sk-autoencoder5-kerastests.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import glob\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from collections import defaultdict\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import keras\n",
    "from keras.optimizers import RMSprop\n",
    "import keras.utils\n",
    "\n",
    "from keras.callbacks import Callback\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.layers.noise import GaussianNoise\n",
    "from keras import backend as K\n",
    "\n",
    "from IPython import display\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'2.3.0'"
      ]
     },
     "metadata": {},
     "execution_count": 146
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf. __version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "LAYER_BLOCKS = 0\n",
    "LAYER_COINS = 1\n",
    "LAYER_ENEMIES = 2\n",
    "NLAYERS = 3\n",
    "\n",
    "class GameMap:\n",
    "    def __init__(self):\n",
    "        self.levels = []\n",
    "        self.widths = []\n",
    "        self.heights = []\n",
    "        self.nmaps = 0\n",
    "        \n",
    "        with open(\"Zelda/zelda.json\") as f:\n",
    "            self.tiledefs = json.load(f)\n",
    "        \n",
    "        for fname in sorted(glob.glob(\"Zelda/zelda/zelda_lvl*.txt\")):\n",
    "            #print(fname)\n",
    "            with open(fname) as f:\n",
    "                data = [line.strip() for line in f.readlines()]\n",
    "                self.levels.append(data)\n",
    "                self.heights.append(len(data))\n",
    "                self.widths.append(len(data[0]))\n",
    "                self.nmaps += 1\n",
    "\n",
    "    def getMaps(self, level=0):\n",
    "        layers = np.ndarray((self.heights[level], self.widths[level], NLAYERS), dtype=np.bool)\n",
    "        layers.fill(0)\n",
    "        for r, row in enumerate(self.levels[level]):\n",
    "            for c, tile in enumerate(row):\n",
    "                if tile in ('1','2','3'):\n",
    "                    layers[r,c,LAYER_ENEMIES] = 1\n",
    "                elif tile in ('+'):\n",
    "                    layers[r,c,LAYER_COINS] = 1\n",
    "                elif tile in ('w'):\n",
    "                    pass\n",
    "                else:\n",
    "                    layers[r,c,LAYER_BLOCKS] = 1\n",
    "                \n",
    "        return layers[:,:,LAYER_BLOCKS]\n",
    "    \n",
    "    def getMapsRange(self, from_level=0, to_level=None):\n",
    "        if to_level == None:\n",
    "            to_level = from_level + 1\n",
    "        return np.concatenate([self.getMaps(i) for i in range(from_level, to_level)], axis=1)\n",
    "            \n",
    "    def getMapsRangeWindowed(self, windowsize = 2, from_level=0, to_level=None):\n",
    "        if to_level == None:\n",
    "            to_level = from_level + 1\n",
    "        a = self.getMapsRange(from_level, to_level)\n",
    "        windows = np.concatenate([np.roll(a,-shift,axis=1) for shift in range(windowsize)])\n",
    "        return windows\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gm = GameMap()\n",
    "a = gm.getMapsRangeWindowed(4,0,5)\n",
    "at = a.transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "class LossHistory(Callback):\n",
    "    def __init__(self, layer_index, X_train=None, y_train=None):\n",
    "        super(Callback, self).__init__()\n",
    "        self.layer_index = layer_index\n",
    "        self.previous_model_params_ = None\n",
    "        if X_train and X_train.shape[0] >= 1000:\n",
    "            mask = np.random.choice(X_train.shape[0], 1000)\n",
    "            self.X_train_subset = X_train[mask]\n",
    "            self.y_train_subset = y_train[mask]\n",
    "        else:\n",
    "            self.X_train_subset = X_train\n",
    "            self.y_train_subset = y_train\n",
    "\n",
    "    def on_train_begin(self, logs={}):\n",
    "        #self.model_params would be ['layer1_W', 'layer2_b', 'layer2_W', 'layer2_b',...]\n",
    "        self.train_batch_loss = []\n",
    "        self.train_acc = []\n",
    "        self.val_acc = []\n",
    "        self.layer_out = []\n",
    "        self.model_params = []\n",
    "        self.gradients = []\n",
    "        self.init_params = self.get_model_params()\n",
