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107 lines
2.1 KiB
107 lines
2.1 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 6.2 循环神经网络"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.4.1\n"
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]
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}
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],
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"source": [
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"import torch\n",
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"\n",
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"print(torch.__version__)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 5.2633, -3.2288, 0.6037, -1.3321],\n",
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" [ 9.4012, -6.7830, 1.0630, -0.1809],\n",
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" [ 7.0355, -2.2361, 0.7469, -3.4667]])"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"X, W_xh = torch.randn(3, 1), torch.randn(1, 4)\n",
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"H, W_hh = torch.randn(3, 4), torch.randn(4, 4)\n",
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"torch.matmul(X, W_xh) + torch.matmul(H, W_hh)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"tensor([[ 5.2633, -3.2288, 0.6037, -1.3321],\n",
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" [ 9.4012, -6.7830, 1.0630, -0.1809],\n",
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" [ 7.0355, -2.2361, 0.7469, -3.4667]])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"torch.matmul(torch.cat((X, H), dim=1), torch.cat((W_xh, W_hh), dim=0))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python [default]",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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