I define a subclass ParserModel whic is inherritted by super(ParserModel, self).__init__(). Can you tell me what if I use super().__init__()? What is the difference between this two?
class ParserModel(nn.Module):
""" Feedforward neural network with an embedding layer and two hidden layers.
The ParserModel will predict which transition should be applied to a
given partial parse configuration.
PyTorch Notes:
- Note that "ParserModel" is a subclass of the "nn.Module" class. In PyTorch all neural networks
are a subclass of this "nn.Module".
- The "__init__" method is where you define all the layers and parameters
(embedding layers, linear layers, dropout layers, etc.).
- "__init__" gets automatically called when you create a new instance of your class, e.g.
when you write "m = ParserModel()".
- Other methods of ParserModel can access variables that have "self." prefix. Thus,
you should add the "self." prefix layers, values, etc. that you want to utilize
in other ParserModel methods.
- For further documentation on "nn.Module" please see https://pytorch.org/docs/stable/nn.html.
"""
def __init__(self, embeddings, n_features=36,
hidden_size=200, n_classes=3, dropout_prob=0.5):
""" Initialize the parser model.
@param embeddings (ndarray): word embeddings (num_words, embedding_size)
@param n_features (int): number of input features
@param hidden_size (int): number of hidden units
@param n_classes (int): number of output classes
@param dropout_prob (float): dropout probability
"""
super(ParserModel, self).__init__()
self.n_features = n_features
self.n_classes = n_classes
self.dropout_prob = dropout_prob
self.embed_size = embeddings.shape[1]
self.hidden_size = hidden_size
self.embeddings = nn.Parameter(torch.tensor(embeddings))
I want to know the differecen between this two types of function call of the super() function.
