FSL_Benchmarks/FSL_ResNet50_XrayReborn_1pc.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "da6eb011"
   },
   "source": [
    "# Fully-Supervised Learning using ResNet50 on 1% of XRay_Reborn Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train on only 1% of the labelled training data in order to compare Fully-Supervised vs Semi-Supervised approaches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2375,
     "status": "ok",
     "timestamp": 1637267862996,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "6131955e",
    "outputId": "c322858c-69d5-43ce-a08b-83ba46b64a0e"
   },
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_format='retina'\n",
    "COLAB = 'google.colab' in str(get_ipython())\n",
    "import tensorflow as tf\n",
    "import os\n",
    "if COLAB:\n",
    "    !nvidia-smi -L\n",
    "    from google.colab import drive\n",
    "    drive.mount('/content/drive')\n",
    "    ![ ! -f \"pip_installed\" ] && pip install -q tensorflow-datasets==4.4.0 && touch pip_installed\n",
    "    ![ ! -d \"/root/tensorflow_datasets\" ] &&  unzip -qq /content/drive/MyDrive/TeamSemiSuperCV/Wing/xray_reborn.zip -d /root/tensorflow_datasets\n",
    "else:\n",
    "    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n",
    "    os.environ[\"CUDA_VISIBLE_DEVICES\"] = '1'\n",
    "    print(tf.config.list_physical_devices('GPU'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "executionInfo": {
     "elapsed": 522,
     "status": "ok",
     "timestamp": 1637267863513,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "32cc774b"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow_datasets as tfds\n",
    "from collections import Counter\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.losses import BinaryCrossentropy\n",
    "from tensorflow.keras.regularizers import l2\n",
    "from tensorflow.keras.metrics import Precision, Recall\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications import ResNet50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1637267863513,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "f41b1bab"
   },
   "outputs": [],
   "source": [
    "RAND_SEED = 42\n",
    "BATCH_SIZE = 128\n",
    "IMG_SIZE = (256, 256, 3)\n",
    "CLASS_WEIGHTS = {0: 0.6, 1: 0.4}\n",
    "SHUFFLE_SIZE = BATCH_SIZE * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 883,
     "status": "ok",
     "timestamp": 1637267864392,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "92298f03",
    "outputId": "75ca0fa7-37d8-4da7-b396-1ff3aa2945ba"
   },
   "outputs": [],
   "source": [
    "ds = tfds.load('xray_reborn')\n",
    "ds_train = ds['train_1pc']  # Train on only 1% (for comparing against SSL)\n",
    "ds_valid = ds['validation']\n",
    "ds_test = ds['test']\n",
    "ds_train.element_spec, ds_valid.element_spec, ds_test.element_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1637267864393,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "a152eefe",
    "outputId": "e9302de0-8ef2-494c-d346-dee3d8d32aa9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(52, 524, 623)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ds_train), len(ds_valid), len(ds_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "executionInfo": {
     "elapsed": 3380,
     "status": "ok",
     "timestamp": 1637267867770,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "7d729856",
    "outputId": "bb1fb7a5-1e0a-4dde-eb13-42940b650e7c"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"Train: Counter({'1': 41, '0': 11}), Valid: Counter({'1': 384, '0': 140}), Test: Counter({'1': 451, '0': 172})\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cnt_train, cnt_valid, cnt_test = Counter(), Counter(), Counter()\n",
    "for ex in ds_train: cnt_train.update(str(ex['label'].numpy()))\n",
    "for ex in ds_valid: cnt_valid.update(str(ex['label'].numpy()))\n",
    "for ex in ds_test: cnt_test.update(str(ex['label'].numpy()))\n",
    "f'Train: {cnt_train}, Valid: {cnt_valid}, Test: {cnt_test}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1637267867771,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "e2dfe225",
    "outputId": "43c581f1-03eb-4ba0-f9e1-320305628e6c"
   },
   "outputs": [],
   "source": [
    "ds_train_xy = ds_train.map(lambda d: (d['image'], d['label']))\n",
    "ds_valid_xy = ds_valid.map(lambda d: (d['image'], d['label']))\n",
    "ds_test_xy = ds_test.map(lambda d: (d['image'], d['label']))\n",
    "ds_train_xy.element_spec, ds_valid_xy.element_spec, ds_test_xy.element_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1637267867771,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "43d1a896"
   },
   "outputs": [],
   "source": [
    "def img_preprocess(imgs):\n",
    "    imgs = tf.image.resize(imgs, IMG_SIZE[:2], method='bicubic')\n",
    "    return imgs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1637267867772,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "d69e47cd"
   },
   "outputs": [],
   "source": [
    "def model_preprocess(x):\n",
    "    x = Rescaling(1/255)(x)\n",
    "    x = RandomFlip(\"horizontal\")(x)\n",
    "    x = RandomTranslation(0.15, 0.15, fill_mode='constant', fill_value=0.0)(x)\n",
    "    x = RandomZoom(0.1, fill_mode='constant', fill_value=0.0)(x)\n",
    "    x = RandomRotation(0.1, fill_mode='constant', fill_value=0.0)(x)\n",
    "    x = RandomContrast(0.15)(x)\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 198,
     "status": "ok",
     "timestamp": 1637267867963,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "241150ab",
    "outputId": "5935a5a2-75ea-4a63-a189-33efb87e75ab"
   },
   "outputs": [],
   "source": [
    "ds_train_xy = ds_train_xy.prefetch(tf.data.AUTOTUNE)\n",
    "ds_train_xy = ds_train_xy.map(lambda x, y: (img_preprocess(x), y))\n",
    "ds_train_xy = ds_train_xy.shuffle(SHUFFLE_SIZE).batch(BATCH_SIZE).cache().prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "ds_valid_xy = ds_valid_xy.prefetch(tf.data.AUTOTUNE)\n",
    "ds_valid_xy = ds_valid_xy.map(lambda x, y: (img_preprocess(x), y))\n",
    "ds_valid_xy = ds_valid_xy.shuffle(SHUFFLE_SIZE).batch(BATCH_SIZE).cache().prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "ds_test_xy = ds_test_xy.prefetch(tf.data.AUTOTUNE)\n",
    "ds_test_xy = ds_test_xy.map(lambda x, y: (img_preprocess(x), y))\n",
    "ds_test_xy = ds_test_xy.shuffle(SHUFFLE_SIZE).batch(BATCH_SIZE).cache().prefetch(tf.data.AUTOTUNE)\n",
    "\n",
    "ds_train_xy.element_spec, ds_valid_xy.element_spec, ds_test_xy.element_spec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1637267867964,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "98b85f92"
   },
   "outputs": [],
   "source": [
    "# This model uses ResNet50 as the backbone\n",
    "def make_model(input_shape):\n",
    "    initializer = 'glorot_uniform'\n",
    "    \n",
    "    base_model = ResNet50(include_top=False, pooling='max', weights=\"imagenet\",\n",
    "                          input_shape=input_shape)\n",
    "    base_model.trainable = False\n",
    "\n",
    "    inputs = Input(shape=input_shape)\n",
    "    x = model_preprocess(inputs)\n",
    "    x = base_model(x)\n",
    "    x = Dropout(0.4)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    x = Dense(64, activation='relu', kernel_initializer=initializer,\n",
    "              kernel_regularizer=l2(1e-4))(x)\n",
    "    x = Dropout(0.4)(x)\n",
    "    x = BatchNormalization()(x)\n",
    "    outputs = Dense(1, activation='sigmoid', kernel_initializer=initializer,\n",
    "                    kernel_regularizer=l2(1e-4))(x)\n",
    "    \n",
    "    model = Model(inputs=inputs, outputs=outputs)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1637267867964,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "553034d9"
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "# Fit <model> using <optimizer> and <class_weight>\n",
    "# while accumulating histories\n",
    "def fit(learning_rate, epochs, reset_hist=False):\n",
    "    if reset_hist:\n",
    "        fit.history = defaultdict(lambda: [])\n",
    "    \n",
    "    optimizer.lr.assign(learning_rate)\n",
    "    history = model.fit(ds_train_xy, validation_data=ds_valid_xy,\n",
    "                        class_weight=CLASS_WEIGHTS, verbose=2,\n",
    "                        epochs=epochs)\n",
    "    for metric, values in history.history.items():\n",
    "        fit.history[metric].extend(values)\n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "executionInfo": {
     "elapsed": 6,
     "status": "ok",
     "timestamp": 1637267867965,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "02baaee6"
   },
   "outputs": [],
   "source": [
    "# Plot Training-vs-Validation Metrics\n",
    "def plot_fit():\n",
    "    # first two log entries will be skipped to not skew y-scale\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    ax = plt.subplot(1, 2, 1)\n",
    "    ax.set_xlabel('Epochs')\n",
    "    ax.set_title('Training Loss, Validation Loss');\n",
    "    ax.plot(fit.history['loss'][2:], label='Train')\n",
    "    ax.plot(fit.history['val_loss'][2:], label='Valid')\n",
    "    ax.set_ylim([0, 0.5])\n",
    "    ax.legend()\n",
    "    \n",
    "    ax = plt.subplot(1, 2, 2)\n",
    "    ax.set_xlabel('Epochs')\n",
    "    ax.set_title('Training Accuracy, Validation Accuracy');\n",
    "    ax.plot(fit.history['accuracy'][2:], label='Train')\n",
    "    ax.plot(fit.history['val_accuracy'][2:], label='Valid')\n",
    "    ax.set_ylim([0.7, 1.0])\n",
    "    ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 2169,
     "status": "ok",
     "timestamp": 1637267870129,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "382d07fb",
    "outputId": "5ed3bbe5-56af-4383-a880-27976f9c5878"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " input_2 (InputLayer)        [(None, 256, 256, 3)]     0         \n",
      "                                                                 \n",
      " rescaling (Rescaling)       (None, 256, 256, 3)       0         \n",
      "                                                                 \n",
      " random_flip (RandomFlip)    (None, 256, 256, 3)       0         \n",
      "                                                                 \n",
      " random_translation (RandomT  (None, 256, 256, 3)      0         \n",
      " ranslation)                                                     \n",
      "                                                                 \n",
      " random_zoom (RandomZoom)    (None, 256, 256, 3)       0         \n",
      "                                                                 \n",
      " random_rotation (RandomRota  (None, 256, 256, 3)      0         \n",
      " tion)                                                           \n",
      "                                                                 \n",
      " random_contrast (RandomCont  (None, 256, 256, 3)      0         \n",
      " rast)                                                           \n",
      "                                                                 \n",
      " resnet50 (Functional)       (None, 2048)              23587712  \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 2048)              0         \n",
      "                                                                 \n",
      " batch_normalization (BatchN  (None, 2048)             8192      \n",
      " ormalization)                                                   \n",
      "                                                                 \n",
      " dense (Dense)               (None, 64)                131136    \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 64)                0         \n",
      "                                                                 \n",
      " batch_normalization_1 (Batc  (None, 64)               256       \n",
      " hNormalization)                                                 \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 65        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 23,727,361\n",
      "Trainable params: 135,425\n",
      "Non-trainable params: 23,591,936\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "\n",
    "model = make_model(IMG_SIZE)\n",
    "optimizer = Adam()\n",
    "model.compile(loss=BinaryCrossentropy(from_logits=False, label_smoothing=0.1),\n",
    "              optimizer=optimizer,\n",
    "              metrics=['accuracy'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 33786,
     "status": "ok",
     "timestamp": 1637267903910,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "08013914",
    "outputId": "6378722e-808b-4d2f-e507-ada8ece8bee5",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fit(1e-5, 5, reset_hist=True);\n",
    "fit(1e-4, 5);\n",
    "fit(1e-3, 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 24334,
     "status": "ok",
     "timestamp": 1637267928235,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "dc80b23d",
    "outputId": "11b367aa-a13b-4bd8-8d22-14ef7bbe4535",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fit(1e-5, 5);\n",
    "fit(1e-4, 5);\n",
    "fit(1e-3, 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 30688,
     "status": "ok",
     "timestamp": 1637267958919,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "e7d51882",
    "outputId": "f6046cd7-e827-45b9-d923-c98f831c2e4a"
   },
   "outputs": [],
   "source": [
    "fit(1e-4, 10);\n",
    "fit(5e-4, 5);\n",
    "fit(1e-4, 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12217,
     "status": "ok",
     "timestamp": 1637267971127,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "4fa570da",
    "outputId": "a464cbec-ea11-4b2d-def5-20fb66874916",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fit(1e-5, 10);\n",
    "# fit(5e-5, 5);\n",
    "# fit(1e-5, 10);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 977,
     "status": "ok",
     "timestamp": 1637267983674,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "d123aa32",
    "outputId": "8fdb6db7-f17d-441f-e36b-5227e64dc14d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 1s 236ms/step - loss: 0.5500 - accuracy: 0.8523\n",
      "['loss', 'accuracy']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5499760508537292, 0.8523274660110474]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# xray\n",
    "results = model.evaluate(ds_test_xy)\n",
    "print(model.metrics_names)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "executionInfo": {
     "elapsed": 671,
     "status": "ok",
     "timestamp": 1637267988043,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "990fc5e3",
    "outputId": "51b5da9e-9b04-4059-f731-39ce577812ff"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 277,
       "width": 706
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 7,
     "status": "ok",
     "timestamp": 1637267976585,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "MyUEIEUsCZe8"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "FSL_ResNet50_XrayReborn_5pc-2.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}