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.
Bu egzersiz
Introduction to Deep Learning in Python
kursunun bir parçasıdırEgzersiz talimatları
- Fit the
model. Remember that the first argument is the predictive features (predictors), and the data to be predicted (target) is the second argument.
Uygulamalı interaktif egzersiz
Bu örnek kodu tamamlayarak bu egzersizi bitirin.
# 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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