CNN_Python_MNIST_and_CIFAR.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cw04b-NnPF_e",
        "outputId": "0d4e6f66-4d27-4ad3-fd67-92cc2e32fc67"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/home/gpuadmin\n",
            "Github Repo exist.\n",
            "/home/gpuadmin/Python_CNN\n",
            "Requirement already satisfied: tqdm in /home/gpuadmin/.local/lib/python3.8/site-packages (from -r requirements.txt (line 1)) (4.64.0)\n",
            "Requirement already satisfied: matplotlib in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (3.6.2)\n",
            "Requirement already satisfied: mnist==0.2.2 in /home/gpuadmin/.local/lib/python3.8/site-packages (from -r requirements.txt (line 3)) (0.2.2)\n",
            "Requirement already satisfied: numpy in /home/gpuadmin/.local/lib/python3.8/site-packages (from -r requirements.txt (line 4)) (1.23.5)\n",
            "Requirement already satisfied: packaging>=20.0 in /home/gpuadmin/.local/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (21.3)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (9.3.0)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (1.4.4)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /home/gpuadmin/.local/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (4.38.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (1.0.6)\n",
            "Requirement already satisfied: cycler>=0.10 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 2)) (0.11.0)\n",
            "Requirement already satisfied: six>=1.5 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from python-dateutil>=2.7->matplotlib->-r requirements.txt (line 2)) (1.16.0)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "Requirement already satisfied: keras==2.2.4 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (2.2.4)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /home/gpuadmin/.local/lib/python3.8/site-packages (from keras==2.2.4) (1.23.5)\n",
            "Requirement already satisfied: pyyaml in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from keras==2.2.4) (6.0)\n",
            "Requirement already satisfied: scipy>=0.14 in /home/gpuadmin/.local/lib/python3.8/site-packages (from keras==2.2.4) (1.8.1)\n",
            "Requirement already satisfied: six>=1.9.0 in /home/gpuadmin/anaconda3/envs/python_cnn/lib/python3.8/site-packages (from keras==2.2.4) (1.16.0)\n",
            "Requirement already satisfied: h5py in /home/gpuadmin/.local/lib/python3.8/site-packages (from keras==2.2.4) (3.7.0)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /home/gpuadmin/.local/lib/python3.8/site-packages (from keras==2.2.4) (1.1.2)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /home/gpuadmin/.local/lib/python3.8/site-packages (from keras==2.2.4) (1.0.8)\n",
            "Note: you may need to restart the kernel to use updated packages.\n",
            "\n",
            "List of files\n",
            "cnn_keras.py\t\t\t  conv.py\t      python_cnn.py\n",
            "cnn.py\t\t\t\t  data.csv\t      README.md\n",
            "CNN_Python_CIFAR.ipynb\t\t  fc.py\t\t      relu.py\n",
            "CNN_Python.ipynb\t\t  filters.npy\t      requirements.txt\n",
            "CNN_Python_MNIST_and_CIFAR.ipynb  LICENSE\t      soft_fc.py\n",
            "CNN_Torch.ipynb\t\t\t  linear_biases.npy   softmax.py\n",
            "cnn_torch.py\t\t\t  linear_filters.npy  softmax_test.py\n",
            "conv_filters_3d.npy\t\t  maxpool.py\t      weights\n",
            "conv_filters.npy\t\t  __pycache__\n"
          ]
        }
      ],
      "source": [
        "%cd ~\n",
        "![ ! -d \"./Python_CNN\" ] && echo \"Github Repo DOES NOT exists.\"\n",
        "![ ! -d \"./Python_CNN\" ] && git clone https://github.com/ShoaibSajid/Python_CNN\n",
        "![ -d \"./Python_CNN\" ] && echo \"Github Repo exist.\"\n",
        "%cd Python_CNN\n",
        "%pip install -r requirements.txt\n",
        "# %pip install tensorflow==2.10.0\n",
        "%pip install keras==2.2.4\n",
        "!echo \n",
        "!echo List of files\n",
        "!ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "I-725DWZTpo6"
      },
      "outputs": [],
      "source": [
        "# Importing libraries\n",
        "\n",
        "from logging import raiseExceptions\n",
        "import os\n",
