eugene.models.zoo.DeepBind

class eugene.models.zoo.DeepBind(input_len, output_dim, conv_kwargs={}, dense_kwargs={}, mode='rbp')

DeepBind architecture implemented from Alipanahi et al 2015 in PyTorch

This is a flexible implementation of the original DeepBind architecture that allows users to modify the number of convolutional layers, the number of fully connected layers, and many more hyperparameters. If parameters for the CNN and FCN are not passed in, the model will be instantiated with the parameters described in Alipanahi et al 2015.

Like the original DeepBind models, this model can be used for both DNA and RNA binding. For DNA, we implemented the “dna” mode which only uses the max pooling of the representation generated by the convolutional layers. For RNA, we implemented the “rbp” mode which uses both the max and average pooling of the representation generated by the convolutional layers.

Parameters:

  • input_len (int) – Length of input sequence

  • output_dim (int) – Number of output classes

  • conv_kwargs (dict) – Keyword arguments for convolutional layers. These come from the models.Conv1DTower class. See the documentation for that class for more information on what arguments are available. If not specified, the default parameters from Alipanahi et al 2015 will be used.

  • dense_kwargs (dict) – Keyword arguments for fully connected layers. These come from the models.DenseBlock class. See the documentation for that class for more information on what arguments are available. If not specified,

  • mode (str) – Mode of model, either “dna” or “rbp”. Controls the pooling layers. if “dna”, only max pooling is used. If “rbp”, both max and average pooling are used.

__init__(input_len, output_dim, conv_kwargs={}, dense_kwargs={}, mode='rbp')

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

Methods

__init__(input_len, output_dim[, ...])

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.

kwarg_handler(conv_kwargs, dense_kwargs)

Sets default kwargs for conv and fc modules if not specified

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