MRNet_Transfer_Learning_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qr36yUFeW2nZ"
   },
   "outputs": [],
   "source": [
    "#imports cell \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils import class_weight\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras.applications.inception_resnet_v2 import InceptionResNetV2\n",
    "from tensorflow.keras.applications.resnet50 import ResNet50\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "from tensorflow.keras.layers import Dense,Input,Flatten,GlobalAveragePooling2D,Dropout,BatchNormalization,Conv2D,Activation,MaxPooling2D\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint,EarlyStopping\n",
    "from tensorflow.keras import backend as K\n",
    "K.set_image_data_format(\"channels_first\")\n",
    "from tensorflow.keras import optimizers,Sequential"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oEEb16xtStmT"
   },
   "source": [
    "## Mount the Google Drive to Google Colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 122
    },
    "colab_type": "code",
    "id": "oiYVvU4lR5Kd",
    "outputId": "eb2f8d02-f0a7-4179-c386-78b42319a86d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
      "\n",
      "Enter your authorization code:\n",
      "··········\n",
      "Mounted at /content/gdrive\n"
     ]
    }
   ],
   "source": [
    "from google.colab import drive\n",
    "drive.mount('/content/gdrive')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YrpPtEzaYAbK"
   },
   "source": [
    "## Method for Loading dataset into Memory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "P-26qZUxXUQm"
   },
   "outputs": [],
   "source": [
    "def load_data(plane,label):\n",
    "  train_X = np.load('/content/gdrive/My Drive/Dataset/Transformations/mid3_train_'+plane+'.npy')\n",
    "  train_y = np.genfromtxt('/content/gdrive/My Drive/Dataset/MRNet-v1.0/train-'+label+'.csv',delimiter=',')[:,1]\n",
    "  val_X = np.load('/content/gdrive/My Drive/Dataset/Transformations/mid3_valid_'+plane+'.npy')\n",
    "  val_y = np.genfromtxt('/content/gdrive/My Drive/Dataset/MRNet-v1.0/valid-'+label+'.csv',delimiter=',')[:,1]\n",
    "  return train_X,train_y,val_X,val_y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fjZznRHnLLRg"
   },
   "source": [
    "## Defining Our Base Model upon which Transfer Learning is done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 156
    },
    "colab_type": "code",
    "id": "MwwjT96WYvxF",
    "outputId": "14337699-e2c7-4584-c7f9-62dbb4b00796"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/keras_applications/resnet50.py:265: UserWarning: The output shape of `ResNet50(include_top=False)` has been changed since Keras 2.2.0.\n",
      "  warnings.warn('The output shape of `ResNet50(include_top=False)` '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94658560/94653016 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "base_model = ResNet50(weights='imagenet',input_shape=(3,256,256),include_top=False,input_tensor=Input(shape=(3,256,256)))\n",
    "for layer in base_model.layers:\n",
    "  layer.trainable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jxxrVaBOMcaR"
   },
   "source": [
    "## Defining our Model Built over Base Model and it's Methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oxFFma0Ufwwc"
   },
   "outputs": [],
   "source": [
    "def build_model():\n",
    "  model = base_model.output\n",
    "  model = Conv2D(64, (3, 3), padding='same', name='block1_conv1')(model)\n",
    "  model = BatchNormalization()(model)\n",
    "  model = Activation('relu')(model)\n",
    "  model = Conv2D(64, (3, 3), padding='same', name='block1_conv2')(model)\n",
    "  model = BatchNormalization()(model)\n",
    "  model = Activation('relu')(model)\n",
    "  model = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(model)\n",
    "  model = Flatten()(model)\n",
    "  model = Dense(64, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(0.001))(model)\n",
    "  model = Dropout(0.5)(model)\n",
    "  model = Dense(64, activation=\"relu\", kernel_regularizer=keras.regularizers.l2(0.001))(model)\n",
    "  model = Dropout(0.5)(model)\n",
    "  preds = Dense(1,activation='sigmoid',name='preds')(model)\n",
    "  model_final=Model(inputs=base_model.input,outputs=preds)\n",
    "  sgd = optimizers.SGD(lr=0.005)\n",
    "  model_final.compile(optimizer= sgd,loss='binary_crossentropy',metrics=['accuracy'])\n",
    "  return model_final\n",
    "  \n",
    "def show_model(model):\n",
    "  model.summary()\n",
    "\n",
    "def train_model(model,plane,label):\n",
    "  train_X,train_y,val_X,val_y = load_data(plane,label)\n",
    "  class_weights = class_weight.compute_class_weight('balanced',np.unique(train_y),train_y)\n",
    "  class_weights_dict = dict(enumerate(class_weights))\n",
    "  val_sample_weights = class_weight.compute_sample_weight('balanced', val_y)\n",
    "  mc = ModelCheckpoint('/content/gdrive/My Drive/Models/TransferLearning/'+plane+'_'+label+'.h5', monitor='val_acc', mode='max', verbose=2, save_best_only=True)\n",
    "  es = EarlyStopping(monitor='val_loss', mode='min', verbose=2, patience= 3)\n",
    "  history = model.fit(x=train_X, y=train_y,  validation_data=(val_X,val_y), batch_size = 20, epochs=50,verbose=2, callbacks=[mc,es], class_weight=class_weights_dict)\n",
    "  return history\n",
    "  \n",
    "def plot_history(histories, key='acc'):\n",
    "  plt.figure(figsize=(16,10))\n",
    "  for name, history in histories:\n",
    "    val = plt.plot(history.epoch, history.history['val_'+key],\n",
    "                   '--', label=name.title()+' Val',color='b')\n",
    "    plt.plot(history.epoch, history.history[key], color='b',\n",
    "             label=name.title()+' Train')\n",
    "  plt.xlabel('Epochs')\n",
    "  plt.ylabel(key.replace('_',' ').title())\n",
    "  plt.legend()\n",
    "  plt.ylim([0,1])\n",
    "  plt.xlim([0,max(history.epoch)])\n",
    "  plt.figure(0)\n",
    "  plt.show()\n",
    "  \n",
    "  plt.figure(figsize=(16,10))\n",
    "  for name, history in histories:\n",
    "    val = plt.plot(history.epoch, history.history['val_loss'],\n",
    "                   '--', label=name.title()+' Val',color='r')\n",
    "    plt.plot(history.epoch, history.history['loss'], color='r',\n",
    "             label=name.title()+' Train')\n",
    "  plt.xlabel('Epochs')\n",
    "  plt.ylabel('Loss')\n",
    "  plt.legend()\n",
    "  plt.ylim([0,1.5])\n",
    "  plt.xlim([0,max(history.epoch)])\n",
    "  plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Y_DdFaVpMtPs"
   },
   "source": [
    "## This is How the Final model looks like "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 6800
    },
    "colab_type": "code",
    "id": "_17T7-HDnI2Q",
    "outputId": "7eaf1b1f-dcc6-4ba9-fa8b-1454e6df205d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            (None, 3, 256, 256)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)       (None, 3, 262, 262)  0           input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "conv1 (Conv2D)                  (None, 64, 128, 128) 9472        conv1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "bn_conv1 (BatchNormalizationV1) (None, 64, 128, 128) 256         conv1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 64, 128, 128) 0           bn_conv1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "pool1_pad (ZeroPadding2D)       (None, 64, 130, 130) 0           activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 64, 64, 64)   0           pool1_pad[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2a (Conv2D)         (None, 64, 64, 64)   4160        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2a (BatchNormalizati (None, 64, 64, 64)   256         res2a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 64, 64, 64)   0           bn2a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2b (Conv2D)         (None, 64, 64, 64)   36928       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2b (BatchNormalizati (None, 64, 64, 64)   256         res2a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 64, 64, 64)   0           bn2a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch2c (Conv2D)         (None, 256, 64, 64)  16640       activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "res2a_branch1 (Conv2D)          (None, 256, 64, 64)  16640       max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch2c (BatchNormalizati (None, 256, 64, 64)  1024        res2a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn2a_branch1 (BatchNormalizatio (None, 256, 64, 64)  1024        res2a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 256, 64, 64)  0           bn2a_branch2c[0][0]              \n",
      "                                                                 bn2a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 256, 64, 64)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2a (Conv2D)         (None, 64, 64, 64)   16448       activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2a (BatchNormalizati (None, 64, 64, 64)   256         res2b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 64, 64, 64)   0           bn2b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2b (Conv2D)         (None, 64, 64, 64)   36928       activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2b (BatchNormalizati (None, 64, 64, 64)   256         res2b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 64, 64, 64)   0           bn2b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2b_branch2c (Conv2D)         (None, 256, 64, 64)  16640       activation_5[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2b_branch2c (BatchNormalizati (None, 256, 64, 64)  1024        res2b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 256, 64, 64)  0           bn2b_branch2c[0][0]              \n",
