Siamese_Network.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xsDyl1Ouvnpg"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from matplotlib.pyplot import imread\n",
        "import pickle\n",
        "import os\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import cv2\n",
        "import time\n",
        "\n",
        "import tensorflow as tf\n",
        "from keras.models import Sequential\n",
        "from keras.optimizers import Adam\n",
        "from keras.layers import Conv2D, ZeroPadding2D, Activation, Input, concatenate\n",
        "from keras.models import Model\n",
        "\n",
        "from keras.layers import BatchNormalization\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Concatenate\n",
        "from keras.layers import Lambda, Flatten, Dense\n",
        "from keras.initializers import glorot_uniform\n",
        "\n",
        "from tensorflow.keras.layers import Layer\n",
        "from keras.regularizers import l2\n",
        "from keras import backend as K\n",
        "\n",
        "from sklearn.utils import shuffle\n",
        "\n",
        "import numpy.random as rng"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/brendenlake/omniglot.git"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcXEnDSFxkfY",
        "outputId": "bfb8fdb7-ad7d-4aeb-c7ad-31c0ecdc441f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'omniglot'...\n",
            "remote: Enumerating objects: 81, done.\u001b[K\n",
            "remote: Total 81 (delta 0), reused 0 (delta 0), pack-reused 81\u001b[K\n",
            "Receiving objects: 100% (81/81), 85.74 MiB | 29.25 MiB/s, done.\n",
            "Resolving deltas: 100% (27/27), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip /content/omniglot/python/images_background.zip\n",
        "!unzip /content/omniglot/python/images_evaluation.zip"
      ],
      "metadata": {
        "id": "OjJQFYY8x9mG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2XjBOPBsvnpi"
      },
      "source": [
        "### Please give below paths as per the paths in your system"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "avAmmxnCvnpi"
      },
      "outputs": [],
      "source": [
        "train_folder = \"/content/images_background\"\n",
        "val_folder = '/content/images_evaluation'\n",
        "save_path = '/content/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TghHmNHrvnpj"
      },
      "outputs": [],
      "source": [
        "def loadimgs(path,n = 0):\n",
        "    '''\n",
        "    path => Path of train directory or test directory\n",
        "    '''\n",
        "    X=[]\n",
        "    y = []\n",
        "    cat_dict = {}\n",
        "    lang_dict = {}\n",
        "    curr_y = n\n",
        "    # we load every alphabet seperately so we can isolate them later\n",
        "    for alphabet in os.listdir(path):\n",
        "        print(\"loading alphabet: \" + alphabet)\n",
        "        lang_dict[alphabet] = [curr_y,None]\n",
        "        alphabet_path = os.path.join(path,alphabet)\n",
        "        # every letter/category has it's own column in the array, so  load seperately\n",
        "        for letter in os.listdir(alphabet_path):\n",
        "            cat_dict[curr_y] = (alphabet, letter)\n",
        "            category_images=[]\n",
        "            letter_path = os.path.join(alphabet_path, letter)\n",
        "            # read all the images in the current category\n",
        "            for filename in os.listdir(letter_path):\n",
        "                image_path = os.path.join(letter_path, filename)\n",
        "                image = imread(image_path)\n",
        "                category_images.append(image)\n",
        "                y.append(curr_y)\n",
        "            try:\n",
        "                X.append(np.stack(category_images))\n",
        "            # edge case  - last one\n",
        "            except ValueError as e:\n",
        "                print(e)\n",
        "                print(\"error - category_images:\", category_images)\n",
        "            curr_y += 1\n",
        "            lang_dict[alphabet][1] = curr_y - 1\n",
        "    y = np.vstack(y)\n",
        "    X = np.stack(X)\n",
        "    return X,y,lang_dict"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m_6uTPrWvnpj"
      },
      "source": [
        "### Loading the train images into tensors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "72oALyXLvnpj",
        "outputId": "3a0c322e-de70-41c3-9a81-1f116de67b34"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading alphabet: N_Ko\n",
            "loading alphabet: Greek\n",
            "loading alphabet: Syriac_(Estrangelo)\n",
            "loading alphabet: Latin\n",
            "loading alphabet: Asomtavruli_(Georgian)\n",
            "loading alphabet: Arcadian\n",
            "loading alphabet: Inuktitut_(Canadian_Aboriginal_Syllabics)\n",
            "loading alphabet: Sanskrit\n",
            "loading alphabet: Gujarati\n",
            "loading alphabet: Bengali\n",
            "loading alphabet: Futurama\n",
            "loading alphabet: Grantha\n",
            "loading alphabet: Mkhedruli_(Georgian)\n",
            "loading alphabet: Blackfoot_(Canadian_Aboriginal_Syllabics)\n",
            "loading alphabet: Tifinagh\n",
            "loading alphabet: Malay_(Jawi_-_Arabic)\n",
            "loading alphabet: Burmese_(Myanmar)\n",
            "loading alphabet: Armenian\n",
            "loading alphabet: Cyrillic\n",
            "loading alphabet: Japanese_(katakana)\n",
            "loading alphabet: Early_Aramaic\n",
            "loading alphabet: Hebrew\n",
            "loading alphabet: Anglo-Saxon_Futhorc\n",
            "loading alphabet: Braille\n",
            "loading alphabet: Tagalog\n",
            "loading alphabet: Japanese_(hiragana)\n",
            "loading alphabet: Balinese\n",
            "loading alphabet: Ojibwe_(Canadian_Aboriginal_Syllabics)\n",
            "loading alphabet: Alphabet_of_the_Magi\n",
            "loading alphabet: Korean\n"
          ]
        }
      ],
      "source": [
        "X,y,c=loadimgs(train_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kUqhefNSvnpj"