    "        self.previous_model_params_ = self.init_params\n",
    "\n",
    "    def on_batch_end(self, batch, logs={}):\n",
    "        self.train_batch_loss.append(logs.get('loss'))\n",
    "\n",
    "    def on_epoch_end(self, epoch, logs={}):\n",
    "        #use W' = W + dW to calculate the gradients\n",
    "        current_model_params = self.get_model_params()\n",
    "        gradients = [(param - prev_param) for (param, prev_param) in zip(current_model_params,\n",
    "                                                                         self.previous_model_params_)]\n",
    "        self.gradients.append(gradients)\n",
    "        self.previous_model_params_ = current_model_params\n",
    "        self.model_params.append(current_model_params)\n",
    "\n",
    "        self.layer_out.append(self.get_layer_out())\n",
    "\n",
    "        if self.X_train_subset:\n",
    "            val_epoch_acc = logs.get('val_acc')\n",
    "            self.val_acc.append(val_epoch_acc)\n",
    "            train_epoch_acc = self.model.evaluate(self.X_train_subset, self.y_train_subset,\n",
    "                                                  show_accuracy=True, verbose=0)[1]\n",
    "            self.train_acc.append(train_epoch_acc)\n",
    "            print('(train accuracy, val accuracy): (%.4f, %.4f)' % (train_epoch_acc, val_epoch_acc))\n",
    "\n",
    "    def get_layer_out(self):\n",
    "        layer_index = self.layer_index\n",
    "        get_activation = theano.function([self.model.layers[0].input],\n",
    "                                       self.model.layers[layer_index].get_output(train=False), allow_input_downcast=True)\n",
    "        return get_activation(self.X_train_subset)\n",
    "\n",
    "    def get_model_params(self):\n",
    "        return [param.get_value() for param in self.model.params]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "in_out_size = at.shape[1]\n",
    "hiddensize =2 #int(in_out_size*.25)\n",
    "model = Sequential()\n",
    "model.add(Dense(in_out_size/1, input_shape=(in_out_size,)))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.add(Dense(in_out_size/2))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.add(Dense(hiddensize))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.add(Dense(in_out_size/2))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "model.add(Dense(in_out_size))\n",
    "model.add(Activation(\"sigmoid\"))\n",
    "\n",
    "# \"mse\" seems to be the best loss function for the problem\n",
    "# \"RMSprop\" seems to be the best, with \"adadelta\" also good\n",
    "optimizer = RMSprop()\n",
    "losshistory = LossHistory(0)\n",
    "model.compile(loss='mse', optimizer=optimizer, metrics=['accuracy'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Dense at 0x1dd99fff6d0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99dd5f70>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99fffe50>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99fff940>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99dcbdc0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd97412700>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd974124c0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd9a09d820>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99df87f0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd9a0f8040>]"
      ]
     },
     "metadata": {},
     "execution_count": 137
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "for i in range(2):\n",
    "    history = model.fit(at, at, epochs=50, batch_size=64, shuffle=True, verbose=False)\n",
    "    pt = model.predict(at)\n",
    "    p = pt.transpose()\n",
    "    fig, axes = plt.subplots(1,1,figsize=(16,4))\n",
    "    axes.imshow(p,cmap=\"gray\", interpolation=\"nearest\");\n",
    "    axes.set_xlim(0,400)\n",
    "    display.clear_output(wait=True)\n",
    "    display.display(plt.gcf());\n",
    "    #print((i, history.history['acc'][-1]))\n",
    "    #axes[1].imshow(a,cmap=\"gray\", interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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       "         -0.00168227]], dtype=float32),\n",
       " array([-0.00164735,  0.00094423,  0.00073783,  0.00125404,  0.00134856,\n",
       "         0.00111424,  0.0010797 ,  0.00084024, -0.00167944, -0.00166212,\n",
