Script for digit recognition model

model.py

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+from pathlib import Path
+import tensorflow as tf
+from tensorflow.keras.datasets import mnist
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D
+
+(x_train, y_train), (x_test, y_test) = mnist.load_data()
+
+# Normalize the pixel values to range [0, 1]
+x_train = x_train / 255.0
+x_test = x_test / 255.0
+
+# Reshape the data to 4D (number of samples, height, width, channels)
+x_train = x_train.reshape(x_train.shape[0], 28, 28, 1)
+x_test = x_test.reshape(x_test.shape[0], 28, 28, 1)
+
+# Create the model
+model = Sequential([
+    Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
+    MaxPooling2D((2, 2)),
+    Conv2D(64, (3, 3), activation='relu'),
+    MaxPooling2D((2, 2)),
+    Flatten(),
+    Dense(128, activation='relu'),
+    Dense(10, activation='softmax')
+])
+
+# Compile the model
+model.compile(optimizer='adam',
+              loss='sparse_categorical_crossentropy',
+              metrics=['accuracy'])
+
+# Train the model
+model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.1)
+
+# Save the model
+path = Path(Path(__file__).parent, 'model.h5')
+model.save(path)