umaradnaan
/

deeplearning

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c1da662

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-MOLhjn8LQ-2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from PIL import Image\n",
        "import pickle\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "import os\n",
        "from tensorflow.keras.applications import ResNet50\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datagen = ImageDataGenerator(rescale=1.0/255.0, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')"
      ],
      "metadata": {
        "id": "j6qtkeCQLzdQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 20\n",
        "train_data_dir = '/content/drive/MyDrive/BoneFractureDataset/training'\n",
        "validation_data_dir = '/content/drive/MyDrive/BoneFractureDataset/testing'\n",
        "train_generator = datagen.flow_from_directory( train_data_dir,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=batch_size,\n",
        "    class_mode='binary',\n",
        "    shuffle=True )\n",
        "validation_generator = datagen.flow_from_directory(\n",
        "    validation_data_dir,\n",
        "    target_size=(224, 224),\n",
        "    batch_size=batch_size,\n",
        "    class_mode='binary',\n",
        "    shuffle=False\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dyr6c1lNMHkG",
        "outputId": "50e93b0b-29fc-470b-abd0-96e02274798a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1141 images belonging to 2 classes.\n",
            "Found 1 images belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /kaggle/input/resnet50-weights/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7kgKKh3MX1s",
        "outputId": "f1bfb5f0-4cd8-4754-d9a4-734e3d1b02ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ls: cannot access '/kaggle/input/resnet50-weights/': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!stat /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vdr-l_dUM6nG",
        "outputId": "f8815a3e-5f22-4c5d-9998-d98d3c1527e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "stat: cannot statx '/kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 -P /kaggle/input/resnet50-weights/"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPDPlQI1NJwo",
        "outputId": "b315d368-d7d5-4488-ab6b-79e9f247fb1c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-02 06:27:07--  https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.2.207, 2607:f8b0:4023:c06::cf, 2607:f8b0:4023:c0d::cf\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.2.207|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 94765736 (90M) [application/octet-stream]\n",
            "/kaggle/input/resnet50-weights: Read-only file system\n",
            "/kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5: No such file or directory\n",
            "\n",
            "Cannot write to ‘/kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5’ (Success).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpmoIUeqNL_I",
        "outputId": "dfffdef9-93ee-43a2-84fb-3c833c6a2568"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cat: /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -l /kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bMbCZOyvNO2Z",
        "outputId": "856a9151-8524-4026-bce8-0595f3715e9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ls: cannot access '/kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5 -P /kaggle/input/resnet50-weights"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FyrbK6mtNlbS",
        "outputId": "53847b33-44b2-442b-e8e1-a59392bd7fe7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-03-02 06:27:14--  https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.251.2.207, 2607:f8b0:4023:c06::cf, 2607:f8b0:4023:c0d::cf\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.251.2.207|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 94765736 (90M) [application/octet-stream]\n",
            "/kaggle/input/resnet50-weights: Read-only file system\n",
            "/kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5: No such file or directory\n",
            "\n",
            "Cannot write to ‘/kaggle/input/resnet50-weights/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5’ (Success).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "resModel = Sequential()\n",
        "resModel.add(ResNet50(\n",
        "    include_top=False,\n",
        "    pooling='avg',\n",
        "    weights=None,\n",
        "    ))\n",
        "resModel.add(Dense(1, activation='sigmoid'))\n",
        "for layer in resModel.layers[0].layers[-50:]:\n",
        "    layer.trainable = True"
      ],
      "metadata": {
        "id": "EeH2rGLQNoQx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras.callbacks import ReduceLROnPlateau\n",
