77 lines
2.3 KiB
Python
77 lines
2.3 KiB
Python
import numpy as np
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from utils import regularize
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import types
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class Encoder:
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def __init__(self,
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in_size: int,
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out_size: int,
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lr: float,
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activation_func: types.FunctionType):
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self.W = np.random.uniform(-1, 1, (in_size, out_size))
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self.lr = lr
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self.last_input = None
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self.last_output = None
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self.activation_func = activation_func
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def forward(self, V: np.ndarray) -> np.ndarray:
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self.last_input = V
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z = V @ self.W
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self.last_output = regularize(self.activation_func(z))
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return self.last_output
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def backprop(self, error: np.ndarray):
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dW = np.outer(self.last_input, error)
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self.W -= self.lr * dW
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return error @ self.W.T
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class Decoder:
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def __init__(self,
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in_size: int,
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out_size: int,
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lr: float,
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activation_func):
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self.W = np.random.uniform(-1, 1, (in_size, out_size))
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self.lr = lr
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self.last_input = None
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self.last_output = None
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self.activation_func = activation_func
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def forward(self, V: np.ndarray) -> np.ndarray:
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self.last_input = V
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z = V @ self.W
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self.last_output = regularize(self.activation_func(z))
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return self.last_output
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def backprop(self, target: np.ndarray):
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error = self.last_output - target
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dW = np.outer(self.last_input, error)
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self.W -= self.lr * dW
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return error @ self.W.T
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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 = Encoder(in_len, bottleneck, lr, activation_func)
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self.decoder = Decoder(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(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 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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