使用model.save(filepath)將Keras模型和權重保存在一個HDF5文件中，該文件將包含：

模型的結構，以便重構該模型 模型的權重 訓練配置（損失函數，優化器等） 優化器的狀態，以便于從上次訓練中斷的地方開始

使用keras.models.load_model(filepath)來重新實例化你的模型，如果文件中存儲了訓練配置的話，該函數還會同時完成模型的編譯

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只保存模型結構,而不包含其權重或配置信息

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#保存成json格式的文件 # save as JSONjson_string = model.to_json() open('my_model_architecture.json','w').write(json_string) from keras.models import model_from_json model = model_from_json(open('my_model_architecture.json').read()) #保存成yaml文件 # save as YAML yaml_string = model.to_yaml() open('my_model_architectrue.yaml','w').write(yaml_string) from keras.models import model_from_yaml model = model_from_yaml(open('my_model_architecture.yaml').read())#這項操作將把模型序列化為json或yaml文件，這些文件對人而言也是友好的，如果需要的話你甚至可以手動打開這些文件并進行編輯。當然，你也可以從保存好的json文件或yaml文件中載入模型：# model reconstruction from JSON: from keras.modelsimport model_from_json model = model_from_json(json_string) # model reconstruction from YAML model =model_from_yaml(yaml_string)

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需要保存模型的權重

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import keras.models import load_model model.save_weights('my_model_weights.h5') #需要在代碼中初始化一個完全相同的模型 model.load_weights('my_model_weights.h5') #需要加載權重到不同的網絡結構（有些層一樣）中，例如fine-tune或transfer-learning，可以通過層名字來加載模型 model.load_weights('my_model_weights.h5', by_name=True) open('my_model_architecture.json','w').write(json_string) model.save_weights('my_model_weights.h5') model = model_from_json(open('my_model_architecture.json').read()) model.load_weights('my_model_weights.h5')

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實時保存模型結構、訓練出來的權重、及優化器狀態并調用

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keras 的callback參數可以幫助我們實現在訓練過程中的適當時機被調用。實現實時保存訓練模型以及訓練參數

keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1 )1. filename：字符串，保存模型的路徑 2. monitor：需要監視的值 3. verbose：信息展示模式，0或1 4. save_best_only：當設置為True時，將只保存在驗證集上性能最好的模型 5. mode：‘auto’，‘min’，‘max’之一，在save_best_only=True時決定性能最佳模型的評判準則，例如，當監測值為val_acc時，模式應為max，當檢測值為val_loss時，模式應為min。在auto模式下，評價準則由被監測值的名字自動推斷。 6. save_weights_only：若設置為True，則只保存模型權重，否則將保存整個模型（包括模型結構，配置信息等） 7. period：CheckPoint之間的間隔的epoch數

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示例

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""" 假如原模型為：model = Sequential()model.add(Dense(2, input_dim=3, name="dense_1"))model.add(Dense(3, name="dense_2"))...model.save_weights(fname) """ # new model model = Sequential() model.add(Dense(2, input_dim=3, name="dense_1")) # will be loaded model.add(Dense(10, name="new_dense")) # will not be loaded# load weights from first model; will only affect the first layer, dense_1. model.load_weights(fname, by_name=True)

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How to Check-Point Deep Learning Models in Keras

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Checkpoint Neural Network Model Improvements

# Checkpoint the weights when validation accuracy improves from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint import matplotlib.pyplot as plt import numpy # fix random seed for reproducibility seed = 7 numpy.random.seed(seed) # load pima indians dataset dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # checkpoint filepath="weights-improvement-{epoch:02d}-{val_acc:.2f}.hdf5" checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks_list = [checkpoint] # Fit the model model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=callbacks_list, verbose=0)

Running the example produces the following output (truncated for brevity):

... Epoch 00134: val_acc did not improve Epoch 00135: val_acc did not improve Epoch 00136: val_acc did not improve Epoch 00137: val_acc did not improve Epoch 00138: val_acc did not improve Epoch 00139: val_acc did not improve Epoch 00140: val_acc improved from 0.83465 to 0.83858, saving model to weights-improvement-140-0.84.hdf5 Epoch 00141: val_acc did not improve Epoch 00142: val_acc did not improve Epoch 00143: val_acc did not improve Epoch 00144: val_acc did not improve Epoch 00145: val_acc did not improve Epoch 00146: val_acc improved from 0.83858 to 0.84252, saving model to weights-improvement-146-0.84.hdf5 Epoch 00147: val_acc did not improve Epoch 00148: val_acc improved from 0.84252 to 0.84252, saving model to weights-improvement-148-0.84.hdf5 Epoch 00149: val_acc did not improve

You will see a number of files in your working directory containing the network weights in HDF5 format. For example:

... weights-improvement-53-0.76.hdf5 weights-improvement-71-0.76.hdf5 weights-improvement-77-0.78.hdf5 weights-improvement-99-0.78.hdf5

Checkpoint Best Neural Network Model Only

# Checkpoint the weights for best model on validation accuracy from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint import matplotlib.pyplot as plt import numpy # fix random seed for reproducibility seed = 7 numpy.random.seed(seed) # load pima indians dataset dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # create model model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # Compile model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # checkpoint filepath="weights.best.hdf5" checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks_list = [checkpoint] # Fit the model model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=callbacks_list, verbose=0)

Running this example provides the following output (truncated for brevity):

... Epoch 00139: val_acc improved from 0.79134 to 0.79134, saving model to weights.best.hdf5 Epoch 00140: val_acc did not improve Epoch 00141: val_acc did not improve Epoch 00142: val_acc did not improve Epoch 00143: val_acc did not improve Epoch 00144: val_acc improved from 0.79134 to 0.79528, saving model to weights.best.hdf5 Epoch 00145: val_acc improved from 0.79528 to 0.79528, saving model to weights.best.hdf5 Epoch 00146: val_acc did not improve Epoch 00147: val_acc did not improve Epoch 00148: val_acc did not improve Epoch 00149: val_acc did not improve

You should see the weight file in your local directory.

weights.best.hdf5

Loading a Check-Pointed Neural Network Model

# How to load and use weights from a checkpoint from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint import matplotlib.pyplot as plt import numpy # fix random seed for reproducibility seed = 7 numpy.random.seed(seed) # create model model = Sequential() model.add(Dense(12, input_dim=8, kernel_initializer='uniform', activation='relu')) model.add(Dense(8, kernel_initializer='uniform', activation='relu')) model.add(Dense(1, kernel_initializer='uniform', activation='sigmoid')) # load weights model.load_weights("weights.best.hdf5") # Compile model (required to make predictions) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print("Created model and loaded weights from file") # load pima indians dataset dataset = numpy.loadtxt("pima-indians-diabetes.csv", delimiter=",") # split into input (X) and output (Y) variables X = dataset[:,0:8] Y = dataset[:,8] # estimate accuracy on whole dataset using loaded weights scores = model.evaluate(X, Y, verbose=0) print("%s: %.2f%%" % (model.metrics_names[1], scores[1]*100))

Running the example produces the following output

Created model and loaded weights from file acc: 77.73%

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參考文獻

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How to Check-Point Deep Learning Models in Keras

http://blog.csdn.net/u010159842/article/details/54602217

用Keras搞一個閱讀理解機器人
Keras中文文檔
如何保存Keras模型
人工神經網絡(三) –keras模型的保存和使用

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