0. 先实现手写体识别的实例：
导入训练数据
from keras.datasets import mnist
from keras.preprocessing.image import ImageDataGenerator
from matplotlib import pyplot
from keras import backend as K
#K.set_image_dim_ordering('th')
(train_data, train_label), (test_data, test_label) = mnist.load_data()
print(train_data.shape)
print(test_data.shape)
train_data = train_data.reshape(train_data.shape[0], 28, 28,1)
train_data = train_data.astype('float32')
from keras.utils import to_categorical
train_label = to_categorical(train_label)
# 创建一个 3* 3 的九宫格，以显示图片
pyplot.subplot(330 + 1)
pyplot.imshow(train_data[1].reshape(28, 28), cmap=pyplot.get_cmap('gray'))
pyplot.show()
(60000, 28, 28)
(10000, 28, 28)
print(test_data.shape)
test_data = test_data.reshape(test_data.shape[0], 28, 28,1)
test_data = test_data.astype('float32')
test_label = to_categorical(test_label)
print(test_data.shape)(10000, 28, 28)
(10000, 28, 28, 1)print(train_data.shape)
print(train_label.shape)
print(len(train_label))
print(test_data.shape)
print(test_label.shape)
print(len(test_label))(60000, 28, 28, 1)
(60000, 10)
60000
(10000, 28, 28, 1)
(10000, 10)
10000设计网络结构
# 随机数种子，重复性设置
np.random.seed(1689)
# 网络结构和训练的参数
EPOCH = 20
BATCH_SIZE = 128
VERBOSE = 1
NB_CLASSES = 10
OPTIMIZER = 'rmsprop'
VALIDATION_SPLIT = 0.2from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D
from keras.layers import Dropout, Flatten, Dense
from keras.models import Sequential
model = Sequential()
### TODO: 定义你的网络架构
model.add(Conv2D(filters=32, kernel_size=3, padding='valid', activation='relu', input_shape=(28, 28,1)))
#model.add(Conv2D(32, (3,3), input_shape=(160, 160, 3), activation="relu"))
model.add(MaxPooling2D(pool_size=2))
#model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.5))
model.add(Conv2D(filters=64, kernel_size=3, padding='valid', activation='relu'))
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.5))
model.add(Conv2D(filters=128, kernel_size=3, padding='valid', activation='relu'))
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(NB_CLASSES, activation='softmax'))
model.summary()Model: "sequential_22"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_58 (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d_37 (MaxPooling (None, 13, 13, 32) 0
_________________________________________________________________
dropout_55 (Dropout) (None, 13, 13, 32) 0
_________________________________________________________________
conv2d_59 (Conv2D) (None, 11, 11, 64) 18496
_________________________________________________________________
max_pooling2d_38 (MaxPooling (None, 5, 5, 64) 0
_________________________________________________________________
dropout_56 (Dropout) (None, 5, 5, 64) 0
_________________________________________________________________
conv2d_60 (Conv2D) (None, 3, 3, 128) 73856
_________________________________________________________________
global_average_pooling2d_19 (None, 128) 0
_________________________________________________________________
dropout_57 (Dropout) (None, 128) 0
_________________________________________________________________
dense_19 (Dense) (None, 10) 1290
=================================================================
Total params: 93,962
Trainable params: 93,962
Non-trainable params: 0
_________________________________________________________________model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])
history = model.fit(train_data, train_label, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=VERBOSE,validation_split=VALIDATION_SPLIT)
Train on 48000 samples, validate on 12000 samplesEpoch 1/20
48000/48000 [==============================] - 10s 208us/step - loss: 2.7828 - acc: 0.3195 - val_loss: 0.6640 - val_acc: 0.8288
Epoch 2/20
48000/48000 [==============================] - 10s 198us/step - loss: 0.7846 - acc: 0.7399 - val_loss: 0.2075 - val_acc: 0.9477
...
