src/my_neuralnetwork_pytorch.ipynb

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    "### Simple neural network developed using pytorch, for binary classification."
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{\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Initialization Makes Me Smile<br>(and it's fun to show off a little...)\"}, \"width\": 400, \"xaxis\": {\"showgrid\": false, \"showticklabels\": false}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('9dc0f18b-ccc7-49f6-b89c-91f5784b773c');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import ipdb\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "from sklearn.model_selection import ShuffleSplit\n",
    "\n",
    "import chart_studio.plotly as ply\n",
    "import chart_studio.tools as plytool\n",
    "import plotly.figure_factory as ff\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as plyoff\n",
    "\n",
    "#plytool.set_credentials_file(username='ahowe42', api_key='')\n",
    "plyoff.init_notebook_mode(connected=True)\n",
    "x1 = [1,4,7]; y1 = [7,5,7]\n",
    "x2 = [1,2,3,4,5,6,7]; y2 = [3,2,1,1,1,2,3]\n",
    "plyoff.iplot(go.Figure(data=[go.Scatter({'x':x1, 'y':y1, 'mode':'markers'}), go.Scatter({'x':x2, 'y':y2, 'mode':'lines'})],\n",
    "                       layout=go.Layout(autosize=False, width=400, showlegend=False,\n",
    "                                        xaxis={'showgrid':False, 'showticklabels':False},\n",
    "                                        title=\"Initialization Makes Me Smile<br>(and it's fun to show off a little...)\")))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "''' define the model class for a neural net with variable hidden layers & dropout;\n",
    "this NN will be used for binary classification '''\n",
    "class myNN(nn.Module):\n",
    "    def __init__(self, layerNodes, wInit, pDropout, activations):\n",
    "        super(myNN, self).__init__()\n",
    "        self.activations = activations[1:]\n",
    "        self.len = len(layerNodes)-1\n",
    "        self.drop = nn.Dropout(pDropout)\n",
    "        self.linears = nn.ModuleList() # could also use nn.Sequential\n",
    "        for I, O in zip(layerNodes, layerNodes[1:]):\n",
    "            # create the layer\n",
    "            lin = nn.Linear(I, O)\n",
    "            # initialize it\n",
    "            nn.init.zeros_(lin.bias)\n",
    "            if wInit == 'uni':\n",
    "                nn.init.uniform_(lin.weight)\n",
    "            elif wInit == 'xav':\n",
    "                nn.init.xavier_uniform_(lin.weight)\n",
    "            elif wInit == 'kai':\n",
    "                nn.init.kaiming_uniform_(lin.weight, nonlinearity='relu')\n",
    "            # and now add it\n",
    "            self.linears.append(lin)\n",
    "\n",
    "    def forward(self,x):\n",
    "        for i, (L, A) in enumerate(zip(self.linears, self.activations)):\n",
    "            # dropout if not the output layer\n",
    "            if i < self.len - 1:\n",
    "                x = self.drop(L(x))\n",
    "            else:\n",
    "                x = L(x)\n",
    "                \n",
    "            # compute the activation\n",
    "            if A == 'relu':\n",
    "                x = nn.functional.relu(x)\n",
    "            elif A == 'sigmoid':\n",
    "                x = torch.sigmoid(x)\n",
    "            elif A == 'leakyrelu':\n",
    "                x = nn.functional.leaky_relu(x)\n",
    "            elif A == 'tanh':\n",
    "                x = nn.functional.tanh(x)\n",
    "\n",
    "        return x\n",
    "    \n",
    "    def __str__(self):\n",
    "        mn = super(myNN, self).__str__()\n",
