Browse Source

docs(keras-2): fix audits format

pull/1687/head
eslopfer 2 years ago
parent
commit
92307d767c
  1. 22
      subjects/ai/keras-2/audit/README.md

22
subjects/ai/keras-2/audit/README.md

@ -8,7 +8,7 @@
###### Does it print `Python 3.x`? x >= 8
##### Does `import jupyter`, `import numpy`, `import pandas` and `import keras` run without any error?
###### Do `import jupyter`, `import numpy`, `import pandas` and `import keras` run without any error?
---
@ -16,7 +16,7 @@
#### Exercise 1: Regression - Optimize
##### The question 1 is validated if the chunk of code is:
###### For question 1, is the chunk of code like this?
```
model.compile(
@ -37,9 +37,9 @@ https://keras.io/api/metrics/regression_metrics/
#### Exercise 2: Regression example
##### The exercice is validated is all questions of the exercice are validated
##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated if the input DataFrames are:
###### For question 1, are these the input DataFrames?
X_train_scaled shape is (313, 5) and the first 5 rows are:
@ -81,7 +81,7 @@ The test target is:
| 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:
###### For question 2, is the mean absolute error on the test set smaller than 10? Here is an architecture that works:
```
# create model
@ -104,7 +104,7 @@ _Hint_: To get the score on the test set, `evaluate` could have been used: `mode
#### Exercise 3: Multi classification - Softmax
##### The question 1 is validated if the code that creates the neural network is:
###### For question 1, is the code that creates the neural network the following?
```
model = keras.Sequential()
@ -119,7 +119,7 @@ model.add(Dense(5, activation= 'softmax'))
#### Exercise 4: Multi classification - Optimize
##### The question 1 is validated if the chunk of code is:
###### For question 1, is the chunk of code the following?
```
model.compile(loss='categorical_crossentropy',
@ -133,7 +133,7 @@ model.compile(loss='categorical_crossentropy',
#### Exercise 4: Multi classification - Optimize
##### The question 1 is validated if the chunk of code is:
###### For question 1, is the chunk of code the following?
```
model.compile(loss='categorical_crossentropy',
@ -147,9 +147,9 @@ model.compile(loss='categorical_crossentropy',
#### Exercise 5: Multi classification example
##### The exercice is validated is all questions of the exercice are validated
##### The exercise is validated if all questions of the exercise are validated
##### The question 1 is validated if the output of the first ten values of the train labels are:
###### For question 1, is the output of the first ten values of the train labels the following?
```
array([[0, 1, 0],
@ -164,7 +164,7 @@ array([[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)`.
###### 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.

Loading…
Cancel
Save