        "import pickle\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "from matplotlib import pyplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fY2ErHnRw3Fx"
      },
      "outputs": [],
      "source": [
        "# Importing network layers\n",
        "\n",
        "from conv import Conv3x3, Conv3x3_n_to_n_padding, Conv3x3_1_to_n_padding\n",
        "from maxpool import MaxPool2\n",
        "from softmax import Softmax\n",
        "from relu import Relu\n",
        "from softmax_test import Softmax_test\n",
        "from fc import FC"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "WmaKrncdvj8Z"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "2022-12-08 12:12:29.129614: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
            "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2022-12-08 12:12:29.344125: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
            "2022-12-08 12:12:30.268260: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory\n",
            "2022-12-08 12:12:30.268342: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory\n",
            "2022-12-08 12:12:30.268349: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n"
          ]
        }
      ],
      "source": [
        "# Importing Dataset\n",
        "\n",
        "import mnist\n",
        "from keras.datasets import cifar10\n",
        "from keras.utils import to_categorical"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mWtCpadIv7-8",
        "outputId": "56f1c85c-446f-4873-f19b-0df0272a3b00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Train: X=(50000, 32, 32, 3), y=(50000, 1)\n",
            "Test: X=(10000, 32, 32, 3), y=(10000, 1)\n"
          ]
        }
      ],
      "source": [
        "# Defining Dataset\n",
        "\n",
        "# Dataset=\"MNIST\" #\"CIFAR\" #\"MNIST\"\n",
        "Dataset=\"CIFAR\" #\"CIFAR\" #\"MNIST\"\n",
        "\n",
        "if Dataset==\"MNIST\":\n",
        "  # ====================== MNIST =============================\n",
        "  # Importing the training dataset - MNIST Dataset\n",
        "  train_images = mnist.train_images()\n",
        "  train_labels = mnist.train_labels()\n",
        "  test_images = mnist.test_images()\n",
        "  test_labels = mnist.test_labels()\n",
        "  # Shuffle Data\n",
        "  permutation = np.random.permutation(len(train_images))\n",
        "  train_images = train_images[permutation]\n",
        "  train_labels = train_labels[permutation]\n",
        "  print(f'Train: {train_images.shape} images and {train_labels.shape} labels.')\n",
        "  print(f'Test : {test_images.shape } images and {test_labels.shape } labels.')\n",
        "\n",
        "elif Dataset==\"CIFAR\":\n",
        "  # ====================== CIFAR10 ===========================\n",
        "  (train_images, train_labels), (test_images, test_labels) = cifar10.load_data()\n",
        "  print('Train: X=%s, y=%s' % (train_images.shape, train_labels.shape))\n",
        "  print('Test: X=%s, y=%s' % (test_images.shape, test_labels.shape))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "7ufjETAUyczt"
      },
      "outputs": [],
      "source": [
        "limit_data=False\n",
        "data_len=100\n",
        "if limit_data:\n",
        "  train_images = train_images[:data_len]\n",
        "  train_labels = train_labels[:data_len]\n",
        "  test_images  = test_images[:int(data_len/10)]\n",
        "  test_labels  = test_labels[:int(data_len/10)]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "Ak65-Qi4z1n1",
        "outputId": "0fe81335-aee8-4685-b2e3-602a388bffdd"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x1000 with 9 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot a few images\n",
        "pyplot.figure(figsize=(10,10))\n",
        "for i in range(9):\n",
        "  pyplot.subplot(330 + 1 + i)\n",
        "  pyplot.imshow(train_images[i])\n",
        "  pyplot.title(f'Class:{train_labels[i]}')\n",
        "pyplot.show()\n",
        "\n",
        "\n",
        "use_one_hot_encoding=False\n",
        "if use_one_hot_encoding:\n",
        "  train_labels = to_categorical(train_labels)\n",
        "  test_labels = to_categorical(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "oidgGH5PVVWS"
      },
      "outputs": [],
