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fix: resolve typos in catdog classifier (#47130) 

* fix: resolve typos in catdog classifier

* fix: missed a couple
pull/47139/head^2
Naomi Carrigan 2022-08-02 22:31:23 -07:00 committed by GitHub
parent af288935b9
commit c17bd7cd89
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curriculum/challenges/english/11-machine-learning-with-python/machine-learning-with-python-projects/cat-and-dog-image-classifier.md
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@ -16,7 +16,7 @@ We are still developing the interactive instructional content for the machine le
# --instructions--
For this challenge, you will complete the code to classify images of dogs and cats. You will use Tensorflow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs at least 63% of the time. (Extra credit if you get it to 70% accuracy!)
For this challenge, you will complete the code to classify images of dogs and cats. You will use TensorFlow 2.0 and Keras to create a convolutional neural network that correctly classifies images of cats and dogs at least 63% of the time. (Extra credit if you get it to 70% accuracy!)
Some of the code is given to you but some code you must fill in to complete this challenge. Read the instruction in each text cell so you will know what you have to do in each code cell.
@ -64,7 +64,7 @@ The `plotImages` function will be used a few times to plot images. It takes an a
Recreate the `train_image_generator` using `ImageDataGenerator`.
Since there are a small number of training examples there is a risk of overfitting. One way to fix this problem is by creating more training data from existing training examples by using random transformations.
Since there are a small number of training examples, there is a risk of overfitting. One way to fix this problem is by creating more training data from existing training examples by using random transformations.
Add 4-6 random transformations as arguments to `ImageDataGenerator`. Make sure to rescale the same as before.
@ -94,7 +94,7 @@ In this cell, get the probability that each test image (from `test_data_gen`) is
Call the `plotImages` function and pass in the test images and the probabilities corresponding to each test image.
After your run the cell, you should see all 50 test images with a label showing the percentage of "sure" that the image is a cat or a dog. The accuracy will correspond to the accuracy shown in the graph above (after running the previous cell). More training images could lead to a higher accuracy.
After you run the cell, you should see all 50 test images with a label showing the percentage of "sure" that the image is a cat or a dog. The accuracy will correspond to the accuracy shown in the graph above (after running the previous cell). More training images could lead to a higher accuracy.
## Cell 11