1，share的内容
- code to create the model, and
- the trained weights, or parameters, for the model
2，ways
There are different ways to save TensorFlow models—depending on the API you're using
3，Checkpoint callback usage
3.1，以callback方式触发对checkpoint的在fit过程中的记录
checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)
# Create checkpoint callback
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,
save_weights_only=True,
verbose=1)
model = create_model()
model.fit(train_images, train_labels, epochs = 10,
validation_data = (test_images,test_labels),
callbacks = [cp_callback]) # pass callback to training
3.2，检查目录
! ls {checkpoint_dir}
3.3，找出最近的
latest=tf.train.latest_checkpoint(checkpoint_dir)
4，恢复至最近的checkpoint
model = create_model()
model.load_weights(latest)
loss, acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
tf.train.latest_checkpoint(checkpoint_dir)
5，手动save和restore
# Save the weights
model.save_weights('./checkpoints/my_checkpoint')
# Restore the weights
model = create_model()
model.load_weights('./checkpoints/my_checkpoint')
loss,acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))
6，保存和恢复整个模型
6.1，save
contains the weight values, the model's configuration, and even the optimizer's configuration (depends on set up). This allows you to checkpoint a model and resume training later—from the exact same state—without access to the original code
model = create_model()
model.fit(train_images, train_labels, epochs=5)
# Save entire model to a HDF5 file
model.save('my_model.h5')
6.2，恢复
new_model = keras.models.load_model('my_model.h5')
new_model.summary()
7，keras如何保存和恢复模型
7.1，创建模型
model = create_model()
model.fit(train_images, train_labels, epochs=5)
7.2，保存模型
Keras saves models by inspecting the architecture. Currently, it is not able to save TensorFlow optimizers (from tf.train). When using those you will need to re-compile the model after loading, and you will lose the state of the optimizer.
saved_model_path = tf.contrib.saved_model.save_keras_model(model, "./saved_models")
!ls -l saved_models
7.3，恢复模型
new_model = tf.contrib.saved_model.load_keras_model(saved_model_path)
new_model.summary()
7.4，编译模型（因为不保存模型的优化器）
# The model has to be compiled before evaluating.
# This step is not required if the saved model is only being deployed.
new_model.compile(optimizer=tf.keras.optimizers.Adam(),
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy'])
# Evaluate the restored model.
loss, acc = new_model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc))