Module `awave.models.models`

Expand source code

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


class Feedforward(nn.Module):
    def __init__(self, input_size, hidden_size=32):
        super(Feedforward, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

        # hidden layers
        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.fc2 = torch.nn.Linear(self.hidden_size, self.hidden_size)
        self.fc3 = torch.nn.Linear(self.hidden_size, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x


class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x


class FFN(nn.Module):
    def __init__(self):
        super(FFN, self).__init__()
        self.fc1 = nn.Linear(784, 500)
        self.fc2 = nn.Linear(500, 500)
        self.fc3 = nn.Linear(500, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


class LSTMNet(nn.Module):
    def __init__(self, D_in, H, p):
        """
        Parameters:
        ==========================================================
            D_in: int
                dimension of input track (ignored, can be variable)

            H: int
                hidden layer size

            p: int
                number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)
        """

        super(LSTMNet, self).__init__()
        self.lstm = nn.LSTM(input_size=1, hidden_size=H, num_layers=1, batch_first=True)
        self.fc = nn.Linear(H + p, 1)

    def forward(self, x1, x2=None):
        x1 = x1.unsqueeze(2)  # add input_size dimension (this is usually for the size of embedding vector)
        outputs, (h1, c1) = self.lstm(x1)  # get hidden vec
        h1 = h1.squeeze(0)  # remove dimension corresponding to multiple layers / directions
        if x2 is not None:
            h1 = torch.cat((h1, x2), 1)
        return self.fc(h1)

Classes

class CNN

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

Ancestors

torch.nn.modules.module.Module

Methods

def forward(self, x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the :class:Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

Expand source code

def forward(self, x):
    x = F.relu(F.max_pool2d(self.conv1(x), 2))
    x = F.relu(F.max_pool2d(self.conv2(x), 2))
    x = x.view(-1, 320)
    x = F.relu(self.fc1(x))
    x = self.fc2(x)
    return x

class FFN

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class FFN(nn.Module):
    def __init__(self):
        super(FFN, self).__init__()
        self.fc1 = nn.Linear(784, 500)
        self.fc2 = nn.Linear(500, 500)
        self.fc3 = nn.Linear(500, 10)

    def forward(self, x):
        x = x.view(-1, 784)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Ancestors

torch.nn.modules.module.Module

Methods

def forward(self, x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Expand source code

def forward(self, x):
    x = x.view(-1, 784)
    x = F.relu(self.fc1(x))
    x = F.relu(self.fc2(x))
    x = self.fc3(x)
    return x

class Feedforward (input_size, hidden_size=32)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Initializes internal Module state, shared by both nn.Module and ScriptModule.

Expand source code

class Feedforward(nn.Module):
    def __init__(self, input_size, hidden_size=32):
        super(Feedforward, self).__init__()
        self.input_size = input_size
        self.hidden_size = hidden_size

        # hidden layers
        self.fc1 = torch.nn.Linear(self.input_size, self.hidden_size)
        self.fc2 = torch.nn.Linear(self.hidden_size, self.hidden_size)
        self.fc3 = torch.nn.Linear(self.hidden_size, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

Ancestors

torch.nn.modules.module.Module

Methods

def forward(self, x)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Expand source code

def forward(self, x):
    x = torch.relu(self.fc1(x))
    x = torch.relu(self.fc2(x))
    x = self.fc3(x)
    return x

class LSTMNet (D_in, H, p)

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes::

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super(Model, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call :meth:to, etc.

:ivar training: Boolean represents whether this module is in training or evaluation mode. :vartype training: bool

Parameters:

D_in: int
    dimension of input track (ignored, can be variable)

H: int
    hidden layer size

p: int
    number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)

Expand source code

class LSTMNet(nn.Module):
    def __init__(self, D_in, H, p):
        """
        Parameters:
        ==========================================================
            D_in: int
                dimension of input track (ignored, can be variable)

            H: int
                hidden layer size

            p: int
                number of additional covariates (such as lifetime, msd, etc..., to be concatenated to the hidden layer)
        """

        super(LSTMNet, self).__init__()
        self.lstm = nn.LSTM(input_size=1, hidden_size=H, num_layers=1, batch_first=True)
        self.fc = nn.Linear(H + p, 1)

    def forward(self, x1, x2=None):
        x1 = x1.unsqueeze(2)  # add input_size dimension (this is usually for the size of embedding vector)
        outputs, (h1, c1) = self.lstm(x1)  # get hidden vec
        h1 = h1.squeeze(0)  # remove dimension corresponding to multiple layers / directions
        if x2 is not None:
            h1 = torch.cat((h1, x2), 1)
        return self.fc(h1)

Ancestors

torch.nn.modules.module.Module

Methods

def forward(self, x1, x2=None)

Defines the computation performed at every call.

Should be overridden by all subclasses.

Note

Expand source code

def forward(self, x1, x2=None):
    x1 = x1.unsqueeze(2)  # add input_size dimension (this is usually for the size of embedding vector)
    outputs, (h1, c1) = self.lstm(x1)  # get hidden vec
    h1 = h1.squeeze(0)  # remove dimension corresponding to multiple layers / directions
    if x2 is not None:
        h1 = torch.cat((h1, x2), 1)
    return self.fc(h1)