logger.plot_model(model, 'Classifier')
log('Create Networks Successfully')
#%%
###################################################################
######################## Start training ###########################
###################################################################
for epoch in range(epochs + 1):
log("Epoch {}".format(epoch + 1))
# train
loss_sum, acc_sum = 0, 0
for i in range(batch_steps):
x_batch, y_batch, showlabels = dataset.train_next_batch()
loss, acc = model.train_on_batch(x_batch, y_batch)
loss_sum = loss_sum + loss
acc_sum = acc_sum + acc
utils.progressbar(i, batch_steps)
logger.write_tensorboard(['train_acc', 'train_loss'],
[acc_sum / batch_steps, loss_sum / batch_steps],
epoch)
print('Training Acc :\t{:.4f}'.format(acc_sum / batch_steps))
# valid
if (epoch % eval_freq == 0):
loss, acc = evaluator.EvaluateModel(model, epoch)
print('Validate Loss:\t{:.4f}'.format(loss))
print('Validate Acc :\t{:.4f}'.format(acc))
logger.save_weights(model, "Round{}_Weights".format(epoch))
