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phsamuel/backpropagation.ipynb

Last active April 10, 2020 15:23
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backpropagation.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!pip install fastai # run this one if you don't have fastai installed"
]
},
{
"cell_type": "code",
"execution_count": 466,
"metadata": {},
"outputs": [],
"source": [
"from fastai import *\n",
"from fastai.vision import *\n",
"\n",
"def softmax(x):\n",
" e=np.exp(x)\n",
" return e/np.sum(e,axis=0)\n",
"\n",
"def cross_entropy_loss(yc,y):\n",
" return -np.sum(yc*np.log(y))\n",
" \n",
"def return_drelu(dL,x): # y=max(x,0)\n",
" dx=np.zeros_like(x)\n",
" dx[x>0]=dL[x>0]\n",
" return dx\n"
]
},
{
"cell_type": "code",
"execution_count": 467,
"metadata": {},
"outputs": [],
"source": [
"# please complete the following functions, they should return gradient for a linear layer, note that x is input\n",
"# as a batch, each column is one sample\n",
"\n",
"def return_dW(dL,W,x): # say y = W x, compute dL/dW given x and dL/dy (=dL), make sure you can handle batch\n",
" fan_out, fan_in = W.shape\n",
" batch_size=x.shape[1]\n",
" \n",
" dW=np.zeros_like(W) # you should replace this line\n",
" return dW\n",
"\n",
"def return_dx(dL,W,x):\n",
" fan_out, fan_in = W.shape\n",
" batch_size=x.shape[1]\n",
" \n",
" dx=np.zeros_like(x) # you should replace this line\n",
" return dx\n"
]
},
{
"cell_type": "code",
"execution_count": 468,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1440 with 25 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mnist=untar_data(URLs.MNIST_TINY)\n",
"tfms = get_transforms(do_flip=False)\n",
"\n",
"data = (ImageList.from_folder(mnist)\n",
" .split_by_folder() \n",
" .label_from_folder()\n",
" .transform(tfms, size=32)\n",
" .databunch()\n",
" .normalize(imagenet_stats))\n",
"\n",
"data.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": 469,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost: 47.797879802297196, Error rate: 0.484375\n",
"Cost: 42.2622450133658, Error rate: 0.421875\n",
"Cost: 37.846401790709955, Error rate: 0.296875\n",
"Cost: 32.516617629101155, Error rate: 0.203125\n",
"Cost: 34.23425805551955, Error rate: 0.296875\n",
"Cost: 31.053905064803214, Error rate: 0.1875\n",
"Cost: 27.79803109718408, Error rate: 0.171875\n",
"Cost: 27.03478421627421, Error rate: 0.125\n",
"Cost: 29.468213532405272, Error rate: 0.15625\n",
"Cost: 24.68582933561504, Error rate: 0.15625\n",
"Cost: 25.550709922115754, Error rate: 0.109375\n",
"Cost: 21.110219028980275, Error rate: 0.046875\n",
"Cost: 21.662174389769625, Error rate: 0.125\n",
"Cost: 20.262755287597066, Error rate: 0.09375\n",
"Cost: 23.602739863639975, Error rate: 0.15625\n",
"Cost: 22.964104143513868, Error rate: 0.09375\n",
"Cost: 23.214759911763913, Error rate: 0.140625\n",
"Cost: 22.298488063265605, Error rate: 0.15625\n",
"Cost: 19.90389806072907, Error rate: 0.140625\n",
"Cost: 20.44047997205177, Error rate: 0.109375\n",
"Cost: 18.400151318778867, Error rate: 0.09375\n",
"Cost: 20.30729932224647, Error rate: 0.15625\n",
"Cost: 23.023255782713367, Error rate: 0.140625\n",
"Cost: 16.95754516727486, Error rate: 0.046875\n",
"Cost: 15.510152035006037, Error rate: 0.0625\n",
"Cost: 16.301180378907418, Error rate: 0.0625\n",
"Cost: 18.381616607209484, Error rate: 0.125\n",
"Cost: 17.248175593269004, Error rate: 0.125\n",
"Cost: 14.039531177138098, Error rate: 0.046875\n",
"Cost: 15.486267213388768, Error rate: 0.078125\n",
"Cost: 15.534414368342059, Error rate: 0.09375\n",
"Cost: 15.064722888005495, Error rate: 0.0625\n",
"Cost: 21.599742797068636, Error rate: 0.140625\n",
"Cost: 14.817018657870282, Error rate: 0.046875\n",
"Cost: 22.717257593400365, Error rate: 0.1875\n",
"Cost: 13.959143965066744, Error rate: 0.0625\n",
"Cost: 23.073114312338127, Error rate: 0.140625\n",
"Cost: 13.074855874016963, Error rate: 0.03125\n",
"Cost: 19.199983950893397, Error rate: 0.171875\n",