      },
      "source": [
        "### Saving the train tensors on disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JjH8dJ2bvnpk"
      },
      "outputs": [],
      "source": [
        "with open(os.path.join(save_path,\"train.pickle\"), \"wb\") as f:\n",
        "    pickle.dump((X,c),f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ui8pswHSvnpk"
      },
      "source": [
        "### Loading the validation images into tensors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tIc0iz2Rvnpk",
        "outputId": "e558d08b-1892-4bb1-bf32-a0ce0f243272"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading alphabet: Malayalam\n",
            "loading alphabet: Mongolian\n",
            "loading alphabet: Sylheti\n",
            "loading alphabet: Atlantean\n",
            "loading alphabet: Gurmukhi\n",
            "loading alphabet: Angelic\n",
            "loading alphabet: Oriya\n",
            "loading alphabet: Tibetan\n",
            "loading alphabet: Glagolitic\n",
            "loading alphabet: Tengwar\n",
            "loading alphabet: Aurek-Besh\n",
            "loading alphabet: Avesta\n",
            "loading alphabet: Syriac_(Serto)\n",
            "loading alphabet: Atemayar_Qelisayer\n",
            "loading alphabet: Old_Church_Slavonic_(Cyrillic)\n",
            "loading alphabet: Keble\n",
            "loading alphabet: ULOG\n",
            "loading alphabet: Manipuri\n",
            "loading alphabet: Ge_ez\n",
            "loading alphabet: Kannada\n"
          ]
        }
      ],
      "source": [
        "Xval,yval,cval=loadimgs(val_folder)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U_Re9pwkvnpk"
      },
      "source": [
        "### Saving the validation tensors on disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JC0dCAB1vnpl"
      },
      "outputs": [],
      "source": [
        "with open(os.path.join(save_path,\"val.pickle\"), \"wb\") as f:\n",
        "    pickle.dump((Xval,cval),f)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P1Q69wKdvnpl"
      },
      "outputs": [],
      "source": [
        "# del X, y ,c ,Xval, yval, cval"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oABHP9ofvnpl"
      },
      "outputs": [],
      "source": [
        "def initialize_weights(shape, dtype=None):\n",
        "    \"\"\"\n",
        "        The paper, http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf\n",
        "        suggests to initialize CNN layer weights with mean as 0.0 and standard deviation of 0.01\n",
        "    \"\"\"\n",
        "    return np.random.normal(loc = 0.0, scale = 1e-2, size = shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "38F3aGBwvnpl"
      },
      "outputs": [],
      "source": [
        "def initialize_bias(shape, dtype=None):\n",
        "    \"\"\"\n",
        "        The paper, http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf\n",
        "        suggests to initialize CNN layer bias with mean as 0.5 and standard deviation of 0.01\n",
        "    \"\"\"\n",
        "    return np.random.normal(loc = 0.5, scale = 1e-2, size = shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3cKJi9lRvnpl"
      },
      "outputs": [],
      "source": [
        "def get_siamese_model(input_shape):\n",
        "    \"\"\"\n",
        "        Model architecture based on the one provided in: http://www.cs.utoronto.ca/~gkoch/files/msc-thesis.pdf\n",
        "    \"\"\"\n",
        "\n",
        "    # Define the tensors for the two input images\n",
        "    left_input = Input(input_shape)\n",
        "    right_input = Input(input_shape)\n",
        "\n",
        "    # Convolutional Neural Network\n",
        "    model = Sequential()\n",
        "    model.add(Conv2D(64, (10,10), activation='relu', input_shape=input_shape,\n",
        "                   kernel_initializer=initialize_weights, kernel_regularizer=l2(2e-4)))\n",
        "    model.add(MaxPooling2D())\n",
        "    model.add(Conv2D(128, (7,7), activation='relu',\n",
        "                     kernel_initializer=initialize_weights,\n",
        "                     bias_initializer=initialize_bias, kernel_regularizer=l2(2e-4)))\n",
        "    model.add(MaxPooling2D())\n",
        "    model.add(Conv2D(128, (4,4), activation='relu', kernel_initializer=initialize_weights,\n",
        "                     bias_initializer=initialize_bias, kernel_regularizer=l2(2e-4)))\n",
        "    model.add(MaxPooling2D())\n",
        "    model.add(Conv2D(256, (4,4), activation='relu', kernel_initializer=initialize_weights,\n",
        "                     bias_initializer=initialize_bias, kernel_regularizer=l2(2e-4)))\n",
        "    model.add(Flatten())\n",
        "    model.add(Dense(4096, activation='sigmoid',\n",
        "                   kernel_regularizer=l2(1e-3),\n",
        "                   kernel_initializer=initialize_weights,bias_initializer=initialize_bias))\n",
        "\n",
        "    # Generate the encodings (feature vectors) for the two images\n",
        "    encoded_l = model(left_input)\n",
        "    encoded_r = model(right_input)\n",
        "\n",
        "    # Add a customized layer to compute the absolute difference between the encodings\n",
        "    L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))\n",
        "    L1_distance = L1_layer([encoded_l, encoded_r])\n",
        "\n",
        "    # Add a dense layer with a sigmoid unit to generate the similarity score\n",
        "    prediction = Dense(1,activation='sigmoid',bias_initializer=initialize_bias)(L1_distance)\n",
        "\n",
        "    # Connect the inputs with the outputs\n",
        "    siamese_net = Model(inputs=[left_input,right_input],outputs=prediction)\n",
        "\n",
        "    # return the model\n",
        "    return siamese_net"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q6a393acvnpl",
        "outputId": "ed6d8bfb-dd9e-4afc-e367-92c2f1e27c06"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_3 (InputLayer)        [(None, 105, 105, 1)]        0         []                            \n",
