2019-11-15 17:15:07 +11:00
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##################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
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2019-11-09 01:36:31 +11:00
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##########################################################################
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# Efficient Neural Architecture Search via Parameters Sharing, ICML 2018 #
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##########################################################################
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import torch
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import torch.nn as nn
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from torch.distributions.categorical import Categorical
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class Controller(nn.Module):
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# we refer to https://github.com/TDeVries/enas_pytorch/blob/master/models/controller.py
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def __init__(self, num_edge, num_ops, lstm_size=32, lstm_num_layers=2, tanh_constant=2.5, temperature=5.0):
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super(Controller, self).__init__()
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# assign the attributes
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self.num_edge = num_edge
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self.num_ops = num_ops
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self.lstm_size = lstm_size
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self.lstm_N = lstm_num_layers
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self.tanh_constant = tanh_constant
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self.temperature = temperature
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# create parameters
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self.register_parameter('input_vars', nn.Parameter(torch.Tensor(1, 1, lstm_size)))
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self.w_lstm = nn.LSTM(input_size=self.lstm_size, hidden_size=self.lstm_size, num_layers=self.lstm_N)
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self.w_embd = nn.Embedding(self.num_ops, self.lstm_size)
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self.w_pred = nn.Linear(self.lstm_size, self.num_ops)
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nn.init.uniform_(self.input_vars , -0.1, 0.1)
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nn.init.uniform_(self.w_lstm.weight_hh_l0, -0.1, 0.1)
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nn.init.uniform_(self.w_lstm.weight_ih_l0, -0.1, 0.1)
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nn.init.uniform_(self.w_embd.weight , -0.1, 0.1)
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nn.init.uniform_(self.w_pred.weight , -0.1, 0.1)
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def forward(self):
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inputs, h0 = self.input_vars, None
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log_probs, entropys, sampled_arch = [], [], []
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for iedge in range(self.num_edge):
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outputs, h0 = self.w_lstm(inputs, h0)
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logits = self.w_pred(outputs)
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logits = logits / self.temperature
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logits = self.tanh_constant * torch.tanh(logits)
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# distribution
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op_distribution = Categorical(logits=logits)
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op_index = op_distribution.sample()
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sampled_arch.append( op_index.item() )
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op_log_prob = op_distribution.log_prob(op_index)
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log_probs.append( op_log_prob.view(-1) )
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op_entropy = op_distribution.entropy()
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entropys.append( op_entropy.view(-1) )
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# obtain the input embedding for the next step
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inputs = self.w_embd(op_index)
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return torch.sum(torch.cat(log_probs)), torch.sum(torch.cat(entropys)), sampled_arch
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