73 lines
2.5 KiB
Python
73 lines
2.5 KiB
Python
import numpy as np
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from utils import (dynamic_loss_plot_init,
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dynamic_loss_plot_update,
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dynamic_loss_plot_finish)
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from tqdm import tqdm
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from layers import NNLayer
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LOADER = ['⡿', '⣟', '⣯', '⣷', '⣾', '⣽', '⣻', '⢿']
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class Autoencoder:
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def __init__(self,
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in_len: int,
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bottleneck: int,
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lr: float,
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activation_func):
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self.encoder = NNLayer(in_len, bottleneck, lr, activation_func)
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self.decoder = NNLayer(bottleneck, in_len, lr, activation_func)
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def train(self, v: np.ndarray) -> float:
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encoded = self.encoder.forward(v)
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reconstructed = self.decoder.forward(encoded)
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error = self.decoder.backprop(reconstructed - v)
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self.encoder.backprop(error)
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error = v - reconstructed
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return np.sum(np.abs(error))
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def train_dataset(self,
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data_set: list[np.ndarray],
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max_epoch: int,
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patience: int,
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display_loss: bool = False) -> list[float]:
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if display_loss is True:
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ax, line = dynamic_loss_plot_init()
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losses = []
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epoch = 0
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no_improv = 0
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prev_error = float('inf')
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with tqdm(bar_format="{desc} {elapsed} {rate_fmt}") as lbar:
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while True:
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lbar.set_description(
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f"{LOADER[epoch % len(LOADER)]} Training ({epoch=} error={prev_error:.2f})", # noqa
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)
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lbar.update()
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error = 0
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for x in data_set:
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input = x.flatten()
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error += self.train(input)
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error /= len(data_set)
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if prev_error - error <= 1e-8:
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no_improv += 1
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else:
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no_improv = 0
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prev_error = float(error)
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losses.append(error)
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if display_loss is True:
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dynamic_loss_plot_update(ax, line, losses)
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if no_improv > patience:
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break
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if epoch > max_epoch:
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break
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epoch += 1
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if display_loss is True:
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dynamic_loss_plot_finish(ax, line)
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print("#Training complete !")
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return losses
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def encode(self, v: np.ndarray) -> np.ndarray:
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return self.encoder.forward(v)
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def decode(self, v: np.ndarray) -> np.ndarray:
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return self.decoder.forward(v)
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