Fitting the model
You're at the most fun part. You'll now fit the model. Recall that the data to be used as predictive features is loaded in a NumPy array called predictors and the data to be predicted is stored in a NumPy array called target. Your model is pre-written and it has been compiled with the code from the previous exercise.
This exercise is part of the course
Introduction to Deep Learning in Python
Exercise instructions
- Fit the
model. Remember that the first argument is the predictive features (predictors), and the data to be predicted (target) is the second argument.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Import necessary modules
from tensorflow.keras.layers import Dense
from tensorflow.keras.models import Sequential
# Specify the model
n_cols = predictors.shape[1]
model = Sequential()
model.add(Dense(50, activation='relu', input_shape = (n_cols,)))
model.add(Dense(32, activation='relu'))
model.add(Dense(1))
# Compile the model
model.compile(optimizer='adam', loss='mean_squared_error')
# Fit the model
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