@菜鳥初學keras錯誤一 20190620
記錄一下今天的錯誤
初次使用keras,不知道model.compile,不曉得model.fit，不懂的如何保存checkpoint等模型。
經過一番掙扎後發現，model相關的保存可以通過Modelcheckpoint()來保存
Modelcheckpoint介紹
原版可以看https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/ModelCheckpoint
tf.keras.callbacks.ModelCheckpoint
解釋：
Class ModelCheckpoint
Inherits From: Callback
Defined in tensorflow/python/keras/callbacks.py.
Save the model after every epoch.
filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end).
For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then the model checkpoints will be saved with the epoch number and the validation loss in the filename.
### Arguments:
1. filepath: string, path to save the model file.
2. monitor: quantity to monitor.
3. verbose: verbosity mode, 0 or 1.
4. save_best_only: if save_best_only=True, the latest best model according to the quantity monitored will not be overwritten.
5. mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.
6. save_weights_only: if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)).
7. period: Interval (number of epochs) between checkpoints.
用法
可以參考http://frankchen.xyz/2018/04/19/keras-reuse-model/
保存模型
如下，我們預定義保存的hdf5文件名，再初始化ModelCheckpoint，將其加入Keras的callback裏（即每個batch結束後做的事情），那麼模型就會在每次batch結束後對比，保存最好的模型。
from keras.callbacks import ModelCheckpoint
# create model
model = Sequential()
model.add(...)
model.add(...)
model.add(...)
# Compile model
model.compile(...)
# checkpoint
filepath="weights-{epoch:02d}-{val_acc:.2f}.hdf5" #文件名
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=[checkpoint], verbose=0)
加載模型
注意，之前保存的只是模型的weights，（ps:想要保存全部模型可以將save_weights_only=False,True是隻保存weight_model）重新load需要再次定義模型結構再load weights並再次combine，例如
from keras.callbacks import ModelCheckpoint
# create model
model = Sequential()
model.add(...)
model.add(...)
model.add(...)
# load weights
model.load_weights("weights.best.hdf5")
# Compile model
model.compile(...)
# estimate accuracy
scores = model.evaluate(X, Y, verbose=0)
print('{}: {:.2%}'.format(model.metrics_names[1], scores[1]))
如果之前選擇了連模型結構也一起保存（即在ModelCheckpoint中選擇save_weights_only=False），那麼load就很簡單，
from keras.callbacks import ModelCheckpoint
from keras.models import load_model
# create model
model = Sequential()
model.add(...)
model.add(...)
model.add(...)
# Compile model
model.compile(...)
# checkpoint
filepath="weights-best.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max', save_weights_only=False)
# Fit the model
model.fit(X, Y, validation_split=0.33, epochs=150, batch_size=10, callbacks=[checkpoint], verbose=0)
# Load the model
model= load_model(filepath)
scores=model.evaluate(X, Y,verbose=0)
print('{}: {:.2%}'.format(model.metrics_names[1], scores[1]))
然後出現了一個大bug
就是在運行的時候報錯：AttributeError: ‘ModelCheckpoint’ object has no attribute ‘on_train_batch_begin’
死活不知道怎麼弄，後來看到https://xbuba.com/questions/55112713
靈光一現，發現在import的時候出錯了
應該將
from keras.callbacks import Modelcheckpoint
改爲從tensorflow中導入，即
from tensorflow.python.keras.callbacks import ModelCheckpoint
然後就能跑代碼了，但是依然還有很多問題，這幾天弄一下model.compile()的相關知識
ps:學個新知識好慢啊，至今還沒有弄明白這個model.fit之類的和tf.session()的適用情況？
ps:有沒有人曉得如何將合成的5D tensor轉換爲4D的，求告知