Last active
August 16, 2021 14:40
-
-
Save jimexist/9b5adca9cc7ad4b8e7c0b02b9a341af1 to your computer and use it in GitHub Desktop.
Revisions
-
jimexist revised this gist
Aug 16, 2021 . 1 changed file with 13 additions and 34 deletions.There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "c1333286", "metadata": {}, "outputs": [], @@ -13,81 +13,60 @@ }, { "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", "rnn = nn.LSTM(input_size=input_size, hidden_size=hidden_size, batch_first=True)" ] }, { "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([1, 4, 8]), torch.Size([1, 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" ] }, { -
Aug 16, 2021 .There are no files selected for viewing
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode charactersOriginal file line number Diff line number Diff line change @@ -0,0 +1,123 @@ { "cells": [ { "cell_type": "code", "execution_count": 2, "id": "c1333286", "metadata": {}, "outputs": [], "source": [ "import torch\n", "from torch import nn" ] }, { "cell_type": "code", "execution_count": 7, "id": "4bd8e564", "metadata": {}, "outputs": [], "source": [ "input_size = 5\n", "hidden_size = 8\n", "seq_length = 11\n", "rnn = nn.LSTM(input_size=input_size, hidden_size=hidden_size, batch_first=True)" ] }, { "cell_type": "code", "execution_count": 8, "id": "e4c9ff01", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "torch.Size([3, 11, 5])" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "input_tensor = torch.randn(3, seq_length, input_size)\n", "input_tensor.shape" ] }, { "cell_type": "code", "execution_count": 11, "id": "d3ad7340", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'states_state' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/var/folders/mn/3932bp3n1hs4cs2nqxj89x_h0000gn/T/ipykernel_99465/1253711468.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0moutput_tensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mhidden_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell_state\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrnn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_tensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0moutput_tensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'states_state' is not defined" ] } ], "source": [ "output_tensor, (hidden_state, cell_state) = rnn(input_tensor)\n", "output_tensor.shape, states_state.shape, cell_state.shape" ] }, { "cell_type": "code", "execution_count": 5, "id": "b7b5e77f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(torch.Size([3, 8]), torch.Size([3, 8]))" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "soutput_tensor.shape, state.shape" ] }, { "cell_type": "code", "execution_count": null, "id": "7bd296a8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.11" } }, "nbformat": 4, "nbformat_minor": 5 }