"Cost: 15.679996138180043, Error rate: 0.078125\n",
"Cost: 13.283783921415024, Error rate: 0.078125\n",
"Cost: 13.556662275752958, Error rate: 0.046875\n",
"Cost: 18.08626402596806, Error rate: 0.125\n",
"Cost: 17.441685121420523, Error rate: 0.125\n",
"Cost: 14.185827016030242, Error rate: 0.125\n",
"Cost: 22.113261344931836, Error rate: 0.140625\n",
"Cost: 13.820855466279717, Error rate: 0.09375\n",
"Cost: 11.286509949101946, Error rate: 0.03125\n",
"Cost: 22.144833642057222, Error rate: 0.171875\n",
"Cost: 13.538716906884886, Error rate: 0.09375\n"
]
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
"\n",
"# read a batch\n",
"batch=data.one_batch()\n",
"# batch=my_batch()\n",
"\n",
"# get image dimensions\n",
"batch_size,no_channels,dimx,dimy=batch[0].shape\n",
"Nx=dimx*dimy+1\n",
"\n",
"# initialize parameters\n",
"learning_rate=0.0005\n",
"N1=200 # number of neurons in first hidden layer\n",
"N2=150 # number of neurons in second hidden layer\n",
"\n",
"W1=np.random.randn(N1,Nx)/np.sqrt(Nx/2) # Xavier initialization\n",
"W2=np.random.randn(2,N1)/np.sqrt(N1/2)\n",
"\n",
"\n",
"for it in range(50):\n",
" # reformat batch\n",
" x=np.array(batch[0])\n",
" x=x[:,0,:,:] # just take the first color (grayscale image anyway)\n",
" x=np.reshape(x,(x.shape[0],-1)).transpose()\n",
" x=np.concatenate((x,np.ones((1,64))))\n",
" label=np.array(batch[1])\n",
" yc=np.zeros((2,len(label)))\n",
" yc[label,range(len(label))]=1\n",
"\n",
" # forward pass\n",
" z1=W1 @ x\n",
" y1=np.maximum(0,z1) # relu\n",
" z2=W2 @ y1\n",
" y=softmax(z2)\n",
"\n",
" # compute gradient\n",
" dz2=y-yc\n",
"\n",
" dW2=return_dW(dz2,W2,y1)\n",
" dy1=return_dx(dz2,W2,y1)\n",
" \n",
" dz1=return_drelu(dy1,z1)\n",
" \n",
" dW1=return_dW(dz1,W1,x)\n",
"\n",
" # gradient descent\n",
" W1=W1-learning_rate*dW1\n",
" W2=W2-learning_rate*dW2\n",
"\n",
" # print cost and training error rate\n",
" print('Cost: %s, Error rate: %s' % (cross_entropy_loss(yc,y),sum(np.abs((y[0]>0.5).astype(np.int)-yc[0]))/batch_size))\n",
" batch=data.one_batch()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Raw
gistfile1.txt
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#!pip install fastai # run this one if you don't have fastai installed"
]
},
{
"cell_type": "code",
"execution_count": 454,
"metadata": {},
"outputs": [],
"source": [
"from fastai import *\n",
"from fastai.vision import *\n",
"\n",
"def softmax(x):\n",
" e=np.exp(x)\n",
" return e/np.sum(e,axis=0)\n",
"\n",
" \n",
"def return_drelu(dL,x): # y=max(x,0)\n",
" dx=np.zeros_like(x)\n",
" dx[x>0]=dL[x>0]\n",
" return dx\n"
]
},
{
"cell_type": "code",
"execution_count": 458,
"metadata": {},
"outputs": [],
"source": [
"# please complete the following functions, they should return gradient for a linear layer, note that x is input\n",
"# as a batch, each column is one sample\n",
"\n",
"def return_dW(dL,W,x): # say y = W x, compute dL/dW given x and dL/dy (=dL), make sure you can handle batch\n",
" fan_out, fan_in = W.shape\n",
" batch_size=x.shape[1]\n",
" \n",
" dW=np.zeros_like(W) # you should replace this line\n",
" return dW\n",
"\n",
"def return_dx(dL,W,x):\n",
" fan_out, fan_in = W.shape\n",
" batch_size=x.shape[1]\n",
" \n",
" dx=np.zeros_like(x) # you should replace this line\n",
" return dx\n"
]
},
{
"cell_type": "code",
"execution_count": 459,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x1440 with 25 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mnist=untar_data(URLs.MNIST_TINY)\n",
"tfms = get_transforms(do_flip=False)\n",
"\n",
"data = (ImageList.from_folder(mnist)\n",
" .split_by_folder() \n",
" .label_from_folder()\n",
" .transform(tfms, size=32)\n",
" .databunch()\n",
" .normalize(imagenet_stats))\n",
"\n",
"data.show_batch()"
]
},
{
"cell_type": "code",
"execution_count": 460,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cost: 5.809259167465824, Error rate: 0.484375\n",
"Cost: 5.9652292364920125, Error rate: 0.53125\n",
