89. tensorflow keras训练、保存、加载模型示例

https://blog.csdn.net/LuohenYJ/article/details/81096886
代码示例

from __future__ import absolute_import, division, print_function

import os

import tensorflow as tf
from tensorflow import keras

tf.__version__

(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

# 模型创建模型
def create_model():
  model = tf.keras.models.Sequential([
    keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(784,)),
    keras.layers.Dropout(0.2),
    keras.layers.Dense(10, activation=tf.nn.softmax)
  ])

  model.compile(optimizer=tf.keras.optimizers.Adam(), 
                loss=tf.keras.losses.sparse_categorical_crossentropy,
                metrics=['accuracy'])

  return model

#创建模型
model = create_model()
model.summary()

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

#创建回调函数
cp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path, 
                                                 save_weights_only=True, #只保存权重
                                                 verbose=1)
model = create_model()
model.fit(train_images, train_labels,  epochs = 10, 
          validation_data = (test_images,test_labels),
          callbacks = [cp_callback])  #保存模型
#对全新没有训练的模型进行预测
model = create_model()
loss, acc = model.evaluate(test_images, test_labels)
print("Untrained model, accuracy: {:5.2f}%".format(100*acc)) #11.4%
#载入权重参数后的模型
model.load_weights(checkpoint_path)
loss,acc = model.evaluate(test_images, test_labels)
print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #86.2
# 保存权重
model.save_weights('./checkpoints/my_checkpoint')
#恢复模型
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)) #87.00%

#将整个模型保存为HDF5文件
# model = create_model()
# model.fit(train_images, train_labels, epochs=5)
# model.save('my_model.h5')
# #载入一个相同的模型
# new_model = keras.models.load_model('my_model.h5')
# new_model.summary()
# loss, acc = new_model.evaluate(test_images, test_labels)
# print("Restored model, accuracy: {:5.2f}%".format(100*acc)) #86.30%
保存权重后生成的目录：
CHH3213 / Note-Ubuntu_CHH3213

89. tensorflow keras训练、保存、加载模型示例 #89
