pytorch-stuff/Visualize_Training.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn.datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = sklearn.datasets.make_moons(200, noise=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x[:,0],x[:,1], s=40, c=y, cmap=plt.cm.Spectral)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = torch.FloatTensor(x)\n",
    "y = torch.LongTensor(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "class FeedForward(torch.nn.Module):\n",
    "    \n",
    "    def __init__(self, input_neurons, hidden_neurons, output_neurons):\n",
    "        super(FeedForward, self).__init__()\n",
    "        self.hidden = nn.Linear(input_neurons, hidden_neurons)\n",
    "        self.out = nn.Linear(hidden_neurons, output_neurons)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.hidden(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.out(x)\n",
    "        return x\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "network = FeedForward(input_neurons=x.shape[1], hidden_neurons=50, output_neurons=len(y.unique()))\n",
    "optimizer = torch.optim.SGD(network.parameters(), lr=0.02)\n",
    "loss_function = torch.nn.CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ion()\n",
    "for epoch in range(10000):\n",
    "    out = network(x)\n",
    "    loss = loss_function(out,y)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    \n",
    "    if epoch%1000 == 0:\n",
    "        # Show learning process till this moment\n",
    "        max_value, prediction = torch.max(out,1)\n",
    "        predicted_y = prediction.data.numpy()\n",
    "        target_y = y.data.numpy()\n",
    "        plt.scatter(x.data.numpy()[:,0],x.data.numpy()[:,1], s=40, c=predicted_y, lw=0, cmap=plt.cm.Spectral)\n",
    "        accuracy = (predicted_y==target_y).sum() / target_y.size\n",
    "        plt.text(3,-1, \"Accuracy = {:.2f}\".format(accuracy), fontdict={'size':14})\n",
    "        plt.pause(0.1)\n",
    "        \n",
    "plt.ioff()\n",
    "plt.show()\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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}