Model_keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import keras\n",
    "from keras.layers import Input, Activation, Dense, Conv2D, MaxPooling2D, Flatten, Lambda, Dropout\n",
    "from keras.models import Model\n",
    "import keras.backend as K\n",
    "from keras.optimizers import SGD\n",
    "from keras.callbacks import LearningRateScheduler\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.regularizers import l2\n",
    "import tensorflow as tf\n",
    "import math\n",
    "from sklearn.manifold import TSNE\n",
    "from keras.optimizers import Adam\n",
    "from keras.callbacks import EarlyStopping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def triplet_mask_acc(y_true,y_pred,N=4096,beta=4096,alpha=2):\n",
    "    #accuracy based on triplets. If anchor is closer to a positive+alpha, than to a    \n",
    "    #negative, then we count that as a correct prediction  \n",
    "    length=K.int_shape(y_pred)[1]\n",
    "    anchor = y_pred[:,0:N]\n",
    "    positive = y_pred[:,N:2*N]\n",
    "    negative = y_pred[:,2*N:3*N]\n",
    "    pos_dist = K.sum(K.square(anchor-positive),axis=1)\n",
    "\n",
    "    # distance between the anchor and the negative\n",
    "    neg_dist = K.sum(K.square(anchor-negative),axis=1)\n",
    "    return K.cast((pos_dist + alpha < neg_dist),float)\n",
    "\n",
    "def triplet_loss(y_true, y_pred, N = 4096, alpha=2):\n",
    "    \"\"\" \n",
    "    Triplet loss function \n",
    "     \n",
    "    Arguments: \n",
    "    y_true -- true labels\n",
    "    y_pred -- list containing three objects: \n",
    "            anchor -- the encodings for the anchor data \n",
    "            positive -- the encodings for the positive data (similar to anchor) \n",
    "            negative -- the encodings for the negative data (different from anchor) \n",
    "    N  --  The number of dimension  \n",
    "     \n",
    "    Returns: \n",
    "    loss -- real number, value of the triplet loss \n",
    "    \"\"\"  \n",
    "\n",
    "    anchor = y_pred[:,0:N]\n",
    "    positive = y_pred[:,N:2*N]\n",
    "    negative = y_pred[:,2*N:3*N]\n",
    "\n",
    "    # distance between the anchor and the positive\n",
    "    pos_dist = K.sum(K.square(anchor-positive),axis=1)\n",
    "\n",
    "    # distance between the anchor and the negative\n",
    "    neg_dist = K.sum(K.square(anchor-negative),axis=1)\n",
    "\n",
    "    # compute loss\n",
    "    basic_loss = pos_dist-neg_dist+alpha\n",
    "    loss = K.maximum(basic_loss,0.0)\n",
    " \n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear previous session if still in cache\n",
    "K.clear_session()\n",
    "def create_model():\n",
    "    # Siamese Model\n",
    "    net = keras.models.Sequential()\n",
    "\n",
    "    net.add(keras.layers.Conv2D(filters=64, kernel_size=(10, 10),\n",
    "                            input_shape=(105, 105, 1),\n",
    "                            activation='relu',name='conv1'))\n",
    "    net.add(keras.layers.MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "    net.add(keras.layers.Conv2D(filters=128, kernel_size=(7, 7),\n",
    "                            activation='relu',name='conv2'))\n",
    "    net.add(keras.layers.MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "    net.add(keras.layers.Conv2D(filters=128, kernel_size=(4, 4),\n",
    "                            activation='relu'))\n",
    "    net.add(keras.layers.MaxPool2D(pool_size=(2, 2),name='conv3'))\n",
    "\n",
    "    net.add(keras.layers.Conv2D(filters=256, kernel_size=(4, 4),\n",
    "                            activation='relu',name='conv4'))\n",
    "    net.add(keras.layers.MaxPool2D(pool_size=(2, 2)))\n",
    "\n",
    "    net.add(keras.layers.Flatten())\n",
