Evaluating model accuracy on validation dataset
Now it's your turn to monitor model accuracy with a validation data set. A model definition has been provided as model
. Your job is to add the code to compile it and then fit it. You'll check the validation score in each epoch.
This exercise is part of the course
Introduction to Deep Learning in Python
Exercise instructions
- Compile your model using
'adam'
as theoptimizer
and'categorical_crossentropy'
for theloss
. To see what fraction of predictions are correct (theaccuracy
) in each epoch, specify the additional keyword argumentmetrics=['accuracy']
inmodel.compile()
. - Fit the model using the
predictors
andtarget
. Create a validation split of 30% (or0.3
). This will be reported in each epoch.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Save the number of columns in predictors: n_cols
n_cols = predictors.shape[1]
input_shape = (n_cols,)
# Specify the model
model = Sequential()
model.add(Dense(100, activation='relu', input_shape = input_shape))
model.add(Dense(100, activation='relu'))
model.add(Dense(2, activation='softmax'))
# Compile the model
____
# Fit the model
hist = ____