       "        -0.0018166 ,  0.00133875,  0.00115811,  0.00153329,  0.00116671,\n",
       "         0.00104572,  0.00099357, -0.00164054, -0.00166963,  0.00115272,\n",
       "        -0.00118429,  0.00131055,  0.00143757,  0.00088527,  0.00142531,\n",
       "         0.00109333, -0.00163065, -0.001678  ,  0.00120193, -0.00206758,\n",
       "         0.00111816,  0.00094618,  0.00125973, -0.0016693 , -0.0021704 ,\n",
       "        -0.00167313], dtype=float32)]"
      ]
     },
     "metadata": {},
     "execution_count": 139
    }
   ],
   "source": [
    "w1 = model.get_weights()\n",
    "model.fit(at, at, epochs=1, batch_size=64, shuffle=True, verbose=False)\n",
    "w2 = model.get_weights()\n",
    "[w2[i]-w1[i] for i in range(len(model.layers))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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       "         -0.28617668,  0.2532372 ,  0.0669202 , -0.13870786,  0.13622583,\n",
       "          0.08489612,  0.05490468, -0.12755856],\n",
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     "metadata": {},
     "execution_count": 140
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   "execution_count": 141,
   "metadata": {
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   "outputs": [
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     "data": {
      "text/plain": [
       "(36,)"
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     "metadata": {},
     "execution_count": 141
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   "source": [
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   "execution_count": 142,
   "metadata": {
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   "outputs": [
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     "data": {
      "text/plain": [
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     "metadata": {},
     "execution_count": 142
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "test_a = gm.getMapsRangeWindowed(4, 1)\n",
    "test_at = test_a.transpose()\n",
    "test_pt = model.predict(test_at)\n",
    "test_p = test_pt.transpose()\n",
    "fig, axes = plt.subplots(3,figsize=(16,6))\n",
    "axes[0].imshow(test_p,cmap=\"gray\", interpolation=\"nearest\")\n",
    "axes[1].imshow(test_a,cmap=\"gray\", interpolation=\"nearest\")\n",
    "axes[2].imshow(test_a-test_p, interpolation=\"nearest\", cmap=\"hot\", vmin=-1, vmax=1)\n",
    "np.sum(pow(test_a-test_p,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Dense at 0x1dd99fff6d0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99dd5f70>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99fffe50>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99fff940>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99dcbdc0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd97412700>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd974124c0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd9a09d820>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99df87f0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd9a0f8040>]"
      ]
     },
     "metadata": {},
     "execution_count": 143
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#modelA = model\n",
    "modelB = model\n",
    "#modelC = model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "model = modelB\n",
    "\n",
    "fig, axes = plt.subplots(1,figsize=(7,5))\n",
    "\n",
    "#get_hidden_layer_output = K.function([model.layers[0].input,  K.learning_phase()], [model.layers[3].output])\n",
    "xd = 0\n",
    "yd = 1\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 0) # W 1-1\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output_A = get_hidden_layer_output([test_at,0])[0]\n",
    "    plt.scatter(hidden_layer_output_A[:,xd], hidden_layer_output_A[:,yd], alpha=.5, marker='^', color=\"#FF8040\", s=50)\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 3) # W 2-1\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output_A = get_hidden_layer_output([test_at,0])[0]\n",
    "    plt.scatter(hidden_layer_output_A[:,xd], hidden_layer_output_A[:,yd], alpha=.5, marker='^', color=\"#FF8040\", s=50,\n",
    "               facecolor='none')\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 2) # W 1-3\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at,0])[0]\n",
