Ledda/perceptron_ffnn
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Added generic pytorch implementation for multi layer linear NN.

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This commit is contained in:
Daniel Ledda
2020-07-11 12:02:22 +02:00
parent ce4172da5d
commit 88508a0d07
15 changed files with 171 additions and 30 deletions
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35
import_data.py
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@@ -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")):
yield img, lbl
accum_x, accum_y = [], []
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")):
yield img, lbl
accum_x, accum_y = [], []
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