Added generic pytorch implementation for multi layer linear NN.

main.py

@@ -1,5 +1,50 @@
-import torch
-from multiclass_perceptron import train_and_test_multiclass_perceptron
-from import_data import show_picture, test_x_y
+from MlpNetwork import train_and_test_multiclass_perceptron
+from mlp_network import train_and_test_neural_network
+from import_data import show_picture, get_test_data_generator, get_training_data_generator, IMAGE_SIZE
+from GenericTorchMlpNetwork import GenericTorchMlpClassifier
+import argparse
 
-train_and_test_multiclass_perceptron()
+
+def main():
+    args = get_args()
+    classifier = GenericTorchMlpClassifier(
+        dims_per_layer=[IMAGE_SIZE, 200, 80, 10],
+        learning_rate=args.learning_rate,
+    )
+    for i in range(args.num_epochs):
+        print(f"Begin training epoch {i + 1}.")
+        classifier.training_epoch(get_training_data_generator(20)())
+        results = classifier.evaluate(get_test_data_generator(20)())
+        print(f"Evaluation results: {results.correct} / {results.total}",
+              f"Accumulated loss = {results.accumulated_loss:.3f}",
+              f"Average loss = {results.accumulated_loss / results.correct:.3f}",
+              f"Accuracy = {100 * float(results.correct) / float(results.total):.2f}%",
+              sep="\n", end="\n\n")
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--num_epochs",
+        "-e",
+        type=int,
+        default=5,
+        help="Number of training epochs to undertake."
+    )
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=0.001,
+        help="Learning rate for the optimiser."
+    )
+    parser.add_argument(
+        "--num_training_samples",
+        type=int,
+        default=-1,
+        help="Number of samples to train with (default = all)."
+    )
+    return parser.parse_args()
+
+
+if __name__ == "__main__":
+    main()