wehub-resource-sync
39 lines
1.5 KiB
Python
Executable File
39 lines
1.5 KiB
Python
Executable File
import numpy as np
|
|
from prml import nn
|
|
|
|
|
|
class Autoencoder(nn.Network):
|
|
|
|
def __init__(self, *args):
|
|
self.n_unit = len(args)
|
|
super().__init__()
|
|
for i in range(self.n_unit - 1):
|
|
self.parameter[f"w_encode{i}"] = nn.Parameter(np.random.randn(args[i], args[i + 1]))
|
|
self.parameter[f"b_encode{i}"] = nn.Parameter(np.zeros(args[i + 1]))
|
|
self.parameter[f"w_decode{i}"] = nn.Parameter(np.random.randn(args[i + 1], args[i]))
|
|
self.parameter[f"b_decode{i}"] = nn.Parameter(np.zeros(args[i]))
|
|
|
|
def transform(self, x):
|
|
h = x
|
|
for i in range(self.n_unit - 1):
|
|
h = nn.tanh(h @ self.parameter[f"w_encode{i}"] + self.parameter[f"b_encode{i}"])
|
|
return h.value
|
|
|
|
def forward(self, x):
|
|
h = x
|
|
for i in range(self.n_unit - 1):
|
|
h = nn.tanh(h @ self.parameter[f"w_encode{i}"] + self.parameter[f"b_encode{i}"])
|
|
for i in range(self.n_unit - 2, 0, -1):
|
|
h = nn.tanh(h @ self.parameter[f"w_decode{i}"] + self.parameter[f"b_decode{i}"])
|
|
x_ = h @ self.parameter["w_decode0"] + self.parameter["b_decode0"]
|
|
self.px = nn.random.Gaussian(x_, 1., data=x)
|
|
|
|
def fit(self, x, n_iter=100, learning_rate=1e-3):
|
|
optimizer = nn.optimizer.Adam(self.parameter, learning_rate)
|
|
for _ in range(n_iter):
|
|
self.clear()
|
|
self.forward(x)
|
|
log_likelihood = self.log_pdf()
|
|
log_likelihood.backward()
|
|
optimizer.update()
|