I am missing information about the 'val_acc' attribute when I fit a compiled sequential model.
I have a sequential model that is compiled with 'accuracy' metrics
model = Sequential()
model.add(Dense(12, input_dim=8, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
and I expect to get info about ['acc', 'loss', 'val_acc', 'val_loss'] attributes after fitting this neural network
history = model.fit(X, Y, epochs=100, batch_size=10)
But the information about val_acc is missing on the progress bar
Epoch 14/100
768/768 [==============================] - 0s 212us/step - loss: 0.4356 - acc: 0.7969
Epoch 15/100
768/768 [==============================] - 0s 219us/step - loss: 0.4388 - acc: 0.8034
Epoch 16/100
768/768 [==============================] - 0s 220us/step - loss: 0.4398 - acc: 0.7956
And it's missed also in object history
>>> print (history.history.keys())
dict_keys(['loss', 'acc'])
How do I get the missing attributes ('val_acc', 'val_loss') when training a neural network?
history = model.fit(X, Y, epochs=100, batch_size=10)
Validation data is missing in your fit method, so it has no way to calculate validation metrics.
Either split some of your train data into validation set and pass it
explicitly via validation_data argument of fit method
or
Use validation_split argument of fit method to use some % of your
train data as validation data. Example: validation_split=0.15 will
use 15% of your train data as validation data.