Create app.py

app.py

@@ -0,0 +1,54 @@
+import numpy as np
+from tensorflow import keras
+from tensorflow.keras import layers
+
+def to_numpy(examples):
+    examples["pixel_values"] = [np.array(image) for image in examples["image"]]
+    return examples
+
+def preprocess():
+    test_dataset = load_dataset("active-learning/test_mnist")
+    train_dataset = load_dataset("active-learning/labeled_samples")
+    train_dataset = train_dataset.map(to_numpy, batched=True)
+    test_dataset = test_dataset.map(to_numpy, batched=True)
+    
+    x_train = train_dataset["train"]["pixel_values"]
+    y_train = train_dataset["train"]["label"]
+    
+    x_test = test_dataset["test"]["pixel_values"]
+    y_test = test_dataset["test"]["label"]
+
+    x_train = np.expand_dims(x_train, -1)
+    x_test = np.expand_dims(x_test, -1)
+
+    num_classes = 10
+    input_shape = (28, 28, 1)
+    
+    y_train = keras.utils.to_categorical(y_train, num_classes)
+    y_test = keras.utils.to_categorical(y_test, num_classes)
+
+    return x_train, y_train, x_test, y_test
+
+def training():
+    x_train, y_train, x_test, y_test = preprocess()
+
+    model = keras.Sequential(
+        [
+            keras.Input(shape=input_shape),
+            layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
+            layers.MaxPooling2D(pool_size=(2, 2)),
+            layers.Flatten(),
+            layers.Dropout(0.5),
+            layers.Dense(num_classes, activation="softmax"),
+        ]
+    )
+    
+    model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
+    model.fit(x_train, y_train, batch_size=128, epochs=15, validation_split=0.1)
+
+    score = model.evaluate(x_test, y_test, verbose=0)
+    print("Test loss:", score[0])
+    print("Test accuracy:", score[1])
+