            "                                                                                                  \n",
            " input_4 (InputLayer)        [(None, 105, 105, 1)]        0         []                            \n",
            "                                                                                                  \n",
            " sequential_1 (Sequential)   (None, 4096)                 3894764   ['input_3[0][0]',             \n",
            "                                                          8          'input_4[0][0]']             \n",
            "                                                                                                  \n",
            " lambda (Lambda)             (None, 4096)                 0         ['sequential_1[0][0]',        \n",
            "                                                                     'sequential_1[1][0]']        \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 1)                    4097      ['lambda[0][0]']              \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 38951745 (148.59 MB)\n",
            "Trainable params: 38951745 (148.59 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = get_siamese_model((105, 105, 1))\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E17p3Gbyvnpm",
        "outputId": "319aec74-c9c9-43e9-9ab9-6f0a53bc8d1b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        }
      ],
      "source": [
        "optimizer = Adam(lr = 0.00006)\n",
        "model.compile(loss=\"binary_crossentropy\",optimizer=optimizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBucYwDcvnpm"
      },
      "source": [
        "### Loading the train tensors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CmD2LZCbvnpm",
        "outputId": "60ec57b5-d9ae-47b0-a85c-57116542b2e9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training alphabets: \n",
            "\n",
            "['N_Ko', 'Greek', 'Syriac_(Estrangelo)', 'Latin', 'Asomtavruli_(Georgian)', 'Arcadian', 'Inuktitut_(Canadian_Aboriginal_Syllabics)', 'Sanskrit', 'Gujarati', 'Bengali', 'Futurama', 'Grantha', 'Mkhedruli_(Georgian)', 'Blackfoot_(Canadian_Aboriginal_Syllabics)', 'Tifinagh', 'Malay_(Jawi_-_Arabic)', 'Burmese_(Myanmar)', 'Armenian', 'Cyrillic', 'Japanese_(katakana)', 'Early_Aramaic', 'Hebrew', 'Anglo-Saxon_Futhorc', 'Braille', 'Tagalog', 'Japanese_(hiragana)', 'Balinese', 'Ojibwe_(Canadian_Aboriginal_Syllabics)', 'Alphabet_of_the_Magi', 'Korean']\n"
          ]
        }
      ],
      "source": [
        "with open(os.path.join(save_path, \"train.pickle\"), \"rb\") as f:\n",
        "    (Xtrain, train_classes) = pickle.load(f)\n",
        "\n",
        "print(\"Training alphabets: \\n\")\n",
        "print(list(train_classes.keys()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U-r99iy7vnpm",
        "outputId": "0669e953-2fff-4abf-a081-88da09dc1cde"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Validation alphabets:\n",
            "\n",
            "['Malayalam', 'Mongolian', 'Sylheti', 'Atlantean', 'Gurmukhi', 'Angelic', 'Oriya', 'Tibetan', 'Glagolitic', 'Tengwar', 'Aurek-Besh', 'Avesta', 'Syriac_(Serto)', 'Atemayar_Qelisayer', 'Old_Church_Slavonic_(Cyrillic)', 'Keble', 'ULOG', 'Manipuri', 'Ge_ez', 'Kannada']\n"
          ]
        }
      ],
      "source": [
        "with open(os.path.join(save_path, \"val.pickle\"), \"rb\") as f:\n",
        "    (Xval, val_classes) = pickle.load(f)\n",
        "\n",
        "print(\"Validation alphabets:\", end=\"\\n\\n\")\n",
        "print(list(val_classes.keys()))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gTzLESKevnpn"
      },
      "outputs": [],
      "source": [
        "def get_batch(batch_size,s=\"train\"):\n",
        "    \"\"\"Create batch of n pairs, half same class, half different class\"\"\"\n",
        "    if s == 'train':\n",
        "        X = Xtrain\n",
        "        categories = train_classes\n",
        "    else:\n",
        "        X = Xval\n",
        "        categories = val_classes\n",
        "    n_classes, n_examples, w, h = X.shape\n",
        "\n",
        "    # randomly sample several classes to use in the batch\n",
        "    categories = rng.choice(n_classes,size=(batch_size,),replace=False)\n",
        "\n",
        "    # initialize 2 empty arrays for the input image batch\n",
        "    pairs=[np.zeros((batch_size, h, w,1)) for i in range(2)]\n",
        "\n",
        "    # initialize vector for the targets\n",
        "    targets=np.zeros((batch_size,))\n",
        "\n",
        "    # make one half of it '1's, so 2nd half of batch has same class\n",
        "    targets[batch_size//2:] = 1\n",
        "    for i in range(batch_size):\n",
        "        category = categories[i]\n",
        "        idx_1 = rng.randint(0, n_examples)\n",
        "        pairs[0][i,:,:,:] = X[category, idx_1].reshape(w, h, 1)\n",
        "        idx_2 = rng.randint(0, n_examples)\n",
        "\n",
        "        # pick images of same class for 1st half, different for 2nd\n",
        "        if i >= batch_size // 2:\n",
        "            category_2 = category\n",
        "        else:\n",
        "            # add a random number to the category modulo n classes to ensure 2nd image has a different category\n",
        "            category_2 = (category + rng.randint(1,n_classes)) % n_classes\n",
        "\n",
        "        pairs[1][i,:,:,:] = X[category_2,idx_2].reshape(w, h,1)\n",
        "\n",
        "    return pairs, targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vr2xDpYWvnpn"
      },
      "outputs": [],
      "source": [
        "def generate(batch_size, s=\"train\"):\n",
        "    \"\"\"a generator for batches, so model.fit_generator can be used. \"\"\"\n",
        "    while True:\n",
        "        pairs, targets = get_batch(batch_size,s)\n",
        "        yield (pairs, targets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XNCx9JYnvnpn"
      },
      "outputs": [],
      "source": [
        "def make_oneshot_task(N, s=\"val\", language=None):\n",
        "    \"\"\"Create pairs of test image, support set for testing N way one-shot learning. \"\"\"\n",
