Documentation for v0.1 version (#199)
* Adding Equations, solving typos * improve _code.rst * the team rst and restuctore index.rst * fixing errors --------- Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
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Dario Coscia
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Nicola Demo
Nicola Demo
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3f9305d475
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8b7b61b3bd
@@ -9,17 +9,21 @@ class ResidualBlock(nn.Module):
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.. seealso::
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**Original reference**: He, Kaiming, et al.
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"Deep residual learning for image recognition."
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*Deep residual learning for image recognition.*
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Proceedings of the IEEE conference on computer vision
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and pattern recognition. 2016..
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<https://arxiv.org/pdf/1512.03385.pdf>`_.
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DOI: `<https://arxiv.org/pdf/1512.03385.pdf>`_.
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"""
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def __init__(self, input_dim, output_dim,
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hidden_dim, spectral_norm=False,
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def __init__(self,
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input_dim,
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output_dim,
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hidden_dim,
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spectral_norm=False,
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activation=torch.nn.ReLU()):
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"""Residual block constructor
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"""
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Initializes the ResidualBlock module.
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:param int input_dim: Dimension of the input to pass to the
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feedforward linear layer.
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@@ -48,9 +52,9 @@ class ResidualBlock(nn.Module):
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self._activation = activation
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# create layers
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self.l1 = self._spect_norm(nn.Linear(input_dim, hidden_dim))
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self.l2 = self._spect_norm(nn.Linear(hidden_dim, output_dim))
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self.l3 = self._spect_norm(nn.Linear(input_dim, output_dim))
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self._l1 = self._spect_norm(nn.Linear(input_dim, hidden_dim))
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self._l2 = self._spect_norm(nn.Linear(hidden_dim, output_dim))
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self._l3 = self._spect_norm(nn.Linear(input_dim, output_dim))
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def forward(self, x):
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"""Forward pass for residual block layer.
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@@ -59,9 +63,9 @@ class ResidualBlock(nn.Module):
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:return: Output tensor for the residual layer.
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:rtype: torch.Tensor
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"""
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y = self.activation(self.l1(x))
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y = self.l2(y)
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x = self.l3(x)
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y = self._activation(self._l1(x))
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y = self._l2(y)
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x = self._l3(x)
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return y + x
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def _spect_norm(self, x):
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@@ -74,32 +78,40 @@ class ResidualBlock(nn.Module):
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"""
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return nn.utils.spectral_norm(x) if self._spectral_norm else x
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@ property
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def spectral_norm(self):
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return self._spectral_norm
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@ property
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def input_dim(self):
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return self._input_dim
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@ property
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def output_dim(self):
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return self._output_dim
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@ property
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def hidden_dim(self):
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return self._hidden_dim
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@ property
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def activation(self):
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return self._activation
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import torch
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import torch.nn as nn
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class EnhancedLinear(torch.nn.Module):
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"""
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TODO
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A wrapper class for enhancing a linear layer with activation and/or dropout.
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:param layer: The linear layer to be enhanced.
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:type layer: torch.nn.Module
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:param activation: The activation function to be applied after the linear layer.
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:type activation: torch.nn.Module
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:param dropout: The dropout probability to be applied after the activation (if provided).
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:type dropout: float
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:Example:
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>>> linear_layer = torch.nn.Linear(10, 20)
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>>> activation = torch.nn.ReLU()
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>>> dropout_prob = 0.5
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>>> enhanced_linear = EnhancedLinear(linear_layer, activation, dropout_prob)
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"""
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def __init__(self, layer, activation=None, dropout=None):
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"""
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Initializes the EnhancedLinear module.
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:param layer: The linear layer to be enhanced.
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:type layer: torch.nn.Module
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:param activation: The activation function to be applied after the linear layer.
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:type activation: torch.nn.Module
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:param dropout: The dropout probability to be applied after the activation (if provided).
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:type dropout: float
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"""
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super().__init__()
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# check consistency
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@@ -108,23 +120,41 @@ class EnhancedLinear(torch.nn.Module):
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check_consistency(activation, nn.Module)
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if dropout is not None:
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check_consistency(dropout, float)
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# assign forward
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if (dropout is None) and (activation is None):
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self._model = torch.nn.Sequential(layer)
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elif (dropout is None) and (activation is not None):
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self._model = torch.nn.Sequential(layer,
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activation)
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self._model = torch.nn.Sequential(layer, activation)
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elif (dropout is not None) and (activation is None):
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self._model = torch.nn.Sequential(layer,
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self._drop(dropout))
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self._model = torch.nn.Sequential(layer, self._drop(dropout))
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elif (dropout is not None) and (activation is not None):
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self._model = torch.nn.Sequential(layer,
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activation,
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self._model = torch.nn.Sequential(layer, activation,
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self._drop(dropout))
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def forward(self, x):
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"""
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Forward pass through the enhanced linear module.
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:param x: Input tensor.
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:type x: torch.Tensor
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:return: Output tensor after passing through the enhanced linear module.
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:rtype: torch.Tensor
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"""
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return self._model(x)
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def _drop(self, p):
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"""
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Applies dropout with probability p.
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:param p: Dropout probability.
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:type p: float
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:return: Dropout layer with the specified probability.
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:rtype: torch.nn.Dropout
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"""
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return torch.nn.Dropout(p)
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