Create and train the teacher.

Author: rodrigogiraoserrao

examples/teacher_student.py

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+import sys, pathlib
+# (Ugly) workaround to enable importing from parent folder without too much hassle.
+sys.path.append(str(pathlib.Path(__file__).parent.parent))
+
+import csv
+import numpy as np
+from nn import NeuralNetwork, Layer, LeakyReLU, CrossEntropyLoss
+
+TRAIN_FILE = pathlib.Path(__file__).parent / "mnistdata/mnist_train.csv"
+TEST_FILE = pathlib.Path(__file__).parent / "mnistdata/mnist_test.csv"
+
+def load_data(filepath, delimiter=",", dtype=float):
+    """Load a numerical numpy array from a file."""
+
+    print(f"Loading {filepath}...")
+    with open(filepath, "r") as f:
+        data_iterator = csv.reader(f, delimiter=delimiter)
+        data_list = list(data_iterator)
+    data = np.asarray(data_list, dtype=dtype)
+    print("Done.")
+    return data
+
+def to_col(x):
+    return x.reshape((x.size, 1))
+
+def test(net, test_data):
+    correct = 0
+    for i, test_row in enumerate(test_data):
+        if not i%1000:
+            print(i)
+
+        t = test_row[0]
+        x = to_col(test_row[1:])/255
+        out = net.forward_pass(x)
+        guess = np.argmax(out)
+        if t == guess:
+            correct += 1
+
+    return correct/test_data.shape[0]
+
+def train(net, train_data):
+    for i, train_row in enumerate(train_data):
+        if not i%1000:
+            print(i)
+
+        net.train(to_col(train_row[1:])/255, train_row[0])
+
+
+if __name__ == "__main__":
+    layers = [
+        Layer(784, 16, LeakyReLU()),
+        Layer(16, 16, LeakyReLU()),
+        Layer(16, 10, LeakyReLU()),
+    ]
+    teacher = NeuralNetwork(layers, CrossEntropyLoss(), 0.001)
+
+    test_data = load_data(TEST_FILE, delimiter=",", dtype=int)
+    accuracy = test(teacher, test_data)
+    print(f"Accuracy is {100*accuracy:.2f}%")     # Expected to be around 10%
+
+    train_data = load_data(TRAIN_FILE, delimiter=",", dtype=int)
+    train(teacher, train_data)
+
+    accuracy = test(teacher, test_data)
+    print(f"Accuracy is {100*accuracy:.2f}%")