chore: import upstream snapshot with attribution

2026-07-13 13:39:55 +08:00
commit 7ee4420c10
87 changed files with 15222 additions and 0 deletions
@@ -0,0 +1,57 @@
+import logging
+
+from mla.datasets import load_mnist
+from mla.metrics import accuracy
+from mla.neuralnet import NeuralNet
+from mla.neuralnet.layers import (
+    Activation,
+    Convolution,
+    MaxPooling,
+    Flatten,
+    Dropout,
+    Parameters,
+)
+from mla.neuralnet.layers import Dense
+from mla.neuralnet.optimizers import Adadelta
+from mla.utils import one_hot
+
+logging.basicConfig(level=logging.DEBUG)
+
+
+# Load MNIST dataset
+X_train, X_test, y_train, y_test = load_mnist()
+
+# Normalize data
+X_train /= 255.0
+X_test /= 255.0
+
+y_train = one_hot(y_train.flatten())
+y_test = one_hot(y_test.flatten())
+print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
+
+# Approx. 15-20 min. per epoch
+model = NeuralNet(
+    layers=[
+        Convolution(n_filters=32, filter_shape=(3, 3), padding=(1, 1), stride=(1, 1)),
+        Activation("relu"),
+        Convolution(n_filters=32, filter_shape=(3, 3), padding=(1, 1), stride=(1, 1)),
+        Activation("relu"),
+        MaxPooling(pool_shape=(2, 2), stride=(2, 2)),
+        Dropout(0.5),
+        Flatten(),
+        Dense(128),
+        Activation("relu"),
+        Dropout(0.5),
+        Dense(10),
+        Activation("softmax"),
+    ],
+    loss="categorical_crossentropy",
+    optimizer=Adadelta(),
+    metric="accuracy",
+    batch_size=128,
+    max_epochs=3,
+)
+
+model.fit(X_train, y_train)
+predictions = model.predict(X_test)
+print(accuracy(y_test, predictions))