      "                                                                 activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 256, 64, 64)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2a (Conv2D)         (None, 64, 64, 64)   16448       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2a (BatchNormalizati (None, 64, 64, 64)   256         res2c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 64, 64, 64)   0           bn2c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2b (Conv2D)         (None, 64, 64, 64)   36928       activation_7[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2b (BatchNormalizati (None, 64, 64, 64)   256         res2c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 64, 64, 64)   0           bn2c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res2c_branch2c (Conv2D)         (None, 256, 64, 64)  16640       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn2c_branch2c (BatchNormalizati (None, 256, 64, 64)  1024        res2c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 256, 64, 64)  0           bn2c_branch2c[0][0]              \n",
      "                                                                 activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 256, 64, 64)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2a (Conv2D)         (None, 128, 32, 32)  32896       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2a (BatchNormalizati (None, 128, 32, 32)  512         res3a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 128, 32, 32)  0           bn3a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2b (Conv2D)         (None, 128, 32, 32)  147584      activation_10[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2b (BatchNormalizati (None, 128, 32, 32)  512         res3a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 128, 32, 32)  0           bn3a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch2c (Conv2D)         (None, 512, 32, 32)  66048       activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3a_branch1 (Conv2D)          (None, 512, 32, 32)  131584      activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch2c (BatchNormalizati (None, 512, 32, 32)  2048        res3a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn3a_branch1 (BatchNormalizatio (None, 512, 32, 32)  2048        res3a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 512, 32, 32)  0           bn3a_branch2c[0][0]              \n",
      "                                                                 bn3a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 512, 32, 32)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2a (Conv2D)         (None, 128, 32, 32)  65664       activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2a (BatchNormalizati (None, 128, 32, 32)  512         res3b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 128, 32, 32)  0           bn3b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2b (Conv2D)         (None, 128, 32, 32)  147584      activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2b (BatchNormalizati (None, 128, 32, 32)  512         res3b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 128, 32, 32)  0           bn3b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3b_branch2c (Conv2D)         (None, 512, 32, 32)  66048       activation_14[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3b_branch2c (BatchNormalizati (None, 512, 32, 32)  2048        res3b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 512, 32, 32)  0           bn3b_branch2c[0][0]              \n",
      "                                                                 activation_12[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 512, 32, 32)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2a (Conv2D)         (None, 128, 32, 32)  65664       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2a (BatchNormalizati (None, 128, 32, 32)  512         res3c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 128, 32, 32)  0           bn3c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2b (Conv2D)         (None, 128, 32, 32)  147584      activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2b (BatchNormalizati (None, 128, 32, 32)  512         res3c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 128, 32, 32)  0           bn3c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3c_branch2c (Conv2D)         (None, 512, 32, 32)  66048       activation_17[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3c_branch2c (BatchNormalizati (None, 512, 32, 32)  2048        res3c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 512, 32, 32)  0           bn3c_branch2c[0][0]              \n",
      "                                                                 activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 512, 32, 32)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2a (Conv2D)         (None, 128, 32, 32)  65664       activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2a (BatchNormalizati (None, 128, 32, 32)  512         res3d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 128, 32, 32)  0           bn3d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2b (Conv2D)         (None, 128, 32, 32)  147584      activation_19[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2b (BatchNormalizati (None, 128, 32, 32)  512         res3d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 128, 32, 32)  0           bn3d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res3d_branch2c (Conv2D)         (None, 512, 32, 32)  66048       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn3d_branch2c (BatchNormalizati (None, 512, 32, 32)  2048        res3d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 512, 32, 32)  0           bn3d_branch2c[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 512, 32, 32)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2a (Conv2D)         (None, 256, 16, 16)  131328      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2a (BatchNormalizati (None, 256, 16, 16)  1024        res4a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 256, 16, 16)  0           bn4a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2b (Conv2D)         (None, 256, 16, 16)  590080      activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2b (BatchNormalizati (None, 256, 16, 16)  1024        res4a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 256, 16, 16)  0           bn4a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch2c (Conv2D)         (None, 1024, 16, 16) 263168      activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4a_branch1 (Conv2D)          (None, 1024, 16, 16) 525312      activation_21[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch2c (BatchNormalizati (None, 1024, 16, 16) 4096        res4a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn4a_branch1 (BatchNormalizatio (None, 1024, 16, 16) 4096        res4a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 1024, 16, 16) 0           bn4a_branch2c[0][0]              \n",
      "                                                                 bn4a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 1024, 16, 16) 0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2a (Conv2D)         (None, 256, 16, 16)  262400      activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2a (BatchNormalizati (None, 256, 16, 16)  1024        res4b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 256, 16, 16)  0           bn4b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2b (Conv2D)         (None, 256, 16, 16)  590080      activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2b (BatchNormalizati (None, 256, 16, 16)  1024        res4b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 256, 16, 16)  0           bn4b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4b_branch2c (Conv2D)         (None, 1024, 16, 16) 263168      activation_26[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4b_branch2c (BatchNormalizati (None, 1024, 16, 16) 4096        res4b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 1024, 16, 16) 0           bn4b_branch2c[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 1024, 16, 16) 0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2a (Conv2D)         (None, 256, 16, 16)  262400      activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2a (BatchNormalizati (None, 256, 16, 16)  1024        res4c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 256, 16, 16)  0           bn4c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2b (Conv2D)         (None, 256, 16, 16)  590080      activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2b (BatchNormalizati (None, 256, 16, 16)  1024        res4c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 256, 16, 16)  0           bn4c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4c_branch2c (Conv2D)         (None, 1024, 16, 16) 263168      activation_29[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4c_branch2c (BatchNormalizati (None, 1024, 16, 16) 4096        res4c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 1024, 16, 16) 0           bn4c_branch2c[0][0]              \n",
      "                                                                 activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 1024, 16, 16) 0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2a (Conv2D)         (None, 256, 16, 16)  262400      activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2a (BatchNormalizati (None, 256, 16, 16)  1024        res4d_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 256, 16, 16)  0           bn4d_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2b (Conv2D)         (None, 256, 16, 16)  590080      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2b (BatchNormalizati (None, 256, 16, 16)  1024        res4d_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 256, 16, 16)  0           bn4d_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4d_branch2c (Conv2D)         (None, 1024, 16, 16) 263168      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4d_branch2c (BatchNormalizati (None, 1024, 16, 16) 4096        res4d_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 1024, 16, 16) 0           bn4d_branch2c[0][0]              \n",