    "    net.add(keras.layers.Dense(units=4096, activation='sigmoid',name='dense'))\n",
    "    return net\n",
    "\n",
    "# Call the Sequential model on each input with shared weights.\n",
    "pair_1st = keras.Input(shape=(105, 105, 1))\n",
    "pair_2nd = keras.Input(shape=(105, 105, 1))\n",
    "pair_3rd = keras.Input(shape=(105, 105, 1))\n",
    "\n",
    "model = create_model()\n",
    "anchor = model(pair_1st)\n",
    "pos = model(pair_2nd)\n",
    "neg = model(pair_3rd)\n",
    "# Merge three outputs into a single vector (12288,1)  \n",
    "outputs = keras.layers.concatenate([anchor, pos, neg])\n",
    "\n",
    "# Return a keras Model architecture.\n",
    "final_model = keras.Model(inputs=[pair_1st, pair_2nd,pair_3rd], outputs=outputs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "decay_slope = 1/10.0\n",
    "\n",
    "class LossHistory(keras.callbacks.Callback):\n",
    "    def on_train_begin(self, logs={}):\n",
    "        self.losses = []\n",
    "        self.val_losses=[]\n",
    "        self.best_val_acc = 0\n",
    "        \n",
    "def step_decay(epoch):\n",
    "    #Although Adam is an adaptive learning rate algorithm, research has proven that \n",
    "    #it is a good practice to decay the learning rate every n steps  \n",
    "    if not epoch%2:\n",
    "        return K.get_value(optimizer.lr)*0.99\n",
    "    return K.get_value(optimizer.lr)\n",
    "  \n",
    "loss_history = LossHistory()\n",
    "lrate = LearningRateScheduler(step_decay)\n",
    "#in case val_loss doesn’t shrink for 6 epochs – stop training  \n",
    "early_stopper = EarlyStopping(monitor='val_loss', min_delta=1e-5, patience=6, verbose=1)\n",
    "\n",
    "callbacks_list = [lrate,loss_history,early_stopper]\n",
    "\n",
    "optimizer = Adam(lr=1e-4,amsgrad=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "final_model.compile(optimizer=optimizer,\n",
    "                   loss=triplet_loss,\n",
    "                   metrics=[triplet_mask_acc],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv1 (Conv2D)               (None, 96, 96, 64)        6464      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 48, 48, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2 (Conv2D)               (None, 42, 42, 128)       401536    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 21, 21, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv2d_1 (Conv2D)            (None, 18, 18, 128)       262272    \n",
      "_________________________________________________________________\n",
      "conv3 (MaxPooling2D)         (None, 9, 9, 128)         0         \n",
      "_________________________________________________________________\n",
      "conv4 (Conv2D)               (None, 6, 6, 256)         524544    \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 3, 3, 256)         0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense (Dense)                (None, 4096)              9441280   \n",
      "=================================================================\n",
      "Total params: 10,636,096\n",
      "Trainable params: 10,636,096\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "final_model.layers[3].summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "%matplotlib inline\n",
    "import glob\n",
    "import cv2\n",
    "import numpy as np\n",
    "from collections import Counter\n",
    "from IPython.core.display import display, HTML\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dataset_train_path , dataset_evaluation_path = (\"python/images_background/\",\"python/images_evaluation/\")\n",
    "\n",
    "languages = [path.split(\"/\")[-1] for path in glob.glob(dataset_train_path+\"*\")]\n",
    "languages_evaluation = [path.split(\"/\")[-1] for path in glob.glob(dataset_evaluation_path+\"*\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "symbols = {language:[] for language in languages}\n",