    "        return '%s\\nActivations: %s'%(mn, self.activations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(100, 2)\n",
      "(100, 1)\n"
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       "                    Plotly.newPlot(\n",
       "                        'e155c9ea-13ea-4b2c-805e-29c420653c5f',\n",
       "                        [{\"marker\": {\"color\": \"red\", \"symbol\": \"circle-open\"}, \"mode\": \"markers\", \"name\": \"train gt\", \"type\": \"scatter\", \"x\": [0.8334936702371015, 0.5574747706642964, 0.7224726030124945, 0.6597492547422605, 0.6841785793772267, 0.2737134352134396, 0.9505242414212675, 0.766338926963951, 0.6767922345827264, 0.47678045901591937, 0.5588669662786234, 0.5226047435259855, 0.6620431812724393, 0.7733449990313263, 0.22224581716408953, 0.9312481037863264, 0.8291507200820026, 0.9027870909708566, 0.37888946792756706, 0.32582541580146873, 0.7279483194597662, 0.45433698425586966, 0.9737518611411031, 0.8917371744695786, 0.6696962762428993, 0.32307278088391356, 0.4475534277896127, 0.436246201135983, 0.9430661677374863, 0.44363868675080675, 0.5256076511344139, 0.7512022680227872, 0.7792688525016935, 0.6329058351241309, 0.5706222302361864], \"y\": [0.5349419047108084, 0.5146146209241975, 0.6484787607988102, 0.39194780415185926, 0.8050368527703533, 0.9153929571965619, 0.6088242515234871, 0.6955295111269058, 0.6254132074294596, 0.7382634913723701, 0.9915015326000602, 0.6753019202489475, 0.9191952117127474, 0.8345220017110738, 0.9496952112679542, 0.22924229444026156, 0.09699448586298942, 0.8192703645053626, 0.892103355062816, 0.8774836456169113, 0.4592997452230799, 0.7986066894598015, 0.46293182350696793, 0.2616232548913431, 0.8498232512632324, 0.8305338387123575, 0.6505287879683889, 0.9363146901386181, 0.3073840025040505, 0.9105970152064617, 0.8157670219147458, 0.9313714690182986, 0.3687212418868979, 0.3626477138960964, 0.96424365219174]}, {\"marker\": {\"color\": \"blue\", \"symbol\": \"square-open\"}, \"mode\": \"markers\", \"name\": \"train lt\", \"type\": \"scatter\", \"x\": [0.22000030092681322, 0.19278593784156317, 0.2774805662551417, 0.5052250425078738, 0.15577742078468892, 0.4801708108802438, 0.1130344548827682, 0.20553062985484793, 0.33725367591720357, 0.40591557578474735, 0.32553319028462435, 0.40850568784161145, 0.521326564775815, 0.6359835172802819, 0.12530035034112919, 0.38924848983781724, 0.5906552962008077, 0.13192431425207474, 0.2821714963570431, 0.7536094257399436, 0.19976223192019793, 0.25592223633967404, 0.28157111745190266, 0.268244221907312, 0.5227662135458723, 0.09768495070108663, 0.05015063777197082, 0.22712642834904806, 0.37288744732632906, 0.4675217596992556, 0.040169529838166906, 0.026223014090914543, 0.2977503091615933, 0.03410828991345549, 0.03462419028477637], \"y\": [0.8563590285381811, 0.14547206603991447, 0.7704025770291012, 0.3352637726084252, 0.5940327217215142, 0.4533870490287639, 0.17675452448107176, 0.6663221400753053, 0.5896725702127464, 0.7492226950266782, 0.36535583605728705, 0.22585800193785133, 0.14460162522855902, 0.2703846523211926, 0.9943698611843019, 0.5886090538719152, 0.2450757603300886, 0.8265662570729043, 0.21817360443427114, 0.07073821756087917, 0.5029895665785145, 0.6478866643989409, 0.07194167180330657, 0.10604592471837393, 0.31093416178866573, 0.9779930271908497, 0.34469624762475093, 0.563678916226927, 0.34558609948710683, 0.32732715710752136, 0.9949217609892183, 0.7132443513739029, 0.42410006327213345, 0.9044026281399044, 0.40627940638604576]}, {\"marker\": {\"color\": \"red\", \"symbol\": \"circle-dot\"}, \"mode\": \"markers\", \"name\": \"test gt\", \"type\": \"scatter\", \"x\": [0.8745296530339364, 0.6678526853434874, 