      "                                                                 activation_30[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 1024, 16, 16) 0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2a (Conv2D)         (None, 256, 16, 16)  262400      activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2a (BatchNormalizati (None, 256, 16, 16)  1024        res4e_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 256, 16, 16)  0           bn4e_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2b (Conv2D)         (None, 256, 16, 16)  590080      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2b (BatchNormalizati (None, 256, 16, 16)  1024        res4e_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 256, 16, 16)  0           bn4e_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4e_branch2c (Conv2D)         (None, 1024, 16, 16) 263168      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4e_branch2c (BatchNormalizati (None, 1024, 16, 16) 4096        res4e_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 1024, 16, 16) 0           bn4e_branch2c[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 1024, 16, 16) 0           add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2a (Conv2D)         (None, 256, 16, 16)  262400      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2a (BatchNormalizati (None, 256, 16, 16)  1024        res4f_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 256, 16, 16)  0           bn4f_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2b (Conv2D)         (None, 256, 16, 16)  590080      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2b (BatchNormalizati (None, 256, 16, 16)  1024        res4f_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 256, 16, 16)  0           bn4f_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res4f_branch2c (Conv2D)         (None, 1024, 16, 16) 263168      activation_38[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn4f_branch2c (BatchNormalizati (None, 1024, 16, 16) 4096        res4f_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_12 (Add)                    (None, 1024, 16, 16) 0           bn4f_branch2c[0][0]              \n",
      "                                                                 activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 1024, 16, 16) 0           add_12[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2a (Conv2D)         (None, 512, 8, 8)    524800      activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2a (BatchNormalizati (None, 512, 8, 8)    2048        res5a_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 512, 8, 8)    0           bn5a_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2b (Conv2D)         (None, 512, 8, 8)    2359808     activation_40[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2b (BatchNormalizati (None, 512, 8, 8)    2048        res5a_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 512, 8, 8)    0           bn5a_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch2c (Conv2D)         (None, 2048, 8, 8)   1050624     activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5a_branch1 (Conv2D)          (None, 2048, 8, 8)   2099200     activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch2c (BatchNormalizati (None, 2048, 8, 8)   8192        res5a_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "bn5a_branch1 (BatchNormalizatio (None, 2048, 8, 8)   8192        res5a_branch1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "add_13 (Add)                    (None, 2048, 8, 8)   0           bn5a_branch2c[0][0]              \n",
      "                                                                 bn5a_branch1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 2048, 8, 8)   0           add_13[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2a (Conv2D)         (None, 512, 8, 8)    1049088     activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2a (BatchNormalizati (None, 512, 8, 8)    2048        res5b_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 512, 8, 8)    0           bn5b_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2b (Conv2D)         (None, 512, 8, 8)    2359808     activation_43[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2b (BatchNormalizati (None, 512, 8, 8)    2048        res5b_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 512, 8, 8)    0           bn5b_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5b_branch2c (Conv2D)         (None, 2048, 8, 8)   1050624     activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5b_branch2c (BatchNormalizati (None, 2048, 8, 8)   8192        res5b_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_14 (Add)                    (None, 2048, 8, 8)   0           bn5b_branch2c[0][0]              \n",
      "                                                                 activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 2048, 8, 8)   0           add_14[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2a (Conv2D)         (None, 512, 8, 8)    1049088     activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2a (BatchNormalizati (None, 512, 8, 8)    2048        res5c_branch2a[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 512, 8, 8)    0           bn5c_branch2a[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2b (Conv2D)         (None, 512, 8, 8)    2359808     activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2b (BatchNormalizati (None, 512, 8, 8)    2048        res5c_branch2b[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 512, 8, 8)    0           bn5c_branch2b[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "res5c_branch2c (Conv2D)         (None, 2048, 8, 8)   1050624     activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "bn5c_branch2c (BatchNormalizati (None, 2048, 8, 8)   8192        res5c_branch2c[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "add_15 (Add)                    (None, 2048, 8, 8)   0           bn5c_branch2c[0][0]              \n",
      "                                                                 activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 2048, 8, 8)   0           add_15[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 64, 8, 8)     1179712     activation_48[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_2 (Batch (None, 64, 8, 8)     32          block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 64, 8, 8)     0           batch_normalization_v1_2[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 64, 8, 8)     36928       activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_v1_3 (Batch (None, 64, 8, 8)     32          block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 64, 8, 8)     0           batch_normalization_v1_3[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "block1_pool (MaxPooling2D)      (None, 64, 4, 4)     0           activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 1024)         0           block1_pool[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 256)          262400      flatten_1[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 256)          0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 256)          65792       dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 256)          0           dense_3[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "preds (Dense)                   (None, 1)            257         dropout_3[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 25,132,865\n",
      "Trainable params: 1,545,121\n",
      "Non-trainable params: 23,587,744\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "show_model(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8isL8vH9NXM6"
   },
   "source": [
    "# Let's Begin Training the Net\n",
    "\n",
    "## Model 1: Axial Plane and ACL Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1865
    },
    "colab_type": "code",
    "id": "YAXM5wNpn2xD",
    "outputId": "99ca9978-d94c-4803-fa54-901c0114cc69"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/100\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.49167, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_acl.h5\n",
      " - 13s - loss: 1.1464 - acc: 0.5487 - val_loss: 1.0067 - val_acc: 0.4917\n",
      "Epoch 2/100\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.49167 to 0.63333, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_acl.h5\n",
      " - 7s - loss: 1.0232 - acc: 0.6248 - val_loss: 0.9743 - val_acc: 0.6333\n",
      "Epoch 3/100\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.63333\n",
      " - 6s - loss: 0.9504 - acc: 0.6611 - val_loss: 1.1624 - val_acc: 0.4500\n",
      "Epoch 4/100\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.63333\n",
      " - 6s - loss: 0.9135 - acc: 0.7027 - val_loss: 0.9625 - val_acc: 0.5833\n",
      "Epoch 5/100\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.63333 to 0.70000, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_acl.h5\n",
      " - 9s - loss: 0.8805 - acc: 0.7319 - val_loss: 0.9267 - val_acc: 0.7000\n",
      "Epoch 6/100\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.70000 to 0.74167, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_acl.h5\n",
      " - 7s - loss: 0.8058 - acc: 0.7885 - val_loss: 0.8987 - val_acc: 0.7417\n",
      "Epoch 7/100\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.74167\n",
      " - 6s - loss: 0.7545 - acc: 0.8150 - val_loss: 0.9200 - val_acc: 0.6750\n",
      "Epoch 8/100\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.74167\n",
      " - 6s - loss: 0.6826 - acc: 0.8549 - val_loss: 1.1101 - val_acc: 0.5750\n",
      "Epoch 9/100\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.74167\n",
      " - 6s - loss: 0.6163 - acc: 0.8929 - val_loss: 0.9728 - val_acc: 0.6333\n",
      "Epoch 00009: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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atct/Te3aOQcyNWlC+xGQWxs2SPfeK02YILVubaGWJ30AhIrevaVZ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\njh9kn3nGpgDv2yd9+609YC1VqvC+h2A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zPmfMkK65xs7+BIqKkiWlpCTp5pul//xHGjLE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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WWRZikpK8HhG85jhWtbB4sXTSSVZ6TOkPAKCyy821Kr+OHaUrrrBQKxFkj1Vs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\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"axial\",\"acl\")\n",
