模型加载与保存

目录

* Outline <https://www.cnblogs.com/nickchen121/p/10922944.html#outline>
* Save/load weights
<https://www.cnblogs.com/nickchen121/p/10922944.html#saveload-weights>
* Save/load entire model
<https://www.cnblogs.com/nickchen121/p/10922944.html#saveload-entire-model>
* saved_model
<https://www.cnblogs.com/nickchen121/p/10922944.html#saved_model>
Outline

*
save/load weights # 记录部分信息

*
save/load entire model # 记录所有信息

*
saved_model # 通用，包括Pytorch、其他语言

Save/load weights

* 保存部分信息 # 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(f'Restored model, accuracy:
{100*acc:5.2f}') import tensorflow as tf from tensorflow.keras import datasets,
layers, optimizers, Sequential, metrics def preprocess(x, y): """ x is a simple
image, not a batch """ x = tf.cast(x, dtype=tf.float32) / 255. x =
tf.reshape(x, [28 * 28]) y = tf.cast(y, dtype=tf.int32) y = tf.one_hot(y,
depth=10) return x, y batchsz = 128 (x, y), (x_val, y_val) =
datasets.mnist.load_data() print('datasets:', x.shape, y.shape, x.min(),
x.max()) db = tf.data.Dataset.from_tensor_slices((x, y)) db =
db.map(preprocess).shuffle(60000).batch(batchsz) ds_val =
tf.data.Dataset.from_tensor_slices((x_val, y_val)) ds_val =
ds_val.map(preprocess).batch(batchsz) sample = next(iter(db))
print(sample[0].shape, sample[1].shape) network = Sequential([
layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'), layers.Dense(32, activation='relu'),
layers.Dense(10) ]) network.build(input_shape=(None, 28 * 28))
network.summary() network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)
network.evaluate(ds_val) network.save_weights('weights.ckpt') print('saved
weights.') del network network = Sequential([ layers.Dense(256,
activation='relu'), layers.Dense(128, activation='relu'), layers.Dense(64,
activation='relu'), layers.Dense(32, activation='relu'), layers.Dense(10) ])
network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
network.load_weights('weights.ckpt') print('loaded weights!')
network.evaluate(ds_val) datasets: (60000, 28, 28) (60000,) 0 255 (128, 784)
(128, 10) Model: "sequential"
_________________________________________________________________ Layer (type)
Output Shape Param #
================================================================= dense (Dense)
multiple 200960
_________________________________________________________________ dense_1
(Dense) multiple 32896
_________________________________________________________________ dense_2
(Dense) multiple 8256
_________________________________________________________________ dense_3
(Dense) multiple 2080
_________________________________________________________________ dense_4
(Dense) multiple 330
================================================================= Total params:
244,522 Trainable params: 244,522 Non-trainable params: 0
_________________________________________________________________ Epoch 1/3
469/469 [==============================] - 5s 12ms/step - loss: 0.2876 -
accuracy: 0.8335 Epoch 2/3 469/469 [==============================] - 5s
11ms/step - loss: 0.1430 - accuracy: 0.9551 - val_loss: 0.1397 - val_accuracy:
0.9634 Epoch 3/3 469/469 [==============================] - 4s 9ms/step - loss:
0.1155 - accuracy: 0.9681 79/79 [==============================] - 1s 8ms/step
- loss: 0.1344 - accuracy: 0.9654 saved weights. loaded weights! 79/79
[==============================] - 1s 13ms/step - loss: 0.1344 - accuracy:
0.9593 [0.13439734456132318, 0.9654]
Save/load entire model

