#### 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?

---

---

#### 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/

---

---

#### 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)`.

---

---

#### 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'))
```

---

---

#### 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'])
```

---

---

#### 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'])
```

---

---

#### 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)
```