    "plot_history([('Axial-ACL',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Syi-ONZEOx1Z"
   },
   "source": [
    "## Model 2: Axial Plane and Abnormal Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1865
    },
    "colab_type": "code",
    "id": "V2_89YnDpmJ3",
    "outputId": "8d1d6e51-4a27-4ef5-fb5f-7c9b240e4614"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/50\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.79167, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_abnormal.h5\n",
      " - 18s - loss: 1.0654 - acc: 0.5823 - val_loss: 1.0030 - val_acc: 0.7917\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.79167 to 0.80000, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_abnormal.h5\n",
      " - 7s - loss: 0.8349 - acc: 0.6903 - val_loss: 0.9054 - val_acc: 0.8000\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.80000 to 0.88333, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_abnormal.h5\n",
      " - 7s - loss: 0.7179 - acc: 0.7522 - val_loss: 0.7091 - val_acc: 0.8833\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.88333\n",
      " - 6s - loss: 0.6228 - acc: 0.8035 - val_loss: 0.6315 - val_acc: 0.8750\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.88333 to 0.89167, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_abnormal.h5\n",
      " - 9s - loss: 0.5486 - acc: 0.8212 - val_loss: 0.6872 - val_acc: 0.8917\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.89167\n",
      " - 6s - loss: 0.4763 - acc: 0.8699 - val_loss: 1.3896 - val_acc: 0.8167\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.89167\n",
      " - 6s - loss: 0.3759 - acc: 0.8991 - val_loss: 0.6972 - val_acc: 0.8167\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.89167\n",
      " - 6s - loss: 0.2956 - acc: 0.9354 - val_loss: 1.2224 - val_acc: 0.8333\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.89167\n",
      " - 6s - loss: 0.2173 - acc: 0.9558 - val_loss: 1.9420 - val_acc: 0.8333\n",
      "Epoch 00009: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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7Aiwqyju9HJk7fdoqrK9YYV8O/PKLrZtPrXr1jAMrldYBgDALFErx8bYH5/Tp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\nRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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REREJb3/9BY89Bl9/7TpJ/mnWDOrWhREjXCcRCb5nn4U//4TBgyP7PLwckdPR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\nkRAUtGLW9/0ZwLFaFV4FvO2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1tf08NhamTYORI22M0Nln29ZjERHJfxddZEXEaae5TiLR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\ngg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"axial\",\"abnormal\")\n",
    "plot_history([('Axial-Abnormal',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cwCGJsTNPa4L"
   },
   "source": [
    "## Model 3: Axial Plane and Meniscus Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 3497
    },
    "colab_type": "code",
    "id": "uUJDSMdvPfzE",
    "outputId": "d0f5955b-c681-4664-c3ec-11aebf6fe19f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/100\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.55000, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 16s - loss: 1.1249 - acc: 0.5416 - val_loss: 1.0056 - val_acc: 0.5500\n",
      "Epoch 2/100\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.55000 to 0.57500, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 7s - loss: 1.0611 - acc: 0.5522 - val_loss: 0.9800 - val_acc: 0.5750\n",
      "Epoch 3/100\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.57500 to 0.60000, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 7s - loss: 1.0224 - acc: 0.5690 - val_loss: 0.9702 - val_acc: 0.6000\n",
      "Epoch 4/100\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.60000 to 0.64167, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 7s - loss: 1.0010 - acc: 0.5814 - val_loss: 0.9585 - val_acc: 0.6417\n",
      "Epoch 5/100\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.9616 - acc: 0.6248 - val_loss: 0.9672 - val_acc: 0.6167\n",
      "Epoch 6/100\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.9643 - acc: 0.6000 - val_loss: 0.9751 - val_acc: 0.6083\n",
      "Epoch 7/100\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.9184 - acc: 0.6451 - val_loss: 0.9530 - val_acc: 0.6417\n",
      "Epoch 8/100\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.9231 - acc: 0.6681 - val_loss: 0.9648 - val_acc: 0.6083\n",
      "Epoch 9/100\n",
      "\n",
      "Epoch 00009: val_acc improved from 0.64167 to 0.65000, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 8s - loss: 0.8996 - acc: 0.6788 - val_loss: 0.9490 - val_acc: 0.6500\n",
      "Epoch 10/100\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.65000\n",
      " - 6s - loss: 0.8899 - acc: 0.6814 - val_loss: 0.9584 - val_acc: 0.6500\n",
      "Epoch 11/100\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.65000 to 0.66667, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 8s - loss: 0.8680 - acc: 0.7115 - val_loss: 0.9398 - val_acc: 0.6667\n",
      "Epoch 12/100\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.66667\n",
      " - 6s - loss: 0.8456 - acc: 0.7283 - val_loss: 0.9607 - val_acc: 0.6500\n",
      "Epoch 13/100\n",
      "\n",
      "Epoch 00013: val_acc did not improve from 0.66667\n",
      " - 6s - loss: 0.8302 - acc: 0.7407 - val_loss: 0.9399 - val_acc: 0.6667\n",
      "Epoch 14/100\n",
      "\n",
      "Epoch 00014: val_acc did not improve from 0.66667\n",
      " - 6s - loss: 0.7944 - acc: 0.7575 - val_loss: 1.0062 - val_acc: 0.5917\n",
      "Epoch 15/100\n",
      "\n",
      "Epoch 00015: val_acc did not improve from 0.66667\n",
      " - 6s - loss: 0.8135 - acc: 0.7434 - val_loss: 0.9612 - val_acc: 0.6583\n",
      "Epoch 16/100\n",
      "\n",
      "Epoch 00016: val_acc improved from 0.66667 to 0.67500, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 8s - loss: 0.7996 - acc: 0.7699 - val_loss: 0.9607 - val_acc: 0.6750\n",
      "Epoch 17/100\n",
      "\n",
      "Epoch 00017: val_acc improved from 0.67500 to 0.68333, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 7s - loss: 0.7759 - acc: 0.7850 - val_loss: 0.9355 - val_acc: 0.6833\n",
      "Epoch 18/100\n",
      "\n",
      "Epoch 00018: val_acc improved from 0.68333 to 0.69167, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 7s - loss: 0.7503 - acc: 0.8035 - val_loss: 0.9336 - val_acc: 0.6917\n",
      "Epoch 19/100\n",
      "\n",
      "Epoch 00019: val_acc did not improve from 0.69167\n",
      " - 6s - loss: 0.7547 - acc: 0.7982 - val_loss: 0.9263 - val_acc: 0.6250\n",
      "Epoch 20/100\n",
      "\n",
      "Epoch 00020: val_acc did not improve from 0.69167\n",
      " - 6s - loss: 0.7198 - acc: 0.8097 - val_loss: 0.9236 - val_acc: 0.6917\n",
      "Epoch 21/100\n",
      "\n",
      "Epoch 00021: val_acc did not improve from 0.69167\n",
      " - 6s - loss: 0.7000 - acc: 0.8230 - val_loss: 0.9253 - val_acc: 0.6750\n",
      "Epoch 22/100\n",
      "\n",
      "Epoch 00022: val_acc did not improve from 0.69167\n",
      " - 6s - loss: 0.6734 - acc: 0.8540 - val_loss: 0.9290 - val_acc: 0.6917\n",
      "Epoch 23/100\n",
      "\n",
      "Epoch 00023: val_acc improved from 0.69167 to 0.70833, saving model to /content/gdrive/My Drive/Models/TransferLearning/axial_meniscus.h5\n",
      " - 12s - loss: 0.6691 - acc: 0.8442 - val_loss: 0.9219 - val_acc: 0.7083\n",
      "Epoch 24/100\n",
      "\n",
      "Epoch 00024: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.6489 - acc: 0.8673 - val_loss: 0.9368 - val_acc: 0.6667\n",
      "Epoch 25/100\n",
      "\n",
      "Epoch 00025: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.6473 - acc: 0.8566 - val_loss: 0.9270 - val_acc: 0.6833\n",
      "Epoch 26/100\n",
      "\n",
      "Epoch 00026: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.6035 - acc: 0.8938 - val_loss: 0.9394 - val_acc: 0.6667\n",
      "Epoch 27/100\n",
      "\n",
      "Epoch 00027: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5955 - acc: 0.8920 - val_loss: 0.9381 - val_acc: 0.6833\n",
      "Epoch 28/100\n",
      "\n",
      "Epoch 00028: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5885 - acc: 0.8876 - val_loss: 0.9519 - val_acc: 0.6667\n",
      "Epoch 29/100\n",
      "\n",
      "Epoch 00029: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5892 - acc: 0.8938 - val_loss: 0.9511 - val_acc: 0.6667\n",
      "Epoch 30/100\n",
      "\n",
      "Epoch 00030: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5711 - acc: 0.9027 - val_loss: 0.9559 - val_acc: 0.6583\n",
      "Epoch 31/100\n",
      "\n",
      "Epoch 00031: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5505 - acc: 0.9080 - val_loss: 0.9490 - val_acc: 0.6750\n",
      "Epoch 32/100\n",
      "\n",
      "Epoch 00032: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5191 - acc: 0.9354 - val_loss: 0.9513 - val_acc: 0.6667\n",
      "Epoch 33/100\n",
      "\n",
      "Epoch 00033: val_acc did not improve from 0.70833\n",
      " - 6s - loss: 0.5197 - acc: 0.9230 - val_loss: 1.0186 - val_acc: 0.6083\n",
