如何保存历史对象的训练历史属性

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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.

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最佳答案

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. Later, when you want to load the history again, you can use:

with open('/trainHistoryDict', "rb") as file_pi:
history = pickle.load(file_pi)

Why choose pickle over json?

The comment under this answer accurately states:

[Storing the history as json] does not work anymore in tensorflow keras. I had issues with: TypeError: Object of type 'float32' is not JSON serializable.

There are ways to tell json how to encode numpy objects, which you can learn about from this other question, so there's nothing wrong with using json in this case, it's just more complicated than simply dumping to a pickle file.

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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)

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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()):
new_hist[key]=history.history[key]
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 file:
json.dump(new_hist, file, separators=(',', ':'), sort_keys=True, indent=4)


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

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I'm sure there are many ways to do this, but I fiddled around and came up with a version of my own.

First, a custom callback enables grabbing and updating the history at the end of every epoch. In there I also have a callback to save the model. Both of these are handy because if you crash, or shutdown, you can pick up training at the last completed epoch.

class LossHistory(Callback):
    

# https://stackoverflow.com/a/53653154/852795
def on_epoch_end(self, epoch, logs = None):
new_history = {}
for k, v in logs.items(): # compile new history from logs
new_history[k] = [v] # convert values into lists
current_history = loadHist(history_filename) # load history from current training
current_history = appendHist(current_history, new_history) # append the logs
saveHist(history_filename, current_history) # save history from current training


model_checkpoint = ModelCheckpoint(model_filename, verbose = 0, period = 1)
history_checkpoint = LossHistory()
callbacks_list = [model_checkpoint, history_checkpoint]

Second, here are some 'helper' functions to do exactly the things that they say they do. These are all called from the LossHistory() callback.

# https://stackoverflow.com/a/54092401/852795
import json, codecs


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


def loadHist(path):
n = {} # set history to empty
if os.path.exists(path): # reload history if it exists
with codecs.open(path, 'r', encoding='utf-8') as f:
n = json.loads(f.read())
return n


def appendHist(h1, h2):
if h1 == {}:
return h2
else:
dest = {}
for key, value in h1.items():
dest[key] = value + h2[key]
return dest

After that, all you need is to set history_filename to something like data/model-history.json, as well as set model_filename to something like data/model.h5. One final tweak to make sure not to mess up your history at the end of training, assuming you stop and start, as well as stick in the callbacks, is to do this:

new_history = model.fit(X_train, y_train,
batch_size = batch_size,
nb_epoch = nb_epoch,
validation_data=(X_test, y_test),
callbacks=callbacks_list)


history = appendHist(history, new_history.history)

Whenever you want, history = loadHist(history_filename) gets your history back.

The funkiness comes from the json and the lists but I wasn't able to get it to work without converting it by iterating. Anyway, I know that this works because I've been cranking on it for days now. The pickle.dump answer at https://stackoverflow.com/a/44674337/852795 might be better, but I don't know what that is. If I missed anything here or you can't get it to work, let me know.

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Another way to do this:

As history.history is a dict, you can convert it as well to a pandas DataFrame object, which can then be saved to suit your needs.

Step by step:

import pandas as pd


# assuming you stored your model.fit results in a 'history' variable:
history = model.fit(x_train, y_train, epochs=10)


# convert the history.history dict to a pandas DataFrame:
hist_df = pd.DataFrame(history.history)


# save to json:
hist_json_file = 'history.json'
with open(hist_json_file, mode='w') as f:
hist_df.to_json(f)


# or save to csv:
hist_csv_file = 'history.csv'
with open(hist_csv_file, mode='w') as f:
hist_df.to_csv(f)

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The easiest way:

Saving:

np.save('my_history.npy',history.history)

Loading:

history=np.load('my_history.npy',allow_pickle='TRUE').item()

Then history is a dictionary and you can retrieve all desirable values using the keys.

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The above answers are useful when saving history at the end of the training process. If you want to save the history during the training, the CSVLogger callback will be helpful.

Below code saves the model weight and history training in form of a datasheet file log.csv.

model_cb = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path)
history_cb = tf.keras.callbacks.CSVLogger('./log.csv', separator=",", append=False)


history = model.fit(callbacks=[model_cb, history_cb])

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You can save History attribute of tf.keras.callbacks.History in .txt form

with open("./result_model.txt",'w') as f:
for k in history.history.keys():
print(k,file=f)
for i in history.history[k]:
print(i,file=f)

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Here is a callback that pickles the logs into a file. Provide the model file path when instantiating the callback obj; this will create an associated file - given model path '/home/user/model.h5', the pickled path '/home/user/model_history_pickle'. Upon reloading the model, the callback will continue from the epoch that it left off at.


import os
import re
import pickle
#
from tensorflow.keras.callbacks import Callback
from tensorflow.keras import backend as K


class PickleHistoryCallback(Callback):
def __init__(self, path_file_model, *args, **kwargs):
super().__init__(*args, **kwargs)
self.__path_file_model = path_file_model
#
self.__path_file_history_pickle = None
self.__history = {}
self.__epoch = 0
#
self.__setup()
#
def __setup(self):
self.__path_file_history_pickle = re.sub(r'\.[^\.]*$', '_history_pickle', self.__path_file_model)
#
if (os.path.isfile(self.__path_file_history_pickle)):
with open(self.__path_file_history_pickle, 'rb') as fd:
self.__history = pickle.load(fd)
# Start from last epoch
self.__epoch = self.__history['e'][-1]
#
else:
print("Pickled history file unavailable; the following pickled history file creation will occur after the first training epoch:\n\t{}".format(
self.__path_file_history_pickle))
#
def __update_history_file(self):
with open(self.__path_file_history_pickle, 'wb') as fd:
pickle.dump(self.__history, fd)
#
def on_epoch_end(self, epoch, logs=None):
self.__epoch += 1
logs = logs or {}
#
logs['e'] = self.__epoch
logs['lr'] = K.get_value(self.model.optimizer.lr)
#
for k, v in logs.items():
self.__history.setdefault(k, []).append(v)
#
self.__update_history_file()