67 lines
1.9 KiB
Python
Executable File
67 lines
1.9 KiB
Python
Executable File
import math
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import torch.nn as nn
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from layers import (
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BBB_Conv2d,
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BBB_Linear,
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BBB_LRT_Conv2d,
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BBB_LRT_Linear,
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FlattenLayer,
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ModuleWrapper,
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)
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class BBB3Conv3FC(ModuleWrapper):
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"""
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Simple Neural Network having 3 Convolution
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and 3 FC layers with Bayesian layers.
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"""
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def __init__(
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self, outputs, inputs, priors, layer_type="lrt", activation_type="softplus"
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):
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super(BBB3Conv3FC, self).__init__()
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self.num_classes = outputs
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self.layer_type = layer_type
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self.priors = priors
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if layer_type == "lrt":
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BBBLinear = BBB_LRT_Linear
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BBBConv2d = BBB_LRT_Conv2d
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elif layer_type == "bbb":
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BBBLinear = BBB_Linear
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BBBConv2d = BBB_Conv2d
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else:
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raise ValueError("Undefined layer_type")
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if activation_type == "softplus":
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self.act = nn.Softplus
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elif activation_type == "relu":
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self.act = nn.ReLU
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else:
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raise ValueError("Only softplus or relu supported")
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self.conv1 = BBBConv2d(inputs, 32, 5, padding=2, bias=True, priors=self.priors)
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self.act1 = self.act()
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self.pool1 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.conv2 = BBBConv2d(32, 64, 5, padding=2, bias=True, priors=self.priors)
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self.act2 = self.act()
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self.pool2 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.conv3 = BBBConv2d(64, 128, 5, padding=1, bias=True, priors=self.priors)
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self.act3 = self.act()
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self.pool3 = nn.MaxPool2d(kernel_size=3, stride=2)
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self.flatten = FlattenLayer(2 * 2 * 128)
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self.fc1 = BBBLinear(2 * 2 * 128, 1000, bias=True, priors=self.priors)
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self.act4 = self.act()
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self.fc2 = BBBLinear(1000, 1000, bias=True, priors=self.priors)
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self.act5 = self.act()
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self.fc3 = BBBLinear(1000, outputs, bias=True, priors=self.priors)
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