加載組件
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
創(chuàng)建訓練測試集
(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
創(chuàng)建模型的函數
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 （這里只保存模型的參數）
# 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%
保存規(guī)則的其他設置
這里可以在迭代中保存最優(yōu)的一組參數，在超長時間的訓練很有用。
# 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%
最后是手動保存參數
# 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))
