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lorne-luo/Neural Network from Scratch with PyTorch.ipynb

Created April 13, 2019 05:26
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Neural Network from Scratch with PyTorch
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Neural Network from Scratch with PyTorch.ipynb
{
"cells": [
{
"metadata": {
"deletable": false,
"editable": false,
"run_control": {
"frozen": true
}
},
"cell_type": "markdown",
"source": "![](https://cdn-images-1.medium.com/max/1600/1*hAg3w3tSNjvoUE2HCro9Ww.png)"
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# pip install torch torchvision\nimport torch\nimport torch.nn as nn",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "# creating some sample data using the torch.tensor\n\n# X represents the amount of hours studied and how much time students spent sleeping\nX = torch.tensor(([2, 9], [1, 5], [3, 6]), dtype=torch.float) # 3 X 2 tensor\n# y represent grades\ny = torch.tensor(([92], [100], [89]), dtype=torch.float) # 3 X 1 tensor\n\n# xPredicted is a single input for which we want to predict a grade \nxPredicted = torch.tensor(([4, 8]), dtype=torch.float) # 1 X 2 tensor\n\n# equivelent to numpy.shape\nprint(X.size())\nprint(y.size())",
"execution_count": 2,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "torch.Size([3, 2])\ntorch.Size([3, 1])\n"
}
]
},
{
"metadata": {
"scrolled": true,
"trusted": true
},
"cell_type": "code",
"source": "# performing some scaling on the sample data\n# scale units\nX_max, _ = torch.max(X, 0)\nxPredicted_max, _ = torch.max(xPredicted, 0)\n\nprint(X)\nX = torch.div(X, X_max)\nprint(X)\n\nxPredicted = torch.div(xPredicted, xPredicted_max)\ny = y / 100 # max test score is 100\n\nprint(xPredicted)",
"execution_count": 3,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "tensor([[2., 9.],\n [1., 5.],\n [3., 6.]])\ntensor([[0.6667, 1.0000],\n [0.3333, 0.5556],\n [1.0000, 0.6667]])\ntensor([0.5000, 1.0000])\n"
}
]
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{
"metadata": {
"trusted": true
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"cell_type": "code",
"source": "# Computate Graph\n\nclass Neural_Network(nn.Module):\n def __init__(self, ):\n super(Neural_Network, self).__init__()\n # parameters\n # TODO: parameters can be parameterized instead of declaring them here\n self.inputSize = 2\n self.outputSize = 1\n self.hiddenSize = 3\n \n # weights matrices are initialized with values randomly chosen from a normal distribution\n self.W1 = torch.randn(self.inputSize, self.hiddenSize) # 3 X 2 tensor\n self.W2 = torch.randn(self.hiddenSize, self.outputSize) # 3 X 1 tensor\n \n def forward(self, X):\n self.z = torch.matmul(X, self.W1) # 3 X 3 \".dot\" does not broadcast in PyTorch\n self.z2 = self.sigmoid(self.z) # activation function\n self.z3 = torch.matmul(self.z2, self.W2)\n o = self.sigmoid(self.z3) # final activation function\n return o\n \n def sigmoid(self, s):\n return 1 / (1 + torch.exp(-s))\n \n def sigmoidPrime(self, s):\n # derivative of sigmoid\n return s * (1 - s)\n \n def backward(self, X, y, o):\n self.o_error = y - o # error in output\n self.o_delta = self.o_error * self.sigmoidPrime(o) # derivative of sig to error\n self.z2_error = torch.matmul(self.o_delta, torch.t(self.W2))\n self.z2_delta = self.z2_error * self.sigmoidPrime(self.z2)\n self.W1 += torch.matmul(torch.t(X), self.z2_delta)\n self.W2 += torch.matmul(torch.t(self.z2), self.o_delta)\n \n def train(self, X, y):\n # forward + backward pass for training\n o = self.forward(X)\n \n self.backward(X, y, o)\n \n def saveWeights(self, model):\n # we will use the PyTorch internal storage functions\n torch.save(model, \"NN\")\n # you can reload model with all the weights and so forth with:\n # torch.load(\"NN\")\n \n def predict(self):\n print (\"Predicted data based on trained weights: \")\n print (\"Input (scaled): \\n\" + str(xPredicted))\n print (\"Output: \\n\" + str(self.forward(xPredicted)))",