      "source": [
        "# Define settings for run\n",
        "\n",
        "debug=False\n",
        "\n",
        "shuffle_data=False\n",
        "\n",
        "run_train=False\n",
        "run_val=True\n",
        "\n",
        "load_saved_weights=True\n",
        "weight_file='weights/best_99.pkl'\n",
        "\n",
        "total_epoch=100\n",
        "training_acc_internal=1000"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "iAoYBSKmWKWE"
      },
      "outputs": [],
      "source": [
        "# Initialize the network layers\n",
        "\n",
        "if Dataset==\"MNIST\":\n",
        "  conv0   = Conv3x3_1_to_n_padding( output=8                        )     # 28x28x1   -> 28x28x8  (Convolution with 8 filters)\n",
        "elif Dataset==\"CIFAR\":\n",
        "  conv0   = Conv3x3_n_to_n_padding( output=8      ,   input=3       )     # 28x28x1   -> 28x28x8  (Convolution with 8 filters)\n",
        "pool0   = MaxPool2              (                                 )     # 28x28x8   -> 14x14x8  (MaxPooling 2x2)\n",
        "conv1   = Conv3x3_n_to_n_padding( output=16     ,   input=8       )     # 14x14x8   -> 14x14x16 (Convolution with 8 filters)\n",
        "pool1   = MaxPool2              (                                 )     # 14x14x16  -> 07x07x16 (MaxPooling 2x2)\n",
        "# conv2   = Conv3x3_n_to_n_padding( output=32     ,   input=16      )\n",
        "# conv3   = Conv3x3_n_to_n_padding( output=64     ,   input=32      )\n",
        "if Dataset==\"MNIST\":\n",
        "  FC_size= 7 * 7 * 16\n",
        "elif Dataset==\"CIFAR\":\n",
        "  FC_size= 8 * 8 * 16\n",
        "  \n",
        "fc0     = FC                    (  FC_size ,   FC_size      )     # 784       -> 784      (FC)\n",
        "fc1     = FC                    (  FC_size ,   10           )     # 784       -> 10       (FC)\n",
        "softmax = Softmax               (                                 )     # 14x14x8   -> 10       (Softmax)\n",
        "relu    = Relu                  (                                 )     # 14x14x8   -> 10       (Softmax)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "EwHWdHFrWSao"
      },
      "outputs": [],
      "source": [
        "# Defining the network - Forward propagation\n",
        "def forward(im, label, debug=False):\n",
        "  im        = (im / 255) - 0.5  \n",
        "    \n",
        "  # Conv 0 with Pool\n",
        "  out_conv0 = conv0.forward   ( im            )\n",
        "  out_pool0 = pool0.forward   ( out_conv0     )\n",
        "  \n",
        "  # Conv 1 with Pool\n",
        "  out_conv1 = conv1.forward   ( out_pool0     )\n",
        "  out_pool1 = pool1.forward   ( out_conv1     )\n",
        "  \n",
        "  # Swap axes to realign for flattening\n",
        "  out_pool2 = np.swapaxes(out_pool1,0,2)\n",
        "  out_pool3 = np.swapaxes(out_pool2,1,2)\n",
        "  \n",
        "  # FC0 and Relu\n",
        "  out_fc0   = fc0.forward     ( out_pool3     )\n",
        "  \n",
        "  # FC1 and SoftMax\n",
        "  out_fc1   = fc1.forward     ( out_fc0       )\n",
        "  out_soft  = softmax.forward ( out_fc1       )\n",
        "  \n",
        "  if debug:\n",
        "    print(f\"Input Image: {im[-4]}\\n\")\n",
        "    print(f\"x_conv0 filters : {conv0.filters[0]}\\n\")\n",
        "    print(f\"x_conv0 : {out_conv0[:,:,0][-1]}\\n\")\n",
        "    print(f\"MaxPool0: {out_pool0[:,:,0][-1]}\\n\")\n",
        "    print(f\"x_conv1 filters : {conv1.filters[0,:,:,0]}\\n\")\n",
        "    print(f\"x_conv1 : {out_conv1[:,:,0][-1]}\\n\")\n",
        "    print(f\"MaxPool1: {out_pool1[:,:,0][-1]}\\n\")\n",
        "    print(f\"FC0 Weights: {fc0.weights[:,0][:10]}\\n\")\n",
        "    print(f\"FC0 output: {out_fc0[:10]}\\n\")\n",
        "    print(f\"FC1 Weights: {fc1.weights[:,0][:10]}\\n\")\n",
        "    print(f\"FC1 output: {out_fc1[:10]}\\n\")\n",
        "    print(f\"SoftMax output: {out_soft}\\n\")\n",
        "\n",
        "  return out_soft"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "IoomUOeIWg1h"
      },
      "outputs": [],
      "source": [
        "# Defining the network - Backward propagation\n",
        "def backward(label, out, loss=1, lr=0.005):\n",
        "  # Calculate initial gradient\n",
