keras: how to save the training history

问题:

In Keras, we can return the output of model.fit to a history as follows:

 history = model.fit(X_train, y_train, 
                     batch_size=batch_size, 
                     nb_epoch=nb_epoch,
                     validation_data=(X_test, y_test))

Now, how to save the history to a file for further uses (e.g. draw plots of acc or loss against epochs)?

回答1:

What I use is the following:

    with open('/trainHistoryDict', 'wb') as file_pi:
        pickle.dump(history.history, file_pi)

In this way I save the history as a dictionary in case I want to plot the loss or accuracy later on.

回答2:

A history objects has a history field is a dictionary which helds different training metrics spanned across every training epoch. So e.g. history.history['loss'][99] will return a loss of your model in a 100th epoch of training. In order to save that you could pickle this dictionary or simple save different lists from this dictionary to appropriate file.

回答3:

The model history can be saved into a file as follows

import json
hist = model.fit(X_train, y_train, epochs=5, batch_size=batch_size,validation_split=0.1)
with open('file.json', 'w') as f:
    json.dump(hist.history, f)

回答4:

I came across the problem that the values inside of the list in keras are not json seriazable. Therefore I wrote this two handy functions for my use cause.

import json,codecs
import numpy as np
def saveHist(path,history):

    new_hist = {}
    for key in list(history.history.keys()):
        if type(history.history[key]) == np.ndarray:
            new_hist[key] == history.history[key].tolist()
        elif type(history.history[key]) == list:
           if  type(history.history[key][0]) == np.float64:
               new_hist[key] = list(map(float, history.history[key]))

    print(new_hist)
    with codecs.open(path, 'w', encoding='utf-8') as f:
        json.dump(new_hist, f, separators=(',', ':'), sort_keys=True, indent=4) 

def loadHist(path):
    with codecs.open(path, 'r', encoding='utf-8') as f:
        n = json.loads(f.read())
    return n

where saveHist just needs to get the path to where the json file should be saved, and the history object returned from the keras fit or fit_generator method.