Keras-保存和恢復模型
轉載自：
1，share的內容

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方式觸發對checkpoint的在fit過程中的記錄

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，檢查目錄

! ls {checkpoint_dir}

3.3，找出最近的

latest=tf.train.latest_checkpoint(checkpoint_dir)

4，恢復至最近的checkpoint

model = create_model()model.load_weights(latest)#用于僅保存了權重時loss, acc = model.evaluate(test_images, test_labels)print("Restored model, accuracy: {:5.2f}%".format(100*acc))tf.train.latest_checkpoint(checkpoint_dir)

5，手動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，保存和恢復整個模型

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，恢復

new_model = keras.models.load_model('my_model.h5') new_model.summary()

7，keras如何保存和恢復模型

7.1，創建模型

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，恢復模型

new_model = tf.contrib.saved_model.load_keras_model(saved_model_path) new_model.summary()

7.4，編譯模型（因為不保存模型的優化器）

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))

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