#### 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
###### Do `import jupyter`, `import numpy`, `import pandas` and `import keras` run without any error?
---
---
#### Exercise 1: Regression - Optimize
###### For question 1, is the chunk of code like this?
```
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/
---
---
#### Exercise 2: Regression example
##### The exercise is validated if all questions of the exercise are validated
###### For question 1, are these the input DataFrames?
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 |
###### For question 2, is the mean absolute error on the test set 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)` .
---
---
#### Exercise 3: Multi classification - Softmax
###### For question 1, is the code that creates the neural network the following?
```
model = keras.Sequential()
model.add(Dense(16, input_shape=(5,), activation= 'sigmoid'))
model.add(Dense(8, activation= 'sigmoid'))
model.add(Dense(5, activation= 'softmax'))
```
---
---
#### Exercise 4: Multi classification - Optimize
###### For question 1, is the chunk of code the following?
```
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
```
---
---
#### Exercise 4: Multi classification - Optimize
###### For question 1, is the chunk of code the following?
```
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
```
---
---
#### Exercise 5: Multi classification example
##### The exercise is validated if all questions of the exercise are validated
###### For question 1, is the output of the first ten values of the train labels the following?
```
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]])
```
###### For question 2, is the accuracy on the test set 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)
```