        "    if s == 'train':\n",
        "        X = Xtrain\n",
        "        categories = train_classes\n",
        "    else:\n",
        "        X = Xval\n",
        "        categories = val_classes\n",
        "    n_classes, n_examples, w, h = X.shape\n",
        "\n",
        "    indices = rng.randint(0, n_examples,size=(N,))\n",
        "    if language is not None: # if language is specified, select characters for that language\n",
        "        low, high = categories[language]\n",
        "        if N > high - low:\n",
        "            raise ValueError(\"This language ({}) has less than {} letters\".format(language, N))\n",
        "        categories = rng.choice(range(low,high),size=(N,),replace=False)\n",
        "\n",
        "    else: # if no language specified just pick a bunch of random letters\n",
        "        categories = rng.choice(range(n_classes),size=(N,),replace=False)\n",
        "    true_category = categories[0]\n",
        "    ex1, ex2 = rng.choice(n_examples,replace=False,size=(2,))\n",
        "    test_image = np.asarray([X[true_category,ex1,:,:]]*N).reshape(N, w, h,1)\n",
        "    support_set = X[categories,indices,:,:]\n",
        "    support_set[0,:,:] = X[true_category,ex2]\n",
        "    support_set = support_set.reshape(N, w, h,1)\n",
        "    targets = np.zeros((N,))\n",
        "    targets[0] = 1\n",
        "    targets, test_image, support_set = shuffle(targets, test_image, support_set)\n",
        "    pairs = [test_image,support_set]\n",
        "\n",
        "    return pairs, targets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fYac7gU1vnpn"
      },
      "outputs": [],
      "source": [
        "def test_oneshot(model, N, k, s = \"val\", verbose = 0):\n",
        "    \"\"\"Test average N way oneshot learning accuracy of a siamese neural net over k one-shot tasks\"\"\"\n",
        "    n_correct = 0\n",
        "    if verbose:\n",
        "        print(\"Evaluating model on {} random {} way one-shot learning tasks ... \\n\".format(k,N))\n",
        "    for i in range(k):\n",
        "        inputs, targets = make_oneshot_task(N,s)\n",
        "        probs = model.predict(inputs)\n",
        "        if np.argmax(probs) == np.argmax(targets):\n",
        "            n_correct+=1\n",
        "    percent_correct = (100.0 * n_correct / k)\n",
        "    if verbose:\n",
        "        print(\"Got an average of {}% {} way one-shot learning accuracy \\n\".format(percent_correct,N))\n",
        "    return percent_correct"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eVap7r-Fvnpn"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IhoLshCsvnpn"
      },
      "outputs": [],
      "source": [
        "# Hyper parameters\n",
        "evaluate_every = 200 # interval for evaluating on one-shot tasks\n",
        "batch_size = 32\n",
        "n_iter = 20000 # No. of training iterations\n",
        "N_way = 20 # how many classes for testing one-shot tasks\n",
        "n_val = 250 # how many one-shot tasks to validate on\n",
        "best = -1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SOjaE1E5vnpn"
      },
      "outputs": [],
      "source": [
        "model_path = '/content/weights'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7uHo8Isivnpo",
        "outputId": "8b0885a8-a9e8-468e-d8ca-7d2f5588d5de"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 6.8% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 16400 iterations: 59.9851034839948 mins\n",
            "Train Loss: 1.3928968906402588\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 5.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 16600 iterations: 60.70341648658117 mins\n",
            "Train Loss: 1.2705355882644653\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Got an average of 3.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 16800 iterations: 61.43017500638962 mins\n",
            "Train Loss: 1.1753381490707397\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
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            "1/1 [==============================] - 0s 48ms/step\n",
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            "1/1 [==============================] - 0s 57ms/step\n",
            "1/1 [==============================] - 0s 46ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "Got an average of 4.0% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 17000 iterations: 62.187262586752574 mins\n",
            "Train Loss: 1.0985093116760254\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
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            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Got an average of 3.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 17200 iterations: 62.89568745295207 mins\n",
            "Train Loss: 1.0351282358169556\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "Got an average of 6.0% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 17400 iterations: 63.59807006120682 mins\n",
            "Train Loss: 0.9821022152900696\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "Got an average of 3.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 17600 iterations: 64.33148243824641 mins\n",
            "Train Loss: 0.9373319149017334\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 19ms/step\n",
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            "1/1 [==============================] - 0s 24ms/step\n",
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            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "Got an average of 5.2% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 17800 iterations: 65.09448037544887 mins\n",
            "Train Loss: 0.8993091583251953\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Got an average of 4.4% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 18000 iterations: 65.80557270844777 mins\n",
            "Train Loss: 0.8669024109840393\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 30ms/step\n",