      "Epoch 00033: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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9XcU3MDAQY8eORb9+/eDj44OjR48if/78mXo9CQkJuHTpEu677z60atUK1atXB8BpzTNn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\nADAcMQsAAMBw/j8V8ISOabqUswAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Fdd09hT5rcLBdUE2bJu3ebWNEPRORjB5tFdtrr7U+2ocOFfrpSo3Ona1S+/nn\n1h3w7rvt4/TpdAsEkJvrSpMm2Y2vr76yfeedl/f3OI40YIB1Q3YcC8ING9rYfoaFAABQer32mvXQ\nmjbNvg4OLrJT+yzMOo4TLWmNpKaO4+xyHGe04zh/cRzHM8J3vqSfJf0o6XVJdxZ5I4KDpTZtvF8/\n/bR02222KO9NN1l14LHHivxpA1q3btKyZRZs69eX/vxnmyhq1iwpM9PfrQPgb5mZ0l//Kj3wgNS/\nv9TqpJXX8ic42JYNe+YZ+xsTHW0hGQAAlA7Z2dLYsXbdcPXV1nO2iPlsnVlfiYiIcOPj4wt3kuxs\nadUqKSbG1mEdOdJm4rz1Vum66+wCrUqVomlwIHNdacECmyRq3TqpSRNp/HjrUliEd1QABIiDB+3/\n/8KF9ub0zDNSUCHvia5ebdPyr1tna9N+9plUrlzRtBcAAPhHero0apQ0e7bNVvzSSzbnUT6VhHVm\nS66gILtoevFFC7KSlJBgk5gMH24V24EDpXfeObcnj3IcqW9f60Y4d65dYN58s1W7Y2LspgCAc8cb\nb1jYnD5d+uc/Cx9kJZsFee1aacYMG0/rCbKMpwUAIHBt2mT54fnnpVdeKVCQLYhzszJ7OtnZ1gX5\nf/+zsLZrl00f3bChdbP94ANbxqZRI+/H1q3PnSpldra9LuPHS1u32oRRTz5plexCTKkNoITLyJDK\nlLG/ARs3Wo8WX9qwQbrySusVcued9twAAKDkO3RIqlzZPt+5U7rggrwffxpUZs9GUJBVCaZMkbZv\nt+5vDRrYsaNHLdjOnCnde680eLDNBOypTk6caF2U77vPZgL+5BPpu+/89qP4RFCQdMMN0jffSG+/\nbZO2DBxoA7oXLiwZ490yMuwmRFKSv1sClA4ff2yzEW/fbn8DfB1kJVvSJyJC+vvfpfBwqwYDAICS\n7auvbFji7Nn29VkG2YIgzJ5OUJDN8Oupuv71r1YuP3hQ+u03m1563jxvxeDwYQuvr7xiMwH37y9d\ndZX3fA89JA0dauPM/u//pEWLpJ9+Kv6fqygEB1t37C1brNthUpIN6o6MlOLifPOc2dm2NuWGDbaU\n0Jdf2v5jx+y17tBBqlvXuihecIHdkJBsLHTLlra+5WefsdwQUBAvvSQNGiRVqyaVLVt8z3vJJd6l\nfNLSbNm1m24qGTfMAADAyT76yFaUCQ21G9HFhG7GRc11LXQlJlqZ3RNo//pXafFiq254lrq57DLr\n1izZskHp6bbURdOm3q1SJb/8GAVy7JhVrJ9+2pZD6tFDeuopC7f5sX+/fd8vv3i3unW9M561aiV9\n/33u2ZRvvtnGNEtWTa9eXaphqjExAAAgAElEQVRXz7b69W3sXbt2tvbl3/5mMzSnp0sVKki9eln7\n2rYtylcBKD2ysqwHytSp1vvi3XelihX905a0NLs5FRwsPfig/Y1NS5PKl/dPewAAQG6eYl779tY7\ntXbtQp8yv92MCbPFLStL2rNH2rbNLsq6dbP9gwdb1XHHDm/14brrbIyqZBdx9etbwG3WTAoLK5rJ\nV4pSWppNDPPssxboe/e20Fi2rIXRXbu8obVmTav6SFY5PbFLdt++VoGVrJodFOQNq/Xq2XjlOnXy\n37bDhy3QLlggzZ9vH5s2tY+LF1tluWtXZlEFJPs//Mgj1s130qSSNS/AvHnSXXfZ0I6hQxmvDwCA\nP61ZY4Wl/v1tmb0iuvlNmA1UaWlWTfz+e6lGDatyHjliAS4lxfu40FCbiGnsWKs4vv++hdymTYtn\nWSHX9V5Erllj3a537rQtMVHau1dKTbUu2TVrSr/+ao+tWNFbOfVUVufMsZDvCap1656+6pKRYeOX\nT7elpXk/z8iwkFqv3sltl6z9zz9vk8wcO2Zt69XLgvRtt5WsC3jJXqMDB+z3QpIefdQmJ4uIsOo1\nk+SgqBw6ZN2Fbr7Z3y052VdfWU+Xdeus98fUqdYLAwAA+Me8eVK/fkV67UyYLW1c18amfv+9d7vi\nCgte335rYcajTh0LtY88Yt2cDx+2cNmoUf5/yVJSLBQ7jrR8uRQb6w2rO3daW/bvt4rpbbfZ2Nly\n5WzCrLAw6y49ebJVX59/3sYa9+9v1c+8wuiZtqysgr1ulSpZWL3nntOP+Tt8WFq61Cq28+fb4374\nwY69846F4cjI4h0zKNl05mvXev+9f/xR6tRJ+vxzO96hg7R+vX1erpyNTxg50i70JRtnXNKq9zDp\n6XazpSQNI9i82W6Qvf22/7oU51d2tvTmm9K4cXbD7OGHbZjD4cP2/8Nx7HffszVrZjfRUlOtB4xn\nv+dxF11kN4lSU20uA89+z8cGDWxmxsOHrddJSIjt4/8XAOBclJxs8+c8/bRdj/oAYfZckpHhreZu\n3eoNPxMmWJhdtEjq08fC2MUXeyu4d9xhAXf5cgttOcPqwYPWHbpOHTvPk09aqLvgAgurF1xgXYhD\nQy0oBwVZBfZUXf5SUmzM25Qpdl7Jqojly+d/Cw0t2OPLl7cLz0cftdlYmzWzYH3llXm/lq5r/0HP\nP98+r1fPfr5KlbxV2759vbNcF0ZiovT117lvUGRkeCe3uvpq6wKdcxx1hw7SjTd62/rzz1YV/+or\n+xgVZRf4qal28d62rVVuO3a0jxdfXPK7ZWZl2WRrR45Yxa1CBX+3qGgcOOBdp/Xzz603wGWXWZfe\nHj3827ZFi6Trr7fAtnSpzUQYCA4csL9N7dvbm+oPP9j/kxO9+qot2L5+/anfdN95x6rQn39+6n+L\nefOkAQNsHFD//ravbl0bCjJkiN3sKmk9OQAA8IWff7Zr1MREm7V48GCfPA1hFl67d9vMoDlD008/\nWQAKD7cKx9ixuYPqBRfYpFTVq1tFtEyZwi92fOyYbeXLF++F36efWmX2p5/s4nPyZPs58yM11WZo\n9oyz3bHDll+aNMmC56pV0uWXn7qL76mq6T/9ZN2qg4Kk22+XZsywx9aubRfhLVrYIHrHsVBdpcrZ\ndR/+9Ve7yI+Pt0qUZxbnl1+28Ya//mo3MTp2tH/r4g64X31l48a3b7ctMdGq9g89ZL8joaH2+pUp\nYyG8WzdbFqo4loUpKnv32s2I886zGyD799tNkqZN7SZT5cp2fMoUmzl9yRK74XLVVTbevLhuPEyf\nbmu5tmplYa0obtT4S1qa3QjJzrbfn+xs2y66yG5MHTxovR1yHnNdu+lTv75VeVesyP39rmthtX59\nG/cfF2c3Wj77zP4upKXZzO7Nmtn/2apVfbYwPACgBMjOtuE4Bw54twsvtOup9HS71qtd266hS1sP\nnrVrrTtxZqbd6O3a1WdPRZhF3jIy7D9YcHDu8a+lVVqaBdBnn7Wf9ZFHLJQWZMIn17WJqipW9Fa0\nu3e3UHLlldbtOyXFgkH16tI//2ndHz3KlbMlR5YutVDz3XcWlps0saVPfCUjw7qix8db1enii21i\nseuvt+O1allgjIiQ/vznk8cYn421a61K5gmr27fbz+mZ9KtePav8S/azN2xo7XnkEdv3ySf2eq9e\nba/zV19Z6LvrLrs5869/WcDt2tXaX1IsWWI3PT77zEKVZEvbzJ1rnyclnb69779vYT4x0b5u2NBC\n7aRJvhsHP2WKzVp89dXSe+95FzlH/qSm2sRy/frZ1zfdZP/2gwdbxbZnT8ayA0BJk5Vl12ueIJqS\nYj0Rmze3m5XPP+/d73nMqFG2bdtmN0hPzE9Tp9psvps2eVfLCAmxXou1a9v159VXW+/H2bNtX+3a\ndk3g+VjSb4Ru2GAFnDp1vBOp+hBhFjiV7dvt4v2DDyzUTZ1qf1zORmqqVdYWLLBt507bv2aNjW1d\nt05audLbRTgsrOR0RUxPty7OObsof/edlJBgdxffeccCr6d7ckSEd+IpyR6/ebO3qrp9u1WkPvzQ\njl96qZ1Xsj/QDRvahf3zz9u+FSvs8Q0b2sczOXLE7oRWqmRhYeBA6zEgWUWsWzfrXdC4cZG9RGeU\nnW1vWps3SyNG2L6ePS2AR0Z6K6zh4fm/M+u6dkc3NtZ+zs2b7aZAUJDN3puSYuft3LloxnD//LOt\ne/3MMyX/TTQQfPqp9N//2uRZqal2U+vOO21MEQCcSw4dkr75xt4nN22yQsLLL9ux/v3tGik42FtY\nad/eu4LHkCE2fC442PuYTp2kF16w48OH281hz/HgYJtN96GH7Phf/mJDzVzXeuQcOGDvx48+au/d\nISEnh9G//91u8B49asObqlSxm+2ebfRo6ZZb7G/7v/6V+1i1anYtUq+e9Pvv9h6+b5+1cd8+2x58\n0G7AL1hgvbVONH++XY8uXWo96zwh1xN0Bw2yYHzkiD3+dEOwjh2znz0tza710tJsa93aXqctW+xa\nLz3de/zYMXuvkuw6bs0a77H0dHv933jDqrEPP2zFoCJYeudMCLNAXmJjpTFjrOvvwIH2B6wwQch1\nrQtyjRola1Kfgjh82P44Oo4FnMmTvRNhSfaHessW+/y66+yGgOPYH++GDe1O5Cuv2PGvv7ZKdFiY\nb8a8Hjtm4x+XL7dt5UoLfhdcYGFi4UILuN26WTW8qHoe7N1r40tjY+1GRlKSvSn+/rtVNbdtszed\noppAKWeviZtvtuppVpadv0cP63p9yy0FO2dSknUtfuSR0t8jw1/S0ux3JCbGfv8ee8x6SNx1l3TN\nNTa2PTTU360EgMLLyrKbsD/84O2l4pkY1KNKFQtyn3xiX//rXxaosrIsXGZl2Y30CRPs+N//bu+n\nWVnex7RrJz33nB0fMsR6aXmOZ2VZ77jJk+14x472vux57mrV7G/v/ffbvmeftWuTnGG0cWO7lvEM\nMfFV8cF1Leh7gq7n44ABdj3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eLR04IHXvLj30kNSnD0sCAQCAEim/YZZ+ZwBQGpUpI7Vt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\nd/4/YhS23onl98oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"axial\",\"meniscus\")\n",
    "plot_history([('Axial-Meniscus',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6YJB516BPlqd"
   },
   "source": [
    "## Model 4: Coronol Plane and ACL Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1865
    },
    "colab_type": "code",
    "id": "Ae3_5SL6QjmH",
    "outputId": "5189ef8d-7604-4814-fd27-50855357ed60"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/50\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.50833, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_acl.h5\n",
      " - 16s - loss: 0.9583 - acc: 0.5451 - val_loss: 0.8840 - val_acc: 0.5083\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.50833 to 0.56667, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_acl.h5\n",
      " - 7s - loss: 0.8803 - acc: 0.5699 - val_loss: 0.8665 - val_acc: 0.5667\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.56667 to 0.61667, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_acl.h5\n",
      " - 7s - loss: 0.8741 - acc: 0.6319 - val_loss: 0.8636 - val_acc: 0.6167\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.61667\n",
      " - 6s - loss: 0.8535 - acc: 0.6442 - val_loss: 0.8656 - val_acc: 0.5500\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.61667\n",
      " - 6s - loss: 0.8526 - acc: 0.6690 - val_loss: 0.8718 - val_acc: 0.6000\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 00006: val_acc did not improve from 0.61667\n",
      " - 6s - loss: 0.8191 - acc: 0.6646 - val_loss: 0.8606 - val_acc: 0.5750\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.61667\n",
      " - 6s - loss: 0.7797 - acc: 0.7018 - val_loss: 0.8758 - val_acc: 0.5417\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.61667\n",
      " - 6s - loss: 0.7603 - acc: 0.7354 - val_loss: 0.8871 - val_acc: 0.5583\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.61667\n",
      " - 6s - loss: 0.6850 - acc: 0.7752 - val_loss: 1.0366 - val_acc: 0.5500\n",
      "Epoch 00009: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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bRoq6w/P8KctcqACAyCPMAgAC7+RJm+Z7pqC6datNJy2seXM/oKallaystmlD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\nkd3XoYPfvCk93W8ycvSoVV3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CQ5XTffukcePsNeucObZ2sfCxAwfs62vUyBohhfqZSHZh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\nQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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2Y9Sf99KGDdHh9vPPrSwrK9xqe8450imnxLeuAAAAQGW2b5fy86Wf/MQaY558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\nzfVBmB6TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"coronal\",\"acl\")\n",
    "plot_history([('Coronal-ACL',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "q3m5qp6DQrhl"
   },
   "source": [
    "## Model 5: Coronol Plane and Abnormal Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1865
    },
    "colab_type": "code",
    "id": "WvgUqiR3QyH1",
    "outputId": "9f0ccfbe-70ba-4eee-c312-5e10196a20ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/50\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.72500, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_abnormal.h5\n",
      " - 14s - loss: 1.1068 - acc: 0.5628 - val_loss: 0.9242 - val_acc: 0.7250\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.72500\n",
      " - 6s - loss: 0.9812 - acc: 0.6487 - val_loss: 0.9212 - val_acc: 0.7000\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.72500\n",
      " - 6s - loss: 0.9318 - acc: 0.6991 - val_loss: 0.9689 - val_acc: 0.5917\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.72500\n",
      " - 6s - loss: 0.8811 - acc: 0.7097 - val_loss: 1.0266 - val_acc: 0.5083\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.72500\n",
      " - 6s - loss: 0.8275 - acc: 0.7664 - val_loss: 0.9161 - val_acc: 0.6583\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.72500 to 0.79167, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_abnormal.h5\n",
      " - 8s - loss: 0.7674 - acc: 0.8186 - val_loss: 0.7987 - val_acc: 0.7917\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.79167\n",
      " - 6s - loss: 0.6889 - acc: 0.8531 - val_loss: 1.2013 - val_acc: 0.4167\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.79167\n",
      " - 6s - loss: 0.6914 - acc: 0.8265 - val_loss: 0.9034 - val_acc: 0.6500\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.79167\n",
      " - 6s - loss: 0.6247 - acc: 0.8619 - val_loss: 0.8595 - val_acc: 0.6917\n",
      "Epoch 00009: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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NG0oDBljyetVVVkoc7hYskO6+W1q0SCpZUurY0b9P9p//tNm3JUt6GyMAhCOS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\nRK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7dJGmT7eW6AAAALg01avb66tJk6TChb2uJjR07GjrZ595xgZiCLQIQTxbXKhC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\njN/XWrVsP+EiRezckiWZJgz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ACGbHjlnjiszrcrdv959TuLA1ecncXblOHbrX4uIdPiy1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\ncwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"coronal\",\"abnormal\")\n",
    "plot_history([('Coronal-Abnormal',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "nBggJ7cZQ2vO"