    "    plt.scatter(hidden_layer_output[:,xd], hidden_layer_output[:,yd], color=\"#9B0000\", alpha=.5, marker=\"s\", s=40)\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 4) # W 3-3\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at,0])[0]\n",
    "    plt.scatter(hidden_layer_output[:,xd], hidden_layer_output[:,yd], color=\"#9B0000\", alpha=.5, marker=\"s\", s=40,\n",
    "               facecolor='None')\n",
    "    \n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 1) # W 1-2\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output_B = get_hidden_layer_output([test_at,0])[0]\n",
    "    plt.scatter(hidden_layer_output_B[:,xd], hidden_layer_output_B[:,yd], color=\"black\", alpha=.5, marker=\"o\", s=50)\n",
    "\n",
    "    \n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 4) # W 4-2\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at,0])[0]\n",
    "    plt.scatter(hidden_layer_output[:,xd], hidden_layer_output[:,yd], color=\"black\", alpha=.5, marker=\"o\", s=50, \n",
    "                facecolor='None')\n",
    "\n",
    "plt.xlim(-0.05,1.05)\n",
    "plt.ylim(-0.05,1.05)\n",
    "\n",
    "plt.savefig(\"hiddenlayerviz.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0.31003457, 0.3168559 , 0.7964645 , 0.54887015, 0.68071306,\n",
       "        0.19018552, 0.31749123, 0.23429897, 0.38306993, 0.74893814,\n",
       "        0.28632516, 0.67876345, 0.7190503 , 0.67066   , 0.82873005,\n",
       "        0.17715687, 0.77876735, 0.23352152, 0.26206315, 0.24786404,\n",
       "        0.7343335 , 0.22425401, 0.6664648 , 0.88748837, 0.55984694,\n",
       "        0.6847653 , 0.5162402 , 0.4979626 , 0.24045545, 0.7011496 ,\n",
       "        0.85577   , 0.7754073 , 0.68948984, 0.45348588, 0.896591  ,\n",
       "        0.76703507],\n",
       "       [0.2836848 , 0.29548395, 0.73212016, 0.6105242 , 0.6548825 ,\n",
       "        0.12592304, 0.47330636, 0.09060907, 0.39043236, 0.70694023,\n",
       "        0.2903972 , 0.5448977 , 0.6237036 , 0.6152326 , 0.6673668 ,\n",
       "        0.36968076, 0.90379494, 0.17327365, 0.1631364 , 0.16044924,\n",
       "        0.71196115, 0.1798943 , 0.54099125, 0.88680905, 0.6311139 ,\n",
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       "        0.86238545, 0.6701453 , 0.6319749 , 0.6363602 , 0.7488635 ,\n",
       "        0.8736772 ],\n",
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       "        0.37678683, 0.49687523, 0.13046199, 0.4017502 , 0.6968259 ,\n",
       "        0.2370677 , 0.5964663 , 0.43589798, 0.5762121 , 0.60382783,\n",
       "        0.33521122, 0.8805481 , 0.28526878, 0.16928348, 0.13654634,\n",
       "        0.57181275, 0.3129809 , 0.57630485, 0.84253013, 0.59083813,\n",
       "        0.61859536, 0.6134462 , 0.5336342 , 0.25939125, 0.5874091 ,\n",
       "        0.83128715, 0.8302283 , 0.65253645, 0.5696303 , 0.56194913,\n",
       "        0.8861735 ],\n",
       "       [0.4299586 , 0.3603578 , 0.8138796 , 0.45388827, 0.3401935 ,\n",
       "        0.29421347, 0.512378  , 0.17756435, 0.54799527, 0.7066951 ,\n",
       "        0.35482582, 0.37299013, 0.5225313 , 0.7487773 , 0.47301075,\n",
       "        0.31672448, 0.869043  , 0.34915727, 0.17284054, 0.4235509 ,\n",
       "        0.554387  , 0.34500384, 0.43743077, 0.791919  , 0.7017783 ,\n",
       "        0.62539846, 0.45948422, 0.33225083, 0.44607732, 0.7522019 ,\n",
       "        0.7026813 , 0.7928622 , 0.6468345 , 0.41265637, 0.7546077 ,\n",
       "        0.76494694],\n",
       "       [0.2526796 , 0.33211136, 0.6135572 , 0.6849607 , 0.6485674 ,\n",
       "        0.26280394, 0.5374058 , 0.18676013, 0.4599187 , 0.82982266,\n",
       "        0.34706882, 0.49645275, 0.33747178, 0.5199902 , 0.45650464,\n",
       "        0.351953  , 0.77943444, 0.39691877, 0.21123657, 0.2845257 ,\n",
       "        0.6975151 , 0.42877188, 0.6147384 , 0.7035007 , 0.6058396 ,\n",
       "        0.68738794, 0.3958236 , 0.5992429 , 0.33479732, 0.6081071 ,\n",
       "        0.77638376, 0.61853033, 0.61525524, 0.57863724, 0.75570035,\n",
       "        0.8217257 ],\n",
       "       [0.25142637, 0.28703976, 0.66872853, 0.6716777 , 0.65353817,\n",
       "        0.22963381, 0.3160835 , 0.21727702, 0.31049535, 0.8176862 ,\n",
       "        0.47315735, 0.6173345 , 0.7461263 , 0.47742817, 0.567963  ,\n",
       "        0.30635595, 0.6100831 , 0.21368513, 0.33728272, 0.3061124 ,\n",
