在Keras中,我们可以将model.fit
的输出返回到历史记录,如下所示:
history = model.fit(X_train, y_train,
batch_size=batch_size,
nb_epoch=nb_epoch,
validation_data=(X_test, y_test))
现在,如何将历史记录对象的历史记录属性保存到文件中以供进一步使用(例如,绘制ACC或针对时期的损失图)?
我使用的是:
with open('/trainHistoryDict', 'wb') as file_pi:
pickle.dump(history.history, file_pi)
以这种方式,我将历史记录保存为词典,以防以后要绘制损失或准确性。稍后,当您想再次加载历史记录时,可以使用:
with open('/trainHistoryDict', "rb") as file_pi:
history = pickle.load(file_pi)
为什么选择泡菜而不是json?
该答案下的评论准确地指出:
[将历史记录作为JSON]在Tensorflow Keras中不再起作用。我有以下问题:typeError:类型" float32"的对象不是JSON序列化。
有多种方法可以告诉json
如何编码numpy
对象,您可以从另一个问题中学到这些对象,因此在这种情况下,使用json
没有错,这比简单地将其简单地倾倒到Pickle文件更为复杂。
另一种方法:
作为history.history
是dict
,您也可以将其转换为pandas
DataFrame
对象,然后可以保存以适合您的需求。
逐步:
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)
最简单的方法:
保存:
np.save('my_history.npy',history.history)
加载:
history=np.load('my_history.npy',allow_pickle='TRUE').item()
然后,历史记录是一个字典,您可以使用键检索所有所需的值。
model
历史记录可以如下保存到文件中
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)
history
对象的 history
字段是一个词典,它可以容纳在每个培训时期跨越不同的训练指标。所以例如history.history['loss'][99]
将在第100个训练时期返回模型的损失。为了节省您可以 pickle
此字典或简单保存该字典的不同列表到适当的文件。
我遇到了一个问题,即keras中列表中的值不可用。因此,我为我的使用原因写了这两个方便的功能。
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
Save Histion只需要获取应保存JSON文件的路径,而历史记录对象则从KERAS fit
或fit_generator
方法返回。
我敢肯定有很多方法可以做到这一点,但是我四处摆弄并提出了自己的版本。
首先,自定义回调可以在每个时期结束时抓取并更新历史记录。在那里,我也有一个回调来保存模型。这两个都方便
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]
第二,这里有一些"助手"功能,可以准确地做他们说的事情。这些都是从LossHistory()
回调中调用的。
# 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
之后,您只需要将history_filename
设置为data/model-history.json
之类的东西,并将model_filename
设置为data/model.h5
之类的东西。最终的调整以确保在培训结束时不要弄乱您的历史,假设您停下来启动并贴在回调中,就是这样做:
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)
随时随地,history = loadHist(history_filename)
将您的历史记录回去。
funkiness来自JSON和列表,但我无法通过迭代进行转换而无法转换它。无论如何,我知道这起作用是因为我已经摇动了几天。https://stackoverflow.com/a/444674337/852795的pickle.dump
答案可能更好,但我不知道那是什么。如果我错过了这里的任何东西,或者您无法正常工作,请告诉我。
您可以保存 .txt form
的tf.keras.callbacks.History
的历史属性 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)
在训练过程结束时保存历史记录时,上述答案很有用。如果您想在培训期间保存历史记录,则CSVlogger回调将有所帮助。
下面的代码以数据表文件的形式保存模型重量和历史培训 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])
这是一个将日志腌入文件的回调。实例化回调OBJ时提供模型文件路径;这将创建一个关联的文件 - 给定的模型路径'/home/user/model.h5',腌制路径'/home/home/user/user/model_history_pickle'。重新加载模型后,回调将继续从其在。
的时代。
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:nt{}".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()