Update README.md

README.md

@@ -3,42 +3,49 @@ library_name: keras
 tags:
 - Image Classification
 ---
-
-## Model description
-
-More information needed
-
-## Intended uses & limitations
-
-More information needed
-
-## Training and evaluation data
-
-More information needed
-
-## Training procedure
-
-### Training hyperparameters
-
-The following hyperparameters were used during training:
-
-| Hyperparameters | Value |
-| :-- | :-- |
-| name | Adam |
-| learning_rate | 0.0010000000474974513 |
-| decay | 0.0 |
-| beta_1 | 0.8999999761581421 |
-| beta_2 | 0.9990000128746033 |
-| epsilon | 1e-07 |
-| amsgrad | False |
-| training_precision | float32 |
-
-
- ## Model Plot
-
-<details>
-<summary>View Model Plot</summary>
-
-![Model Image](./model.png)
-
-</details>
+# Cifar-CNN (Teeny-Tiny Castle)
+
+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. 
+
+## How to Use
+
+```python
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from huggingface_hub import from_pretrained_keras
+
+# Download the CIFAR-10 dataset
+(x_train, y_train), (x_test, y_test)  = tf.keras.datasets.cifar10.load_data()
+
+class_names = ['Airplane', 'Automobile', 'Bird', 'Cat', 'Deer',
+               'Dog', 'Frog', 'Horse', 'Ship', 'Truck']
+
+plt.figure(figsize=[10, 10])
+for i in range(25):
+ plt.subplot(5, 5, i+1)
+ plt.xticks([])
+ plt.yticks([])
+ plt.grid(False)
+ plt.imshow(x_test[i], cmap=plt.cm.binary)
+ plt.xlabel(class_names[y_test[i][0]])
+
+plt.show()
+
+# Load the model from the Hub
+model = from_pretrained_keras("AiresPucrs/Cifar-CNN")
+model.compile(
+    loss=tf.keras.losses.CategoricalCrossentropy(),
+    metrics=['categorical_accuracy']
+ )
+x_train = x_train.astype('float32')
+x_train = x_train / 255.
+y_train = tf.keras.utils.to_categorical(y_train, 10)
+x_test = x_test.astype('float32')
+x_test = x_test / 255.
+y_test = tf.keras.utils.to_categorical(y_test, 10)
+test_loss_score, test_acc_score = model.evaluate(x_test, y_test, verbose=0)
+model.summary()
+print(f'Loss: {round(test_loss_score, 2)}.')
+print(f'Accuracy: {round(test_acc_score * 100, 2)} %.')
+```