public/subjects/ai/keras-2/audit

Exercise 0: Environment and libraries

The exercise is validated is all questions of the exercise are validated.

Activate the virtual environment. If you used conda run conda activate your_env.

Run python --version.

Does it print Python 3.x? x >= 8

Does import jupyter, import numpy, import pandas and import keras run without any error?

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Exercise 1: Regression - Optimize

The question 1 is validated if the chunk of code is:

model.compile(
  optimizer='adam',
  loss='mse',
  metrics=['mse']
)

All regression metrics or losses used are correct. As explained before, the loss functions are chosen thanks to nice mathematical properties. That is why most of the time the loss function used for regression is the MSE or MAE.

https://keras.io/api/losses/regression_losses/ https://keras.io/api/metrics/regression_metrics/

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Exercise 2: Regression example

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).

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Exercise 3: Multi classification - Softmax

The question 1 is validated if the code that creates the neural network is:

model = keras.Sequential()
model.add(Dense(16, input_shape=(5,), activation= 'sigmoid'))
model.add(Dense(8, activation= 'sigmoid'))
model.add(Dense(5, activation= 'softmax'))

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Exercise 4: Multi classification - Optimize

The question 1 is validated if the chunk of code is:

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

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Exercise 4: Multi classification - Optimize

The question 1 is validated if the chunk of code is:

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

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Exercise 5: Multi classification example

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

The question 1 is validated if the output of the first ten values of the train labels are:

array([[0, 1, 0],
       [0, 0, 1],
       [0, 1, 0],
       [0, 0, 1],
       [0, 0, 1],
       [1, 0, 0],
       [0, 1, 0],
       [1, 0, 0],
       [0, 1, 0],
       [0, 0, 1]])

The question 2 is validated if the accuracy on the test set is bigger than 90%. To evaluate the accuracy on the test set you can use: model.evaluate(X_test_sc, y_test_multi_class).

Here is an implementation that gives 96% accuracy on the test set.

model = Sequential()
model.add(Dense(10, input_dim=4, activation='sigmoid'))
model.add(Dense(3, activation='softmax'))
# Compile model
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X_train_sc, y_train_multi_class, epochs = 1000, batch_size=20)