0.6538800379877407, 0.9429543014631075, 0.9639274757564721, 0.45803356455921773, 0.6612153982256154, 0.8387266153238556, 0.6834113416592633, 0.8540490137954712, 0.7489290935912524, 0.24093585524888528, 0.6805153333090559, 0.8875779110750167, 0.2670863306541751], \"y\": [0.6160125578779879, 0.19498424635718536, 0.6373699094212194, 0.19084599903223498, 0.9279351954427358, 0.6153370726700812, 0.2643543930566584, 0.3721637921916645, 0.8799601471934245, 0.3742960845147498, 0.17045942046068419, 0.8783434504195272, 0.5212588505115836, 0.8313073613617206, 0.8248676239496718]}, {\"marker\": {\"color\": \"blue\", \"symbol\": \"square-dot\"}, \"mode\": \"markers\", \"name\": \"test lt\", \"type\": \"scatter\", \"x\": [0.4021574950529281, 0.19138431789498345, 0.3494119007624502, 0.07781979077166712, 0.13276278754745463, 0.2703217751018999, 0.052457810444296205, 0.04798376239733371, 0.4947903320762377, 0.22901319031618916, 0.12457888874984857, 0.03539931734494417, 0.06123343578559637, 0.24568967604358605, 0.8079561203986311], \"y\": [0.383455802934048, 0.7050049348542148, 0.5587184916944284, 0.2725334405339699, 0.15763238886217235, 0.17897800957909382, 0.3803357166034277, 0.34677268992619625, 0.002571979346945308, 0.7450405902826022, 0.8166234574250403, 0.10382069450907438, 0.29184118423448624, 0.12576127244447366, 0.00935631192203068]}],\n",
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Data\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('e155c9ea-13ea-4b2c-805e-29c420653c5f');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create simulated data\n",
    "np.random.seed = 42\n",
    "n = 100\n",
    "p = 2\n",
    "trainPerc = 0.7\n",
    "\n",
    "x = np.random.rand(n, p)\n",
    "print(x.shape)\n",
    "y = x[:,0]*5 + x[:,1]*3 + np.random.rand(n)\n",
    "y = np.atleast_2d(y > np.median(y)).T\n",
    "print(y.shape)\n",
    "\n",
    "# partition randomly into the training & testing sets\n",
    "ss = ShuffleSplit(n_splits=1, train_size=trainPerc)\n",
    "(trn, tst) = next(ss.split(x))\n",
    "trnX = x[trn,:]; trnY = y[trn,:]\n",
    "tstX = x[tst,:]; tstY = y[tst,:]\n",
    "\n",
    "# plot the data - training\n",
    "trnYFlat = np.squeeze(trnY); tstYFlat = np.squeeze(tstY); # squeee y for indexing into x\n",
    "trc = [go.Scatter({'x':trnX[trnYFlat,0], 'y':trnX[trnYFlat,1], 'name':'train gt', 'mode':'markers',\n",
    "                   'marker':{'color':'red', 'symbol':'circle-open'}}), \n",
    "        go.Scatter({'x':trnX[~trnYFlat,0], 'y':trnX[~trnYFlat,1], 'name':'train lt', 'mode':'markers',\n",
    "                   'marker':{'color':'blue', 'symbol':'square-open'}}),\n",
    "        go.Scatter({'x':tstX[tstYFlat,0], 'y':tstX[tstYFlat,1], 'name':'test gt', 'mode':'markers',\n",
    "                   'marker':{'color':'red', 'symbol':'circle-dot'}}), \n",
    "        go.Scatter({'x':tstX[~tstYFlat,0], 'y':tstX[~tstYFlat,1], 'name':'test lt', 'mode':'markers',\n",
    "                   'marker':{'color':'blue', 'symbol':'square-dot'}})]\n",
    "plyoff.iplot(go.Figure(data=trc, layout=go.Layout(title = 'Data')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now make the datasets & dataloaders\n",
    "batchSize = 5\n",
    "\n",
    "# Create the data class\n",
    "class Data(Dataset):\n",
    "    def __init__(self, x, y):\n",
    "        self.x = torch.FloatTensor(x)\n",
    "        self.y = torch.Tensor(y.astype(int))\n",
    "        self.len, self.p = self.x.shape\n",
    "    def __getitem__(self, index):      \n",
    "        return self.x[index], self.y[index]\n",
    "    def __len__(self):\n",
    "        return self.len\n",
    "\n",
    "trainData = Data(trnX, trnY)\n",