   },
   "source": [
    "## Model 6: Coronol Plane and Meniscus Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2001
    },
    "colab_type": "code",
    "id": "82NTNclIQ7Cd",
    "outputId": "11cb4397-b4cb-40e9-8c0e-0c3904847160"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/50\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.45000, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_meniscus.h5\n",
      " - 16s - loss: 1.0996 - acc: 0.5531 - val_loss: 1.0499 - val_acc: 0.4500\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 00002: val_acc improved from 0.45000 to 0.58333, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_meniscus.h5\n",
      " - 7s - loss: 1.0167 - acc: 0.5735 - val_loss: 0.9777 - val_acc: 0.5833\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.58333 to 0.63333, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_meniscus.h5\n",
      " - 7s - loss: 0.9740 - acc: 0.6274 - val_loss: 0.9751 - val_acc: 0.6333\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.63333\n",
      " - 6s - loss: 0.9656 - acc: 0.6372 - val_loss: 0.9693 - val_acc: 0.5583\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.63333\n",
      " - 6s - loss: 0.9311 - acc: 0.6823 - val_loss: 0.9617 - val_acc: 0.6000\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.63333 to 0.64167, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_meniscus.h5\n",
      " - 9s - loss: 0.8979 - acc: 0.7097 - val_loss: 0.9556 - val_acc: 0.6417\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.8701 - acc: 0.7150 - val_loss: 0.9573 - val_acc: 0.6333\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.8283 - acc: 0.7504 - val_loss: 0.9509 - val_acc: 0.6250\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.7838 - acc: 0.7938 - val_loss: 0.9848 - val_acc: 0.6417\n",
      "Epoch 10/50\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.64167\n",
      " - 6s - loss: 0.7400 - acc: 0.8159 - val_loss: 0.9789 - val_acc: 0.6250\n",
      "Epoch 11/50\n",
      "\n",
      "Epoch 00011: val_acc improved from 0.64167 to 0.65833, saving model to /content/gdrive/My Drive/Models/TransferLearning/coronal_meniscus.h5\n",
      " - 8s - loss: 0.6615 - acc: 0.8566 - val_loss: 0.9544 - val_acc: 0.6583\n",
      "Epoch 00011: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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4sfe2YoVtr1pV6tlTuvFGC7MxMVJISKBbDCDYEWwBAABwTvbt8w+xS5ZIBw7Y\nvho1pPh46aGHLMheeKFUpUpg2wug/CHYAgAAoMiOHZNSUrwhdtEiaeNG2xcSInXsKA0fLiUmWoiN\nipJCQwPbZgDlH8EWAAAAhXJd6Zdf/EPsihUWbiWpaVMLsOPGWYiNjZVq1gxsmwFUTARbAAAASLIl\ndpKTLcB6uhTv2WP7qla14HrnnRZmExOl5s2Z6AlA2UCwBQAAqIBycqTVq70hdvFi6ccfvfujoqRB\ng7whNjpaCgsLXHsB4FQItgAAAOWc60rbtvlP8LRsmZSVZfsbNrTwet119m98vFSnTmDbDABngmAL\nAABQzmRkSEuX+gfZHTtsX+XKUrdu0vjx3gmeWremSzGA4EawBQAACGK5udK6df4hdu1aWzNWks47\nT+rb1xtiY2Is3AJAeUKwBQAACCI7dvjPUrx0qXT4sO2rW1dKSJCGDbMgm5Ag1a8f2PYCQGkg2AIA\nAJRRR45Iy5d7Q+zixTZWVpIqVZK6dJFuuME7wVO7dnQpBlAxEWwBAADKgLw8af16/1mKV62yrsaS\njYPt3t0bYrt2tSV4AAAEWwAAgIDYs8d/XOySJVJ6uu2rWdO6Ed9/vzfINm4c2PYCQFlGsAUAAChh\nR49KK1b4B9lNm2xfSIjUqZN0zTXeCZ4uuMC2AwCKhmALAABQjFxX2rjRP8SmpEjHjtn+Zs0swN5y\ni/0bGytVrx7YNgNAsCPYAgAAnIMDB6wbsW+Q3bfP9lWrJsXHSxMnersUN2sW2PYCQHlEsAUAACii\n48dtQiff5XbWr7d9jiN16CBdeaU3xHbsaLMXAwBKFr9qAQAACuG60tat/kvtLF8uZWfb/saNLbx6\nltuJj5dq1QpsmwGgoiLYAgAASDp0SFq61H+5nV27bF94uNStm3Trrd4Jnlq2ZM1YACgrCLYAACCo\nuK5NxJSdLWVl2b+em+/9U+3zvZ+ZKa1eLa1bZ88tSe3bS/37W4BNTJQ6d5bCwgL7ugEAJ0ewBQAA\nZ8w3XJ5LqDzbY8+F40hVq9otPNxu7dtLI0daiE1IkOrWLZ73CQBQOgi2AAAEKde1yYxKOkSe7L6n\nunk2POHSEyx9vw4Pt9mE69UrfN+p7hfl2EqV6EIMAOUNwRYAgDLCdaXt26W1a+22bp20YYN1lT1Z\nyDzXcHmqYFi1qjdcnm2IPNm+sDDCJQCg+BBsAQAoZb4Bdt06/yB76JD3uEaNpPPPl5o0Kd5Q6blP\nuAQAlBcEWwAASojrSmlp3uDqCa/r1knp6d7jGjWy9U9/+1tb97RjR7vfoEHg2g4AQDAh2AIAcI58\nA+yJFVjfANuwoYXW0aMtuHo1AetEAAAgAElEQVRCLAEWAIBzQ7AFAKCIXFfasaNgBXbt2oIBtkMH\n6brr/CuwDRsGru0AAJRnBFsAAE7gG2BPrMAePOg9rkEDC63XXedfgSXAAgBQugi2AIAKy3WlnTsL\nr8D6Btj69S2wjhrlrb527GhjYwEAQOARbAEA5Z5vgD2xAnvggPc4T4C99lpv9dVTgWX2YAAAyi6C\nLQCg3HBdadeuwiuwvgG2Xj0LrNdcU7ACS4AFACD4EGwBAEHHE2B9q6+eELt/v/c4T4AdOdK/AkuA\nBQCgfCHYAgDKLNeVdu8uWH1du9Y/wNata4H16qv9J3Fq3JgACwBARUCwBQAEnCfAFlaB3bfPe1yd\nOhZYR4zwX0anSRMCLAAAFRnBFgBQqk5WgS0swA4f7l+BJcACAIDCEGwBACXiZBXYvXu9x9SubYF1\n2DD/CmzTpgRYAABQdARbAMA52bOn8ApsYQH2qqv8J3EiwAIAgOJAsAUAnJGNG6VZs6Qvv5S++86C\nrUetWv4B1tONOCKCAAsAAEoOwRYAcErp6dKcORZkZ82SNm2y7ZGR0uDBUqdO3gosARYAAAQCwRYA\n4Cc3V1q61ELsrFnS99/btho1pL59pXvukfr3l9q2JcQCAICygWALANDWrd4gO3u2dOCAhda4OOmB\nByzIXnSRFBYW6JYCAAAURLAFgAooM1OaN8/bvfjHH217s2bS0KEWZPv1kxo0CGw7AQAAioJgCwAV\nQF6etHKld9Knb7+Vjh2TqlaVevWSbr7ZwmxUFN2LAQBA8CHYAkA5tWOH9NVXFma/+srWlZWkmBjp\nrrssyF58sRQeHth2AgAAnCuCLQCUE9nZ0oIF3rGyq1bZ9kaNLMT27y9deqnUpElg2wkAAFDcCLYA\nEKRcV1q3zjtOdt48C7eVK1sl9plnLMx27iyFhAS6tQAAACWHYAsAQWTvXpu12BNm09Jse1SUjZO9\n7DKpZ0+pevXAthMAAKA0EWwBoAw7dszWkfV0L162zCq1detat2JP9+KWLQPdUgAAgMAh2AJAGeK6\n0s8/eyuyc+ZIhw9LoaG2juzjj1uYjY21bQAAACDYAkDAHTwoffONN8xu3mzb27aVfvtb617cp49U\nq1ZAmwkAAFBmEWwBoJTl5EjJyd41ZRcvtnVma9aU+vWT7rvPqrJt2wa6pQAAAKVj/37piSekI0fO\n7vEEWwAoBZs3e8fJfv21VWkdR4qPlx5+2IJsYqIUFhbolgIAAJQM15U2bZKWLvXeevWSHntMqlZN\nevddqVu3s3tugi0AlIDDh218rCfMrl9v25s3l4YPt+7F/fpJ9eoFtp0AAAAlwXWlLVuk3bulhATb\ndsEF3nOiypWlLl1sQkxJCg+3qm1IiF38P1MEWwAoBnl50ooV3nGy330nHT9uVx9795Zuu83C7Pnn\nn90vawAAgLJuzhzrmeapxu7bZ2H2hx9s/+23S1WrSnFxUseOFm59hYSc/fcm2ALAWdq+XfrqKwuz\ns2fbGrOS1LWrdPfd1r24Rw+pSpXAthMAAKA47dzpDa/r1klTp9qF+/fek/7xDyk6WrrqKlvFIT7e\n+7g77yy5NhFsAaCIsrKk+fO9kz6tXWvbGzeWLr/cKrKXXGL3AQAAyoM9e6Tata26+t570kMP2cV9\nycJsVJRVZhs0kJ55Rnr1VavKljaCLQCchOtKa9Z4uxfPny8dPWoV2KQk6YYbLMx26kT3YgAAEPwO\nH5YWLfKf3GnLFunbb6Xu3aUmTWyIVVyc3bp0kWrU8D6+UaOANZ1gCwC+du+2bsWeMLtzp23v0MHG\nyfbvL/XsaWNnAQAAglV6urR8ubRsmZ3bJCTYfCGXXmr727aVLrxQmjBBatHCtvXvb7eyiGALoEI7\netQmevJ0L16xwrbXr2+/2Pv3t3+bNw9sOwEAAM6W61rvsowM6ZZbrBLrmZ1Ykp5+2oJtbKzNHxIb\n652tOFgQbAFUKK4r/fSTdxmeuXOlzEypUiXrYvPkk9a9uGtXKTQ00K1FRZWRIS1caLNL3nyzXTVf\nsUJ65RWpZk3/21VXSU2b2hioLVv891Wvfm4zTAIoW1zXqmx16tj9WbOk5GQpLc1uhw5JrVtL77xj\n+xctko4dkyIi7EZvo4rhyBFp5Ur/7sQXXyy98Yb9XUhJsZ5o119v3YljY218rGQ/I5dcEtj2ny2C\nLYBy78AB617sCbNbt9r2886TxoyxqmyfPhYEgEDZvVt66SULs8nJUm6uFBZm3cDatpV27JC++MJC\n7+HDdoIr2fimpk2lTz+Vxo4t+LyrVtk48H/9S3rhhYLB+KmnrIfC0qUWnk/c36mTXfjJybGLPYwn\nB4qf61oo3b7dAmq/fvZZ++AD6aOPvME1Lc22Z2V590+ZYmuiR0TYZ3bzZu/zTp5sS6941Kljs/V/\n+qndf+stey5P8I2IsN8nzOYfPLKz7ff8nj3SoEG2LTZW+vFH+7pxY5uVODbW7oeEeCe/LG8ItgCC\nXl6eneynp3tvBw9aOPjyS/s3L0+qVctOFh56yLoXt2kT6JajosrKkr7/3noMREVJo0ZZaHz+eTv5\nuP9+u9jSvbu3wjJwoHcWyrw8uyKfkWEntJL9TM+cadt8b02b2v7q1e0EJyND2rbNu//xx23/zJnS\nE08UbOuhQ3ayfP/90ssv2yQhntBbq5Z15Q8Jkf7f/5OWLPEPxXXrSr/9rT3Pzz/b6/bdf+L6hUB5\ndPiwN5Tu2GH/jhtns8z+/e/WBTQtzT7THnv2WAVt82abxDAiwipunvCZm2sXnF58UXrtNSk8vPDv\n/frr9hye7799u3/30pdfLhhy+vb1hmHPxTLf4HvBBXZD4Hz6qfTf/9oFydWrpePHbVInz4WPP/7R\nLk7Exdn/WUW5IOm4nku+QSIuLs5dunRpoJsBoJi4rv3R9w2kvgG1KNszMrzVK18hITZepH9/616c\nkGAnAkCg/OlPVnVdtMjGd4eESHfcYSeXkgW/QCyRINlJ9f79BYPxkCHWzi+/lBYs8N939KgFYkma\nONHWLszIsJMsyU7M9+yxr4cNk2bM8P+e550nbdhgX992m3Wd8w2+7dpJDz5o+2fMsOf23d+wofcC\nlWf8GFCa0tMtWPhWVNPSpEcekc4/X3r33cJ7UqxYYb0t/vtf603hGxybNZMSE0unapqXZ59737bX\nry9deaXt793bLkrt3GlhWrIVAaZMsc9cZKS3Wuy59ekj9epl+3ftss8pQ3vOXE6OrQ/r6UqckmIX\nQytXln7/e7uYGBvrnZ04Lk5q2bL8/B50HGeZ67pxZ/QYgi1QunJzbYzneecFf7XCde1k+FxC6aFD\n9of1VCpVsivbnludOv73T7a9Xbvgm/gA5cOxY9ZTYO5cC3YvvWTbe/e2cNanj92SkqzqWd4cPWqv\nMyvLO5NmcrJVjnyDcbVq0t132/4HH7SKr+/+9u2lb76x/V272omdr1697D2WrIK0bZt/8L30UltT\nUbKKc06O//4OHawKJlmorlbNu69atfJzgogzc+yY/V0KD7chAv/+d8Hg+txz0oABdqHq8su9j61S\nxcLde+/Zz9batdJnn/l3842IsM99MP185eba77K0NPtsXHCBfc4nTPB/X3bvlh5+2Hp/eNY1DQ21\naqLnPRg3Tho82M4f5s/3bq9fP7jek+LkOTds2dJ6xbz3nk3wlJVl+2vWtBD7/vv2XmVl2c9neX6/\nzibYUrsASoGnkpCVZdWFnTvtiuxdd0njx1sIC0SbsrPPPpB6vvZcwT2ZkJCC4bNVqzMLqlWrlu9f\n3ig/pk+X3nzTJn7ydCuMi7PPSWiojfWuCL0GqlQpWG2Kj7fbyTz99Kmfc9Ys+53jG3x9x8XffLN1\nszwxOHt8/LHtz8z0bhszxsKH69r/U06Od19IiFWhn3/eKtA9evh3w65Z007OBw2y36Xvv19wfHKL\nFlbN8tQQ+D0WWDk5Frw8VcnISGnvXumBB/zD2Z49NlHb7bfb8RMm2OfWE0rbt/f+bMXHW7ht1sz2\n1a3r///csaPdgp0nnDZp4t1WpYr9vvN1/Li3x0ZYmPS3v/m/t5s2WYVYshl5fS8KVK5s7/HLL1vF\neOtWb5DzvL8REXZeEOyfpQMHpP/9z1uNXb7cfjd9/rm9Jx06WLD1VGLPO89/IsBA9ewp66jYAiUk\nLc3GQMycab+MPN31HnvMFq+ePt0qETVrStOmnfmaYEePnlsgTU/3/vE5Gcexq8pnUiU9cXv16sH/\nBwg4UW6uVfjmzLHbu+9ad7vnn7cxc56KbK9e3pkmUTbk5toJZEaGnXg3amTBs7DxyQkJ0hVX2PFX\nX11w/913WyhKTfVWpn0995x0zz12At+xY8Hg+9BD9vxbttixJ+7v3dsuBGZk2Em+776KcIGkqPLy\nLKD6BqjWrW1m16NH7aJEWpp1i/X0EHrgAbuYkp5u49x9u9JGRFi4iI+3v5MHDtjnmBnGi1dmpv0e\nPbEaftttNmne//5ncwuc6H//s2r5t99Kf/2rfyU8IsL+32rUKP3XUxjXtTC/bJkF2L59re1r1tjE\nfOHh1hvFE2D79/e/eFCRUbEFyoB//MN+0SYn2/3ISGnECG/V9rHH7A/l1VfbL+U337QxpjNm2Jib\n7GwLg6cLqkePnr4tNWv6B88mTWzMT1GDao0a/CEHfK1ZY93s5s+3z6Vk1Ztt2yzY3n23BRmUXaGh\ndsHOtwu443jHFBamenWrpJxMkyYFu1lnZEjR0ba/Vi3p3nsL7g8Ls/07d0r//Kdt8+0FM326Bdtv\nv/WvbEl2QvzppzYh3jffSI8+WjAYT5xoj9+woeDEXjVr2t+nYBgS88MPFuw9Ey+lpdlM4b//ve1v\n3NiCra/rrrNgW6WKXXTo0sU/uHqqqLVr2/OdjOfiB4pf9eo2Qd7JXH65nR/5/r+npVkglOz/fPly\n+xz49sRYvdo+e++8Iz37bMGLFuPG2c9/RoZdICqu6qdneFb16naONniwBdoDB2x/5crWg2PAALuY\nsnKlVWa5SFV8eCuBc3DsmJ3gzpxp65/WqmUnKKGh0v/9n0260qGDnTSlp9tx06bZJCy+wfR//yv4\n3OHh1lXKEzIbNLA/5CcLpSduq1mTyRqAs+W6NmmHpyJ7zTXSyJF2YrJ2rV2s6tPHKmoREd7H0Tuh\nYqpUyQLkyTRpcuqu1omJdvLrGSLiCb6eQBUTI334YcFg7Ps9w8KsC+2mTd79N9xgx8yebVWwE/34\no13sfPFF6Q9/KBh8p02zv0Off26fgxP3Dxtm33fHDjuh92w/3dg/17WhOZ7uvJ99Zp8rz1I3aWlS\n8+bS1Km2/+qr/WfurVvX/r56PPigfTZ9w4tv1evEScsQPKpXt264551XcN+VV9rNde3n3ROA27a1\n/U2a2AWNtDSbsG/7djv3uvFG2/9//2cT+tWp4/+z8/bb9nO9Zo09r+fn6cThFdu3e7sSeyqyPXva\n56ZKFXuOESO81djoaO+FpNBQqXPnknvfKiq6IgNnKCPDAurMmTau5tAhu9o3a5Z3rJbnD/r+/d4w\nO2uWVWqbN7eTgfPOKxhKXdeOfestO0Hp1s0mYCisKw6A4nf8uDR6tE1ItHu3bWvVytaCHDcuoE0D\nzprnpL+wGa9r1LCf98K6Yn/2mQXVP/7RAkB2tv/zHjtmJ++33y69+qp3e2ioVaY8n6Enn7Tvcfy4\nN7g2aiT98ovtHzDALvjWquUNF127WvdsSZo3zzvGtWlTxhfi7LiuXUDyjINeuNBmevetBh88aBc1\nHceWKvvnP72Pb9DAJs1asMDuJyRY77zQUOsBEBdnPSh+85vAvL7yhlmRgRKycaN1D2vf3q4aR0fb\n1bsrrrATg379vH9o9+61SUqmTbN14HJybKzPiBF2i48/fffe7Gzr0vz88zZpxe2321XG48fLzrgR\nIJi5rn2uPRXZmjWlN96wff36eZes6NPHumsCsL9nvsHX05136VL723j4sHdfTo53Ruonn7SQHBZm\nkwA1bWoXjO66y/bv2uWdkRooK37+2brx+wZfydYNluxvR5UqVhX2nagOxYNgCxST3Fwbj+SpzK5b\nJ11/va0Z5ro2pqNrV29A3bXLwux//mNXpXNzrSvMiBHWhapbt7PropiXZ88VFmYn3Q8+KN16q4Vd\nJhcAzs4TT9jY9tRUu9+4sTR0qPdkBQAABBaTRwHnwLMch