在Jupyter Notebook中使用TensorFlow

Jupyter Notebook的环境配置

首先需要下载安装Anaconda

由于我现在使用的最新版本Anaconda自带的Python版本是3.8.5，而截止到写作这篇文章的时候，TensorFlow只支持3.5.X,3.6.X,3.7.X版本的Python，所以安装TensorFlow时需要创建一个新Python 3.7的环境

创建名为tensorflow的python为3.7的环境

conda create -n tensorflow python=3.7

激活新创建的环境

conda activate tensorflow

由于我只需要tensorflow,所以这里只安装tensorflow，需要其他包可以自行conda或者pip安装

conda install tensorflow

安装jupyter

conda install ipython
conda install jupyter

启动notebook

jupyter notebook

使用TensorFlow

下面是notebook上的操作，参考了官网的新手教程

import tensorflow as tf

mnist = tf.keras.datasets.mnist

(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 102s 9us/step

上面这一步由于需要从google下载数据，需要连接外网

model = tf.keras.models.Sequential([
  tf.keras.layers.Flatten(input_shape=(28, 28)),
  tf.keras.layers.Dense(128, activation='relu'),
  tf.keras.layers.Dropout(0.2),
  tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5)

Epoch 1/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.2979 - accuracy: 0.9137
Epoch 2/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.1435 - accuracy: 0.9573
Epoch 3/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.1071 - accuracy: 0.9677
Epoch 4/5
1875/1875 [==============================] - 5s 3ms/step - loss: 0.0888 - accuracy: 0.9728
Epoch 5/5
1875/1875 [==============================] - 5s 2ms/step - loss: 0.0750 - accuracy: 0.9764





<tensorflow.python.keras.callbacks.History at 0x7fa24c207510>

model.evaluate(x_test,  y_test, verbose=2)

313/313 - 0s - loss: 0.0764 - accuracy: 0.9749





[0.07642737776041031, 0.9749000072479248]

以上为在jypyter中使用Tensorflow的教程，由于本人是第一次使用python和anaconda以及tensorflow，教程中有不足之处还请谅解。