eugene.models.zoo.Kopp21CNN¶
- class eugene.models.zoo.Kopp21CNN(input_len, output_dim, aggr='max', filters=[10, 8], conv_kernel_size=[11, 3], maxpool_kernel_size=30, stride=1)¶
Custom convolutional model used in Kopp et al. 2021 paper
PyTorch implementation of the TensorFlow model described here: https://github.com/wkopp/janggu_usecases/tree/master/01_jund_prediction
- Parameters:
input_len (int) – Length of the input sequence.
output_dim (int) – Dimension of the output.
aggr (str, optional) – Aggregation method. Either “concat”, “max”, or “avg”. By default “max” for this model.
filters (list, optional) – Number of filters in the convolutional layers.
conv_kernel_size (list, optional) – Kernel size of the convolutional layers.
maxpool_kernel_size (int, optional) – Kernel size of the maxpooling layer.
stride (int, optional) – Stride of the convolutional layers.
- __init__(input_len, output_dim, aggr='max', filters=[10, 8], conv_kernel_size=[11, 3], maxpool_kernel_size=30, stride=1)¶
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Methods
__init__
(input_len, output_dim[, aggr, ...])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