        "  gradient = np.zeros(10)\n",
        "  gradient[label] = -1 / out[label]\n",
        "  # gradient[label] = -loss / out[label]\n",
        "\n",
        "  # ------------------------------Backprop-----------------------------------\n",
        "  # SoftMax\n",
        "  gradient_softmax = softmax.backprop ( gradient                              )     \n",
        "\n",
        "  # FC1 and FC0 \n",
        "  gradient_fc1 = fc1.backprop         ( gradient_softmax  ,               lr  )\n",
        "  gradient_fc0 = fc0.backprop         ( gradient_fc1      ,               lr  )\n",
        "\n",
        "  # Swap axes to realign for flattening\n",
        "  gradient_swap0 = np.swapaxes        ( gradient_fc0      ,       1   ,   2   )\n",
        "  gradient_swap1 = np.swapaxes        ( gradient_swap0    ,       0   ,   2   )\n",
        "\n",
        "  # Conv 1 with Pool \n",
        "  gradient_pool1 = pool1.backprop     ( gradient_swap1                        )\n",
        "  gradient_conv1 = conv1.backprop     ( gradient_pool1     ,              lr  )\n",
        "\n",
        "  # Conv 0 with Pool \n",
        "  gradient_pool0 = pool0.backprop     ( gradient_conv1                        ) \n",
        "  gradient_conv0 = conv0.backprop     ( gradient_pool0     ,              lr  )\n",
        "  return None"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "yhMPrtRNXBXT"
      },
      "outputs": [],
      "source": [
        "# Defining the network - Loss Function (Cross Entropy)\n",
        "# Calculate cross-entropy loss and accuracy. np.log() is the natural log.\n",
        "def cal_loss(out_soft, label):\n",
        "  loss = -np.log(out_soft[label])\n",
        "  acc = 1 if np.argmax(out_soft) == label else 0\n",
        "  return out_soft, loss, acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "jbypb9x2XRej"
      },
      "outputs": [],
      "source": [
        "# Defining the network - Training Function\n",
        "def train(im, label, debug=False, lr=.005):\n",
        "  pred = forward(im, label, debug)\n",
        "  out_soft, loss, acc = cal_loss(pred, label)\n",
        "  backward(label, out_soft, loss=loss, lr=0.005)\n",
        "  return loss, acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "Qlrl0fzUXV_M"
      },
      "outputs": [],
      "source": [
        "# Defining the network - Validation Function\n",
        "def val(im, label):\n",
        "  pred = forward(im, label)\n",
        "  out_soft, loss, acc = cal_loss(pred, label)\n",
        "  predLabel = np.argmax(pred)\n",
        "  return loss, acc, predLabel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "n3132AoGXzc0"
      },
      "outputs": [],
      "source": [
        "# Function definitions to save and load weight files\n",
        "\n",
        "def save_weights(name,lr=0,max_acc=0):\n",
        "  print(f\"\\nSaving new weights ({name}).\")\n",
        "  weights = dict()\n",
        "  weights[\"conv0\"]        = conv0.filters\n",
        "  weights[\"conv1\"]        = conv1.filters\n",
        "  weights[\"fc0_weights\"]  = fc0.weights\n",
        "  weights[\"fc0_biases\" ]  = fc0.biases\n",
        "  weights[\"fc1_weights\"]  = fc1.weights\n",
        "  weights[\"fc1_biases\" ]  = fc1.biases\n",
        "  weights[\"lr\" ]          = lr\n",
        "  weights[\"max_acc\"]      = max_acc\n",
        "  weight_file = open(str(name), \"wb\")\n",
        "  pickle.dump(weights, weight_file)\n",
        "  weight_file.close()\n",
        "  \n",
        "def load_weights(name):\n",
        "  if os.path.isfile(name): \n",
        "    weight_file = open(str(name), \"rb\")\n",
        "    weights = pickle.load(weight_file)\n",
        "    conv0.filters  = weights[\"conv0\"]      \n",
        "    conv1.filters  = weights[\"conv1\"]      \n",
        "    fc0.weights    = weights[\"fc0_weights\"]\n",
        "    fc0.biases     = weights[\"fc0_biases\" ]\n",
        "    fc1.weights    = weights[\"fc1_weights\"]\n",
        "    fc1.biases     = weights[\"fc1_biases\" ]\n",
        "    lr             = weights[\"lr\" ]\n",
        "    max_acc        = weights[\"max_acc\"]\n",
        "    print(f\"\\nLoading weights from {name} file. LR restored to {lr}. Last Accuracy {max_acc}%\")\n",
