import sys
import numpy as np
import keras.backend as K
from keras.callbacks import Callback
from keras.models import Model, Sequential
class UnsupervisedLoss2Logger(Callback):
def __init__(self, X_train, X_test, loss, verbose=1, batch_size = 1,
label='loss', every_n_epochs=1, display_delta=True):
super(UnsupervisedLoss2Logger, self).__init__()
self.X_train = X_train
self.X_test = X_test
self.loss = loss
self.verbose = verbose
self.label = label
self.every_n_epochs = every_n_epochs
self.display_delta = display_delta
self.prev_loss = None
self.batch_size = batch_size
input_train = K.placeholder(shape=self.X_train.shape)
input_test = K.placeholder(shape=self.X_test.shape)
loss = self.loss(input_train, input_test)
ins = [input_train, input_test]
self.loss_function = K.function(ins, loss)
def on_epoch_end(self, epoch, logs={}):
if((epoch+1) % self.every_n_epochs == 0):
loss = np.mean(self.loss_function([self.X_train, self.X_test]))
if(self.prev_loss):
delta_loss = loss - self.prev_loss
else:
delta_loss = None
self.prev_loss = loss
if(self.display_delta and delta_loss):
print(' - %s: %f (%+f)' % (self.label, loss, delta_loss))
else:
print(' - %s: %f' % (self.label, loss))
#sys.stdout.flush()
