Added generic pytorch implementation for multi layer linear NN.

.idea/MNIST.iml

@@ -4,7 +4,7 @@
     <content url="file://$MODULE_DIR$">
       <excludeFolder url="file://$MODULE_DIR$/venv" />
     </content>
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (perceptron_ffnn)" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
   <component name="TestRunnerService">

.idea/inspectionProfiles/Project_Default.xml

@@ -0,0 +1,12 @@
+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyUnresolvedReferencesInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredIdentifiers">
+        <list>
+          <option value="Uebung1.anagramme.CaseAgnosticWord" />
+        </list>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>

.idea/misc.xml

@@ -3,5 +3,5 @@
   <component name="JavaScriptSettings">
     <option name="languageLevel" value="ES6" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.6 (Perceptron)" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (perceptron_ffnn)" project-jdk-type="Python SDK" />
 </project>

GenericTorchMlpNetwork.py

@@ -0,0 +1,50 @@
+import torch
+import torch.nn as nn
+from typing import List, Generator
+from custom_types import TrainingBatch, EvaluationResults, DEVICE
+from tqdm import tqdm
+
+
+class GenericTorchMlpClassifier(nn.Module):
+    def __init__(self, dims_per_layer: List[int], learning_rate: float):
+        super(GenericTorchMlpClassifier, self).__init__()
+        self.layers = []
+        for i, layer_dims in enumerate(dims_per_layer[1:], 1):
+            self.layers.append(nn.Linear(dims_per_layer[i - 1], layer_dims))
+        self.loss_fn = nn.CrossEntropyLoss()
+        self.optimiser = torch.optim.Adam(params=[{"params": layer.parameters()} for layer in self.layers], lr=learning_rate)
+        self.to(torch.device(DEVICE))
+
+    def forward(self, input_batch: List[int]) -> torch.Tensor:
+        x = torch.tensor(input_batch, dtype=torch.float)
+        for layer in self.layers[:-1]:
+            x = torch.sigmoid(layer(x))
+        x = self.layers[-1](x)
+        return x
+
+    def training_epoch(self, training_data: Generator[TrainingBatch, None, None]) -> None:
+        self.train(True)
+        for x, y in tqdm(training_data):
+            prediction_probs, targets = self.forward(x), torch.tensor(y, dtype=torch.long, device=torch.device(DEVICE))
+            self.optimiser.zero_grad()
+            self.loss_fn(prediction_probs, targets).backward()
+            self.optimiser.step()
+
+    def evaluate(self, evaluation_data: Generator[TrainingBatch, None, None]) -> EvaluationResults:
+        self.train(False)
+        accumulated_loss = 0.0
+        total = 0
+        total_correctly_classified = 0
+        for x, y in tqdm(evaluation_data):
+            prediction_probs, targets = self.forward(x), torch.tensor(y, device=torch.device(DEVICE))
+            predictions = torch.argmax(prediction_probs, dim=1)
+            total += len(targets)
+            total_correctly_classified += sum(predictions == targets)
+            accumulated_loss += self.loss_fn(prediction_probs, targets)
+        return EvaluationResults(
+            total=total,
+            correct=total_correctly_classified,
+            accumulated_loss=accumulated_loss
+        )
+
+

MlpNetwork.py

@@ -1,5 +1,5 @@
 from typing import Tuple, List, Callable, Generator
-from import_data import test_x_y, train_x_y, IMAGE_SIZE, print_img_to_console, show_picture
+from import_data import get_test_data_generator, get_training_data_generator, IMAGE_SIZE, print_img_to_console
 import numpy as np
 
 
@@ -44,15 +44,15 @@ class MulticlassPerceptron:
         return self.classifiers[classifier_index].get_normalised_weight_array()
 
 
-def train_and_test_multiclass_perceptron(iterations: int = 5, training_inputs: int = 5000, test_inputs: int = 1000):
+def train_and_test_multiclass_perceptron(iterations: int = 5, training_inputs: int = -1, test_inputs: int = -1):
     print("Loading data")
-    training_data_gen = train_x_y(training_inputs)
+    training_data_gen = get_training_data_generator(training_inputs)
     print("Begin training model!")
     model = MulticlassPerceptron(IMAGE_SIZE, 10)
     model.train(training_data_gen, iterations)
     print("Model successfully trained.")
     print("Testing model...")
-    test_data = list(test_x_y(test_inputs)())
+    test_data = list(get_test_data_generator(test_inputs)())
     n_correct = sum(model.prediction(x) == y for x, y in test_data)
     accuracy = n_correct / len(test_data)
     print(f"Accuracy: {accuracy} ({n_correct} correctly classified out of {len(test_data)} total test inputs.)")
@@ -60,8 +60,8 @@ def train_and_test_multiclass_perceptron(iterations: int = 5, training_inputs: i
         print_img_to_console(model.view_for_classifier(i))
 
