Branch-AI/one_exercise_per_file/week03/day03/ex02/audit/readme.md

The exercice is validated is all questions of the exercice are validated

The question 1 is validated if the input DataFrames are:

X_train_scaled shape is (313, 5) and the first 5 rows are:

	cylinders	displacement	horsepower	weight	acceleration
0	1.28377	0.884666	0.48697	0.455708	-1.19481
1	1.28377	1.28127	1.36238	0.670459	-1.37737
2	1.28377	0.986124	0.987205	0.378443	-1.55992
3	1.28377	0.856996	0.987205	0.375034	-1.19481
4	1.28377	0.838549	0.737087	0.393214	-1.74247

The train target is:

	mpg
0	18
1	15
2	18
3	16
4	17

X_test_scaled shape is (79, 5) and the first 5 rows are:

	cylinders	displacement	horsepower	weight	acceleration
315	-1.00255	-0.554185	-0.5135	-0.113552	1.76253
316	0.140612	0.128347	-0.5135	0.31595	1.25139
317	-1.00255	-1.05225	-0.813641	-1.03959	0.192584
318	-1.00255	-0.710983	-0.5135	-0.445337	0.0830525
319	-1.00255	-0.840111	-0.888676	-0.637363	0.813262

The test target is:

	mpg
315	24.3
316	19.1
317	34.3
318	29.8
319	31.3

The question 2 is validated if the mean absolute error on the test set is smaller than 10. Here is an architecture that works:

# create model
model = Sequential()
model.add(Dense(30, input_dim=5, activation='sigmoid'))
model.add(Dense(30, activation='sigmoid'))
model.add(Dense(1))
# Compile model
model.compile(loss='mean_squared_error',
                optimizer='adam', metrics='mean_absolute_error')

The output neuron has to be Dense(1) - by defaut the activation funtion is linear. The loss has to be mean_squared_error and the input_dim has to be 5. All variations on the others parameters are accepted.

Hint: To get the score on the test set, evaluate could have been used: model.evaluate(X_test_scaled, y_test).