    "for path in glob.glob(dataset_train_path+\"/*/*\"):\n",
    "    symbols[path.split(\"/\")[2]].append(path.split(\"/\")[3])\n",
    "symbols_eval = {language:[] for language in languages_evaluation}\n",
    "for path in glob.glob(dataset_evaluation_path+\"/*/*\"):\n",
    "    symbols_eval[path.split(\"/\")[2]].append(path.split(\"/\")[3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read and process data to arrays and dicts  \n",
    "all_alphabets = {language:{symbol:[cv2.cvtColor(cv2.imread(image),cv2.COLOR_RGB2GRAY) for image in glob.glob(dataset_train_path+language+'/'+symbol+\"/*\")] for symbol in symbols[language]}\\\n",
    "                                                                  for language in languages }\n",
    "all_images = []\n",
    "for language in all_alphabets:\n",
    "    for char in all_alphabets[language]:\n",
    "        all_images.append(all_alphabets[language][char])\n",
    "all_images = np.array(all_images)       \n",
    "all_evaluation_alphabets = {language:{symbol:[cv2.cvtColor(cv2.imread(image),cv2.COLOR_RGB2GRAY) for image in glob.glob(dataset_evaluation_path+language+'/'+symbol+\"/*\")] for symbol in symbols_eval[language]}\\\n",
    "                                                                  for language in languages_evaluation[5:] }\n",
    "all_test_alphabets = {language:{symbol:[cv2.cvtColor(cv2.imread(image),cv2.COLOR_RGB2GRAY) for image in glob.glob(dataset_evaluation_path+language+'/'+symbol+\"/*\")] for symbol in symbols_eval[language]}\\\n",
    "                                                                  for language in languages_evaluation[:5] }\n",
    "all_evaluation_images = []\n",
    "for language in all_evaluation_alphabets:\n",
    "    for char in all_evaluation_alphabets[language]:\n",
    "        all_evaluation_images.append(all_evaluation_alphabets[language][char])\n",
    "all_evaluation_images = np.array(all_evaluation_images)\n",
    "        \n",
    "all_test_images = []\n",
    "for language in all_test_alphabets:\n",
    "    for char in all_test_alphabets[language]:\n",
    "        all_test_images.append(all_test_alphabets[language][char])\n",
    "all_test_images = np.array(all_test_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#create a batch of triplets (batch_size,3,105,105,1) to feed it into the model  \n",
    "def augmented_generator_triplet(features,batch_size,validation):\n",
    "    \n",
    "    batch_features = np.zeros((batch_size, 3,105, 105,1))\n",
    "    y = np.ones(batch_size)\n",
    "    ceil = all_evaluation_images.shape[0] if validation else all_images.shape[0]\n",
    "    while True:\n",
    "        for i in range(batch_size):\n",
    "            anchor = np.random.choice(range(ceil),2,replace=False)\n",
    "            indices = np.random.randint(0,20,3)\n",
    "            image1,image2,image3 = features[anchor[0],indices[0]].reshape(105,105,1),features[anchor[0],indices[1]].reshape(105,105,1),features[anchor[1],indices[2]].reshape(105,105,1)\n",
    "            batch_features[i] = [image1,image2,image3]\n",
    "            \n",
    "        yield [batch_features[:,0],batch_features[:,1],batch_features[:,2]], y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " - 20s - loss: 0.8655 - triplet_mask_acc: 0.5771 - val_loss: 0.9444 - val_triplet_mask_acc: 0.5309\n",
      "Epoch 2/100\n",
      " - 16s - loss: 0.7494 - triplet_mask_acc: 0.6024 - val_loss: 0.9291 - val_triplet_mask_acc: 0.5662\n",
      "Epoch 3/100\n",
      " - 16s - loss: 0.7457 - triplet_mask_acc: 0.6147 - val_loss: 0.9273 - val_triplet_mask_acc: 0.5552\n",
      "Epoch 4/100\n",
      " - 16s - loss: 0.7114 - triplet_mask_acc: 0.6348 - val_loss: 0.8540 - val_triplet_mask_acc: 0.4984\n",