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            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "Got an average of 4.4% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 18200 iterations: 66.51585303147634 mins\n",
            "Train Loss: 0.8392331600189209\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Got an average of 4.4% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 18400 iterations: 67.26360778808593 mins\n",
            "Train Loss: 0.8155995607376099\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 43ms/step\n",
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            "1/1 [==============================] - 0s 37ms/step\n",
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            "1/1 [==============================] - 0s 37ms/step\n",
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            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "Got an average of 3.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 18600 iterations: 68.01407235860825 mins\n",
            "Train Loss: 0.7954276204109192\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Got an average of 5.2% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 18800 iterations: 68.71380533774693 mins\n",
            "Train Loss: 0.7782388925552368\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Got an average of 5.2% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 19000 iterations: 69.44145757357279 mins\n",
            "Train Loss: 0.7636280059814453\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
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            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "Got an average of 4.8% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 19200 iterations: 70.19447576999664 mins\n",
            "Train Loss: 0.7512471675872803\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 35ms/step\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
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            "1/1 [==============================] - 0s 33ms/step\n",
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            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "Got an average of 4.0% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 19400 iterations: 70.9040577530861 mins\n",
            "Train Loss: 0.7407953143119812\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 2.8% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 19600 iterations: 71.6104561328888 mins\n",
            "Train Loss: 0.7320096492767334\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Got an average of 5.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 19800 iterations: 72.34998387495676 mins\n",
            "Train Loss: 0.7246600389480591\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 5.6% 20 way one-shot learning accuracy \n",
            "\n",
            "\n",
            " ------------- \n",
            "\n",
            "Time for 20000 iterations: 73.05493399302165 mins\n",
            "Train Loss: 0.7185438871383667\n",
            "Evaluating model on 250 random 20 way one-shot learning tasks ... \n",
            "\n",
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            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 48ms/step\n",
            "1/1 [==============================] - 0s 77ms/step\n",
            "1/1 [==============================] - 0s 54ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 51ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 52ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "Got an average of 2.8% 20 way one-shot learning accuracy \n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"Starting training process!\")\n",
        "print(\"-------------------------------------\")\n",
        "t_start = time.time()\n",
        "for i in range(1, n_iter+1):\n",
        "    (inputs,targets) = get_batch(batch_size)\n",
        "    loss = model.train_on_batch(inputs, targets)\n",
        "    if i % evaluate_every == 0:\n",
        "        print(\"\\n ------------- \\n\")\n",
        "        print(\"Time for {0} iterations: {1} mins\".format(i, (time.time()-t_start)/60.0))\n",
        "        print(\"Train Loss: {0}\".format(loss))\n",
        "        val_acc = test_oneshot(model, N_way, n_val, verbose=True)\n",
        "        model.save_weights(os.path.join(model_path, 'weights.{}.h5'.format(i)))\n",
        "        if val_acc >= best:\n",
        "            print(\"Current best: {0}, previous best: {1}\".format(val_acc, best))\n",
        "            best = val_acc"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U28m16yqvnpo"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wzDibJuLvnpo"
      },
      "source": [
        "### Load model weights"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vPmYBxRvvnpo"
      },
      "outputs": [],
      "source": [
        "model.load_weights(os.path.join(model_path, \"weights.20000.h5\"))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHU7zxS_vnpo"
      },
      "source": [
        "### Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KriJh-ayvnpo"
      },
      "source": [
        "### Baseline model based on Nearest Neighbors using Euclidean distance (L2 distance)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BHIGNZMsvnpo"
      },
      "outputs": [],
      "source": [
        "def nearest_neighbour_correct(pairs,targets):\n",
        "    \"\"\"returns 1 if nearest neighbour gets the correct answer for a one-shot task\n",
        "        given by (pairs, targets)\"\"\"\n",
        "    L2_distances = np.zeros_like(targets)\n",
        "    for i in range(len(targets)):\n",
        "        L2_distances[i] = np.sum(np.sqrt(pairs[0][i]**2 - pairs[1][i]**2))\n",
        "    if np.argmin(L2_distances) == np.argmax(targets):\n",
        "        return 1\n",
        "    return 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MZjsegiOvnpo"
      },
      "outputs": [],
      "source": [
        "def test_nn_accuracy(N_ways,n_trials):\n",
        "    \"\"\"Returns accuracy of NN approach \"\"\"\n",
        "    print(\"Evaluating nearest neighbour on {} unique {} way one-shot learning tasks ...\".format(n_trials,N_ways))\n",