 
-def get_trained_digit_model(iterations: int = 5, training_inputs: int = 5000, test_inputs: int = 1000):
+def make_trained_digit_model(iterations: int = 5, training_inputs: int = 5000, test_inputs: int = 1000):
-    training_data_gen = train_x_y(training_inputs)
+    training_data_gen = get_training_data_generator(training_inputs)
     model = MulticlassPerceptron(IMAGE_SIZE, 10)
     model.train(training_data_gen, iterations)
     return model

custom_types.py

@@ -0,0 +1,22 @@
+import torch
+from typing import Tuple, NamedTuple, List, Callable
+import numpy as np
+
+TrainingBatch = Tuple[List[List[float]], List[int]]
+
+
+class LossFun(NamedTuple):
+    exec: Callable[[np.array, np.array], float]
+    deriv: Callable[[np.array, np.array], np.array]
+
+
+class EvaluationResults(NamedTuple):
+    total: int
+    correct: int
+    accumulated_loss: float
+
+
+if torch.cuda.is_available():
+    DEVICE = "cuda:0"
+else:
+    DEVICE = "cpu"

import_data.py

@@ -1,5 +1,6 @@
 from PIL import Image
-from typing import Union, List, Generator, Callable
+from typing import Tuple, Union, List, Generator, Callable
+from custom_types import TrainingBatch
 
 
 BOX_SHADING = " ░▒▓██"
@@ -24,7 +25,7 @@ def print_img_to_console(img: Union[bytes, List[int]]):
     print()
 
 
-def read_labels(file_location: str):
+def read_labels(file_location: str) -> int:
     with open(file_location, 'rb') as img_file:
         img_data = img_file.read()
         num_items = int.from_bytes(img_data[4:8], byteorder="big")
@@ -32,7 +33,7 @@ def read_labels(file_location: str):
             yield int.from_bytes(img_data[i:i + 1], byteorder="big")
 
 
-def read_imgs(file_location: str, as_bytes=False):
+def read_imgs(file_location: str, as_bytes=False) -> List[int]:
     with open(file_location, 'rb') as img_file:
         img_data = img_file.read()
         num_items = int.from_bytes(img_data[4:8], byteorder="big")
@@ -50,28 +51,42 @@ def read_imgs(file_location: str, as_bytes=False):
                 start_byte = end_byte
 
 
-def read_img_lbl_pairs(imgs_file: str, lbls_file: str):
+def read_img_lbl_pairs(imgs_file: str, lbls_file: str) -> Tuple[List[int], int]:
     for img, label in zip(read_imgs(imgs_file), read_labels(lbls_file)):
         yield img, label
 
 
-def test_x_y(num: int = -1) -> Callable[[], Generator]:
+def get_test_data_generator(batch_size: int = 1, num: int = -1) -> Callable[[], Generator[TrainingBatch, None, None]]:
     if num == -1:
         num = 9992
 
     def generator():
-        for i, (img, lbl) in zip(range(num), read_img_lbl_pairs("t10k-images.idx3-ubyte", "t10k-labels.idx1-ubyte")):
+        accum_x, accum_y = [], []
-            yield img, lbl
+        for i, (img, lbl) in zip(range(num), read_img_lbl_pairs("data/t10k-images.idx3-ubyte", "data/t10k-labels.idx1-ubyte")):
+            accum_x.append(img)
+            accum_y.append(lbl)
+            if (i + 1) % batch_size == 0:
+                yield accum_x, accum_y
+                accum_x, accum_y = [], []
+            elif i == num:
+                yield accum_x, accum_y
 
     return generator
 
 
-def train_x_y(num: int = -1) -> Callable[[], Generator]:
+def get_training_data_generator(batch_size: int = 1, num: int = -1) -> Callable[[], Generator[TrainingBatch, None, None]]:
     if num == -1:
         num = 60000
 
     def generator():
-        for i, (img, lbl) in zip(range(num), read_img_lbl_pairs("train-images.idx3-ubyte", "train-labels.idx1-ubyte")):
+        accum_x, accum_y = [], []
-            yield img, lbl
+        for i, (img, lbl) in zip(range(num), read_img_lbl_pairs("data/train-images.idx3-ubyte", "data/train-labels.idx1-ubyte")):
+            accum_x.append(img)
+            accum_y.append(lbl)
+            if (i + 1) % batch_size == 0:
+                yield accum_x, accum_y
+                accum_x, accum_y = [], []
+            elif i == num:
+                yield accum_x, accum_y
 
     return generator

main.py

@@ -1,5 +1,50 @@
-import torch
+from MlpNetwork import train_and_test_multiclass_perceptron
-from multiclass_perceptron import train_and_test_multiclass_perceptron
+from mlp_network import train_and_test_neural_network
-from import_data import show_picture, test_x_y
+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()

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)