      "Epoch 5/100\n",
      " - 16s - loss: 0.6346 - triplet_mask_acc: 0.6791 - val_loss: 0.7828 - val_triplet_mask_acc: 0.6709\n",
      "Epoch 6/100\n",
      " - 16s - loss: 0.5262 - triplet_mask_acc: 0.7302 - val_loss: 0.6361 - val_triplet_mask_acc: 0.7250\n",
      "Epoch 7/100\n",
      " - 16s - loss: 0.4059 - triplet_mask_acc: 0.7892 - val_loss: 0.5580 - val_triplet_mask_acc: 0.7817\n",
      "Epoch 8/100\n",
      " - 16s - loss: 0.3007 - triplet_mask_acc: 0.8384 - val_loss: 0.4665 - val_triplet_mask_acc: 0.7922\n",
      "Epoch 9/100\n",
      " - 16s - loss: 0.2676 - triplet_mask_acc: 0.8637 - val_loss: 0.4268 - val_triplet_mask_acc: 0.8177\n",
      "Epoch 10/100\n",
      " - 16s - loss: 0.2148 - triplet_mask_acc: 0.8852 - val_loss: 0.4131 - val_triplet_mask_acc: 0.8206\n",
      "Epoch 11/100\n",
      " - 16s - loss: 0.2134 - triplet_mask_acc: 0.8870 - val_loss: 0.3790 - val_triplet_mask_acc: 0.8430\n",
      "Epoch 12/100\n",
      " - 16s - loss: 0.2028 - triplet_mask_acc: 0.8927 - val_loss: 0.3649 - val_triplet_mask_acc: 0.8358\n",
      "Epoch 13/100\n",
      " - 16s - loss: 0.1702 - triplet_mask_acc: 0.9096 - val_loss: 0.3469 - val_triplet_mask_acc: 0.8595\n",
      "Epoch 14/100\n",
      " - 16s - loss: 0.1596 - triplet_mask_acc: 0.9186 - val_loss: 0.3728 - val_triplet_mask_acc: 0.8535\n",
      "Epoch 15/100\n",
      " - 16s - loss: 0.1656 - triplet_mask_acc: 0.9171 - val_loss: 0.3415 - val_triplet_mask_acc: 0.8712\n",
      "Epoch 16/100\n",
      " - 16s - loss: 0.1426 - triplet_mask_acc: 0.9262 - val_loss: 0.3370 - val_triplet_mask_acc: 0.8666\n",
      "Epoch 17/100\n",
      " - 16s - loss: 0.1407 - triplet_mask_acc: 0.9286 - val_loss: 0.3174 - val_triplet_mask_acc: 0.8628\n",
      "Epoch 18/100\n",
      " - 16s - loss: 0.1410 - triplet_mask_acc: 0.9266 - val_loss: 0.3198 - val_triplet_mask_acc: 0.8660\n",
      "Epoch 19/100\n",
      " - 16s - loss: 0.1199 - triplet_mask_acc: 0.9387 - val_loss: 0.3090 - val_triplet_mask_acc: 0.8865\n",
      "Epoch 20/100\n",
      " - 16s - loss: 0.1257 - triplet_mask_acc: 0.9373 - val_loss: 0.3219 - val_triplet_mask_acc: 0.8759\n",
      "Epoch 21/100\n",
      " - 16s - loss: 0.1221 - triplet_mask_acc: 0.9390 - val_loss: 0.3275 - val_triplet_mask_acc: 0.8663\n",
      "Epoch 22/100\n",
      " - 16s - loss: 0.1250 - triplet_mask_acc: 0.9373 - val_loss: 0.3593 - val_triplet_mask_acc: 0.8625\n",
      "Epoch 23/100\n",
      " - 16s - loss: 0.1211 - triplet_mask_acc: 0.9406 - val_loss: 0.3272 - val_triplet_mask_acc: 0.8787\n",
      "Epoch 24/100\n",
      " - 16s - loss: 0.1138 - triplet_mask_acc: 0.9391 - val_loss: 0.2969 - val_triplet_mask_acc: 0.8862\n",
      "Epoch 25/100\n",
      " - 16s - loss: 0.1122 - triplet_mask_acc: 0.9428 - val_loss: 0.2883 - val_triplet_mask_acc: 0.8938\n",
      "Epoch 26/100\n",
      " - 16s - loss: 0.1032 - triplet_mask_acc: 0.9496 - val_loss: 0.3124 - val_triplet_mask_acc: 0.8828\n",
      "Epoch 27/100\n",
      " - 16s - loss: 0.1108 - triplet_mask_acc: 0.9479 - val_loss: 0.2735 - val_triplet_mask_acc: 0.8911\n",
      "Epoch 28/100\n",
      " - 16s - loss: 0.0999 - triplet_mask_acc: 0.9493 - val_loss: 0.3097 - val_triplet_mask_acc: 0.8867\n",
      "Epoch 29/100\n",
      " - 16s - loss: 0.1082 - triplet_mask_acc: 0.9491 - val_loss: 0.2798 - val_triplet_mask_acc: 0.8900\n",
      "Epoch 30/100\n",
      " - 16s - loss: 0.0941 - triplet_mask_acc: 0.9525 - val_loss: 0.2967 - val_triplet_mask_acc: 0.8966\n",
      "Epoch 31/100\n",
      " - 16s - loss: 0.1074 - triplet_mask_acc: 0.9513 - val_loss: 0.2866 - val_triplet_mask_acc: 0.8723\n",
      "Epoch 32/100\n",
      " - 16s - loss: 0.1104 - triplet_mask_acc: 0.9471 - val_loss: 0.2952 - val_triplet_mask_acc: 0.8757\n",
      "Epoch 33/100\n",
