CNN2_BCELoss.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "talented-scientist",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(253, 1)\n",
      "[202, 51]\n",
      "(3, 200, 200)\n",
      "[0]\n"
     ]
    }
   ],
   "source": [
    "#https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#training-on-gpu\n",
    "import torch, torchvision, os, cv2\n",
    "from torch.utils.data import random_split, Dataset\n",
    "from torch.nn import Sigmoid, Tanh, Linear, ReLU, Sequential, Conv2d, MaxPool2d, Sigmoid, BatchNorm2d, Flatten, ConvTranspose2d\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "images = np.load('LogImages.npy') #Rade the trianing data.\n",
    "images = np.moveaxis(images, -1, 1) #Reshape channeL from [B, H, W, C] to [B, C, H, W]\n",
    "labels = np.load('Labels.npy') #Rade the trianing data. \n",
    "labels = labels.reshape(labels.shape[0],1)\n",
    "# images = images.astype(np.float32)\n",
    "# labels = labels.astype(np.int)\n",
    "\n",
    "print(labels.shape)\n",
    "labels2D = np.zeros((labels.shape[0],2))\n",
    "for i in range(len(labels)):\n",
    "    lab = labels[i]\n",
    "    if lab == 0:\n",
    "        labels2D[i,0] = 1\n",
    "    if lab == 1:\n",
    "        labels2D[i,1] = 1\n",
    "lengths = [round(len(images)*0.8), round(len(images)*0.2)]\n",
    "print(lengths)\n",
    "trainImg, testImg = random_split(images, lengths ,generator=torch.random.manual_seed(42)) #Shuffle data with random seed 42 before split train and test\n",
    "trainLab, testLab = random_split(labels, lengths ,generator=torch.random.manual_seed(42)) #Shuffle data with random seed 42 before split train and test\n",
    "\n",
    "print(trainImg[0].shape)\n",
    "print(trainLab[25])\n",
    "\n",
    "\n",
    "trainData = [] \n",
    "for i in range(len(trainImg)):\n",
    "    trainData.append([torch.tensor(trainImg[i], dtype=torch.float32), torch.tensor(trainLab[i],dtype=torch.float32)])\n",
    "trainLoader = torch.utils.data.DataLoader(trainData, shuffle=True, batch_size=5)\n",
    "\n",
    "testData = []\n",
    "for i in range(len(testImg)):\n",
    "    testData.append([torch.tensor(testImg[i], dtype=torch.float32), torch.tensor(testLab[i],dtype=torch.float32)])\n",
    "testLoader = torch.utils.data.DataLoader(testData, shuffle=False, batch_size=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "complicated-millennium",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (2): ReLU(inplace=True)\n",
      "  (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (4): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (5): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (6): ReLU(inplace=True)\n",
      "  (7): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (8): Conv2d(8, 4, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (9): BatchNorm2d(4, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (10): ReLU(inplace=True)\n",
      "  (11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  (12): Conv2d(4, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "  (13): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
      "  (14): ReLU(inplace=True)\n",
      "  (15): Flatten(start_dim=1, end_dim=-1)\n",
      "  (16): Linear(in_features=1250, out_features=1, bias=True)\n",
      "  (17): Sigmoid()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "net = Sequential(\n",
    "            # Defining 1st 2D convolution layer\n",
    "            Conv2d(3, 16, kernel_size=3, stride=1, padding=1), #200@3 \n",
    "            BatchNorm2d(16),\n",
    "            ReLU(inplace=True),\n",
    "            MaxPool2d(kernel_size=2, stride=2),\n",
    "            # Defining 2nd 2D convolution layer\n",
    "            Conv2d(16, 8, kernel_size=3, stride=1, padding=1), #100@3 \n",
    "            BatchNorm2d(8),\n",
    "            ReLU(inplace=True),\n",
    "            MaxPool2d(kernel_size=2, stride=2),\n",
    "            # Defining 3rd 2D convolution layer\n",
    "            Conv2d(8, 4, kernel_size=3, stride=1, padding=1), #50@3 \n",
    "            BatchNorm2d(4),\n",
    "            ReLU(inplace=True),\n",
    "            MaxPool2d(kernel_size=2, stride=2),\n",
    "            # Defining 4th 2D convolution layer\n",
    "            Conv2d(4, 2, kernel_size=3, stride=1, padding=1), #25@3 \n",
    "            BatchNorm2d(2),\n",
    "            ReLU(inplace=True),\n",
    "            Flatten(),\n",
    "            Linear(2 * 25 * 25, 1),\n",
    "            Sigmoid()\n",
    "        )\n",
    "\n",
    "net = net.cuda()\n",
    "#criterion = nn.CrossEntropyLoss()\n",
    "criterion = nn.BCELoss()\n",
