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Fuyang Liu
293
tutorial-contents-notebooks/202_variable.ipynb
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293
tutorial-contents-notebooks/202_variable.ipynb
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{
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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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"# 202 Variable\n",
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"\n",
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"View more, visit my tutorial page: https://morvanzhou.github.io/tutorials/\n",
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"My Youtube Channel: https://www.youtube.com/user/MorvanZhou\n",
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"\n",
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"Dependencies:\n",
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"* torch: 0.1.11\n",
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"\n",
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"Variable in torch is to build a computational graph,\n",
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"but this graph is dynamic compared with a static graph in Tensorflow or Theano.\n",
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"So torch does not have placeholder, torch can just pass variable to the computational graph.\n"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import torch\n",
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"from torch.autograd import Variable"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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" 1 2\n",
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" 3 4\n",
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"[torch.FloatTensor of size 2x2]\n",
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"\n",
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"Variable containing:\n",
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" 1 2\n",
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" 3 4\n",
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"[torch.FloatTensor of size 2x2]\n",
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"\n"
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]
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}
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],
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"source": [
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"tensor = torch.FloatTensor([[1,2],[3,4]]) # build a tensor\n",
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"variable = Variable(tensor, requires_grad=True) # build a variable, usually for compute gradients\n",
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"\n",
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"print(tensor) # [torch.FloatTensor of size 2x2]\n",
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"print(variable) # [torch.FloatTensor of size 2x2]"
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]
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},
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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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"Till now the tensor and variable seem the same.\n",
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"\n",
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"However, the variable is a part of the graph, it's a part of the auto-gradient.\n"
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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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"name": "stdout",
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"output_type": "stream",
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"text": [
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"7.5\n",
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"Variable containing:\n",
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" 7.5000\n",
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"[torch.FloatTensor of size 1]\n",
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"\n"
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]
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}
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],
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"source": [
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"t_out = torch.mean(tensor*tensor) # x^2\n",
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"v_out = torch.mean(variable*variable) # x^2\n",
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"print(t_out)\n",
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"print(v_out)"
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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": 4,
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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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"v_out.backward() # backpropagation from v_out"
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]
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},
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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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"$$ v_{out} = {{1} \\over {4}} sum(variable^2) $$\n",
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"\n",
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"the gradients w.r.t the variable, \n",
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"\n",
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"$$ {d(v_{out}) \\over d(variable)} = {{1} \\over {4}} 2 variable = {variable \\over 2}$$\n",
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"\n",
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"let's check the result pytorch calculated for us below:"
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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": 5,
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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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"Variable containing:\n",
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" 0.5000 1.0000\n",
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" 1.5000 2.0000\n",
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"[torch.FloatTensor of size 2x2]"
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]
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},
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"execution_count": 5,
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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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"variable.grad"
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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": 6,
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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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"Variable containing:\n",
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" 1 2\n",
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" 3 4\n",
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"[torch.FloatTensor of size 2x2]"
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]
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},
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"execution_count": 6,
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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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"variable # this is data in variable format"
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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": 7,
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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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"\n",
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" 1 2\n",
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" 3 4\n",
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"[torch.FloatTensor of size 2x2]"
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]
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},
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"execution_count": 7,
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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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"variable.data # this is data in tensor format"
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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": 8,
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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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"array([[ 1., 2.],\n",
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" [ 3., 4.]], dtype=float32)"
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]
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},
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"execution_count": 8,
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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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"variable.data.numpy() # numpy format"
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]
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},
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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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"Note that we did `.backward()` on `v_out` but `variable` has been assigned new values on it's `grad`.\n",
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"\n",
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"As this line \n",
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"```\n",
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"v_out = torch.mean(variable*variable)\n",
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"``` \n",
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"will make a new variable `v_out` and connect it with `variable` in computation graph."
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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": 9,
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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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"torch.autograd.variable.Variable"
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]
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},
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"execution_count": 9,
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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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"type(v_out)"
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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": 10,
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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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"torch.FloatTensor"
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]
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},
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"execution_count": 10,
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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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"type(v_out.data)"
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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 3",
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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": "3.5.2"
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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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