68 lines
2.0 KiB
Python
68 lines
2.0 KiB
Python
import numpy as np
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from utils import normalize
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from activations import ActivationFunc
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class NNLayer:
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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: ActivationFunc):
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self.W = np.random.uniform(-1, 1, (in_size, out_size))
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self.B = np.zeros((out_size))
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self.lr = lr
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self.input = None
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self.output = None
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self.output_linear = None
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self.activation_func = activation_func
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def __str__(self):
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return f'[ {self.W.shape[0]} => {self.W.shape[1]}\tlr:{self.lr}\tactivation:{self.activation_func.__class__.__name__} ]' # noqa
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def forward(self, V: np.ndarray) -> np.ndarray:
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self.input = normalize(V)
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self.output_linear = self.input @ self.W + self.B
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self.output = self.activation_func(
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self.output_linear
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)
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return self.output
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def backprop(self, error: np.ndarray) -> np.ndarray:
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error *= self.activation_func.derivative(self.output_linear)
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ret = self.W @ error
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dW = np.outer(self.input, error) * self.lr
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dB = error * self.lr
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self.W -= dW
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self.B -= dB
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return ret
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class DeepNNLayer:
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def __init__(self,
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layers: list[int],
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lr: float,
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activation_func: ActivationFunc):
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self.layers: list[NNLayer] = []
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for i in range(len(layers) - 1):
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self.layers.append(
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NNLayer(
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layers[i],
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layers[i+1],
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lr,
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activation_func)
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)
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def __str__(self):
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return '\n'.join([str(layer) for layer in self.layers])
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def forward(self, v: np.ndarray) -> np.ndarray:
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for layer in self.layers:
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v = layer.forward(v)
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return v
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def backprop(self, error: np.ndarray) -> np.ndarray:
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for layer in self.layers[::-1]:
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error = layer.backprop(error)
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return error
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