michael-iuzzolino

michael-iuzzolino

michael-iuzzolino

class HelloRNN(nn.Module):
    cells = {
        "LSTM"    : LSTMCell,
        "GRU"     : GRUCell,
        "vanilla" : VanillaCell
    }
    
    def __init__(self, num_chars, num_hidden=10, cell_type='LSTM'):
        super().__init__()

michael-iuzzolino

class VanillaCell(nn.Module):
    def __init__(self, num_chars, num_hidden):
        super().__init__()
        
        self.num_chars = num_chars
        self.num_hidden = num_hidden
        
        # Network Parameters
        # Potential Input
        self.Wxh = nn.Parameter(torch.randn((num_chars, num_hidden)))

michael-iuzzolino

class GRUCell(nn.Module):
    def __init__(self, num_chars, num_hidden):
        super().__init__()
        
        self.num_chars = num_chars
        self.num_hidden = num_hidden
        
        # Network Parameters
        # Potential Input
        self.Wxh = nn.Parameter(torch.randn((num_chars, num_hidden)))

michael-iuzzolino

class LSTMCell(nn.Module):
    def __init__(self, num_chars, num_hidden):
        super().__init__()
        
        self.num_chars = num_chars
        self.num_hidden = num_hidden
        
        # Network Parameters
        # Potential Input
        self.Wxh = nn.Parameter(torch.randn((num_chars, num_hidden)))

michael-iuzzolino

michael-iuzzolino

N_EPOCHS = 5000
LR = 0.005
end_early = False
seq_i = ""

net.train() # Ensure net in training mode
for epoch_i in range(N_EPOCHS):
    # Zero out gradients
    optimizer.zero_grad()
    

michael-iuzzolino

# import torch.nn as nn 

class HelloRNN(nn.Module):
    def __init__(self, num_chars, num_hidden=10):
        super().__init__()
        self.num_chars = num_chars
        self.num_hidden = num_hidden
        
        # Network Parameters
        # Connection Matrices

michael-iuzzolino

class DataHandler:
    def __init__(self, string):
        self.string = string
        
        characters = np.sort(list(set(string)))
        self.num_characters = len(characters)
        
        self.char_to_idx = { ch : i for i, ch in enumerate(characters) }
        self.idx_to_char = { i : ch for ch, i in self.char_to_idx.items() }