    "trainLoad = DataLoader(dataset=trainData, batch_size=batchSize)\n",
    "testData = Data(tstX, tstY)\n",
    "testLoad = DataLoader(dataset=testData, batch_size=len(testData))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "myNN(\n",
      "  (drop): Dropout(p=0.2)\n",
      "  (linears): ModuleList(\n",
      "    (0): Linear(in_features=2, out_features=10, bias=True)\n",
      "    (1): Linear(in_features=10, out_features=20, bias=True)\n",
      "    (2): Linear(in_features=20, out_features=10, bias=True)\n",
      "    (3): Linear(in_features=10, out_features=1, bias=True)\n",
      "  )\n",
      ")\n",
      "Activations: ('relu', 'relu', 'relu', 'sigmoid')\n"
     ]
    }
   ],
   "source": [
    "# define the modeling objects\n",
    "layers = (p, 10, 20, 10, 1) # input layer size, hidden layer sizes, output layer size\n",
    "activations = (None, 'relu', 'relu', 'relu', 'sigmoid')\n",
    "learningRate = 0.1\n",
    "pDropout = 0.2\n",
    "weightInit = 'kai'\n",
    "\n",
    "\n",
    "model = myNN(layers, weightInit, pDropout, activations)\n",
    "print(model)\n",
    "optimizer = torch.optim.SGD(model.parameters(), lr = learningRate)\n",
    "loss = nn.BCELoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 0 Training (Testing) Loss = 0.61 (0.59), & Accuracy = 0.54 (0.70)\n",
      "Epoch 100 Training (Testing) Loss = 0.11 (0.16), & Accuracy = 0.96 (0.90)\n",
      "Epoch 200 Training (Testing) Loss = 0.13 (0.11), & Accuracy = 0.94 (0.93)\n",
      "Epoch 300 Training (Testing) Loss = 0.09 (0.16), & Accuracy = 0.96 (0.90)\n",
      "Epoch 400 Training (Testing) Loss = 0.06 (0.37), & Accuracy = 0.99 (0.90)\n",
      "==========\n",
      "Training Initial Loss (Accuracy) = 0.61 (0.54), Final Loss (Accuracy) = 0.06 (0.99)\n",
      "Testing Initial Loss (Accuracy) = 0.59 (0.70), Final Loss (Accuracy) = 0.30 (0.93)\n"
     ]
    }
   ],
   "source": [
    "# train!\n",
    "epochs = 500\n",
    "talkFreq = 0.2\n",
    "\n",
    "trnLoss = [np.inf]*epochs\n",
    "trnAcc = [0]*epochs\n",
    "tstLoss = [np.inf]*epochs\n",
    "tstAcc = [0]*epochs\n",
    "for i in range(epochs):\n",
    "    # train with minibatch gradient descent\n",
    "    model.train(True)\n",
    "    for x, y in trainLoad:\n",
    "        # forward step\n",
    "        yhat = model(x)\n",
    "        # compute loss (not storing for now, will do after minibatching)\n",
    "        l = loss(yhat, y)\n",
    "        # backward step\n",
    "        optimizer.zero_grad()\n",
    "        l.backward()\n",
    "        optimizer.step()\n",
    "    with torch.no_grad():\n",
    "        model.train(False) # model.eval()\n",
    "        # evaluate loss & accuracy on training set\n",
    "        yhat = model(trainData.x)\n",
    "        trnLoss[i] = loss(yhat, trainData.y)\n",
    "        trnAcc[i] = np.sum((yhat.detach().numpy()>0.5) == trainData.y.numpy())/len(trainData)\n",
    "        # evaluate loss & accuracy on testing set\n",
    "        yhat = model(testData.x)\n",
    "        tstLoss[i] = loss(yhat, testData.y)\n",
    "        tstAcc[i] = np.sum((yhat.detach().numpy()>0.5) == testData.y.numpy())/len(testData)\n",
    "        # maybe talk\n",
    "        if i % (epochs*talkFreq) == 0:\n",
    "            print('Epoch %d Training (Testing) Loss = %0.2f (%0.2f), & Accuracy = %0.2f (%0.2f)'%\n",
    "                  (i, trnLoss[i], tstLoss[i], trnAcc[i], tstAcc[i]))\n",
    "    \n",
    "print('==========\\nTraining Initial Loss (Accuracy) = %0.2f (%0.2f), Final Loss (Accuracy) = %0.2f (%0.2f)'%\\\n",
    "      (trnLoss[0], trnAcc[0], trnLoss[-1], trnAcc[-1]))\n",
