Stacked LSTM Output

stacked_lstm_output.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c1333286",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4bd8e564",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 4\n",
    "input_size = 5\n",
    "hidden_size = 8\n",
    "seq_length = 31\n",
    "num_layers = 3\n",
    "rnn = nn.LSTM(input_size=input_size, hidden_size=hidden_size, batch_first=True, num_layers=num_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e4c9ff01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([4, 31, 5])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_tensor = torch.randn(batch_size, seq_length, input_size)\n",
    "input_tensor.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3ad7340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([4, 31, 8]), torch.Size([3, 4, 8]), torch.Size([3, 4, 8]))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_tensor, (hidden_state, cell_state) = rnn(input_tensor)\n",
    "output_tensor.shape, hidden_state.shape, cell_state.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7bd296a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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