        "\n",
        "    n_right = 0\n",
        "\n",
        "    for i in range(n_trials):\n",
        "        pairs,targets = make_oneshot_task(N_ways,\"val\")\n",
        "        correct = nearest_neighbour_correct(pairs,targets)\n",
        "        n_right += correct\n",
        "    return 100.0 * n_right / n_trials"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Kzr6h-iIvnpp"
      },
      "outputs": [],
      "source": [
        "ways = np.arange(1,20,2)\n",
        "resume =  False\n",
        "trials = 50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_FjLAjIrvnpp",
        "outputId": "88a51418-094b-4fbe-cdc6-d0c0bd757c4b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Evaluating model on 50 random 1 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 469ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 100.0% 1 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 1 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 100.0% 1 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 1 way one-shot learning tasks ...\n",
            "NN Accuracy =  100.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 3 way one-shot learning tasks ... \n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-47-0e6416d38168>:6: RuntimeWarning: invalid value encountered in sqrt\n",
            "  L2_distances[i] = np.sum(np.sqrt(pairs[0][i]**2 - pairs[1][i]**2))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 485ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "Got an average of 34.0% 3 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 3 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 83ms/step\n",
            "1/1 [==============================] - 0s 60ms/step\n",
            "1/1 [==============================] - 0s 44ms/step\n",
            "1/1 [==============================] - 0s 67ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 80ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 40.0% 3 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 3 way one-shot learning tasks ...\n",
            "NN Accuracy =  26.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 5 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 508ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Got an average of 22.0% 5 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 5 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 18ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 19ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Got an average of 16.0% 5 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 5 way one-shot learning tasks ...\n",
            "NN Accuracy =  18.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 7 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 523ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "Got an average of 18.0% 7 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 7 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 12.0% 7 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 7 way one-shot learning tasks ...\n",
            "NN Accuracy =  12.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 9 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 594ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 10.0% 9 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 9 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 39ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 10.0% 9 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 9 way one-shot learning tasks ...\n",
            "NN Accuracy =  8.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 11 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 613ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "Got an average of 22.0% 11 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 11 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 14.0% 11 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 11 way one-shot learning tasks ...\n",
            "NN Accuracy =  10.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 13 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 653ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 42ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 41ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "Got an average of 6.0% 13 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 13 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 8.0% 13 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 13 way one-shot learning tasks ...\n",
            "NN Accuracy =  10.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 15 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 672ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 36ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 38ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "Got an average of 8.0% 15 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 15 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 6.0% 15 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 15 way one-shot learning tasks ...\n",
            "NN Accuracy =  6.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 17 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 688ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Got an average of 8.0% 17 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 17 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 34ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 31ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "1/1 [==============================] - 0s 37ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 26ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "Got an average of 6.0% 17 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 17 way one-shot learning tasks ...\n",
            "NN Accuracy =  4.0\n",
            "---------------------------------------------------------------------------------------------------------------\n",
            "Evaluating model on 50 random 19 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 1s 765ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 27ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 33ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "Got an average of 2.0% 19 way one-shot learning accuracy \n",
