deepSIP.architecture

class deepSIP.architecture.DropoutCNN(spec_len, kernel=15, filters=8, fc_size=32, drop_rate=0.1)

Bases: torch.nn.modules.module.Module

core model architecture

4 x (Conv + ReLU + Max Pooling + Dropout) + (Linear + ReLU + Dropout) + Linear

Parameters:
spec_len : int,

number of wavelength bins for pre-processed spectra

kernel : odd int, optional

convolutional kernel size

filters : int, optional

number of filters in first convolution layer

fc_size : int, optional

number of neurons in fully connected layer

drop_rate : float, optional

dropout probability
Attributes:
conv[1-4] : torch.nn.Sequential

convolution block [1-4]

fc : torch.nn.Sequential

fully connected block

out : toch.nn.Sequential

output block

Methods

__call__(self, *input, **kwargs) Call self as a function.
add_module(self, name, module) Adds a child module to the current module.
apply(self, fn) Applies fn recursively to every submodule (as returned by .children()) as well as self.
buffers(self[, recurse]) Returns an iterator over module buffers.
children(self) Returns an iterator over immediate children modules.
cpu(self) Moves all model parameters and buffers to the CPU.
cuda(self[, device]) Moves all model parameters and buffers to the GPU.
double(self) Casts all floating point parameters and buffers to double datatype.
eval(self) Sets the module in evaluation mode.
extra_repr(self) Set the extra representation of the module
float(self) Casts all floating point parameters and buffers to float datatype.
forward(self, x) forward pass
half(self) Casts all floating point parameters and buffers to half datatype.
load_state_dict(self, state_dict[, strict]) Copies parameters and buffers from state_dict into this module and its descendants.
modules(self) Returns an iterator over all modules in the network.
named_buffers(self[, prefix, recurse]) Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children(self) Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules(self[, memo, prefix]) Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters(self[, prefix, recurse]) Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters(self[, recurse]) Returns an iterator over module parameters.
register_backward_hook(self, hook) Registers a backward hook on the module.
register_buffer(self, name, tensor) Adds a persistent buffer to the module.
register_forward_hook(self, hook) Registers a forward hook on the module.
register_forward_pre_hook(self, hook) Registers a forward pre-hook on the module.
register_parameter(self, name, param) Adds a parameter to the module.
state_dict(self[, destination, prefix, …]) Returns a dictionary containing a whole state of the module.
to(self, *args, **kwargs) Moves and/or casts the parameters and buffers.
train(self[, mode]) Sets the module in training mode.
type(self, dst_type) Casts all parameters and buffers to dst_type.
zero_grad(self) Sets gradients of all model parameters to zero.
share_memory  
forward(self, x)

forward pass

Parameters:
x : torch.tensor of shape (batch size, 1, number of wavelength bins)

inputs to the network