows10 平台安装Tensorflow-gpu 2.10.0。
Tensorflow-gpu 2.10.0 之后的版本，不再支持这样的安装方式，如果有需要，请参考wsl安装ubuntu的方式，进行安装。

1.安装miniconda

https://docs.anaconda.com/free/miniconda/index.html

2.安装CUDA

tensorflow-cuda-cudnn对应版本
tensorflow-cuda-cudnn

下载 CUDA 11.2.2
cuda11.2.2 | https://developer.nvidia.com/cuda-toolkit-archive

cuda安装完之后，已经配置好环境路径了，直接在cmd中查看

nvcc -V
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下载cudnn 8.1.1
cudnn 8.1.1 | https://developer.nvidia.com/rdp/cudnn-archive

把cudnn8.10解压出来的文件，拷贝到cuda下，有对应的文件下名称，对应拷贝过去。

3.创建python环境

conda create --name tf2.10 python==3.10.14

conda activate tf2.10
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4.安装Tensorflow-GPU 2.10.0

Tensorflow-GPU 2.10.0

pip install tensorflow-gpu==2.10.0 -i https://pypi.tuna.tsinghua.edu.cn/simple/
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安装一些常用常用包

pip install scikit-learn einops ipywidgets pandas tqdm jupyterlab matplotlib seaborn -i https://pypi.tuna.tsinghua.edu.cn/simple/
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测试

python ./mnist.py

import tensorflow as tf
print(tf.__version__)
print(tf.config.list_physical_devices('GPU'))
print(tf.test.is_built_with_cuda())

import tensorflow as tf
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg

print(tf.__version__)
print(tf.config.list_physical_devices('GPU'))

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data(path="mnist.npz")

input_shape = (28, 28, 1)

x_train=x_train.reshape(x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)
x_train=x_train / 255.0
x_test = x_test.reshape(x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)
x_test=x_test/255.0

y_train = tf.one_hot(y_train.astype(np.int32), depth=10)
y_test = tf.one_hot(y_test.astype(np.int32), depth=10)

batch_size = 64
num_classes = 10
epochs = 5

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(32, (5,5), padding='same', activation='relu', input_shape=input_shape),
    tf.keras.layers.Conv2D(32, (5,5), padding='same', activation='relu'),
    tf.keras.layers.MaxPool2D(),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation='relu'),
    tf.keras.layers.Conv2D(64, (3,3), padding='same', activation='relu'),
    tf.keras.layers.MaxPool2D(strides=(2,2)),
    tf.keras.layers.Dropout(0.25),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.Dense(num_classes, activation='softmax')
])

model.compile(optimizer=tf.keras.optimizers.RMSprop(epsilon=1e-08), loss='categorical_crossentropy', metrics=['acc'])


class myCallback(tf.keras.callbacks.Callback):
  def on_epoch_end(self, epoch, logs={}):
    if(logs.get('acc')>0.995):
      print("\nReached 99.5% accuracy so cancelling training!")
      self.model.stop_training = True

callbacks = myCallback()


history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    validation_split=0.1,
                    callbacks=[callbacks])

test_loss, test_acc = model.evaluate(x_test, y_test)

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