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import torch
from torch import nn
def t1():
rnn = nn.RNN(10, 20, 2)
input = torch.randn(5, 3, 10)
h0 = torch.randn(2, 3, 20)
output, hn = rnn(input, h0)
print()
def t2():
data = torch.Tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20])
print("Data: ", data.shape, "\n\n", data)
###################### OUTPUT ######################
# Number of features used as input. (Number of columns)
INPUT_SIZE = 1
# Number of previous time stamps taken into account.
SEQ_LENGTH = 5
# Number of features in last hidden state ie. number of output time-
# steps to predict.See image below for more clarity.
HIDDEN_SIZE = 2
# Number of stacked rnn layers.
NUM_LAYERS = 1
# We have total of 20 rows in our input.
# We divide the input into 4 batches where each batch has only 1
# row. Each row corresponds to a sequence of length 5.
BATCH_SIZE = 4
# Initialize the RNN.
rnn = nn.RNN(input_size=INPUT_SIZE, hidden_size=HIDDEN_SIZE, num_layers = 1, batch_first=True)
# input size : (batch, seq_len, input_size)
inputs = data.view(BATCH_SIZE, SEQ_LENGTH, INPUT_SIZE)
# out shape = (batch, seq_len, num_directions * hidden_size)
# h_n shape = (num_layers * num_directions, batch, hidden_size)
out, h_n = rnn(inputs)
if __name__ == '__main__':
nn.Linear
t1()
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