"Cost: 5.602260006646008, Error rate: 0.53125\n",
"Cost: 5.8391559203038, Error rate: 0.5\n",
"Cost: 5.741447309828689, Error rate: 0.515625\n",
"Cost: 5.611209179869465, Error rate: 0.46875\n",
"Cost: 6.033907366329177, Error rate: 0.515625\n",
"Cost: 5.784288888928853, Error rate: 0.53125\n",
"Cost: 5.896431556336266, Error rate: 0.53125\n",
"Cost: 5.947769443672568, Error rate: 0.53125\n",
"Cost: 5.8931924784975775, Error rate: 0.484375\n",
"Cost: 6.12974913666471, Error rate: 0.578125\n",
"Cost: 6.021590445829703, Error rate: 0.546875\n",
"Cost: 5.908051839584128, Error rate: 0.515625\n",
"Cost: 5.7731212308340805, Error rate: 0.46875\n",
"Cost: 5.789261725028032, Error rate: 0.5\n",
"Cost: 5.646568204539585, Error rate: 0.421875\n",
"Cost: 5.896817804876285, Error rate: 0.5625\n",
"Cost: 6.059646322789454, Error rate: 0.609375\n",
"Cost: 5.884097497680961, Error rate: 0.5\n",
"Cost: 5.667666657300261, Error rate: 0.390625\n",
"Cost: 5.865567311822412, Error rate: 0.515625\n",
"Cost: 5.400387897677762, Error rate: 0.375\n",
"Cost: 5.960702815591999, Error rate: 0.578125\n",
"Cost: 5.740528686383339, Error rate: 0.453125\n",
"Cost: 6.146677742683279, Error rate: 0.5625\n",
"Cost: 5.666020563097864, Error rate: 0.453125\n",
"Cost: 5.713605285051963, Error rate: 0.5\n",
"Cost: 5.9240327592314515, Error rate: 0.5625\n",
"Cost: 6.01840010488619, Error rate: 0.53125\n",
"Cost: 6.01991155728844, Error rate: 0.53125\n",
"Cost: 5.885152737676546, Error rate: 0.578125\n",
"Cost: 5.814729734311979, Error rate: 0.46875\n",
"Cost: 5.616095983152885, Error rate: 0.484375\n",
"Cost: 6.168541365816532, Error rate: 0.609375\n",
"Cost: 5.653377579199163, Error rate: 0.515625\n",
"Cost: 5.64974336677615, Error rate: 0.484375\n",
"Cost: 6.0309608764611164, Error rate: 0.5\n",
"Cost: 5.5413318730357295, Error rate: 0.46875\n",
"Cost: 5.864856827134741, Error rate: 0.5\n",
"Cost: 5.937659012726162, Error rate: 0.578125\n",
"Cost: 5.6982478325953965, Error rate: 0.484375\n",
"Cost: 5.712105071278958, Error rate: 0.546875\n",
"Cost: 5.64308797172937, Error rate: 0.46875\n",
"Cost: 5.408052237371329, Error rate: 0.4375\n",
"Cost: 5.981379148888182, Error rate: 0.546875\n",
"Cost: 5.977477757696311, Error rate: 0.59375\n",
"Cost: 5.855293123162325, Error rate: 0.4375\n",
"Cost: 5.87553188133754, Error rate: 0.59375\n",
"Cost: 5.847126661335028, Error rate: 0.5625\n"
]
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"import torch\n",
"\n",
"# read a batch\n",
"batch=data.one_batch()\n",
"# batch=my_batch()\n",
"\n",
"# get image dimensions\n",
"batch_size,no_channels,dimx,dimy=batch[0].shape\n",
"Nx=dimx*dimy+1\n",
"\n",
"# initialize parameters\n",
"learning_rate=0.0005\n",
"N1=200 # number of neurons in first hidden layer\n",
"N2=150 # number of neurons in second hidden layer\n",
"\n",
"W1=np.random.randn(N1,Nx)/np.sqrt(Nx/2) # Xavier initialization\n",
"W2=np.random.randn(2,N1)/np.sqrt(N1/2)\n",
"\n",
"\n",
"for it in range(50):\n",
" # reformat batch\n",
" x=np.array(batch[0])\n",
" x=x[:,0,:,:] # just take the first color (grayscale image anyway)\n",
" x=np.reshape(x,(x.shape[0],-1)).transpose()\n",
" x=np.concatenate((x,np.ones((1,64))))\n",
" label=np.array(batch[1])\n",
" yc=np.zeros((2,len(label)))\n",
" yc[label,range(len(label))]=1\n",
"\n",
" # forward pass\n",
" z1=W1 @ x\n",
" y1=np.maximum(0,z1) # relu\n",
" z2=W2 @ y1\n",
" y=softmax(z2)\n",
"\n",
" # compute gradient\n",
" dz2=y-yc\n",
"\n",
" dW2=return_dW(dz2,W2,y1)\n",
" dy1=return_dx(dz2,W2,y1)\n",
" \n",
" dz1=return_drelu(dy1,z1)\n",
" \n",
" dW1=return_dW(dz1,W1,x)\n",
"\n",
" # gradient descent\n",
" W1=W1-learning_rate*dW1\n",
" W2=W2-learning_rate*dW2\n",
"\n",
" print('Cost: %s, Error rate: %s' % (np.linalg.norm(dz2),sum(np.abs((y[0]>0.5).astype(np.int)-yc[0]))/batch_size))\n",
" batch=data.one_batch()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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