        "optimizer = Adam(learning_rate=0.001)\n",
        "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=3, min_lr=0.0001)\n",
        "resModel.compile(optimizer=optimizer, loss='binary_crossentropy', metrics=['accuracy'])\n",
        "epochs = 10\n",
        "history = resModel.fit(train_generator, epochs=epochs, validation_data=validation_generator, callbacks=[reduce_lr])\n",
        "evaluation = resModel.evaluate(train_generator)\n",
        "print(f\"Test Accuracy: {evaluation[1] * 100:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "arSXsPFCOpoZ",
        "outputId": "89d7b0f0-db68-49d6-d96f-192d652ea862"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "58/58 [==============================] - 771s 13s/step - loss: 0.1156 - accuracy: 0.9904 - val_loss: 3.4440 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 2/10\n",
            "58/58 [==============================] - 733s 13s/step - loss: 0.0398 - accuracy: 0.9930 - val_loss: 1.0681 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 3/10\n",
            "58/58 [==============================] - 726s 13s/step - loss: 0.0285 - accuracy: 0.9912 - val_loss: 1.5398 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 4/10\n",
            "58/58 [==============================] - 727s 13s/step - loss: 0.0244 - accuracy: 0.9904 - val_loss: 0.6431 - val_accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 5/10\n",
            "58/58 [==============================] - 727s 13s/step - loss: 0.0178 - accuracy: 0.9921 - val_loss: 7.1402 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 6/10\n",
            "58/58 [==============================] - 729s 13s/step - loss: 0.0160 - accuracy: 0.9930 - val_loss: 0.1447 - val_accuracy: 1.0000 - lr: 0.0010\n",
            "Epoch 7/10\n",
            "58/58 [==============================] - 735s 13s/step - loss: 0.0234 - accuracy: 0.9921 - val_loss: 1.0688 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 8/10\n",
            "58/58 [==============================] - 731s 13s/step - loss: 0.0148 - accuracy: 0.9912 - val_loss: 1.1508 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 9/10\n",
            "58/58 [==============================] - 731s 13s/step - loss: 0.0284 - accuracy: 0.9912 - val_loss: 5.4258 - val_accuracy: 0.0000e+00 - lr: 0.0010\n",
            "Epoch 10/10\n",
            "58/58 [==============================] - 734s 13s/step - loss: 0.0147 - accuracy: 0.9912 - val_loss: 0.8876 - val_accuracy: 0.0000e+00 - lr: 2.0000e-04\n",
            "58/58 [==============================] - 185s 3s/step - loss: 0.0117 - accuracy: 0.9930\n",
            "Test Accuracy: 99.30%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "initial_epoch = 0\n",
        "saved_history = {\n",
        "    'loss': history.history['loss'],\n",
        "    'accuracy': history.history['accuracy'],\n",
        "    'val_loss': history.history['val_loss'],\n",
        "    'val_accuracy': history.history['val_accuracy'],\n",
        "}"
      ],
      "metadata": {
        "id": "4BCBOUsO009V"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.lines import Line2D\n",
        "from matplotlib.legend_handler import HandlerLine2D\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Bp4g1HqlTxQT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "initial_epoch = 10\n",
        "saved_history = {\n",
        "    'loss': history.history['loss'],\n",
        "    'accuracy': history.history['accuracy'],\n",
        "    'val_loss': history.history['val_loss'],\n",
        "    'val_accuracy': history.history['val_accuracy'],\n",
        "}"
      ],
      "metadata": {
        "id": "TIZqpl8bUWlM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls /kaggle/working"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ffb04lhnUdsO",
        "outputId": "35f74640-06f8-4b63-f9df-4124dd569375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ls: cannot access '/kaggle/working': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -l /kaggle/working/saved_D201history.npy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i8m6lQOkU5se",
        "outputId": "a3b322b4-90c3-47b9-d33d-1325891d29a7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ls: cannot access '/kaggle/working/saved_D201history.npy': No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!find / -name saved_D201history.npy"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2R3mN-wVw2D",
        "outputId": "8b8ff5e3-9eb0-48dd-8c63-f35dfa3695d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "find: ‘/proc/59/task/59/net’: Invalid argument\n",
            "find: ‘/proc/59/net’: Invalid argument\n",
            "find: ‘/proc/37519’: No such file or directory\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, log_loss, jaccard_score\n",