      " - 16s - loss: 0.0985 - triplet_mask_acc: 0.9484 - val_loss: 0.2865 - val_triplet_mask_acc: 0.8927\n",
      "Epoch 00033: early stopping\n"
     ]
    }
   ],
   "source": [
    "#train out Siamese Neural Network using the above triplet generator\n",
    "history = final_model.fit_generator(augmented_generator_triplet(all_images,128,False),\n",
    "        validation_data= augmented_generator_triplet(all_evaluation_images,128,True),\n",
    "        validation_steps = 100,\n",
    "        steps_per_epoch=100,\n",
    "        verbose=2,\n",
    "        epochs=100,\n",
    "        callbacks=callbacks_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f6900280b38>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax=plt.subplots(figsize=(15,10)) # Visualize loss and accuracy\n",
    "length = len(history.history['loss'])\n",
    "for i in history.history:\n",
    "    plt.plot(range(1,length+1),history.history[i])\n",
    "plt.legend(['val_loss','val_triplet_mask_acc','loss','triplet_mask_acc'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_model = Model(inputs=final_model.layers[3].get_input_at(-1),outputs=final_model.layers[3].get_output_at(-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "images = np.vstack((all_images,all_evaluation_images,all_test_images))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1623, 20, 105, 105)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def L2_distance(a,b):\n",
    "    return np.linalg.norm(a-b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Score on  20  task:  87.35000000000001 %\n"
     ]
    }
   ],
   "source": [
    "N_way = 20 # against what amount of random samples to test\n",
    "batch = np.random.randint(0,1623,10000)\n",
    "score = 0\n",
    "#for every random character create a set of N_way random characters and find   \n",
    "#the closes character emebedding by predicting it with the model. In every set   \n",
    "#there are guaranteed two images of the same class.  \n",
    "for i in batch:\n",
    "    ind_true = np.random.choice(20,2)\n",
    "    indicies = np.random.randint(0,20,N_way-2)\n",
    "    all_ind = np.hstack((ind_true,indicies))\n",
    "    classes = np.random.randint(0,1623,N_way-2)\n",
    "    imgs = [images[i,ind_true[0],:,:],images[i,ind_true[1],:,:]]\n",
    "    for k,val in enumerate(indicies):\n",
    "        imgs.append(images[classes[k],val,:,:])\n",
    "    imgs = np.array(imgs).reshape(N_way,105,105,1)\n",
    "    prediction = new_model.predict(imgs)\n",
    "    distances = []\n",
    "    classes = np.hstack((i,classes))\n",
    "    for j in prediction[1:]:\n",
    "        distances.append(L2_distance(prediction[0],j))\n",
    "    predicted = distances.index(np.min(distances))\n",
    "    if classes[predicted]==i: score += 1\n",
    "print(\"Score on \",N_way,\" task: \",score/batch.shape[0]*100,\"%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "preds = np.array([new_model.predict(all_test_images[i].reshape(20,105,105,1)) for i in range(20)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs,ys,vec = preds.shape\n",
    "preds = preds.reshape(xs*ys,vec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(n_components=2)\n",
    "preds_transformed = tsne.fit_transform(preds) # reduce the dimensionality of our 4096 vectors \n",
    "#and save the higher dimension representation for visualization  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['r', 'g', 'b', 'c', 'm', 'y', 'k', 'w', 'orange', 'purple','pink','lime',\n",
    "          'gray','snow','teal','aliceblue','navy','dimgray','lightsalmon','indigo']\n",
    "plt.figure(figsize=(13, 10))\n",
    "for i in range(20): #visualize  \n",
    "    plt.scatter(preds_transformed.T[0][i*20:(i+1)*20],preds_transformed.T[1][i*20:(i+1)*20],c=colors[i])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}