* 完美保存所有信息 network.save('model.h5') print('saved total model.') del network
print('load model from file') network = tf.keras.models.load_model('model.h5')
network.evaluate(x_val, y_val) import tensorflow as tf from tensorflow.keras
import datasets, layers, optimizers, Sequential, metrics def preprocess(x, y):
""" x is a simple image, not a batch """ x = tf.cast(x, dtype=tf.float32) /
255. x = tf.reshape(x, [28 * 28]) y = tf.cast(y, dtype=tf.int32) y =
tf.one_hot(y, depth=10) return x, y batchsz = 128 (x, y), (x_val, y_val) =
datasets.mnist.load_data() print('datasets:', x.shape, y.shape, x.min(),
x.max()) db = tf.data.Dataset.from_tensor_slices((x, y)) db =
db.map(preprocess).shuffle(60000).batch(batchsz) ds_val =
tf.data.Dataset.from_tensor_slices((x_val, y_val)) ds_val =
ds_val.map(preprocess).batch(batchsz) sample = next(iter(db))
print(sample[0].shape, sample[1].shape) network = Sequential([
layers.Dense(256, activation='relu'), layers.Dense(128, activation='relu'),
layers.Dense(64, activation='relu'), layers.Dense(32, activation='relu'),
layers.Dense(10) ]) network.build(input_shape=(None, 28 * 28))
network.summary() network.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
network.fit(db, epochs=3, validation_data=ds_val, validation_freq=2)
network.evaluate(ds_val) network.save('model.h5') print('saved total model.')
del network print('load model from file') network1 =
tf.keras.models.load_model('model.h5')
network1.compile(optimizer=optimizers.Adam(lr=0.01),
loss=tf.losses.CategoricalCrossentropy(from_logits=True), metrics=['accuracy'])
x_val = tf.cast(x_val, dtype=tf.float32) / 255. x_val = tf.reshape(x_val, [-1,
28 * 28]) y_val = tf.cast(y_val, dtype=tf.int32) y_val = tf.one_hot(y_val,
depth=10) ds_val = tf.data.Dataset.from_tensor_slices((x_val,
y_val)).batch(128) network1.evaluate(ds_val) datasets: (60000, 28, 28) (60000,)
0 255 (128, 784) (128, 10) Model: "sequential_4"
_________________________________________________________________ Layer (type)
Output Shape Param #
================================================================= dense_20
(Dense) multiple 200960
_________________________________________________________________ dense_21
(Dense) multiple 32896
_________________________________________________________________ dense_22
(Dense) multiple 8256
_________________________________________________________________ dense_23
(Dense) multiple 2080
_________________________________________________________________ dense_24
(Dense) multiple 330
================================================================= Total params:
244,522 Trainable params: 244,522 Non-trainable params: 0
_________________________________________________________________ Epoch 1/3
469/469 [==============================] - 6s 13ms/step - loss: 0.2851 -
accuracy: 0.8405 Epoch 2/3 469/469 [==============================] - 6s
13ms/step - loss: 0.1365 - accuracy: 0.9580 - val_loss: 0.1422 - val_accuracy:
0.9590 Epoch 3/3 469/469 [==============================] - 5s 11ms/step -
loss: 0.1130 - accuracy: 0.9661 79/79 [==============================] - 1s
10ms/step - loss: 0.1201 - accuracy: 0.9714 saved total model. load model from
file W0525 16:44:50.178785 4587234752 hdf5_format.py:266] Sequential models
without an `input_shape` passed to the first layer cannot reload their
optimizer state. As a result, your model isstarting with a freshly initialized
optimizer. 79/79 [==============================] - 1s 7ms/step - loss: 0.1201
- accuracy: 0.9672 [0.12005392337660747, 0.9714]
saved_model

*
通用，包括Pytorch、其他语言

*
用于工业环境的部署
tf.saved_model.save(m, '/tmp/saved_model/') imported =
tf.saved_model.load(path) f = imported.signatures['serving_default']
print(f(x=tf.ones([1, 28, 28, 3])))

• « 上一篇：零基础现在开始学web前端多久可以工作
• » 下一篇：痛！“做C#半年，挣的不如做AI 1个月?”看到第二句泪目……