Epoch 20/20
48000/48000 [==============================] - 9s 198us/step - loss: 0.1141 - acc: 0.9655 - val_loss: 0.0360 - val_acc: 0.9898
## 评估模型
```python
score = model.evaluate(test_data, test_label, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])
10000/10000 [==============================] - 1s 78us/step
Test score: 0.028671354174159933
Test accuracy: 0.991100013256073可视化训练效果
from keras.models import Model
import matplotlib.pyplot as plt### print the keys contained in the history object
print(history.history.keys())
### plot the training and validation loss for each epoch
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model mean squared error loss')
plt.ylabel('mean squared error loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validation set'], loc='upper right')
plt.show()dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])
1. 完整保存模型所有状态
可以使用 model.save(filepath) 将 Keras 模型和权重保存在一个 HDF5 文件中，该文件将包含：
模型的结构，以便重构该模型
模型的权重
训练配置（损失函数，优化器等）
优化器的状态，以便于从上次训练中断的地方开始
使用 keras.models.load_model(filepath) 来重新实例化你的模型，如果文件中存储了训练配置的话，该函数还会同时完成模型的编译
优势 ：不仅保存了模型结构和参数，也保存了训练配置等信息，可以方便从上次训练中断的地方继续优化；
劣势 ：占用空间太大
保存整个模型
model_all = 'my_model_all.h5'
model.save(model_all)加载整个模型
model_01 = load_model(model_all)
# 也可以继续训练模型
#history_01 = model_01.fit(train_data, train_label, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT)# 评估模型
score = model_01.evaluate(test_data, test_label, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])10000/10000 [==============================] - 1s 86us/step
Test score: 0.028671354174159933
Test accuracy: 0.9911000132560732. 分别保存 / 加载模型结构和权重
优势 : 节省硬盘空间，方便同步和协作
劣势 : 丢失了训练的一些配置信息
保存模型权重
model_save_weights = "my_model_weights.h5"
# 保存模型权重
model.save_weights(model_save_weights)加载模型权重, 原 model 加载参数可以继续训练
model.load_weights(model_save_weights)
score = model.evaluate(test_data, test_label, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])10000/10000 [==============================] - 1s 65us/step
Test score: 0.028671354174159933
Test accuracy: 0.991100013256073保存网络结构
方法 1：保存为 json
json_string = model.to_json()
open('my_model_arthitecture.json', 'w').write(json_string) #重命名 从 json 中读出数据
from keras.models import model_from_json
model_02 = model_from_json(json_string)加载模型权重, 原 model 加载参数可以继续训练
model_02.load_weights(model_save_weights)model_02.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])
#history_02 = model_02.fit(train_data, train_label, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT)score = model_02.evaluate(test_data, test_label, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])10000/10000 [==============================] - 1s 86us/step
Test score: 0.028671354174159933
Test accuracy: 0.991100013256073方法 2：保存为 yaml
yaml_string = model.to_yaml()
open('my_model_arthitecture.yaml', 'w').write(yaml_string) #重命名 从 json 中读出数据
from keras.models import model_from_yaml
model_03 = model_from_yaml(yaml_string)
# 加载模型权重, 原 model 加载参数可以继续训练
model_03.load_weights(model_save_weights)model_03.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['accuracy'])
#history_03 = model_03.fit(train_data, train_label, batch_size=BATCH_SIZE, epochs=EPOCH, verbose=VERBOSE, validation_split=VALIDATION_SPLIT)score = model_03.evaluate(test_data, test_label, verbose=1)
print('Test score:', score[0])
print('Test accuracy:', score[1])10000/10000 [==============================] - 1s 87us/step
Test score: 0.028671354174159933
Test accuracy: 0.991100013256073这几种保存模型的联系与区别
| 方法 | 保存模型结构 | 保存模型权重 | 是否能进行模型预测 |
|---|---|---|---|
| model.save() | 是 | 是 | 是 |
| model.save_weights() | 否 | 是 | 是 |
| model.to_json() | 是 | 否 | 加载权重，编译后能进行正常预测 |
| model.to_yaml() | 是 | 否 | 加载权重，编译后能进行正常预测 |
ps: 重新加载模型后，可以重新训练；或者加载模型和权重后，在上一次权重基础上继续训练；
compile 做什么？
compile 定义了 loss function 损失函数、optimizer 优化器和 metrics 度量。它与权重无关，也就是说 compile 并不会影响权重，不会影响之前训练的问题。
3. 保存最佳模型
from keras.callbacks import ModelCheckpointkeras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1)
在每个训练期之后保存模型。
