Module `rlmodels.nets.FullyConnected`

Expand source code

import random
import numpy as np

import torch
import torch.autograd as autograd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim

import itertools

class FullyConnected(nn.Module):
  """neural network with variable number and size of hidden layers. Uses ReLu for all of them and an allows specifying the activation function type of the output layer 
  
  **Parameters**: 

  *layer_sizes* (*list* of *int*s): list with hidden layer sizes 

  *input_size* (*int*): input size 

  *output_size* (*int*): output size 

  *final_activation* (*function*): torch activation function for output layer. Can be `None`

  """


  def __init__(self,layer_sizes,input_size,output_size,final_activation):

    super(FullyConnected,self).__init__()
    self.n_hidden_layers = len(layer_sizes)
    layers = [input_size] + layer_sizes + [output_size]
    for layer in range(len(layers)-1):
        setattr(self,"linear{n}".format(n=layer),nn.Linear(layers[layer],layers[layer+1]))

    self.final_activation = final_activation

  def forward(self,x):
    if isinstance(x,np.ndarray):
      x = torch.from_numpy(x).float()

    for layer in range(self.n_hidden_layers):
      x = F.relu(getattr(self,"linear{n}".format(n=layer))(x))
    if self.final_activation is None:
      x = (getattr(self,"linear{n}".format(n=self.n_hidden_layers))(x))
    else:
      x = self.final_activation(getattr(self,"linear{n}".format(n=self.n_hidden_layers))(x))
    return x

Classes

class FullyConnected (layer_sizes, input_size, output_size, final_activation)

neural network with variable number and size of hidden layers. Uses ReLu for all of them and an allows specifying the activation function type of the output layer

Parameters:

layer_sizes (list of ints): list with hidden layer sizes

input_size (int): input size

output_size (int): output size

final_activation (function): torch activation function for output layer. Can be None

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

Expand source code

class FullyConnected(nn.Module):
  """neural network with variable number and size of hidden layers. Uses ReLu for all of them and an allows specifying the activation function type of the output layer 
  
  **Parameters**: 

  *layer_sizes* (*list* of *int*s): list with hidden layer sizes 

  *input_size* (*int*): input size 

  *output_size* (*int*): output size 

  *final_activation* (*function*): torch activation function for output layer. Can be `None`

  """


  def __init__(self,layer_sizes,input_size,output_size,final_activation):

    super(FullyConnected,self).__init__()
    self.n_hidden_layers = len(layer_sizes)
    layers = [input_size] + layer_sizes + [output_size]
    for layer in range(len(layers)-1):
        setattr(self,"linear{n}".format(n=layer),nn.Linear(layers[layer],layers[layer+1]))

    self.final_activation = final_activation

  def forward(self,x):
    if isinstance(x,np.ndarray):
      x = torch.from_numpy(x).float()

    for layer in range(self.n_hidden_layers):
      x = F.relu(getattr(self,"linear{n}".format(n=layer))(x))
    if self.final_activation is None:
      x = (getattr(self,"linear{n}".format(n=self.n_hidden_layers))(x))
    else:
      x = self.final_activation(getattr(self,"linear{n}".format(n=self.n_hidden_layers))(x))
    return x

Ancestors

torch.nn.modules.module.Module

Subclasses

Class variables

var dump_patches : bool
var training : bool

Methods

def forward(self, x) ‑> Callable[..., Any]

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):
  if isinstance(x,np.ndarray):
    x = torch.from_numpy(x).float()

  for layer in range(self.n_hidden_layers):
    x = F.relu(getattr(self,"linear{n}".format(n=layer))(x))
  if self.final_activation is None:
    x = (getattr(self,"linear{n}".format(n=self.n_hidden_layers))(x))
  else:
    x = self.final_activation(getattr(self,"linear{n}".format(n=self.n_hidden_layers))(x))
  return x