        "    return lr, max_acc\n",
        "  else:\n",
        "    print(\"Weights file not found.\")\n",
        "    lr=0.005\n",
        "    max_acc=0\n",
        "    return lr, max_acc\n",
        "   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Y1WIfF2UYstz"
      },
      "outputs": [],
      "source": [
        "# Defining the network - Adjust learning rate\n",
        "def adjust_lr(acc, lr=.005):\n",
        "  if   acc > 98: lr=0.00001\n",
        "  elif acc > 95: lr=0.0005\n",
        "  elif acc > 90: lr=0.001\n",
        "  elif acc > 80: lr=0.002\n",
        "  elif acc > 70: lr=0.003\n",
        "  elif acc > 60: lr=0.004\n",
        "  return lr"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1RYPUzZDYHcC",
        "outputId": "ded4c9ea-f531-43a8-889b-0e3faf2f7cb6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "Loading weights from weights/best_99.pkl file. LR restored to 0.0005. Last Accuracy 99%\n"
          ]
        }
      ],
      "source": [
        "# Load Weights\n",
        "if load_saved_weights:\n",
        "    lr, max_acc = load_weights(weight_file)\n",
        "else:\n",
        "    lr, max_acc = 0.005, 0 \n",
        "\n",
        "if debug: save_weights(f'weights/debug.pkl', lr, max_acc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "N02tnG1ZVSyk",
        "outputId": "8c1f46f4-d785-4648-ceca-5e88bd1e826a"
      },
      "outputs": [],
      "source": [
        " # Main function to run the training\n",
        "\n",
        "if run_train:\n",
        "  print(f'Training Initialized.')\n",
        "  print(f\"\\tTotal number of training   images: {len(train_labels)}\")\n",
        "  print(f\"\\tTotal number of validation images: {len(test_labels)}\")\n",
        "  print(f\"\\tTraining will run for {total_epoch} epochs.\")\n",
        "  print(f\"\\tResults will be logged after every {training_acc_internal} images.\")\n",
        "  for epoch in range(total_epoch):\n",
        "    print('\\n--- Epoch %d ---' % (epoch + 1))\n",
        "            \n",
        "    # Initialize Variables\n",
        "    loss, num_correct = 0, 0\n",
        "    for i, (im, label) in tqdm(enumerate(zip(train_images, train_labels))):\n",
        "      \n",
        "      # Logging results\n",
        "      if i % training_acc_internal == training_acc_internal-1:\n",
        "        lr = adjust_lr(num_correct)\n",
        "        print(f'\\n[Step {(i+1)}] : For {training_acc_internal} iterations, the Avg Loss is {np.round((loss / training_acc_internal),2)} | Training Acc: {num_correct/training_acc_internal} | LR: {lr}')\n",
        "        loss, num_correct = 0, 0\n",
        "          \n",
        "      # Train the network\n",
        "      l, acc = train(im, label, debug, lr=lr)\n",
        "      loss += l\n",
        "      num_correct += acc\n",
        "           \n",
        "    print(f\"End of epoch {epoch+1}\")      \n",
        "\n",
        "    print(f\"\\n\\nCalculating validation scores at the end of epoch.\")\n",
        "    loss, num_correct, predLabels, imagesList, gtLabels = 0, 0, [], [], []\n",
        "    for im, label in tqdm(zip(test_images, test_labels)):\n",
        "      l, acc, predLabel = val(im, label)\n",
        "      loss += l\n",
        "      num_correct += acc\n",
        "      predLabels.append(predLabel)\n",
        "      gtLabels.append(label)\n",
        "      imagesList.append(im)\n",
        "    num_tests = len(test_images)\n",
        "    test_loss, test_acc =  loss / num_tests , num_correct / num_tests\n",
        "    print('Test Loss:', test_loss*100+\"%\")\n",
        "    print('Test Accuracy:', test_acc)\n",
        "\n",
        "    if epoch == 0:\n",
        "      max_acc = test_acc\n",
        "    elif test_acc > max_acc: \n",
        "      max_acc = test_acc\n",
        "      save_weights(f'weights/best_{test_acc}.pkl', lr, max_acc)\n",
        "      save_weights(f'weights/last.pkl', lr, max_acc)\n",
        "\n",
        "    pyplot.close()\n",
        "    pyplot.figure(figsize=(10,10))\n",
        "    for i in range(9):\n",
        "      pyplot.subplot(330 + 1 + i)\n",
        "      pyplot.imshow(imagesList[i])\n",
        "      Str=\"Correct\" if predLabels[i]==gtLabels[i] else \"Wrong\"\n",
