def train(self,
epochs,
steps_per_epoch,
initial_epoch=0,
end_of_epoch_callback=None,
verbose=1):
epoch = initial_epoch
logger = ProgbarLogger(count_mode='steps')
logger.set_params({
'epochs': epochs,
'steps': steps_per_epoch,
'verbose': verbose,
'metrics': self.metric_names
})
logger.on_train_begin()
while epoch < epochs:
step = 0
batch = 0
logger.on_epoch_begin(epoch)
while step < steps_per_epoch:
self.batch_logs['batch'] = batch
logger.on_batch_begin(batch, self.batch_logs)
for i in range(len(self.models)):
x, y = self.output_generators[i].__next__()
if x is None:
x, y = self.output_generators[i].__next__()
outs = self.models[i].train_on_batch(x, y)
if not isinstance(outs, list):
outs = [outs]
if self.print_full_losses:
for l, o in zip(self.metric_names, outs):
self.batch_logs[l] = o
else:
self.batch_logs[self.metric_names[i]] = outs[0]
logger.on_batch_end(batch, self.batch_logs)
step += 1
batch += 1
logger.on_epoch_end(epoch)
if end_of_epoch_callback is not None:
end_of_epoch_callback(epoch)
epoch += 1
def on_epoch_end(self, epoch, logs=None):
if 'roc_auc' not in self.params['metrics']:
# self.params['metrics'].append('roc_auc')
self.params['metrics'].append('val_roc_auc')
# X_train, Y_train, _, _ = self.model.training_data
X_test = list(self.model.validation_data[0:-3])
Y_test = self.model.validation_data[-3]
# train_pred = self.model.predict_proba(X_train, verbose=0)
val_pred = self.model.predict_proba(X_test, verbose=0)
# logs['roc_auc'] = roc_auc_score(Y_train[:,1], train_pred[:,1])
logs['val_roc_auc'] = roc_auc_score(Y_test[:, 1], val_pred[:, 1])
ProgbarLogger.on_epoch_end(self, epoch, logs)