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.
Questo esercizio fa parte del corso
Introduction to Deep Learning in Python
Istruzioni dell'esercizio
- Fit the
model. Remember that the first argument is the predictive features (predictors), and the data to be predicted (target) is the second argument.
Esercizio pratico interattivo
Prova a risolvere questo esercizio completando il codice di esempio.
# 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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