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chore(i18n,learn): processed translations (#54399)

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2024-04-16 03:25:52 +05:30
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curriculum/challenges/german/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md
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@@ -45,7 +45,7 @@ Now it is your turn! Set each of the variables in this cell correctly. (They sho
Create image generators for each of the three image data sets (train, validation, test). Use `ImageDataGenerator` to read / decode the images and convert them into floating point tensors. Use the `rescale` argument (and no other arguments for now) to rescale the tensors from values between 0 and 255 to values between 0 and 1.
For the `*_data_gen` variables, use the `flow_from_directory` method. Pass in the batch size, directory, target size (`(IMG_HEIGHT, IMG_WIDTH)`), class mode, and anything else required. `test_data_gen` will be the trickiest one. For `test_data_gen`, make sure to pass in `shuffle=False` to the `flow_from_directory` method. This will make sure the final predictions stay is in the order that our test expects. For `test_data_gen` it will also be helpful to observe the directory structure.
For the `*_data_gen` variables, use the `flow_from_directory` method. Pass in the batch size, directory, target size (`(IMG_HEIGHT, IMG_WIDTH)`), class mode, and anything else required. `test_data_gen` will be the trickiest one. For `test_data_gen`, make sure to pass in `shuffle=False` to the `flow_from_directory` method. This will make sure the final predictions stay in the order that our test expects. For `test_data_gen` it will also be helpful to observe the directory structure.
After you run the code, the output should look like this: