FSL_Benchmarks/FSL_ResNet50_XrayReborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "da6eb011"
   },
   "source": [
    "# Fully-Supervised Learning using ResNet50 on XRay_Reborn Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "6131955e"
   },
   "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": 541,
     "status": "ok",
     "timestamp": 1636929169326,
     "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": 1636929169327,
     "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": 3669,
     "status": "ok",
     "timestamp": 1636929172993,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "92298f03",
    "outputId": "1ddabb45-e21e-4d71-90bb-9091ab6f2229"
   },
   "outputs": [],
   "source": [
    "ds = tfds.load('xray_reborn')\n",
    "ds_train = ds['train']\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": 7,
     "status": "ok",
     "timestamp": 1636929172994,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "a152eefe",
    "outputId": "e617c017-a0ac-4ab3-d98b-5f7d784dc658"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4708, 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": 16105,
     "status": "ok",
     "timestamp": 1636929189095,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "7d729856",
    "outputId": "7a103212-114a-486e-bccc-df72e10a449e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "\"Train: Counter({'1': 3437, '0': 1271}), 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": 14,
     "status": "ok",
     "timestamp": 1636929189096,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "e2dfe225",
    "outputId": "40ace763-d279-4773-9a61-8eca3a487bfa"
   },
   "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": 11,
     "status": "ok",
     "timestamp": 1636929189096,
     "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": 12,
     "status": "ok",
     "timestamp": 1636929189097,
     "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": 11,
     "status": "ok",
     "timestamp": 1636929189097,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "241150ab",
    "outputId": "2b978030-c89f-4144-8d08-232aaa94fdbb"
   },
   "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": 202,
     "status": "ok",
     "timestamp": 1636929189291,
     "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_normal'\n",
    "    \n",
    "    base_model = ResNet50(include_top=False, pooling='max', weights=\"imagenet\",\n",
    "                          input_shape=input_shape)\n",
    "    base_model.trainable = True\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": 6,
     "status": "ok",
     "timestamp": 1636929189292,
     "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": 5,
     "status": "ok",
     "timestamp": 1636929189292,
     "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": 2457,
     "status": "ok",
     "timestamp": 1636929191745,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "382d07fb",
    "outputId": "cd86adaa-a2e6-40ea-fea9-597a27ee23ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94773248/94765736 [==============================] - 0s 0us/step\n",
      "94781440/94765736 [==============================] - 0s 0us/step\n",
      "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: 23,670,017\n",
      "Non-trainable params: 57,344\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": 1049598,
     "status": "ok",
     "timestamp": 1636930241340,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "08013914",
    "outputId": "7a067596-b772-4d0f-da0f-6af181fe386c",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "fit(1e-5, 5, reset_hist=True);\n",
    "fit(1e-4, 5);\n",
    "fit(1e-3, 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 797202,
     "status": "ok",
     "timestamp": 1636931038539,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "dc80b23d",
    "outputId": "ee5bf72b-a51c-4a9d-9934-dfda3f8401aa",
    "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": 1389465,
     "status": "ok",
     "timestamp": 1636932427993,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "e7d51882",
    "outputId": "fd78416c-4b7a-4e7d-b219-0f21e8e57e1b"
   },
   "outputs": [],
   "source": [
    "fit(1e-4, 15);\n",
    "fit(5e-4, 5);\n",
    "fit(1e-4, 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1412117,
     "status": "ok",
     "timestamp": 1636933840106,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "4fa570da",
    "outputId": "1a5fe54b-67f3-4298-f1b2-ba9130f64cd0",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "fit(1e-5, 15);\n",
    "fit(5e-5, 5);\n",
    "fit(1e-5, 15);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 479998,
     "status": "ok",
     "timestamp": 1636934320095,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "7672af6e",
    "outputId": "328a8c14-5217-463f-ed0e-bfc15945f0af"
   },
   "outputs": [],
   "source": [
    "fit(1e-6, 5);\n",
    "fit(5e-6, 2);\n",
    "fit(1e-6, 5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5029,
     "status": "ok",
     "timestamp": 1636934325120,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "d123aa32",
    "outputId": "ae2edc85-dd65-4d9a-e914-4282423196a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5/5 [==============================] - 5s 375ms/step - loss: 0.2267 - accuracy: 0.9888\n",
      "['loss', 'accuracy']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.22672413289546967, 0.9887640476226807]"
      ]
     },
     "execution_count": 20,
     "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": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 294
    },
    "executionInfo": {
     "elapsed": 2592,
     "status": "ok",
     "timestamp": 1636934327708,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "990fc5e3",
    "outputId": "40d3d7e8-6d1f-4798-f575-c389e8992991"
   },
   "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": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 881,
     "status": "ok",
     "timestamp": 1636934733624,
     "user": {
      "displayName": "Sim Clr",
      "photoUrl": "https://lh3.googleusercontent.com/a/default-user=s64",
      "userId": "09230613867822379570"
     },
     "user_tz": 480
    },
    "id": "09537479"
   },
   "outputs": [],
   "source": [
    "model.save_weights('/content/drive/MyDrive/TeamSemiSuperCV/Wing/FSL_Benchmarks/FSL_ResNet50_XrayReborn.h5')"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "FSL_ResNet50_XrayReborn.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
}