            "\n",
            "Evaluating model on 50 random 19 way one-shot learning tasks ... \n",
            "\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 25ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 23ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 22ms/step\n",
            "1/1 [==============================] - 0s 24ms/step\n",
            "1/1 [==============================] - 0s 21ms/step\n",
            "1/1 [==============================] - 0s 20ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n",
            "1/1 [==============================] - 0s 40ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 29ms/step\n",
            "1/1 [==============================] - 0s 32ms/step\n",
            "Got an average of 2.0% 19 way one-shot learning accuracy \n",
            "\n",
            "Evaluating nearest neighbour on 50 unique 19 way one-shot learning tasks ...\n",
            "NN Accuracy =  4.0\n",
            "---------------------------------------------------------------------------------------------------------------\n"
          ]
        }
      ],
      "source": [
        "val_accs, train_accs,nn_accs = [], [], []\n",
        "for N in ways:\n",
        "    val_accs.append(test_oneshot(model, N, trials, \"val\", verbose=True))\n",
        "    train_accs.append(test_oneshot(model, N, trials, \"train\", verbose=True))\n",
        "    nn_acc = test_nn_accuracy(N, trials)\n",
        "    nn_accs.append(nn_acc)\n",
        "    print (\"NN Accuracy = \", nn_acc)\n",
        "    print(\"---------------------------------------------------------------------------------------------------------------\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EEI8wz1cvnpz"
      },
      "source": [
        "### Save the accuracies on disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fvc832gUvnpz"
      },
      "outputs": [],
      "source": [
        "with open(os.path.join(save_path,\"accuracies.pickle\"), \"wb\") as f:\n",
        "    pickle.dump((val_accs,train_accs,nn_accs),f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p4sVfVLXvnpz"
      },
      "source": [
        "### Load the accuracies from disk"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DvdLCA9uvnpz"
      },
      "outputs": [],
      "source": [
        "with open(os.path.join(save_path, \"accuracies.pickle\"), \"rb\") as f:\n",
        "    (val_accs, train_accs, nn_accs) = pickle.load(f)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RRbN4wtPvnpz"
      },
      "source": [
        "### Below two functions are used for visualizing test image and support set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tLoCbgNvvnp0"
      },
      "outputs": [],
      "source": [
        "def concat_images(X):\n",
        "    \"\"\"Concatenates a bunch of images into a big matrix for plotting purposes.\"\"\"\n",
        "    nc, h , w, _ = X.shape\n",
        "    X = X.reshape(nc, h, w)\n",
        "    n = np.ceil(np.sqrt(nc)).astype(\"int8\")\n",
        "    img = np.zeros((n*w,n*h))\n",
        "    x = 0\n",
        "    y = 0\n",
        "    for example in range(nc):\n",
        "        img[x*w:(x+1)*w,y*h:(y+1)*h] = X[example]\n",
        "        y += 1\n",
        "        if y >= n:\n",
        "            y = 0\n",
        "            x += 1\n",
        "    return img"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcjbW7F2vnp0"
      },
      "outputs": [],
      "source": [
        "def plot_oneshot_task(pairs):\n",
        "    fig,(ax1,ax2) = plt.subplots(nrows=1, ncols=2)\n",
        "    ax1.matshow(pairs[0][0].reshape(105,105), cmap='gray')\n",
        "    img = concat_images(pairs[1])\n",
        "    ax1.get_yaxis().set_visible(False)\n",
        "    ax1.get_xaxis().set_visible(False)\n",
        "    ax2.matshow(img,cmap='gray')\n",
        "    plt.xticks([])\n",
        "    plt.yticks([])\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "0Wqra09jvnp0",
        "outputId": "2e9845a1-7be4-4930-d984-046cf04e98f6"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Example of concat image visualization\n",
        "pairs, targets = make_oneshot_task(16,\"train\",\"Sanskrit\")\n",
        "plot_oneshot_task(pairs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xy7bqBvgvnp0"
      },
      "source": [
        "### Resuts"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 717
        },
        "id": "Otkind7Avnp0",
        "outputId": "9f230aba-d5c3-4384-e5bd-d7e70534964f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAD1CAYAAADNj/Z6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAATSklEQVR4nO3dy47bOBYAUFVjtkn2Qef/P6yBfEBqH8+i4W6PRrIoio9L8Rwgm4qrTNMSeXnFx8fj8XgsAMC0/uhdAACgL8EAAExOMAAAkxMMAMDkBAMAMDnBAABMTjAAAJMTDADA5P6T8qLfv38vP3/+XL58+bJ8fHzULhOw8ng8ls/Pz+X79+/LH3+ME8NrO6Cv1LYjKRj4+fPn8uPHj2KFA/L89ddfy59//tm7GMm0HRDDUduRFAx8+fLlnz/29evXMiUDkv369Wv58ePHP/fiKLQd0Fdq25EUDDzTe1+/fnVDQ0ejpdq1HRDDUduRFAwARJYSJDmTrY3X70Kdj0MwAAxrtEzJaPbqVyd/P4IBYEjrjupdB/Xx8bF8fHw06cRyA5QoHWxK+c/UPWMYZ40SwI7Uzqh2JuHK34+Y5Xg8Hv/3b8tW2QUIY5EZAIZz9rn04/H453dKZwhSO/GUjrRV9iLFXjlefx617JwnGACG1bPzeRcEnCnX87W1gpVcKeXYKnsvOe8doZ6femdXBAPAUJ6N5tmG8jU7UFqJRrtm+WpbBwUtXX000ysgSJ2b0ap8ggEgjKMGslTDeLWRvfPyuStBSeuApsR75QaXV9/v7Otrl08wAISQM4v9rNKdVa0GOtKjgmU5P0GzdtlLrWboHbgclbvlnAzBABDKmRnruX+/5VLDMyI8Kth6/0h1VXJZY6v6zs0ktXz8YmkhEIrdBPvpHYgcWXeqPVaFXHG1vDXLKDMAhHA0ShMA1LM12l7/rPdGQ7XmaUTIxhxpUUaZASCc6I3znex18kebDfX6jmoGISNcd7XKKBgAwjD6b+tM2r10Wj6SET6X1QQAFHd1Uhv3IjMAhDRzyra15+qKu3yeqNZLBSPVuWAACKXFGvWr73Hn0XGkDuouRrhePCYAhlFqh8KW6+Z7z8I/4uChtqJeD4IBIKzcrVuf1g1tjR3y7jSKXi9hixgQRCxTiq3lgVubCvX6bB4TAENaL307s7VrqffPeW304GEvgKKsd9dPj0c1ggHgNlovf0s54vd13f4o1vUYISBYl2f9L9XW6yN8viM2HQKmdXb0n/o3S3l97FCifNE6pcgBwdpR+dZBQM7BQaWc/RwtHiMIBoCh5BzeEqEjO6NlFqHVsdEl5GxXvQ4Ceu2qmDJf5ahMTi0EyFSrMd1r3EfY676WmidCnvke9+o/pUxRJii2LoNgYGAjNDgRbipYlvjXYuv7+UzQEvnY51e5QUCUVRQ1Vruk8pgAGErKs+Gc34sicmfby9nsTk4d9pw0mfrowBHG/I9RGrVlyXvGx9zOjFZT/n/mtH1PNUbXqd/51fd4Xi89MgRb79eiTDIDwDByswI5kw5b6bn9b0q9zLg9cesMQcp71C6TzEBgd78Br0zygXdGvoZ6Pq++YxYlty