From 92307d767cb20180e622f05e97043b4d337b7d21 Mon Sep 17 00:00:00 2001 From: eslopfer Date: Wed, 21 Dec 2022 16:06:56 +0000 Subject: [PATCH] docs(keras-2): fix audits format --- subjects/ai/keras-2/audit/README.md | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/subjects/ai/keras-2/audit/README.md b/subjects/ai/keras-2/audit/README.md index 38fcedcd..c10d0a32 100644 --- a/subjects/ai/keras-2/audit/README.md +++ b/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.