eugene.models.zoo.dsHybrid

class eugene.models.zoo.dsHybrid(input_len, output_dim, conv_kwargs, recurrent_kwargs, aggr='concat_cnn', dense_kwargs={})

Basic hybrid network with reverse complement

A hybrid model that uses both a CNN and an RNN to extract features then passes the features through a set of fully connected layers.

By default, the CNN is used to extract features from the input sequence, and the RNN is used to to combine those features. The output of the RNN is passed to a set of fully connected layers to make the final prediction.

Parameters:

• input_len (int) – The length of the input sequence.

• output_dim (int) – The dimension of the output.

• conv_kwargs (dict) – The keyword arguments for the convolutional layers. These come from the models.Conv1DTower class. See the documentation for that class for more information on what arguments are available.

• recurrent_kwargs (dict) – The keyword arguments for the recurrent layers. These come from the models.RecurrentBlock class. See the documentation for that class for more information on what arguments are available.

• aggr – The method for aggregating the output of the forward pass of the reverse complement. If “concat_cnn”, the output of the forward pass and the reverse complement pass are concatenated and passed to the recurrent block. If “concat_rnn”, the output of the recurrent block is concatenated with the output of the reverse complement recurrent block and passed to the dense block. If “max” or “avg”, the output of the forward pass and the reverse complement pass are passed to the dense block and the max or average of the two outputs is returned.

• dense_kwargs (dict) – The keyword arguments for the fully connected layer.

__init__(input_len, output_dim, conv_kwargs, recurrent_kwargs, aggr='concat_cnn', dense_kwargs={})

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

Methods

__init__(input_len, output_dim, conv_kwargs, ...)	Initializes internal Module state, shared by both nn.Module and ScriptModule.
add_module(name, module)	Adds a child module to the current module.
apply(fn)	Applies fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Returns an iterator over module buffers.
children()	Returns an iterator over immediate children modules.
cpu()	Moves all model parameters and buffers to the CPU.
cuda([device])	Moves all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Sets the module in evaluation mode.
extra_repr()	Set the extra representation of the module
float()	Casts all floating point parameters and buffers to float datatype.
forward(x)	Defines the computation performed at every call.
get_buffer(target)	Returns the buffer given by target if it exists, otherwise throws an error.
get_extra_state()	Returns any extra state to include in the module's state_dict.
get_parameter(target)	Returns the parameter given by target if it exists, otherwise throws an error.
get_submodule(target)	Returns the submodule given by target if it exists, otherwise throws an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Moves all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict])	Copies parameters and buffers from state_dict into this module and its descendants.
modules()	Returns an iterator over all modules in the network.
named_buffers([prefix, recurse])	Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse])	Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Returns an iterator over module parameters.
register_backward_hook(hook)	Registers a backward hook on the module.
register_buffer(name, tensor[, persistent])	Adds a buffer to the module.
register_forward_hook(hook)	Registers a forward hook on the module.
register_forward_pre_hook(hook)	Registers a forward pre-hook on the module.
register_full_backward_hook(hook)	Registers a backward hook on the module.
register_load_state_dict_post_hook(hook)	Registers a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Adds a parameter to the module.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	This function is called from load_state_dict() to handle any extra state found within the state_dict.
share_memory()	See torch.Tensor.share_memory_()
state_dict(*args[, destination, prefix, ...])	Returns a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Moves and/or casts the parameters and buffers.
to_empty(*, device)	Moves the parameters and buffers to the specified device without copying storage.
train([mode])	Sets the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Moves all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Sets gradients of all model parameters to zero.

Attributes

T_destination
dump_patches
training