28 lines
1.0 KiB
Python
28 lines
1.0 KiB
Python
import torch.nn as nn
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import torch
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import torch.nn.functional as F
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class Conv_Neural_Network_Model(nn.Module):
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def __init__(self, num_classes=5,hidden_layer1 = 512,hidden_layer2 = 256):
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super(Conv_Neural_Network_Model, self).__init__()
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self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, stride=1, padding=1)
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self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
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self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1)
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self.fc1 = nn.Linear(64*25*25,hidden_layer1)
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self.fc2 = nn.Linear(hidden_layer1,hidden_layer2)
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self.out = nn.Linear(hidden_layer2,num_classes)
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def forward(self, x):
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x = self.pool1(F.relu(self.conv1(x)))
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x = self.pool1(F.relu(self.conv2(x)))
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x = x.view(-1, 64*25*25) #<----flattening the image
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x = self.fc1(x)
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x = torch.relu(x)
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x = self.fc2(x)
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x = torch.relu(x)
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x = self.out(x)
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return F.log_softmax(x, dim=-1)
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