2QzKC5ebF2fevWyZSSuuML2OY6tJbZj\nh/TRR1aZnT/fQmj79jbT4ogRNibqXMfbhYR4w3N8vFWPnn7aumddf71NUnPBBef2PYDyKjXVW5Fd\ntMguSIWH24WiCy/0VmQvuICxsQAABDsqtqjQduzwLsmzcqWN9wkNtYWxK1e2cT916niPT021WSqn\nTbNZKl3XJofyVGY7diz5E+QNG6QXXpCmTLHwvGhRyX4/IFh4xrd/9pnNBvvzz7a9bl2b5Olvf7Mu\nkAAAoGyjYgsU0ZdfSo884l2Sp3Vrm9ApM9Mmr7j+eu+xW7Z4w+z339u2zp1tMo3hwy3YlqZ27azL\n5B//6J2YY+9em7H19tulq65iNmRUDLt3W9d/T1X2+eelQYNs2Z2oKJsFtk8f+7yybBUAAOUbwRbl\nnu+SPDfcYN2IK1WyE92nnrLJn06stG7aZEF22jRv+O3a1aaGHz7cuhwHWqNG3qUgfvnFAviIETbb\n8t13S2PGMHMkype8PPvc7tpl61OuWWPba9SQkpK8k3ckJNjnHQAAVBx0RUa5dPSoVVlnzrQ1Yg8d\nsrF1r71mga8w69fbY/7zH2nFCtsWH29hcfhw77poZVVuro35/fOfLYw3bGivybcrNRBMDh60i1Ke\niuyFF0qvv24B9+qrbWmFPn3sYlVYWKBbCwAAigtdkVGhbdwo7dxpEz9J0i23WAVn5EjvkjwnTsf+\nww/eyuyqVbbtoousS+OwYdZFOViEhtrJ/ogRFgYWLPCG2nfesTGGZT2co2I7dsy7eP2wYdInn1iI\nDQ+Xune3XhOSVW2nTw9cOwEAQNlDxRZBKy/PZi7+73+tMrt2rXUp9nRP/PlnqU0b/7F1rmvHTZtm\nlVnPItw9elggHDZMatEiMK+npOzfLzVvblXsYcOkSZOsqyYQaJmZNgmbpyKbmipt22afyWeflY4c\nsYpsYqKFWwAAUDFQsUW5l5XlHTf6u9/ZzMCeJXnGj/cuySPZWFPJwuzKld7K7E8/Wdjt2dMmlxk6\nVIqIKPWXUmrq1bNq9l/+Yl2xp02z1/7qq3YhACgtWVlWkQ0NlV58Ubr/fun4cfsMJyTYMIGjRy3E\n3ndfoFsLAACCSYlWbB3HGSDpZUmhkt52XfdPhRwzUtJjklxJK13X/c2pnpOKbcXjuyTP7NlWcW3T\nxrrapqbakjx16/o/xnWlZcu8YXbjRguzffpYZXboUKlx48C8nkDKyJDefttC7fz5tvTJtm02CVWV\nKoFuHcqbo0dtOSrftWQXLLAQO2+ejX/v08d6TNSoEejWAgCAsuJsKrYlFmwdxwmVtF7SpZJSJSVL\nGuW67jqfY9pJ+rekvq7rHnAcp5HrurtP9bwE24pj9Wpp3DhpyRK737q1jZW9+26pVauCx7uuHesJ\ns5s3WyWoXz8Ls1deaRMqwbvep2Rjb3/6SbrzThuXfOJFAqCojh2TsrNtyazlyy2wZmfbz1q3bhZi\nb77Z25sCAACgMGWtK3KCpJ9d190kSY7jfCjpSknrfI65SdLfXNc9IEmnC7Uov3yX5ElIkEaPtmri\nqZbkkWyc7fffW5CdPt2qj2Fh0qWXSn/4g4XZevUC85rKMt/3cfJkm0n5oYfsvb7pJmnixMIvHgC+\ncnKsZ4SnIrtwof3sPPWUrSN766124aRnT2bnBgAAJaskg20zSdt87qdKSjzhmPaS5DjOt7Luyo+5\nrvtFCbYJZczUqdKMGf5L8nhOgBs0sNB6otxcm3DGE2bT0qwb7WWX2Qn1FVdwEn0mLrnEbitX2mzQ\nr7xioXbiRP/KLpCbazOPN2tmPxuRkTYcQLILT2PHSv372/2qVaUXXghcWwEAQMUS6MmjKklqJ6m3\npOaS5juO08l13YO+BzmOM17SeElq2bJlabcRxWjTJuuiOGKE3f/LX2z24quvtqrsJZcUXJJHssrQ\n/PkWZj/6SNq1y0Lw5ZfbYwcNsu6POHsxMdJ779nFAU935Hfflf71L5tJuX9/Qm5Fk5dny2DNnWsV\n2fnzbYbt1avtZ+GBB6x7f+/eNk4bAAAgUEoy2G6X5LtwSvNft/lKlbTYdd3jkn5xHGe9LOgm+x7k\nuu6bkt6UbIxtibUYxS4vz8a9zpzpXZInLMyqqzVrWsW1USP/JXk8jh+3k+lp06yqu3evhd5BgywY\nDxzIhDMlwXe5o9BQWxJpwACpc2fp3nula6+1/0OUP64r/fijdSOWrFv63/9uX593nn3u+vb1VvJv\nvz1wbQUAAPBVkpNHVZJNHtVPFmiTJf3Gdd21PscMkE0odYPjOA0krZDUxXXdfSd7XiaPKvuOHLFA\nVKWKdUW85x6737OnVWWvuEJq27bwxx47Jn39tYXZjz+2NVhr1JAGD7bK7IABhVd0UXKOHrWq7XPP\nWcgdOFD67LNAtwrFwXVt4jDPGNm5c6U9e6xnRWSkzVy8datVZMvb+s4AAKDsKlOTR7mum+M4zh2S\nvpSNn/2767prHcd5XNJS13Vn/rqvv+M46yTlSpp0qlCLsmvnTu+SPF99Jf3jH1bdGTbMJoEqbEke\nj+xse8y0adInn0jp6dateMgQe47+/b1r16L0Vaki3XijdMMN0hdf2DqkknTwoPTss9Idd5TvdYDL\nE9e1pa9q17YuxNOn2wUjyboYDxhgMxd7Pqu9egWurQAAAGeiRNexLQlUbMuWAwdsnOvixXbfsyTP\nuHFSp04nf1xWlvTll9J//iP997+2vmqdOtJVV1mYveQS1lUt6z7+WBo+3Krx111n3ZQ7dgx0q3Ci\nzZu9Fdk5c2yyp5deku66y6qzH39sYbZtW8ZQAwCAsqFMrWNbUgi2gXP8uHdJnmrVpKeftgrQ8OFS\nbKwF2ujok58cZ2ba7MfTpll1NzPTluIZOtQ7ds9TDURw2LTJQtI771gX9IEDbXIvLkoEzrZtVk3v\n1Ml7wSgvzzvJU58+9v/Eck4AAKCsKlNdkVF+fPaZ9M9/WihNT7fZiEeOtH2OY0HmZA4ftsdPmyZ9\n/rmFn4YNbZ3aESOsqyMTEQWvNm1sZutHH5Vee83Ga3pC7fffS/HxUiV+y5SoHTtsbOw331hFduNG\nC6/ffGMTtL3/vl1wKmwdaAAAgPKCii0K+OUXC6O33mrdTO+6S/rwQ5vAybMkT/XqJ398erpVZKdN\nszGZ2dlSkyY23nbECCkpibBT3m3bZpMPtWgh3X23rW96qp8ZFN3u3VJKine92EsusQnXate2C0V9\n+kj9+p16KAAAAEBZRldknJPjxy2EvPKK3V+yxCpuGRkWSgpbksfjwAHrojxtmjRrls1u3KyZdVMe\nMULq3t1CMiqGvDz7efjzn6XvvrMu57fdJk2cKNWvH+jWBZf9+73ryM6ZY0tmhYTY9tq1pUWL7EJR\n1658xgAAQPlAsMVZ27PHuhfPnStNmGBV2pMtyeOxb5/NYjxtmjR7tgXjFi0syF59tZSYeOowjIrh\nu+8s4H7+ufTzz/Yzcvw4XdBP5uBBG8vevbvUoIF19b7rLhvXfvHFVpHt00eKiyPIAgCA8olgi7PW\nt68FkLfftvGvJ7N7t82iOm2ajeHLzbUupyNG2C0+nnF8KNzu3VKjRvb1gAG2hNOkSRbgKrKsLO/4\n2DlzpBUrbFK299+XfvMbG0O7aZN9tphcDQAAVAQEW5wx17UgumqVdPSonTyfaOdOacYMW5pn3jzr\nZnreed7KbNeuhFkUXV6e9Mc/Wpf3/fuliy6ygDtkSMWoQGZmSt9+a92IExNtOZ7ISAutF15oF5n6\n9LF9zC4NAAAqIoItiiwvT/rDHyxYvPpqwf3bt9tsx9OmSQsWWAA+/3wLsiNGSJ07E2ZxbjIzpXff\nlV54wSYse+UV6fbbA92qkjFnjk3wNGeOjV3PyZGuvVb64APbP3++XVSqWjWw7QQAACgLCLYokvR0\n62786afSdddZmEhNtcrR5s3WFfL77+3Yjh29YbZDB8Isil9Ojl1E6d/f1lydMUNavdp+LoNxoqns\nbGnxYpsZ2tOtv3Nnad06GxfrGSPbowczRQMAABSGYAs/rivt2uUNrFu22DIh//2vVcvCwmwSH191\n6kjt2lm30OHDpaioQLQcFdndd0svvmjVy7Fj7X6bNoFu1amtXGmzQM+ZYxeFsrOtq/G+fda9+ocf\nbJbwWrUC3VIAAICyj2BbweTl2fhX3+Dq+XrzZmnrVjvB9hUSYlXX7t2t62Pr1lKrVt5/a9cu5RcB\nFGLtWun556V//tMmKLvvPunppwPdKpOTIy1daiH2zjut6vrII9KTT0oxMd6KbM+edqEIAAAAZ4Zg\nW87k5tqMqKcKrseO+T+mYUP/sNq6tdSypU1O06qVVZPat7ftQFmXlmbL3XTtKl1zja2pPG+eNHBg\n6S4llZZmsxTPmWNjzg8ftu0LFtgSPLt3W2U2GLtOAwAAlDUE2yCTk2OTNPkGVt+vt261Y3w1blww\nuHrut2pVcMxeVpY0bpyUlCTdckvJvyagJP3tb9Idd9h473vvteVwinvm4Lw8myV8zhybpfiii6Tk\nZCkhQbrgAm9Ftlcv7/JFAAAAKD5nE2wrlVRjYONXU1MLVlo997dts6qsr6ZNLagmJkojR/oH15Yt\npWrViv79t22TrrrKJoPq3LmYXhQQQOPHW3f5P//Zxt8+/LB01122XNC5VHCPH5dee83C7Lx50oED\ntv0Pf7Bg262bVW2bNi2e1wEAAIDiRcX2HBw7ZuHxxEqr5+vUVKv+eDiOFBHhH1Z9q64tWkjh4cXT\ntoULbfKnrCzpX/+SBmNcqcQAABfWSURBVA8unucFygLXlb76ygJubq70zTe2PT399OPEXVf68UcL\nsZJ02222LSLCPn+eimyfPlLz5iX7OgAAAFAQXZGL2dGj1h24sPGtW7ZYN2Lfty8kxGY+PbGLsG9w\nrVy55Nu9davNbNyqlfTJJ8xsjPItK8tmUE5Ls5/7oUOtghsT43/c9Om2LvPcuTbpmmTV2O++s6/3\n75fq1SvVpgMAAKAQdEU+Q1lZ3uBaWHjdscP/+NBQC6etWkn9+hUMr82b2xI6geK6VhVu2VKaMkW6\n/HJmZUX5V7Wq/VupknTzzdJbb9lET5deap/L11+3i05ffWWh1rci27at93kItQAAAMGrXFdsjxw5\n+fjWzZttjVdflSpZKDyx0ur5ulkzO6Ys2r1bGjXKxgT26hXo1gCBc+CA9MYbNptybq5N/NSypf0+\nqFrVLv4AAACg7KpwFdvDh08+vnXzZmnPHv/jw8K8IXXw4IJdhiMirCobbFaskK680l7via8ZqGjq\n1pUeeEC6/3677wmyZzLxGgAAAIJL0AXbjRuluDgLrvv2+e+rUsUbXLt2LVh1bdq0dNe+LA0ffmiz\nw9avbxNGxcYGukVA2UBlFgAAoOIIumCbnS01bCjFxxecVbhRo/IXXE/lm2+s+/HFF9ukOI0bB7pF\nAAAAAFD6gi7Yduwo/e9/gW5FYHkmierd28YSjhlTOrMtAwAAAEBZVIHqm+XDTz9JSUnWFTskRBo/\nnlALAAAAoGIj2AaRzz+XEhKk9eu963ACAAAAQEVHsA0Cris984zN5Ny2rbR0qXThhYFuFQAAAACU\nDQTbIPDKK7Z8yTXX2MzHLVsGukUAAAAAUHYE3eRRFdGNN0rh4dK4cSxhAgAAAAAnomJbRs2fL11y\niXT4sFSjhnTTTYRaAAAAACgMwbYMeu01qV8/KTVV2rs30K0BAAAAgLKNYFuGHDsm3XyzdNttUv/+\n0uLFUuvWgW4VAAAAAJRtBNsy5M47pTfflB58UJo5U6pdO9AtAgAAAICyj8mjypAHH7QuyFdfHeiW\nAAAAAEDwoGIbYP/6lzR6tJSXJ7VqRagFAAAAgDNFsA2Q3Fzpvvuk666Ttm6VMjMD3SIAAAAACE4E\n2wA4cEAaPFj685+lW2+VZs+WatYMdKsAAAAAIDgxxraUua50xRXSkiXSG29I48cHukUAAAAAENwI\ntqXMcaQ//cm+vvjiwLYFAAAAAMoDgm0pcF3p6adtndrHHiPQAgAAAEBxYoxtCcvMlK65Rnr4YWnj\nRgu5AAAAAIDiQ7AtQZs3Sz16SNOn20RR771nXZEBAAAAAMWHrsgl5MgRqXt3+/ezz6QBAwLdIgAA\nAAAonwi2JaRaNenFF6WuXaX27QPdGgAAAAAov07bFdlxnEjHccJ97ld1HKd1STYqWB09Kt10kzRt\nmt2/5hpCLQAAAACUtKKMsf2PpDyf+7m/boOPnTulPn2kt9+Wfvwx0K0BAAAAgIqjKF2RK7mue8xz\nx3XdY47jVC7BNgWd5GRp6FDpwAHp3/+Wrr460C0CAAAAgIqjKBXbPY7jDPHccRznSkl7S65JwWXj\nRikpSapUSfruO0ItAAAAAJS2olRsb5H0vuM4r/x6P1XS9SXXpODStq303HM2nrZhw0C3BgAAAAAq\nntNWbF3X3ei67oWSOkjq4Lpud9d1fy75ppVdBw5Y1+OUFLt/xx2EWgAAAAAIlKLMivx/juPUcV33\nsOu6hx3Hqes4zpOl0biyaO1aKT7e1qZlkijg/7d397GW1PUdxz9fF1R8rBWKyqKQSLHrUzUroVqp\ngjFQFUitCqmKxgRLqoCYFmyNVeofahuLUmJKrYr1AS31YdNS0CBYjZWyVasimm7QCnTVpfUJqyLy\n7R/nUG/XXdhd9uzs7+zrlWzuzNy5c783mWzu+86cOQAAML1teY3tMd39ndtWuvvbSX5zcSPtvj78\n4eTww5ObbkquuCI54YSpJwIAAGBbwnZVVd3ttpWq2ifJ3W5n/6V06aXJ8ccnD3tYsn598vjHTz0R\nAAAAybY9POrdSS6rqrcnqSQvSHLBIofaHR15ZPK61yWnnprss8/U0wAAAHCbbXl41OuTvDbJryQ5\nNMmlSR6y4Ll2C1/9avKMZySbNiV7752ceaaoBQAA2N1sy63ISfLNJJ3kWUmOTHLNwibaTVx2WbJ2\nbfLJTyYb9uhnQAMAAOzetnorclX9cpIT5/9uTPK+JNXdT95Fs02iOzn33OSMM5JDD509MOqhD516\nKgAAALbm9q7Yfjmzq7NP7+5f7+5zk/x014w1nXPOSU47bXYL8qc/LWoBAAB2d7f38KjfSnJCksur\n6pIkF2b28Kil9rznJbfemrzsZcldtvVGbQAAACaz1XTr7g919wlJHpbk8iSnJ/mlqnpLVT11Vw24\nK1x5ZfKc5yQ335zsu2/y8peLWgAAgFFsy1ORf9Dd7+nuZyRZneSzSc5c+GS7yDvekRxxRHLVVcnG\njVNPAwAAwPbaruuS3f3t7j6/u49a1EC7yi23JKefnrzwhckTnzgL24fsEW9iBAAAsFz22BtuX/zi\n5E1vmsXtJZck97//1BMBAACwI27v4VFL7fTTZ1dqX/CCqScBAADgztijrth+4AOz96dNkkc+UtQC\nAAAsgz0ibG+9NXn1q5NnPjP51KeSH/xg6okAAADYWZY+bL///VnQvuY1yUknJVdckdzznlNPBQAA\nwM6y1K+x7U6e+tTZE4/POSc59dSkauqpAAAA2JmWOmyrkle8YnaF9qjh36AIAACALVm6sO2eXZ29\n+92TU05Jjj126okAAABYpKV6je2PfjR70vEZZyQf//gscgEAAFhuSxO2N9yQHHFE8s53Jmefnbzn\nPV5PCwAAsCdYiluRv/e95LDDZh8/+MHk+OOnnggAAIBdZSnC9j73SV75ytkV24c/fOppAAAA2JWG\nvRX5Jz9JTj89ufzy2fopp4haAACAPdGQV2xvvDF59rNnUXu/+yVPfvLUEwEAADCV4cL2hz9MHve4\nZOPG5IILkuc/f+qJAAAAmNJwYfvlLycPeEDyiU/MAhcAAIA923Bhu//+yfr1yQMfOPUkAAAA7A6G\ne3jUgx4kagEAAPiZ4cIWAAAAVhK2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0\nYQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCE\nLQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDW2jYVtXRVfWVqtpQVWfdzn7PrKquqrWLnAcA\nAIDls7CwrapVSc5LckySNUlOrKo1W9jv3klOS3LlomYBAABgeS3yiu1hSTZ097XdfXOSC5Mct4X9\n/iTJ65P8aIGzAAAAsKQWGbYHJLluxfr1823/p6oem+TA7v6HBc4BAADAEpvs4VFVdZckb0zy8m3Y\n9+SqWl9V6zdt2rT44QAAABjGIsP2hiQHrlhfPd92m3sneUSSK6rqa0kOT7JuSw+Q6u7zu3ttd6/d\nb7/9FjgyAAAAo1lk2F6V5JCqOriq7prkhCTrbvtkd3+3u/ft7oO6+6Akn05ybHevX+BMAAAALJmF\nhW1335LkJUkuTXJNkvd399VVdXZVHbuo7wsAAMCeZa9FHry7L05y8WbbXrWVfZ+0yFkAAABYTpM9\nPAoAAAB2BmELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0\nYQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCE\nLQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2\nAAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgC\nAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsA\nAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAA\nAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAA\nDE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAw\nNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQ\nhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMT\ntgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADG2h\nYVtVR1fVV6pqQ1WdtYXPn1FVX6qqz1fVZVX1kEXOAwAAwPJZWNhW1aok5yU5JsmaJCdW1ZrNdvts\nkrXd/agkFyV5w6LmAQAAYDkt8ortYUk2dPe13X1zkguTHLdyh+6+vLv/Z7766SSrFzgPAAAAS2iR\nYXtAkutWrF8/37Y1L0ryjwucBwAAgCW019QDJElVPTfJ2iS/sZXPn5zk5CR58IMfvAsnAwAAYHe3\nyCu2NyQ5cMX66vm2/6eqnpLkj5Ic290/3tKBuvv87l7b3Wv322+/hQwLAADAmBYZtlclOaSqDq6q\nuyY5Icm6lTtU1WOS/GVmUfutBc4CAADAklpY2Hb3LUlekuTSJNckeX93X11VZ1fVsfPd/jTJvZL8\nbVV9rqrWbeVwAAAAsEULfY1td1+c5OLNtr1qxfJTFvn9AQAAWH6LvBUZAAAAFk7YAgAAMDRhCwAA\nwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAA\nQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAM\nTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0\nYQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCE\nLQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2\nAAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgC\nAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsA\nAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAA\nAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAwNGELAADA0IQtAAAAQxO2AAAA\nDE3YAgAAMDRhCwAAwNCELQAAAEMTtgAAAAxN2AIAADA0YQsAAMDQhC0AAABDE7YAAAAMTdgCAAAw\nNGELAADA0IQtAAAAQxO2AAAADE3YAgAAMDRhCwAAwNCELQAAAENbaNhW1dFV9ZWq2lBVZ23h83er\nqvfNP39lVR20yHkAAABYPgsL26paleS8JMckWZPkxKpas9luL0ry7e5+aJI/T/L6Rc0DAADAclrk\nFdvDkmzo7mu7++YkFyY5brN9jktywXz5oiRHVVUtcCYAAACWzCLD9oAk161Yv36+bYv7dPctSb6b\n5P4LnAkAAIAls9fUA2yLqjo5ycnz1R9X1RennAd2kn2T3Dj1EHAnOY9ZFs5lloHzmGVx6PZ+wSLD\n9oYkB65YXz3ftqV9rq+qvZLcN8l/bX6g7j4/yflJUlXru3vtQiaGXci5zDJwHrMsnMssA+cxy6Kq\n1m/v1yzyVuSrkhxSVQdX1V2TnJBk3Wb7rEty0nz5t5N8rLt7gTMBAACwZBZ2xba7b6mqlyS5NMmq\nJG/r7qur6uwk67t7XZK/TvI3VbUhyX9nFr8AAACwzRb6GtvuvjjJxZtte9WK5R8ledZ2Hvb8nTAa\n7A6cyywD5zHLwrnMMnAesyy2+1wud/4CAAAwskW+xhYAAAAWbqiwraqjq+orVbWhqs6aeh7YXlV1\nYFVdXlVfqqqrq+q0qWeCO6OqVlXVZ6vq76eeBXZEVf1CVV1UVV+uqmuq6temngl2RFW9bP67xRer\n6r1VdfepZ4JtUVVvq6pvrXxL16r6xar6aFX9+/zj/e7oOMOEbVWtSnJekmOSrElyYlWtmXYq2G63\nJHl5d69JcniS33MeM7jTklwz9RBwJ7wpySXd/bAkj47zmQFV1QFJTk2ytrsfkdmDWz2UlVG8I8nR\nm207K8ll3X1Iksvm67drmLBNcliSDd19bXffnOTCJMdNPBNsl+7e2N2fmS9/P7NfoA6YdirYMVW1\nOsnTkrx16llgR1TVfZMckdm7NKS7b+7u70w7FeywvZLsU1V7JblHkv+ceB7YJt39T5m9Q85KxyW5\nYL58QZLj7+g4I4XtAUmuW7F+fQQBA6uqg5I8JsmV004CO+ycJH+Q5NapB4EddHCSTUnePr+l/q1V\ndc+ph4Lt1d03JPmzJF9PsjHJd7v7I9NOBXfK/t29cb78jST739EXjBS2sDSq6l5J/i7J6d39vann\nge1VVU9P8q3u/tepZ4E7Ya8kj03ylu5+TJIfZBtud4Pdzfz1h8dl9seaByW5Z1U9d9qpYOfo2dv4\n3OFb+YwUtjckOXDF+ur5NhhKVe2dWdS+u7s/MPU8sIOekOTYqvpaZi8NObKq3jXtSLDdrk9yfXff\ndufMRZmFLozmKUm+2t2buvsnST6Q5PETzwR3xjer6oFJMv/4rTv6gpHC9qokh1TVwVV118xeEL9u\n4plgu1RVZfZarmu6+41TzwM7qrtf0d2ru/ugzP4//lh3uzrAULr7G0muq6pD55uOSvKlCUeCHfX1\nJIdX1T3mv2scFQ9CY2zrkpw0Xz4pyYfv6Av2Wug4O1F331JVL0lyaWZPentbd1898ViwvZ6Q5HlJ\nvlBVn5tv+8PuvnjCmQD2ZC9N8u75H82vTfLCieeB7dbdV1bVRUk+k9k7MHw2yfnTTgXbpqrem+RJ\nSfatquuT/HGS1yV5f1W9KMl/JHn2HR5ndssyAAAAjGmkW5EBAADg5whbAAAAhiZsAQAAGJqwBQAA\nYGjCFgAAgKEJWwBYoKr6aVV9bsW/s3bisQ+qqi/urOMBwKiGeR9bABjUD7v7V6ceAgCWmSu2ADCB\nqvpaVb2hqr5QVf9SVQ+dbz+oqj5WVZ+vqsuq6sHz7ftX1Qer6t/m/x4/P9Sqqvqrqrq6qj5SVfvM\n9z+1qr40P86FE/2YALBLCFsAWKx9NrsV+TkrPvfd7n5kkr9Ics5827lJLujuRyV5d5I3z7e/OcnH\nu/vRSR6b5Or59kOSnNfdD0/ynSTPnG8/K8lj5sf53UX9cACwO6junnoGAFhaVXVTd99rC9u/luTI\n7r62qvZO8o3uvn9V3Zjkgd39k/n2jd29b1VtSrK6u3+84hgHJflodx8yXz8zyd7d/dqquiTJTUk+\nlORD3X3Tgn9UAJiMK7YAMJ3eyvL2+PGK5Z/mZ8/PeFqS8zK7untVVXmuBgBLS9gCwHSes+LjP8+X\nP5XkhPny7yT5xHz5siSnJElVraqq+27toFV1lyQHdvflSc5Mct8kP3fVGACWhb/eAsBi7VNVn1ux\nfkl33/aWP/erqs9ndtX1xPm2lyZ5e1X9fpJNSV44335akvOr6kWZXZk9JcnGrXzPVUneNY/fSvLm\n7v7OTvuJAGA34zW2ADCB+Wts13b3jVPPAgCjcysyAAAAQ3PFFgAAgKG5YgsAAMDQhC0AAABDE7YA\nAAAMTdgCAAAwNGELAADA0IQtAAAAQ/tfGR06eR/+WhAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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JF18c7NpVTnGxter6ht1166xLdnGxHRMZabMvl23h7dBBCmMCbgAAAABndi7B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\nGNr/ArJgyeudNdlEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"coronal\",\"meniscus\")\n",
    "plot_history([('Coronal-Meniscus',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "QmahIYIbRW0f"
   },
   "source": [
    "## Model 7: Sagittal Plane and ACL Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "ZOaVSMVBRWDM",
    "outputId": "07f3ffc0-9ecc-4af6-fc5a-bfe609c0cb48"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n"
     ]
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"sagittal\",\"acl\")\n",
    "plot_history([('Sagittal-Meniscus',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VOVrmbmERwkA"
   },
   "source": [
    "## Model 8: Sagittal Plane and Abnormal Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1933
    },
    "colab_type": "code",
    "id": "Eft84txzRvq1",
    "outputId": "c1ca3171-90d6-43e0-9fed-1d509af05b51"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/50\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.79167, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_abnormal.h5\n",
      " - 23s - loss: 1.0668 - acc: 0.5584 - val_loss: 1.5503 - val_acc: 0.7917\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.79167\n",
      " - 6s - loss: 0.8664 - acc: 0.7115 - val_loss: 1.4010 - val_acc: 0.7917\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 00003: val_acc improved from 0.79167 to 0.80000, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_abnormal.h5\n",
      " - 8s - loss: 0.7560 - acc: 0.7080 - val_loss: 0.9348 - val_acc: 0.8000\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 00004: val_acc improved from 0.80000 to 0.83333, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_abnormal.h5\n",
      " - 7s - loss: 0.6792 - acc: 0.7584 - val_loss: 0.8042 - val_acc: 0.8333\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 00005: val_acc did not improve from 0.83333\n",
      " - 6s - loss: 0.5961 - acc: 0.8159 - val_loss: 0.6469 - val_acc: 0.8250\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.83333 to 0.88333, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_abnormal.h5\n",
      " - 8s - loss: 0.4440 - acc: 0.8664 - val_loss: 0.9691 - val_acc: 0.8833\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.88333\n",
      " - 6s - loss: 0.3667 - acc: 0.9274 - val_loss: 0.6322 - val_acc: 0.7500\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.88333\n",
      " - 6s - loss: 0.3839 - acc: 0.9097 - val_loss: 1.1312 - val_acc: 0.8167\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.88333\n",
      " - 6s - loss: 0.2429 - acc: 0.9442 - val_loss: 1.9202 - val_acc: 0.8250\n",
      "Epoch 10/50\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.88333\n",
      " - 6s - loss: 0.1781 - acc: 0.9805 - val_loss: 1.5156 - val_acc: 0.8333\n",
      "Epoch 00010: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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yygmFCklPPy01aGBlytdeazPAknTNNdKzz1qArVkzZ54PAOB/CLMAABRwS5ZY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\n5C1YfNarErsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wxk+IaRSziDwLFkiXXy79/rvvJAAAAH6MGGEfmzXzmwPwiGIWkee006TZs6VOnXwnAQAA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\nv+oqO1PLTD9kVKNGdhb700/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t6o784YepW3LXrjB6dApn9erBj34Ew4en8/q2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\n+CA7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"sagittal\",\"abnormal\")\n",
    "plot_history([('Sagittal-Abnormal',history)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_bsqw1TUSYVe"
   },
   "source": [
    "## Model 9: Sagittal Plane and Meniscus Labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2069
    },
    "colab_type": "code",
    "id": "i0zV0xfISXF7",
    "outputId": "0217395a-61a4-4c8c-f4c9-0dd76b5279ab"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 1130 samples, validate on 120 samples\n",
      "Epoch 1/50\n",
      "\n",
      "Epoch 00001: val_acc improved from -inf to 0.58333, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_meniscus.h5\n",
      " - 23s - loss: 1.0663 - acc: 0.5009 - val_loss: 0.9167 - val_acc: 0.5833\n",
      "Epoch 2/50\n",
      "\n",
      "Epoch 00002: val_acc did not improve from 0.58333\n",
      " - 6s - loss: 0.9159 - acc: 0.5106 - val_loss: 0.8971 - val_acc: 0.5667\n",
      "Epoch 3/50\n",
      "\n",
      "Epoch 00003: val_acc did not improve from 0.58333\n",
      " - 6s - loss: 0.8765 - acc: 0.5106 - val_loss: 0.8588 - val_acc: 0.5667\n",
      "Epoch 4/50\n",
      "\n",
      "Epoch 00004: val_acc did not improve from 0.58333\n",
      " - 6s - loss: 0.8452 - acc: 0.5292 - val_loss: 0.8308 - val_acc: 0.5750\n",
      "Epoch 5/50\n",
      "\n",
      "Epoch 00005: val_acc improved from 0.58333 to 0.64167, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_meniscus.h5\n",
      " - 8s - loss: 0.8171 - acc: 0.5027 - val_loss: 0.8071 - val_acc: 0.6417\n",
      "Epoch 6/50\n",
      "\n",
      "Epoch 00006: val_acc improved from 0.64167 to 0.65833, saving model to /content/gdrive/My Drive/Models/TransferLearning/sagittal_meniscus.h5\n",
      " - 7s - loss: 0.7837 - acc: 0.5841 - val_loss: 0.7654 - val_acc: 0.6583\n",
      "Epoch 7/50\n",
      "\n",
      "Epoch 00007: val_acc did not improve from 0.65833\n",
      " - 6s - loss: 0.7424 - acc: 0.6593 - val_loss: 0.7850 - val_acc: 0.6000\n",
      "Epoch 8/50\n",
      "\n",
      "Epoch 00008: val_acc did not improve from 0.65833\n",
      " - 6s - loss: 0.6919 - acc: 0.7053 - val_loss: 0.7405 - val_acc: 0.5917\n",
      "Epoch 9/50\n",
      "\n",
      "Epoch 00009: val_acc did not improve from 0.65833\n",
      " - 6s - loss: 0.5982 - acc: 0.7681 - val_loss: 0.7149 - val_acc: 0.6583\n",
      "Epoch 10/50\n",
      "\n",
      "Epoch 00010: val_acc did not improve from 0.65833\n",
      " - 6s - loss: 0.4718 - acc: 0.8274 - val_loss: 0.9262 - val_acc: 0.6333\n",
      "Epoch 11/50\n",
      "\n",
      "Epoch 00011: val_acc did not improve from 0.65833\n",
      " - 6s - loss: 0.3885 - acc: 0.8858 - val_loss: 0.8091 - val_acc: 0.6083\n",
      "Epoch 12/50\n",
      "\n",
      "Epoch 00012: val_acc did not improve from 0.65833\n",
      " - 6s - loss: 0.2679 - acc: 0.9265 - val_loss: 1.1408 - val_acc: 0.6583\n",
      "Epoch 00012: early stopping\n"
     ]
    },
    {
     "data": {
      "image/png": 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sszpw4ICSk5PVpk2bbD2ufv362rp1q6ZOnap27dplObZw4ULNmTMnY81tSkqKtm3bJklq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\nBQAAYDj/H1Xb7VRKcFK5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Pyy+X+vXztpYcCliYdV33a0kHTnPKtZL+55rvJVVxHKdOoOrJF716WZf2ppuk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\nUulYZiVJkiRJpfP/AX5QEm61XNY3AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = build_model()\n",
    "history = train_model(model,\"sagittal\",\"meniscus\")\n",
    "plot_history([('Sagittal-Meniscus',history)])"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "name": "MRNet Transfer Learning Model",
   "provenance": [],
   "version": "0.3.2"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}