init: first draft of autoencoder

autoencoder.py

@@ -0,0 +1,76 @@
+import numpy as np
+from utils import regularize
+import types
+
+
+class Encoder:
+    def __init__(self,
+                 in_size: int,
+                 out_size: int,
+                 lr: float,
+                 activation_func: types.FunctionType):
+        self.W = np.random.uniform(-1, 1, (in_size, out_size))
+        self.lr = lr
+        self.last_input = None
+        self.last_output = None
+        self.activation_func = activation_func
+
+    def forward(self, V: np.ndarray) -> np.ndarray:
+        self.last_input = V
+        z = V @ self.W
+        self.last_output = regularize(self.activation_func(z))
+        return self.last_output
+
+    def backprop(self, error: np.ndarray):
+        dW = np.outer(self.last_input, error)
+        self.W -= self.lr * dW
+        return error @ self.W.T
+
+
+class Decoder:
+    def __init__(self,
+                 in_size: int,
+                 out_size: int,
+                 lr: float,
+                 activation_func):
+        self.W = np.random.uniform(-1, 1, (in_size, out_size))
+        self.lr = lr
+        self.last_input = None
+        self.last_output = None
+        self.activation_func = activation_func
+
+    def forward(self, V: np.ndarray) -> np.ndarray:
+        self.last_input = V
+        z = V @ self.W
+        self.last_output = regularize(self.activation_func(z))
+        return self.last_output
+
+    def backprop(self, target: np.ndarray):
+        error = self.last_output - target
+        dW = np.outer(self.last_input, error)
+        self.W -= self.lr * dW
+        return error @ self.W.T
+
+
+class Autoencoder:
+    def __init__(self,
+                 in_len: int,
+                 bottleneck: int,
+                 lr: float,
+                 activation_func):
+        self.encoder = Encoder(in_len, bottleneck, lr, activation_func)
+        self.decoder = Decoder(bottleneck, in_len, lr, activation_func)
+
+    def train(self, v: np.ndarray) -> float:
+        encoded = self.encoder.forward(v)
+        reconstructed = self.decoder.forward(encoded)
+        error = self.decoder.backprop(v)
+        self.encoder.backprop(error)
+        error = v - reconstructed
+        return np.sum(np.abs(error))
+
+    def encode(self, v: np.ndarray) -> np.ndarray:
+        return self.encoder.forward(v)
+    
+    def decode(self, v: np.ndarray) -> np.ndarray:
+        return self.decoder.forward(v)