        "true_classes = [1, 0, 1, 1, 0]\n",
        "predicted_classes = [1, 1, 0, 1, 0]\n",
        "print(f\"Accuracy: {accuracy_score(true_classes, predicted_classes)}\")\n",
        "print(f\"Precision: {precision_score(true_classes, predicted_classes)}\")\n",
        "print(f\"Recall: {recall_score(true_classes, predicted_classes)}\")\n",
        "print(f\"F1 Score: {f1_score(true_classes, predicted_classes)}\")\n",
        "print(f\"Log Loss: {log_loss(true_classes, predicted_classes)}\")\n",
        "print(f\"Jaccard Score: {jaccard_score(true_classes, predicted_classes)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UBh7jLGuWdne",
        "outputId": "936069b3-acbc-4cf2-8d7a-38e24c36ca72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.6\n",
            "Precision: 0.6666666666666666\n",
            "Recall: 0.6666666666666666\n",
            "F1 Score: 0.6666666666666666\n",
            "Log Loss: 14.41746135564686\n",
            "Jaccard Score: 0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "K-dwI-JbW76t"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"\\nClassification Report:\")\n",
        "print(classification_report(true_classes, predicted_classes,digits=4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qtfZRkgnXZDb",
        "outputId": "cf038a37-cbad-4edf-e0c8-46685e672165"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Classification Report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0     0.5000    0.5000    0.5000         2\n",
            "           1     0.6667    0.6667    0.6667         3\n",
            "\n",
            "    accuracy                         0.6000         5\n",
            "   macro avg     0.5833    0.5833    0.5833         5\n",
            "weighted avg     0.6000    0.6000    0.6000         5\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_curve, roc_auc_score\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.patches import Patch"
      ],
      "metadata": {
        "id": "qxqGxcfLXjfz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def save_and_display_gradcam(img_path, heatmap, alpha=0.7):\n",
        "    img = cv2.imread(img_path)\n",
        "    img = cv2.resize(img, (299, 299))\n",
        "    heatmap = cv2.resize(heatmap, (img.shape[1], img.shape[0]))\n",
        "    heatmap = np.uint8(255 * heatmap)\n",
        "    heatmap = cv2.applyColorMap(heatmap, cv2.COLORMAP_PLASMA)\n",
        "    superimposed_img = cv2.addWeighted(heatmap, alpha, img, 1 - alpha, 0)\n",
        "    plt.figure(figsize=(4, 4))\n",
        "    plt.imshow(cv2.cvtColor(superimposed_img, cv2.COLOR_BGR2RGB))\n",
        "    plt.title('GradCAM', fontdict={'family': 'Serif', 'weight': 'bold', 'size': 12})\n",
        "    plt.axis('off')\n",
        "    plt.tight_layout()\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "lyeCeQUIX4Lf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def make_gradcam_heatmap(img_array, model, last_conv_layer_name, pred_index=None):\n",
        "    model.layers[-1].activation = None\n",
        "    grad_model = tf.keras.models.Model(\n",
        "        [model.inputs], [model.get_layer(last_conv_layer_name).output, model.output]\n",
        "    )\n",
        "    with tf.GradientTape() as tape:\n",
        "        last_conv_layer_output, preds = grad_model(img_array)\n",
        "        if pred_index is None:\n",
        "            pred_index = tf.argmax(preds[0])\n",
        "        class_channel = preds[:, pred_index]\n",
        "    grads = tape.gradient(class_channel, last_conv_layer_output)\n",
        "    pooled_grads = tf.reduce_mean(grads, axis=(0, 1, 2))\n",
        "    last_conv_layer_output = last_conv_layer_output[0]\n",
        "    heatmap = last_conv_layer_output @ pooled_grads[..., tf.newaxis]\n",
        "    heatmap = tf.squeeze(heatmap)\n",
        "    heatmap = tf.maximum(heatmap, 0) / tf.math.reduce_max(heatmap)\n",
        "    return heatmap.numpy()"
      ],
      "metadata": {
        "id": "7Z3AEJgGYDSa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import cv2"
      ],
      "metadata": {
        "id": "RNAbWnoPYNJY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "abcd = cv2.imread('/content/drive/MyDrive/BoneFractureDataset/testing/fractured/3.jpg')"
      ],
      "metadata": {
        "id": "IAjsdeJwYS3W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(abcd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "MU-tZljUYkNU",
        "outputId": "cc4c09bd-dc2a-4203-b75d-4e6f1f0414c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7da20a16ff70>"
            ]
          },
          "metadata": {},
          "execution_count": 66
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}