'loss': np.mean(training_loss),
'val_loss': np.mean(testing_loss),
'val_acc': np.mean(testing_acc)
}
modelcheckpoint.on_epoch_end(epoch, logs)
earlystop.on_epoch_end(epoch, logs)
reduce_lr.on_epoch_end(epoch, logs)
tensorboard.on_epoch_end(epoch, logs)
print(
"accuracy: {}, loss: {}, validation accuracy: {}, validation loss: {}".
format(np.mean(training_acc), np.mean(training_loss),
np.mean(testing_acc), np.mean(testing_loss)))
if model.stop_training:
break
earlystop.on_train_end()
modelcheckpoint.on_train_end()
reduce_lr.on_train_end()
tensorboard.on_train_end()
# confusion metric for training
y_train_pred = model.predict(x_train).argmax(axis=1)
conf_mat = confusion_matrix(y_train, y_train_pred)
class_label = [
"airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse",
"ship", "truck"
]
df = pd.DataFrame(conf_mat, index=class_label, columns=class_label)
sns.heatmap(df, annot=True, cmap="YlGnBu", fmt="d")
plt.title("Confusion Matrix for Training data")
plt.xlabel("Predicted Label")
