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1 files changed, 43 insertions, 0 deletions

diff --git a/learn_torch/seq/basic_rnn.py b/learn_torch/seq/basic_rnn.py
new file mode 100644
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+++ b/learn_torch/seq/basic_rnn.py
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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()