4j1kXNMskMAGEdrQvfe22J17XSeqOkrfd/OnqsN0qQVeo7bvGZc1e7lL6OZQaCidZQ9RD1IA/a2JrZveVsZun5d6NeT72fVb+WY1QjtB1XM6K1HnkJBoBw9karRw1hiwlmNfUKCKLXy9NenfTcTbCE3NUPJQkGgNCOZlfvvWZUEZ9VR3J2sl0UEcv0SjAQhJt/3+hRP3VEvwZKlC/yY42WUlLj6ukawQBAhrN7zD9/PurINgL1Uo9goLOSGYFIN0rtTMcIE4Vgy92v1egTNdlmaSEARQkExiMz0FiNEXPEG6/1jm89D/gAGJ3MAABMTmZgQCONflPPDy9FhgDgPJkBAJiczEAjJUa/dx3trj9XibqSIQBIJzMAwJBs1laOzEBFJU/Omsne53Xjz+fqfhJ33bIYSpMZAIYhIOTJtVCWYAAI6ezRxczDNfC3j4+PYnXhMUEFHg/EYSJhbGf36T97bzkB8JwzdXWXe0ob8TfBANCFA3viyAmYenWigrs6BAMFXblINXrHrowQie3s8lIH4dRzdgLvyN9Fj3Yk5z1b1K9gAJhCtE5rr1NoWcYzqzW2/u/5+691WzNjUGO30pZy37PFqhjBQAFGqe2dPQjJErN5RZs3ECXrUeIY8JZ1u76HI32nKa60Qa+/WyvYspoAGMYIgdy7Gd45wWsLJeq1V+ecM6O+dVmvDkYej0eVnVpfyQx0MkKjBjWNOLq7IrVD2Eq913T1PXp9j+v3Tc10tM4wlMxK1jwaXjAAhHPnYPnos7XuoEb7u6/1965zPCrLyNdYjWtEMHBB1FmhM6kZKVOf+6GvUvXfO8szQjsQ/VoXDABw2VbavnQHmJJVIY9gIEPk6BPu7A73XoQlhbRR83otnY2xmgCgkHeNc2pn3zLguUNwNYIRAj2ZgUZGuBhgFNHvp73ynSl3zc/4Oqosmc7vPXeAfIIBYBrRdiFca9mRrgOC58/elSdy3Y0qyoZoHhMAQ7j72R89OoWtzv/1XxQjrBbIceXzWFo4kBEaoBk5snQ8UUZPNfTu4N51tHer6162MlLPn0XJVgkGgLDunKbuHQSsjfK8P0rneVbOxkgteUxwQrTUGf/a2rubsd31XktpRyKcqOd+um6kOpQZAEK5Y6eU0rlHeS4etb5HyVysvdZn5MddggEgrKsNZpT5Ib3f/8iInSxleUwAhBF55NRC7+BlxjpvpeS17dTCQbih4JoS95DRLhFF7R9kBoBb69H4CkTqirgXwjslMz61PrNg4MBoFx3Qz95kMcrY6kxnrefSQa7HBAAF1dr3fzS1PvdWwHUUEPT8DkoGKzUDH5kBgMJkCGK523dQI7iRGQBuZ8TGf8Qy95QyO//1NTNnaVIIBgpyoUEsPe/JnFMBt17TS+TNn1KX6a03cho5IKi97NRjAuBWIo2wz54KGLmjilSvJUXZ+fGdZxBT8/oQDAC3EXHTopRyRDlbw8qp8kptMFT7e/GYAAjnTEp0hM4rQkf/zlY6fQSl67XWY4TnI6Mzf7/1YxrBABBWTqcUveON7N1hQHeu15aHIB0Fur0eHwkGdowSGcOd5DbKd+6oWlOXdayv7TMnWbYgGABCObPLnI5rbmfT+r2vlzPBbuuyCgYK6H2Bwd25x7iLqNey1QTA7Xnsdy9ndng8MxKP2lHvKVlmwQBwe88Gc7TGnn3rgCDy+QQjEAwAU9AZtNFyhL23qRPnmTMAwLC2JuUJCM4TDAAwtKM5BLJCxwQDANyGjj+POQMAMDnBAABMzmMCALIn3UnL34NgAGBiZt6zLIIBYDBnO68II9eoZyu8q8ujsr0ed9zzc0Td6380ggEgtKsj16MjY2s6s1Vuy/Kty5Xz3i2P/V3Led87LzkscY0LBrgFqc57utqAr4+M7dXhppxd36p8JQKBXnKvh732oWegGI3VBEBIJUZyvRr51BF/6/Kty3X1/Z+/3yIYv3I9PD/r3mc2mJAZYHBu4vspPXJ97bBqjwRz0v7r8tUoW4tsQM3MRunHKWdOPawpUvslMwCEJX1b1ojH9LaaV9G6Y44UCCyLzAATGq0xnFXp76nlhLcrZS89wq79mVvVa637tvVEyBoTGUuUXzAAUIDJaOVFGz1fFXnypmCAIY241pz7ulunlav3ngPv9A7Wei0jTWXOAEAhERv5iGWaTfRAYFlkBqaXM6KJEFlDFK5LUkUNBJZFMABQRKklkLR3p1UKuTwmAEIq3YiO0ijXpA7YIzNQQO+JKWeUaAxabtzCfHrueX9XZ+s09x6PcP5D1HY4armeBANAWCVnp0dvjFs5qtNR5hHd+eChHgQDBUXOEIww0qpRxojfBcdeR7IpAcEoo8OeUrIDkduJo7L5zq8RDAAhpQYEW53E8/VRO5Deo9qt+hw1oBqlnEd6XxMmEN7c6+EsMxlxD3b+37sDZbau7SgH0LwTpVyv9Rc1EFjfx+tyRSnnFe/a6JbXimAAGMZe45ga/K1fN9vhNFv1FDUQePUsd4nNe3p/B0+pA7WU15QY/HhMsKPlOd1XtS5j1MaCe1rfi0ed12unEem62Ct3jzYmd8VGz530RtjFL9fW53n9vC22eRYMAMPKbSBrLF9MndcQpSPbypDkzMtoLec9Iwzq3mW19n7estyCgQM5KcVWo5LSF0rvRurMSKl3WWlvfX1EuQb2JjpG6IBK28rStPge7liXT1EOdzJnAJhaiY5mPWg4OwqMrueE3IhZlTNGmcwsM1DR1Yu4RTQc8SKNWCbup3Qa9t3fu9s1fXYfiFLvObIzj2Vef6dVPQsGAAoZYWnjbHwPaTwmAChs9NR2ihE+190CgZr7xsgMnHBlYlBqWqjVxTvCjQwjGjkQiDKZbcvZsm19DyMGB63KLjMAUMjIgcAIzq7oiu7MSN8+AzczykUKtNVzVN5jQmCq9cTMs+dURPosT1uf6fnzPbX7DsEAQGFnOqDWm8uklCNC5/Rqr/M8+p3Itr73np9LMJBhpOdP0W8III6cTreV1JUaI7V5R+dEHL2+JMEAQGEjzx04m6kYZUviK7/XSs/yCQYuiJYhiH6hw4xGOBVwbYQyUpbVBACFjbIFLTzJDBRw9rlP6fcDzqt5nz5nvEfLHsIewQAwpeez8VrBdfTn7vBKMFBBrdGABgPS7oPUSXCl76mje989TFSCAeCWena8On1GIxioqNQJZhoWAGqymgAAJicYAIDJeUxQ0dUJhB4PANCCYACA4UU5CnhUgoFgXKgAaXKyryknMs5IMADAMK7s4bA+kXHkgKB0UCMYqCAnWh35ooQScufYuHfqiTTvaassZ//+1hHNrp+/CQaA7q50Ohr0skrunFrquyl58qPzIrYJBoCuXhvls43883c9By6j5DbKpTrbK9fHO88sgWDyb4IBIIScBnk9yovQsJfsvFoFOSVH3iXVCgRaGyFYFQwUZK4A9NczIFi3ASN0ZlEDgZYiBJG92YEQGN66Ie/xPPjoPZ8p6UgiBwItAqnWn7fG91/qb8oMdBLppoNRvBvB9XpkkNppRXqU8SzHU4Ty7IlctlSvqxiifP9rMgPAMCI2oqlGLvsMamdtSp1iW4tgABhCznaztRvdUUbX7Gv5vUW+RgQDBUR8Fgh3kbP8K9LjgVezrHEfYfZ8L6/XQKTrQDAAhLDVMEZqLPeM2uGNWu47iFj3JhBeYCkhXLfeInbvNbSV0r75XtLt1WeUCYWCASCcs4G2tHSerV34ztT9Xr37Hv71blfHSJkvwQAQ2lHH0qtBvVuHd/YgIAf+nLcVNEXZEtmcASCM9aSqEoFAtIla0Rzt2/Du96Ivl4ukd2d/RDDQyPrGAf6Vc0DROmiImkGIVoYtV9qn1ucm1Pr7vdrnKCtMBANACOsOaa9xPrPfQNQgPGKZliVWuSKVJVfvQOMMwUAGaUfoY+T7buSy9zZD3fUOGAQDwBDedQjvlm1F60h6N/r8K8q1EeGasJqgoghfMNxN7gmFPe/HKJ3OaFKW3737f21wOpkBILyczlQHfE20+tvK8lwJFEZW43PJDFQkYoV8W2uvc+6bSPdapLKMYuto6r3XvDrTYY70vdQKcGQGgPCirgpIMdrodL13QJTRd+5+CHdSs64FA0AoZzujUeTspdDL1ryM138pv1OrXKXeZ6Rlf1tKl9tjAiCcvdRwTgPYc6vX3A69RSBw5T3Wv9uyfnOOju7tauDRor4FA8AwzhyoE+kgmFE6rVytRtmjBQLra7DE9Vjrc3tMAIS1lRbOeXTQIyiInBU4K3U3x5plrxUI1K7vkmWtGQDJDADhvcsGvGsgI2UHUqw7vJpl3ztkaOvnKfX++trRRu21yzxCfcgMAMN4HaGmTiaLchDMkejlOzJCh7c2YplrEQwA04ja4fackPfufc8EXlHrNtXo5b/KY4JORlrWctfNO5hHy5TwGVGCgBRbu/9tlbfWZzh6bJLbmUe9NloTDADTad3op3RUkTuhdwdB9ZiXUfo8CgGBxwTAJHIPOKot8u6K65Ube6s7WjlTT1fqNMq10ZLMADCNlpMJo3bw76Sep7KXDWi1C2HNvztrZkAwAExnxsb+nXep/tnOBLjr5zoiGAAIqmXHNGsnOLKS35k5AwAwOcEAAExOMAAAkxMMAMDkBAMAMDmrCTKMcvAJRPHt27feRQDekBkAgMnJDDRmLS8A0cgMAMDkZAYuMMoH4A5kBgBgcoIBAJicYAAAJicYAIDJCQYAYHKCAQCYnGAAACYnGACAyQkGAGByggEAmJztiDmUcmSzrZkBxiUzAACTkxkgmdE/wD3JDADA5AQDADA5wQAATE4wAACTEwwAwOQEAwAwOcEAAExOMAAAkxMMAMDkBAMAMDnBAABMTjAAAJMTDADA5AQDADA5wQAATE4wAACTEwwAwOQEAwAwOcEAAExOMAAAkxMMAMDkBAMAMDnBAABMTjAAAJMTDADA5AQDADC5/6S86PF4LMuyLL9+/apaGGDb89573oujGK28cFdH92JSMPD5+bksy7L8+PHjeomAbJ+fn8u3b996FyPZs+0A+jpqOz4eCaH779+/l58/fy5fvnxZPj4+ihYQOPZ4PJbPz8/l+/fvyx9/jPN0T9sBfaW2HUnBAABwX+MMMQCAKgQDADA5wQAATE4wAACTEwwAwOQEAwAwOcEAAEzuv5o8U4LgKy9BAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "fig,ax = plt.subplots(1)\n",
        "ax.plot(ways, val_accs, \"m\", label=\"Siamese(val set)\")\n",
        "ax.plot(ways, train_accs, \"y\", label=\"Siamese(train set)\")\n",
        "plt.plot(ways, nn_accs, label=\"Nearest neighbour\")\n",
        "\n",
        "ax.plot(ways, 100.0/ways, \"g\", label=\"Random guessing\")\n",
        "plt.xlabel(\"Number of possible classes in one-shot tasks\")\n",
        "plt.ylabel(\"% Accuracy\")\n",
        "plt.title(\"Omiglot One-Shot Learning Performance of a Siamese Network\")\n",
        "box = ax.get_position()\n",
        "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
        "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5))\n",
        "inputs,targets = make_oneshot_task(20, \"val\", 'Oriya')\n",
        "plt.show()\n",
        "\n",
        "plot_oneshot_task(inputs)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.6.5"
    },
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}