tensorflow基础流程

引入包并查看版本号

import tensorflow as tf import tensorflow.keras as keras 

print(tf.__version__) print(keras.__version__) 

2.0.0 2.2.4-tf 

引入数据集

如果下载不顺利的话可以从 https://www.kaggle.com/vikramtiwari/mnist-numpy/data 手动下载好mnist文件，然后把它复制到keras/datasets这个文件夹下面。

mnist = keras.datasets.mnist (train_images, train_labels), (test_images, test_labels) = mnist.load_data() 

print(train_images.shape) print(test_images.shape) print(train_labels.shape) print(test_labels.shape) 

(60000, 28, 28) (10000, 28, 28) (60000,) (10000,) 

搭建模型进行预测

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']) 

最下面输出的两个数字分别代表损失函数和准确率，要是能顺便把accu和loss的值画成曲线图就更好啦。乍看起来tf比pytorch更简单，可以少写很多代码，但其实高度封装的后果就是你不需要操心底层的实现，没办法把学到的原理落实到每个细节，头重脚轻，如果涉及到不能采取拿来主义的地方就很难办。另外一点就是你很难访问计算过程中的中间结果，就好像做饭时只能拿着大刀砍骨头，不能用小刀雕萝卜。

model.fit(train_images, train_labels, epochs=5) model.evaluate(test_images, test_labels, verbose=2) 

Train on 60000 samples Epoch 1/5 60000/60000 [==============================] - 6s 93us/sample - loss: 2.5732 - accuracy: 0.7574 Epoch 2/5 60000/60000 [==============================] - 5s 84us/sample - loss: 0.6132 - accuracy: 0.8468 Epoch 3/5 60000/60000 [==============================] - 5s 81us/sample - loss: 0.4904 - accuracy: 0.8767 Epoch 4/5 60000/60000 [==============================] - 4s 59us/sample - loss: 0.4061 - accuracy: 0.8967 Epoch 5/5 60000/60000 [==============================] - 4s 59us/sample - loss: 0.3761 - accuracy: 0.9045 10000/1 - 0s - loss: 0.1850 - accuracy: 0.9370      [0.2977512352705002, 0.937]