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]