    "print('Testing Initial Loss (Accuracy) = %0.2f (%0.2f), Final Loss (Accuracy) = %0.2f (%0.2f)'%\\\n",
    "      (tstLoss[0], tstAcc[0], tstLoss[-1], tstAcc[-1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Modeling Results\"}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('62835883-518e-4e6e-b21a-e618ee1d40d8');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize loss & accuracy progressions\n",
    "x = list(range(epochs))\n",
    "lossesAccs = np.asarray([(rl.item(), tl.item(), ra.item(), ta.item()) for (rl, tl, ra, ta)\n",
    "                         in zip(trnLoss, tstLoss, trnAcc, tstAcc)])\n",
    "\n",
    "trc = [go.Scatter({'x':x, 'y':lossesAccs[:,0], 'name':'Train Loss', 'mode':'lines',\n",
    "                   'line':{'color':'red', 'dash':'dash'}}),\n",
    "       go.Scatter({'x':x, 'y':lossesAccs[:,1], 'name':'Test Loss', 'mode':'lines',\n",
    "                   'line':{'color':'red'}}),\n",
    "       go.Scatter({'x':x, 'y':lossesAccs[:,2], 'name':'Train Acc.', 'mode':'lines',\n",
    "                   'line':{'color':'green', 'dash':'dash'}}),\n",
    "       go.Scatter({'x':x, 'y':lossesAccs[:,3], 'name':'Test Acc', 'mode':'lines',\n",
    "                   'line':{'color':'green'}})]\n",
    "plyoff.iplot(go.Figure(data=trc, layout=go.Layout(title='Modeling Results')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Set Results\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>y</th>\n",
       "      <th>yhat</th>\n",
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       "      <td>0</td>\n",
       "      <td>False</td>\n",
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      ],
      "text/plain": [
       "      y      yhat  Predict  Correct\n",
       "0   1.0  0.999998        1     True\n",
       "1   0.0  0.000939        0     True\n",
       "2   1.0  0.006012        0    False\n",
       "3   0.0  0.000926        0     True\n",
       "4   0.0  0.001166        0     True\n",
       "5   1.0  0.999306        1     True\n",
       "6   1.0  0.999648        1     True\n",
       "7   0.0  0.000279        0     True\n",
       "8   1.0  1.000000        1     True\n",
       "9   1.0  0.778061        1     True\n",
       "10  1.0  0.568029        1     True\n",
       "11  0.0  0.000267        0     True\n",
       "12  0.0  0.000393        0     True\n",
       "13  1.0  0.999602        1     True\n",
       "14  0.0  0.000308        0     True\n",
       "15  1.0  0.999997        1     True\n",
       "16  1.0  0.999767        1     True\n",
       "17  0.0  0.000290        0     True\n",
       "18  0.0  0.000567        0     True\n",
       "19  0.0  0.001162        0     True\n",
       "20  0.0  0.000963        0     True\n",
       "21  0.0  0.000204        0     True\n",
       "22  0.0  0.000276        0     True\n",
       "23  0.0  0.000330        0     True\n",
       "24  1.0  0.789059        1     True\n",
       "25  0.0  0.372490        0     True\n",
       "26  1.0  0.501632        1     True\n",
       "27  1.0  0.996954        1     True\n",
       "28  1.0  1.000000        1     True\n",
       "29  1.0  0.195588        0    False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display targets and predictions\n",
    "model.train(False)\n",
    "results = pd.DataFrame({'y':np.squeeze(testData.y.detach().numpy()),\n",
    "                        'yhat':np.squeeze(model(testData.x).detach().numpy())})\n",
    "results['Predict'] = (results.yhat >= 0.5)*1\n",
    "results['Correct'] = results.Predict == results.y\n",
    "print('Testing Set Results')\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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