import keras
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
import keras.backend as K
import numpy as np
class LossHistory(keras.callbacks.Callback):
"""
Record the training details during the process
"""
def __init__(self,datapath,model,model_path):
self.save_path=datapath
self.model_path=model_path
self.model=model
self.losses = {'batch': [], 'epoch': []}
self.accuracy = {'batch': [], 'epoch': []}
self.val_loss = {'batch': [], 'epoch': []}
self.val_acc = {'batch': [], 'epoch': []}
#def on_train_begin(self, logs={}):
def on_batch_end(self, batch, logs={}):
self.losses['batch'].append(logs.get('loss'))
self.accuracy['batch'].append(logs.get('acc'))
self.val_loss['batch'].append(logs.get('val_loss'))
self.val_acc['batch'].append(logs.get('val_acc'))
def on_epoch_end(self, batch, logs={}):
self.losses['epoch'].append(logs.get('loss'))
self.accuracy['epoch'].append(logs.get('acc'))
self.val_loss['epoch'].append(logs.get('val_loss'))
self.val_acc['epoch'].append(logs.get('val_acc'))
self.loss_plot('batch')
self.loss_plot('epoch')
tmp_path=os.path.join(self.model_path,str(len(self.accuracy['epoch']))+'_epoch.h5')
self.model.save_weights(tmp_path)
lr=K.get_value(self.model.optimizer.lr)
tmp_path = os.path.join(self.model_path, str(len(self.accuracy['epoch'])) + '_lr.txt')
lr_result=np.zeros(1)
lr_result[0]=lr
np.savetxt(tmp_path,lr_result)
def loss_plot(self, loss_type):
iters = range(len(self.losses[loss_type]))
#create figure
plt.figure()
# loss
plt.plot(iters, self.losses[loss_type], 'g', label='train loss')
plt.grid(True) # grid true
plt.xlabel(loss_type)
plt.ylabel('Loss') # add comment
plt.legend(loc="upper right") # lengend settings
if loss_type == 'batch':
plt.savefig(self.save_path)
else:
plt.savefig(self.save_path[:-4] + '_epoch_loss.jpg')
plt.figure()
# val_loss
plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')
plt.grid(True)#grid true
plt.xlabel(loss_type)
plt.ylabel('Val Loss')#add comment
plt.legend(loc="upper right")#lengend settings
#plt.show()
if loss_type=='epoch':
plt.savefig(self.save_path[:-4]+'_epoch_val_loss.jpg')
plt.figure()
# acc
plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc') # plt.plot(x,y)
# val_acc
plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')
plt.xlabel(loss_type)
plt.ylabel('accuracy') # add comment
plt.grid(True) # grid true
plt.legend(loc="upper right") # lengend settings
# plt.show()
if loss_type=='batch':
plt.savefig(self.save_path[:-4]+'_accu.jpg')
else:
plt.savefig(self.save_path[:-4] + '_epoch_accu.jpg')
