SimpleModel definition

SimpleModel.py

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F

words_dim = 300
input_channel = 1
output_channel = 100
dropout_rate = 0.5
Ks = 3
target_class = 5
class SimpleModel(nn.Module):
	def __init__(self):
		super(SimpleModel, self).__init__()
		

		self.conv1 = nn.Conv2d(input_channel, output_channel, (3, words_dim), padding=(2,0))
		self.conv2 = nn.Conv2d(input_channel, output_channel, (4, words_dim), padding=(3,0))
		self.conv3 = nn.Conv2d(input_channel, output_channel, (5, words_dim), padding=(4,0))

		self.dropout = nn.Dropout(dropout_rate)

		self.fc1 = nn.Linear(Ks * output_channel, target_class)

		
	def forward(self, x):
		x1 = F.relu(self.conv1(x)).squeeze(3)
		x2 = F.relu(self.conv2(x)).squeeze(3)
		x3 = F.relu(self.conv3(x)).squeeze(3)


		x1 = F.max_pool1d(x1, x1.shape[2]).view(-1, x1.shape[1])
		x2 = F.max_pool1d(x2, x2.shape[2]).view(-1, x1.shape[1])
		x3 = F.max_pool1d(x3, x3.shape[2]).view(-1, x1.shape[1])

		x = torch.cat((x1,x2,x3), 1)
		x = self.dropout(x)
		x = self.fc1(x)

		return x