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--- |
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library_name: keras |
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tags: |
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- Image Classification |
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--- |
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# Cifar-CNN (Teeny-Tiny Castle) |
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This model is part of a tutorial tied to the [Teeny-Tiny Castle](https://github.com/Nkluge-correa/TeenyTinyCastle), an open-source repository containing educational tools for AI Ethics and Safety research. |
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## How to Use |
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```python |
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import numpy as np |
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import tensorflow as tf |
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import matplotlib.pyplot as plt |
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from huggingface_hub import from_pretrained_keras |
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# Download the CIFAR-10 dataset |
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(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() |
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class_names = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer', |
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'Dog', 'Frog', 'Horse', 'Ship', 'Truck'] |
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plt.figure(figsize=[10, 10]) |
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for i in range(25): |
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plt.subplot(5, 5, i+1) |
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plt.xticks([]) |
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plt.yticks([]) |
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plt.grid(False) |
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plt.imshow(x_test[i], cmap=plt.cm.binary) |
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plt.xlabel(class_names[y_test[i][0]]) |
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plt.show() |
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# Load the model from the Hub |
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model = from_pretrained_keras("AiresPucrs/Cifar-CNN") |
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model.compile( |
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loss=tf.keras.losses.CategoricalCrossentropy(), |
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metrics=['categorical_accuracy'] |
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) |
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x_train = x_train.astype('float32') |
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x_train = x_train / 255. |
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y_train = tf.keras.utils.to_categorical(y_train, 10) |
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x_test = x_test.astype('float32') |
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x_test = x_test / 255. |
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y_test = tf.keras.utils.to_categorical(y_test, 10) |
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test_loss_score, test_acc_score = model.evaluate(x_test, y_test, verbose=0) |
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model.summary() |
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print(f'Loss: {round(test_loss_score, 2)}.') |
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print(f'Accuracy: {round(test_acc_score * 100, 2)} %.') |
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``` |
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