"execution_count": 9,
"outputs": []
},
{
"metadata": {
"trusted": true
},
"cell_type": "code",
"source": "NN = Neural_Network()\nfor i in range(1000): # trains the NN 1,000 times\n print (\"#\" + str(i) + \" Loss: \" + str(torch.mean((y - NN(X))**2).detach().item())) # mean sum squared loss\n NN.train(X, y)\nNN.saveWeights(NN)\nNN.predict()",
"execution_count": 10,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "#0 Loss: 0.007429896388202906\n#1 Loss: 0.007136822212487459\n#2 Loss: 0.00687433173879981\n#3 Loss: 0.006638271268457174\n#4 Loss: 0.006425193976610899\n#5 Loss: 0.006232195068150759\n#6 Loss: 0.006056810263544321\n#7 Loss: 0.005896960850805044\n#8 Loss: 0.005750857759267092\n#9 Loss: 0.005616967100650072\n#10 Loss: 0.005493972450494766\n#11 Loss: 0.005380699876695871\n#12 Loss: 0.0052761803381145\n#13 Loss: 0.005179520696401596\n#14 Loss: 0.005089965183287859\n#15 Loss: 0.005006836261600256\n#16 Loss: 0.004929537419229746\n#17 Loss: 0.00485753221437335\n#18 Loss: 0.004790371749550104\n#19 Loss: 0.004727609921246767\n#20 Loss: 0.004668896552175283\n#21 Loss: 0.004613881465047598\n#22 Loss: 0.004562269896268845\n#23 Loss: 0.00451379781588912\n#24 Loss: 0.004468204919248819\n#25 Loss: 0.0044252811931073666\n#26 Loss: 0.004384817089885473\n#27 Loss: 0.0043466477654874325\n#28 Loss: 0.0043105920776724815\n#29 Loss: 0.004276504274457693\n#30 Loss: 0.004244245123118162\n#31 Loss: 0.004213692154735327\n#32 Loss: 0.004184729885309935\n#33 Loss: 0.004157244693487883\n#34 Loss: 0.00413114856928587\n#35 Loss: 0.00410634558647871\n#36 Loss: 0.004082761239260435\n#37 Loss: 0.004060313571244478\n#38 Loss: 0.004038934130221605\n#39 Loss: 0.004018553998321295\n#40 Loss: 0.0039991228841245174\n#41 Loss: 0.0039805760607123375\n#42 Loss: 0.003962861839681864\n#43 Loss: 0.003945937845855951\n#44 Loss: 0.003929756581783295\n#45 Loss: 0.003914279397577047\n#46 Loss: 0.0038994650822132826\n#47 Loss: 0.0038852717261761427\n#48 Loss: 0.0038716711569577456\n#49 Loss: 0.00385863333940506\n#50 Loss: 0.0038461231160908937\n#51 Loss: 0.0038341206964105368\n#52 Loss: 0.00382259301841259\n#53 Loss: 0.0038115207571536303\n#54 Loss: 0.0038008783012628555\n#55 Loss: 0.0037906423676759005\n#56 Loss: 0.003780798986554146\n#57 Loss: 0.003771319054067135\n#58 Loss: 0.0037621997762471437\n#59 Loss: 0.0037534136790782213\n#60 Loss: 0.0037449419032782316\n#61 Loss: 0.00373678095638752\n#62 Loss: 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Loss: 0.0034489023964852095\n#183 Loss: 0.0034481019247323275\n#184 Loss: 0.0034473019186407328\n#185 Loss: 0.0034465075004845858\n#186 Loss: 0.003445720300078392\n#187 Loss: 0.003444937989115715\n#188 Loss: 0.003444146132096648\n#189 Loss: 0.0034433745313435793\n#190 Loss: 0.0034425982739776373\n#191 Loss: 0.003441832261160016\n#192 Loss: 0.0034410618245601654\n#193 Loss: 0.00344030256383121\n#194 Loss: 0.003439543768763542\n#195 Loss: 0.003438790561631322\n#196 Loss: 0.003438035724684596\n#197 Loss: 0.0034372862428426743\n#198 Loss: 0.0034365393221378326\n#199 Loss: 0.003435796359553933\n#200 Loss: 0.0034350629430264235\n#201 Loss: 0.0034343237057328224\n#202 Loss: 0.0034335919190198183\n#203 Loss: 0.003432856872677803\n#204 Loss: 0.0034321274142712355\n#205 Loss: 0.003431403310969472\n#206 Loss: 0.0034306757152080536\n#207 Loss: 0.003429955570027232\n#208 Loss: 0.0034292389173060656\n#209 Loss: 0.003428521566092968\n#210 Loss: 0.003427801886573434\n#211 Loss: 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