Added generic pytorch implementation for multi layer linear NN.

mlp_network.py

@@ -1,15 +1,11 @@
 import numpy as np
 from recordclass import recordclass
 from typing import NamedTuple, Tuple, List, Callable, Generator
-from import_data import train_x_y, test_x_y
+from import_data import get_training_data_generator, get_test_data_generator
+from custom_types import LossFun
 import sys
 
 
-class LossFun(NamedTuple):
-    exec: Callable[[np.array, np.array], float]
-    deriv: Callable[[np.array, np.array], np.array]
-
-
 def sum_squares_loss_func(predicted: np.array, gold: np.array) -> float:
     return sum((predicted - gold) ** 2)
 
@@ -106,10 +102,11 @@ class FFNeuralNetwork:
 def train_and_test_neural_network():
     model = FFNeuralNetwork([28**2, 100, 10], sum_squares_loss, 0.0001)
     training_data_gen = train_x_y(1000)
-    test_data = test_x_y(10)()
+    test_data = get_test_data_generator(10)()
     model.train(training_data_gen, 5)
     for test_datum, label in test_data:
-        print(model.feed_forward(test_datum), label)
+        prediction = model.feed_forward(test_datum)
+        print(prediction, label, label == prediction)
     np.set_printoptions(threshold=sys.maxsize)
     print(model.layers[0].weights)