keras 保存训练的最佳模型
转自:https://anifacc.github.io/deeplearning/machinelearning/python/2017/08/30/dlwp-ch14-keep-best-model-checkpoint/,感谢分享
深度学习模型花费时间大多很长, 如果一次训练过程意外中断, 那么后续时间再跑就浪费很多时间. 这一次练习中, 我们利用 Keras checkpoint 深度学习模型在训练过程模型, 我的理解是检查训练过程, 将好的模型保存下来. 如果训练过程意外中断, 那么我们可以加载最近一次的文件, 继续进行训练, 这样以前运行过的就可以忽略.
那么如何 checkpoint 呢, 通过练习来了解.
- 数据: Pima diabete 数据
- 神经网络拓扑结构: 8-12-8-1
1.效果提升检查
如果神经网络在训练过程中, 其训练效果有所提升, 则将该次模型训练参数保存下来.
代码
:
# -*- coding: utf-8 -*- # Checkpoint NN model imporvements from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint import numpy as np import urllib url = "http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" raw_data = urllib.urlopen(url) dataset = np.loadtxt(raw_data, delimiter=",") X = dataset[:, 0:8] y = dataset[:, 8] seed = 42 np.random.seed(seed) # create model model = Sequential() model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu')) model.add(Dense(1, init='uniform', activation='sigmoid')) # compile model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # checkpoint filepath = "weights-improvement-{epoch:02d}-{val_acc:.2f}.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] # Fit model.fit(X, y, validation_split=0.33, nb_epoch=150, batch_size=10, callbacks=callbacks_list, verbose=0)
部分结果
:
Epoch 00139: val_acc did not improve Epoch 00140: val_acc improved from 0.70472 to 0.71654, saving model to weights-improvement-140-0.72.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 did not improve Epoch 00147: val_acc did not improve Epoch 00148: val_acc did not improve Epoch 00149: val_acc did not improve
在运行程序的本地文件夹下, 我们会发现许多性能提升时, 程序自动保存的 hdf5 文件.
2.检查最好模型
检查训练过程中训练效果最好的那个模型.
代码
:
# -*- coding: utf-8 -*- # # checkpoint the weights for the best model on validation accuracy from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint import numpy as np import urllib url = "http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" raw_data = urllib.urlopen(url) dataset = np.loadtxt(raw_data, delimiter=",") X = dataset[:, 0:8] y = dataset[:, 8] seed = 42 np.random.seed(seed) # create model model = Sequential() model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu')) model.add(Dense(1, init='uniform', activation='sigmoid')) # compile model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # checkpoint filepath='weights.best.hdf5' # 有一次提升, 则覆盖一次. checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max') callbacks_list = [checkpoint] # fit model.fit(X, y, validation_split=0.33, nb_epoch=150, batch_size=10, callbacks=callbacks_list, verbose=0)
部分结果
:
df5 Epoch 00044: val_acc did not improve Epoch 00045: val_acc improved from 0.69685 to 0.69685, saving model to weights.best.hdf5 Epoch 00046: val_acc did not improve Epoch 00047: val_acc did not improve Epoch 00048: val_acc did not improve Epoch 00049: val_acc improved from 0.69685 to 0.70472, saving model to weights.best.hdf5 ... Epoch 00140: val_acc improved from 0.70472 to 0.71654, saving model to weights.best.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 did not improve Epoch 00147: val_acc did not improve Epoch 00148: val_acc did not improve Epoch 00149: val_acc did not improve
文件weights.best.hdf5
将第140迭代时的模型权重保存.
3.加载保存模型
上面我们将训练过程中最好的模型保存下来, 如果训练有中断, 那么我们可以直接采用本次模型.
代码
:
# -*- coding: utf-8 -*- # Load and use weights from a checkpoint from keras.models import Sequential from keras.layers import Dense from keras.callbacks import ModelCheckpoint import numpy as np import urllib url = "http://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data" raw_data = urllib.urlopen(url) dataset = np.loadtxt(raw_data, delimiter=",") X = dataset[:, 0:8] y = dataset[:, 8] seed = 42 np.random.seed(seed) # create model model = Sequential() model.add(Dense(12, input_dim=8, init='uniform', activation='relu')) model.add(Dense(8, init='uniform', activation='relu')) model.add(Dense(1, init='uniform', activation='sigmoid')) # load weights 加载模型权重 model.load_weights('weights.best.hdf5') # compile 编译 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print('Created model and loaded weights from hdf5 file') # estimate scores = model.evaluate(X, y, verbose=0) print("{0}: {1:.2f}%".format(model.metrics_names[1], scores[1]*100))
结果
:
Created model and loaded weights from hdf5 file acc: 74.74%
4.Sum
本次练习如何将神经网络模型训练过程中, 训练效果最好的模型参数保存下来, 为以后的时候准备, 以备意外发生, 节省时间, 提高效率.