filepath 可以包括命名格式选项，可以由 epoch 的值和 logs 的键（由 on_epoch_end 参数传递）来填充。
例如：如果 filepath 是 weights.{epoch:02d}-{val_loss:.2f}.hdf5，那么模型被保存的的文件名就会有训练轮数和验证损失。
filepath: 字符串，保存模型的路径。
monitor: 被监测的数据。
verbose: 详细信息模式，0 或者 1。
save_best_only: 如果 save_best_only=True，只保存在验证集上性能最好的模型。
mode: {auto, min, max} 的其中之一。如果 save_best_only=True，那么是否覆盖保存文件的决定就取决于被监测数据的最大或者最小值。
对于 val_acc，模式就会是 max，
而对于 val_loss，模式就需要是 min，等等。
在 auto 模式中，方向会自动从被监测的数据的名字中判断出来。
save_weights_only: 如果 True，那么只有模型的权重会被保存 (model.save_weights(filepath))，否则的话，整个模型会被保存 (model.save(filepath))。
period: 每个 CheckPoint 之间的间隔（训练轮数）。
#from keras.callbacks import Tensorboad
from keras.callbacks import ModelCheckpoint
filepath='weights.best.hdf5'
checkpoint = ModelCheckpoint(filepath,
monitor='val_acc',
verbose=1,
save_best_only=True,
mode='max',
period=1)
callbacks_list = [checkpoint]
model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=['acc'])
history_04 = model.fit(train_data, train_label,validation_data=(test_data, test_label), batch_size=BATCH_SIZE, epochs=EPOCH,validation_split=VALIDATION_SPLIT,callbacks=callbacks_list)Train on 60000 samples, validate on 10000 samples
Epoch 1/20
60000/60000 [==============================] - 12s 204us/step - loss: 1.3368 - acc: 0.6510 - val_loss: 0.1234 - val_acc: 0.9631
...
Epoch 00018: val_acc improved from 0.99110 to 0.99160, saving model to weights.best.hdf5
Epoch 19/20
60000/60000 [==============================] - 12s 195us/step - loss: 0.1075 - acc: 0.9693 - val_loss: 0.0313 - val_acc: 0.9896
Epoch 00019: val_acc did not improve from 0.99160
Epoch 20/20
60000/60000 [==============================] - 12s 195us/step - loss: 0.1064 - acc: 0.9693 - val_loss: 0.0313 - val_acc: 0.9913
Epoch 00020: val_acc did not improve from 0.99160### print the keys contained in the history object
print(history_04.history.keys())
### plot the training and validation loss for each epoch
plt.plot(history_05.history['loss'])
plt.plot(history_05.history['val_loss'])
plt.title('model mean squared error loss')
plt.ylabel('mean squared error loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validation set'], loc='upper right')
plt.show()dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])
from keras.callbacks import ModelCheckpoint
# checkpoint
filepath = "weights-improvement-{epoch:02d}-{val_acc:.3f}.hdf5"
# 中途训练效果提升, 则将文件保存, 每提升一次, 保存一次
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True,mode='max')
callbacks_list = [checkpoint]
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])history_05 = model.fit(train_data, train_label,validation_data=(test_data, test_label),
batch_size=BATCH_SIZE,
epochs=EPOCH,
validation_split=VALIDATION_SPLIT,
callbacks=callbacks_list)Train on 60000 samples, validate on 10000 samples
Epoch 1/20
60000/60000 [==============================] - 12s 205us/step - loss: 1.9712 - acc: 0.4447 - val_loss: 0.3115 - val_acc: 0.9287
Epoch 00001: val_acc improved from -inf to 0.92870, saving model to weights-improvement-01-0.929.hdf5
Epoch 2/20
60000/60000 [==============================] - 12s 195us/step - loss: 0.5117 - acc: 0.8366 - val_loss: 0.1148 - val_acc: 0.9692
...
Epoch 19/20
60000/60000 [==============================] - 12s 196us/step - loss: 0.1014 - acc: 0.9696 - val_loss: 0.0274 - val_acc: 0.9910
Epoch 00019: val_acc did not improve from 0.99130
Epoch 20/20
60000/60000 [==============================] - 12s 196us/step - loss: 0.0958 - acc: 0.9702 - val_loss: 0.0279 - val_acc: 0.9918
Epoch 00020: val_acc improved from 0.99130 to 0.99180, saving model to weights-improvement-20-0.992.hdf5### print the keys contained in the history object
print(history_05.history.keys())
### plot the training and validation loss for each epoch
plt.plot(history_05.history['loss'])
plt.plot(history_05.history['val_loss'])
plt.title('model mean squared error loss')
plt.ylabel('mean squared error loss')
plt.xlabel('epoch')
plt.legend(['training set', 'validation set'], loc='upper right')
plt.show()dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])
正文完