        "      pyplot.title(f'{Str} -- PD:{predLabels[i]} GT:{gtLabels[i]}')\n",
        "    pyplot.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "PonPg6kBZT1J"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "--- Testing the CNN for 10000 images---\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "0it [00:00, ?it/s]\n"
          ]
        },
        {
          "ename": "IndexError",
          "evalue": "too many indices for array: array is 3-dimensional, but 4 were indexed",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[19], line 8\u001b[0m\n\u001b[1;32m      6\u001b[0m num_correct \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m      7\u001b[0m \u001b[39mfor\u001b[39;00m im, label \u001b[39min\u001b[39;00m tqdm(\u001b[39mzip\u001b[39m(test_images, test_labels)):\n\u001b[0;32m----> 8\u001b[0m   l, acc, predLabel \u001b[39m=\u001b[39m val(im, label)\n\u001b[1;32m      9\u001b[0m   loss \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m l\n\u001b[1;32m     10\u001b[0m   num_correct \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m acc\n",
            "Cell \u001b[0;32mIn[13], line 3\u001b[0m, in \u001b[0;36mval\u001b[0;34m(im, label)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mval\u001b[39m(im, label):\n\u001b[0;32m----> 3\u001b[0m   pred \u001b[39m=\u001b[39m forward(im, label)\n\u001b[1;32m      4\u001b[0m   out_soft, loss, acc \u001b[39m=\u001b[39m cal_loss(pred, label)\n\u001b[1;32m      5\u001b[0m   predLabel \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margmax(pred)\n",
            "Cell \u001b[0;32mIn[9], line 6\u001b[0m, in \u001b[0;36mforward\u001b[0;34m(im, label, debug)\u001b[0m\n\u001b[1;32m      3\u001b[0m im        \u001b[39m=\u001b[39m (im \u001b[39m/\u001b[39m \u001b[39m255\u001b[39m) \u001b[39m-\u001b[39m \u001b[39m0.5\u001b[39m  \n\u001b[1;32m      5\u001b[0m \u001b[39m# Conv 0 with Pool\u001b[39;00m\n\u001b[0;32m----> 6\u001b[0m out_conv0 \u001b[39m=\u001b[39m conv0\u001b[39m.\u001b[39;49mforward   ( im            )\n\u001b[1;32m      7\u001b[0m out_pool0 \u001b[39m=\u001b[39m pool0\u001b[39m.\u001b[39mforward   ( out_conv0     )\n\u001b[1;32m      9\u001b[0m \u001b[39m# Conv 1 with Pool\u001b[39;00m\n",
            "File \u001b[0;32m~/Python_CNN/conv.py:280\u001b[0m, in \u001b[0;36mConv3x3_n_to_n_padding.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    278\u001b[0m \u001b[39mfor\u001b[39;00m im_region, i, j \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39miterate_regions(\u001b[39minput\u001b[39m):\n\u001b[1;32m    279\u001b[0m   \u001b[39mfor\u001b[39;00m \u001b[39mfilter\u001b[39m \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mnum_filters):\n\u001b[0;32m--> 280\u001b[0m     output[i, j, \u001b[39mfilter\u001b[39m] \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39msum(im_region \u001b[39m*\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfilters[\u001b[39mfilter\u001b[39;49m,:,:,:], axis\u001b[39m=\u001b[39m(\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m))\n\u001b[1;32m    283\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlast_output \u001b[39m=\u001b[39m output\n\u001b[1;32m    284\u001b[0m \u001b[39mreturn\u001b[39;00m output\n",
            "\u001b[0;31mIndexError\u001b[0m: too many indices for array: array is 3-dimensional, but 4 were indexed"
          ]
        }
      ],
      "source": [
        "# Main function to validate the weights\n",
        "\n",
        "if run_val:\n",
        "  print(f'\\n--- Testing the CNN for {len(test_labels)} images---')\n",
        "  loss = 0\n",
        "  num_correct = 0\n",
        "  for im, label in tqdm(zip(test_images, test_labels)):\n",
        "    l, acc, predLabel = val(im, label)\n",
        "    loss += l\n",
        "    num_correct += acc\n",
        "    \n",
        "\n",
        "  num_tests = len(test_images)\n",
        "  print('\\nTest Loss:', loss / num_tests)\n",
        "  print('Test Accuracy:', num_correct / num_tests)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.10.8 ('python_cnn')",
      "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": "3.8.15"
    },
    "vscode": {
      "interpreter": {
        "hash": "b9450f1e70c40e3e13ef2de08476fb3dcd992e7612be6477af641a838aca42a3"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}