NLP保存与加载模型

加载组件

import os
import tensorflow as tf
from tensorflow import keras
from keras import Sequential
from keras.layers import Dense, Dropout
from keras.losses import SparseCategoricalCrossentropy
from keras.callbacks import ModelCheckpoint
from keras.metrics import SparseCategoricalAccuracy
from tensorflow import train

创建训练测试集

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

train_labels = train_labels[:1000]
test_labels = test_labels[:1000]

train_images = train_images[:1000].reshape(-1, 28 * 28) / 255.0
test_images = test_images[:1000].reshape(-1, 28 * 28) / 255.0

创建模型的函数

def create_model():
    model = Sequential([
        Dense(512, activation='relu', input_shape=(784,)),
        Dropout(0.2),
        Dense(10)
    ])
    model.compile(optimizer='adam',
                  loss=SparseCategoricalCrossentropy(from_logits=True),
                  metrics=[SparseCategoricalAccuracy()])
    return model


# Create a basic model instance
model = create_model()

# Display the model's architecture
model.summary()

model = create_model()

model.summary()

保存模型 （这里只保存模型的参数）

# Save checkpoints during training

checkpoint_path = "training_1/cp.ckpt"
checkpoint_dir = os.path.dirname(checkpoint_path)

# Create a callback that saves the model's weights
cp_callback = ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1)

# Train the model with the new callback
# model.fit(train_images, train_labels, epochs=10, validation_data=(test_images, test_labels), callbacks=[cp_callback])  # Pass callback to training

loss, acc = model.evaluate(test_images, test_labels, verbose=2)
print("accuracy: {:5.2f}%".format(100 * acc))

# build new model without training , its accuracy would be around 10%
model2 = create_model()
loss, acc = model2.evaluate(test_images, test_labels, verbose=2)
print("Untrained model, accuracy: {:5.2f}%".format(100 * acc))
# Untrained model, accuracy: 10.60%


# load wight for the new model
model2.load_weights(checkpoint_path)
loss, acc = model2.evaluate(test_images, test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100 * acc))
# Restored model, accuracy: 87.20%

保存规则的其他设置
这里可以在迭代中保存最优的一组参数，在超长时间的训练很有用。

# Checkpoint callback options

checkpoint_path_2 = 'training_2/cp-{epoch:04d}.ckpt'
checkpoint_dir_2 = os.path.dirname(checkpoint_path_2)

batch_size = 32

cp_callback2 = ModelCheckpoint(filepath=checkpoint_path_2, verbose=1, save_weights_only=True, save_freq=5 * batch_size)
# cp_callback = ModelCheckpoint(filepath=checkpoint_path_2, verbose=1, save_weights_only=True, save_best_only=True)
model3 = create_model()

# model3.save_weights(checkpoint_path_2.format(epoch=0))

# model3.fit(train_images, train_labels, epochs=50, batch_size=batch_size, callbacks=[cp_callback2], validation_data=(test_images, test_labels), verbose=0)

latest = train.latest_checkpoint(checkpoint_dir_2)
model4 = create_model()

print(latest)
model4.load_weights(latest)

# Re-evaluate the model
loss, acc = model4.evaluate(test_images, test_labels, verbose=2)
print("Restored model, accuracy: {:5.2f}%".format(100 * acc))
# 32/32 - 0s - loss: 0.4787 - sparse_categorical_accuracy: 0.8750 - 95ms/epoch - 3ms/step
# Restored model, accuracy: 87.50%

最后是手动保存参数

# Manually save weights

#  after train, save down the weights
model4.save_weights('./checkpoints/my_checkpoint')

model5 = create_model()

# load the weights
model5.load_weights('./checkpoints/my_checkpoint')

# Evaluate the model
loss, acc = model5.evaluate(test_images, test_labels, verbose=2)
print("Restored model5, accuracy: {:5.2f}%".format(100 * acc))

代码传送门