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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
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+mnist_ann_model.keras filter=lfs diff=lfs merge=lfs -text

README.md

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+
+---
+tags:
+- computer-vision
+- tensorflow
+- keras
+- mnist
+- classification
+license: mit
+---
+
+# MNIST Digit Recognition (ANN - TensorFlow/Keras)
+
+This model is a simple Artificial Neural Network (ANN) trained on the MNIST dataset to classify handwritten digits (0–9).
+
+## Architecture
+- Input: 28x28 grayscale image
+- Flatten layer
+- Dense(128, ReLU)
+- Dense(10, Softmax)
+
+## Training
+- Dataset: MNIST
+- Optimizer: Adam
+- Loss: Sparse Categorical Crossentropy
+- Epochs: 5
+
+## Performance
+Achieves ~97–98% test accuracy.
+
+## Usage
+
+```python
+import tensorflow as tf
+import numpy as np
+
+model = tf.keras.models.load_model("mnist_ann_model.keras")
+
+# Example input (28x28 image normalized)
+sample = np.random.rand(1, 28, 28)
+
+pred = model.predict(sample)
+print(np.argmax(pred))
+
+Notes
+
+This is a beginner-friendly ANN model (not CNN).

inference.py

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+
+import tensorflow as tf
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model("mnist_ann_model.keras")
+
+def predict_digit(image_array):
+    # Expect shape (28, 28)
+    image_array = image_array / 255.0
+    image_array = np.expand_dims(image_array, axis=0)
+
+    prediction = model.predict(image_array)
+    return np.argmax(prediction)
+
+# Example usage
+if __name__ == "__main__":
+    sample = np.random.rand(28, 28)
+    print("Predicted digit:", predict_digit(sample))

mnist_ann_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ab8646cce0e048343b035af046addaee433c7894d03f5e6c6008ea90ed72527d
+size 1244433

requirements.txt

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+tensorflow
+numpy