    "optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)\n",
    "print(net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "clean-approval",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    },
    {
     "name": "stdout",
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     "text": [
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      "Train epoch: 9968, Loss: 0.0000036683\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train epoch: 9969, Loss: 0.0000035781\n",
      "Train epoch: 9970, Loss: 0.0000031208\n",
      "Train epoch: 9971, Loss: 0.0000035376\n",
      "Train epoch: 9972, Loss: 0.0000034175\n",
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      "Train epoch: 9989, Loss: 0.0000037384\n",
      "Train epoch: 9990, Loss: 0.0000031944\n",
      "Train epoch: 9991, Loss: 0.0000030153\n",
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      "Train epoch: 9993, Loss: 0.0000035481\n",
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      "Train epoch: 9995, Loss: 0.0000030835\n",
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      "Train epoch: 9997, Loss: 0.0000031521\n",
      "Train epoch: 9998, Loss: 0.0000034926\n",
      "Train epoch: 9999, Loss: 0.0000037313\n",
      "Train epoch: 10000, Loss: 0.0000029865\n",
      "Finished Training\n"
     ]
    }
   ],
   "source": [
    "#%%time\n",
    "n_epoch = 10000\n",
    "for epoch in range(n_epoch):  # loop over the dataset multiple times\n",
    "#epoch = 0\n",
    "#while True:\n",
    "    train_loss = 0.0\n",
    "    for i, data in enumerate(trainLoader, 0):\n",
    "        # get the inputs; data is a list of [inputs, labels]\n",
    "        inputs, labels = data[0].cuda(), data[1].cuda() #Reg\n",
    "        \n",
    "        # zero the parameter gradients\n",
    "        optimizer.zero_grad()\n",
    "\n",
    "        # forward + backward + optimize\n",
    "        outputs = net(inputs)\n",
    "        labels = torch.reshape(labels, (-1,))\n",
    "        outputs = torch.reshape(outputs, (-1,))\n",
    "\n",
    "        loss = criterion(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # print statistics\n",
    "        train_loss += loss.item()\n",
    "    logStr = 'Train epoch: %d, Loss: %.10f' % (epoch + 1, train_loss/ (i+1))\n",
    "    print(logStr)\n",
    "    epoch += 1\n",
    "\n",
    "print('Finished Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "proprietary-delaware",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PATH = './CNN2_SGDlr0.001_BCEL_EP10000_72%.pth'\n",
    "# torch.save(net.state_dict(), PATH)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "rolled-lambda",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 202 test images: 100 %\n",
      "tensor([[120.,   0.],\n",
      "        [  0.,  82.]])\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "nb_classes = 2\n",
    "confusion_matrix = torch.zeros(nb_classes, nb_classes)\n",
    "with torch.no_grad():\n",
    "    for data in trainLoader:\n",
    "        images, labels = data[0].cuda(), data[1].cuda()\n",
    "        outputs = net(images)\n",
    "        predicted = torch.round(outputs)\n",
    "\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        for t, p in zip(labels.view(-1), predicted.view(-1)):\n",
    "                confusion_matrix[t.long(), p.long()] += 1\n",
    "        \n",
    "\n",
    "print('Accuracy of the network on the %s test images: %d %%' % (total,\n",
    "    100 * correct / total))\n",
    "\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "curious-plate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of the network on the 51 test images: 72 %\n",
      "tensor([[28.,  7.],\n",
      "        [ 7.,  9.]])\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "total = 0\n",
    "nb_classes = 2\n",
    "confusion_matrix = torch.zeros(nb_classes, nb_classes)\n",
    "with torch.no_grad():\n",
    "    for data in testLoader:\n",
    "        images, labels = data[0].cuda(), data[1].cuda()\n",
    "        outputs = net(images)\n",
    "        predicted = torch.round(outputs)\n",
    "\n",
    "        total += labels.size(0)\n",
    "        correct += (predicted == labels).sum().item()\n",
    "        for t, p in zip(labels.view(-1), predicted.view(-1)):\n",
    "                confusion_matrix[t.long(), p.long()] += 1\n",
    "        \n",
    "\n",
    "print('Accuracy of the network on the %s test images: %d %%' % (total,\n",
    "    100 * correct / total))\n",
    "\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "commercial-pointer",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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