       "        0.7385103 , 0.16073945, 0.64453524, 0.7804177 , 0.7089335 ,\n",
       "        0.614159  , 0.5148631 , 0.47142082, 0.2521009 , 0.628575  ,\n",
       "        0.7473526 , 0.6595761 , 0.6085339 , 0.53916234, 0.7193706 ,\n",
       "        0.7363223 ],\n",
       "       [0.319054  , 0.26436588, 0.6127852 , 0.537534  , 0.7038766 ,\n",
       "        0.19154552, 0.46228176, 0.16534275, 0.39126995, 0.75680363,\n",
       "        0.33693492, 0.65801114, 0.60223776, 0.37135762, 0.5832842 ,\n",
       "        0.27304065, 0.80874467, 0.14830387, 0.3232324 , 0.32248023,\n",
       "        0.65559655, 0.26821774, 0.65268993, 0.8452827 , 0.7053137 ,\n",
       "        0.5747036 , 0.603412  , 0.69215417, 0.19283175, 0.5316749 ,\n",
       "        0.90031826, 0.7889475 , 0.5162755 , 0.5751974 , 0.65354717,\n",
       "        0.83526903],\n",
       "       [0.44384605, 0.24854681, 0.76876366, 0.55875075, 0.6939473 ,\n",
       "        0.14182308, 0.4161247 , 0.08944893, 0.2846264 , 0.70427775,\n",
       "        0.22095975, 0.62464815, 0.68216705, 0.6769078 , 0.8174709 ,\n",
       "        0.35191488, 0.9018114 , 0.2973982 , 0.17097265, 0.40756452,\n",
       "        0.6270191 , 0.24437052, 0.5224509 , 0.91800237, 0.49784613,\n",
       "        0.716668  , 0.61245686, 0.44378152, 0.25789925, 0.8225483 ,\n",
       "        0.78028524, 0.8339472 , 0.5214396 , 0.508376  , 0.7660465 ,\n",
       "        0.882208  ],\n",
       "       [0.41373006, 0.22669154, 0.83855134, 0.4725869 , 0.7245717 ,\n",
       "        0.11185002, 0.40680766, 0.10545233, 0.46014428, 0.7713208 ,\n",
       "        0.1978006 , 0.68983006, 0.51361126, 0.7607248 , 0.80890286,\n",
       "        0.22875649, 0.9265777 , 0.18437997, 0.16155419, 0.3591373 ,\n",
       "        0.71859086, 0.15191343, 0.648051  , 0.8914639 , 0.63361937,\n",
       "        0.615954  , 0.4339571 , 0.6615603 , 0.2818179 , 0.7571658 ,\n",
       "        0.8367182 , 0.7715585 , 0.6870313 , 0.50200003, 0.8245463 ,\n",
       "        0.90440035],\n",
       "       [0.31785843, 0.47228384, 0.7904031 , 0.43314895, 0.5504467 ,\n",
       "        0.4037877 , 0.3677652 , 0.1275408 , 0.3839814 , 0.8390145 ,\n",
       "        0.2129761 , 0.5601194 , 0.4426496 , 0.7351278 , 0.7613348 ,\n",
       "        0.36325938, 0.9301201 , 0.30392036, 0.23825726, 0.15773332,\n",
       "        0.6544347 , 0.3142044 , 0.6825894 , 0.7552644 , 0.5433067 ,\n",
       "        0.6937719 , 0.67407507, 0.50348604, 0.27146804, 0.49804777,\n",
       "        0.6120732 , 0.77315485, 0.62046003, 0.67677134, 0.7767407 ,\n",
       "        0.82982916],\n",
       "       [0.4488465 , 0.4306695 , 0.70290005, 0.4779428 , 0.3820425 ,\n",
       "        0.49357116, 0.5761486 , 0.33788925, 0.46042618, 0.49879494,\n",
       "        0.24506578, 0.33244264, 0.5040456 , 0.74343127, 0.43936315,\n",
       "        0.48436397, 0.8763454 , 0.5192176 , 0.31477058, 0.37536204,\n",
       "        0.53381467, 0.37356764, 0.27769035, 0.76063967, 0.644742  ,\n",
       "        0.44207388, 0.35879722, 0.39865282, 0.29720867, 0.5649366 ,\n",
       "        0.7286044 , 0.64739823, 0.5668478 , 0.508619  , 0.62989235,\n",
       "        0.73154193],\n",
       "       [0.44534567, 0.30307913, 0.5043588 , 0.56827366, 0.7730163 ,\n",
       "        0.10850465, 0.4989186 , 0.27242255, 0.4185989 , 0.48951316,\n",
       "        0.39959532, 0.45025328, 0.53948736, 0.53226   , 0.5449131 ,\n",
       "        0.51133096, 0.7227121 , 0.4835294 , 0.3123947 , 0.5793817 ,\n",
       "        0.705762  , 0.21774417, 0.41037056, 0.7706246 , 0.69416356,\n",
       "        0.4880598 , 0.24301407, 0.486231  , 0.4510708 , 0.61144465,\n",
       "        0.7298204 , 0.4595908 , 0.45772496, 0.45754746, 0.6253294 ,\n",
       "        0.74377793],\n",
       "       [0.4406136 , 0.2763049 , 0.6518173 , 0.4741304 , 0.77226484,\n",
       "        0.11057228, 0.37898374, 0.23278579, 0.4280715 , 0.78620297,\n",
       "        0.48298562, 0.79396605, 0.55588347, 0.45829692, 0.8187387 ,\n",
       "        0.30251893, 0.6764956 , 0.24926332, 0.34074712, 0.34530717,\n",
       "        0.73065084, 0.23840356, 0.78205144, 0.7124467 , 0.48364317,\n",
       "        0.75487417, 0.59242535, 0.73499113, 0.42202026, 0.49234927,\n",
       "        0.75249124, 0.69341236, 0.61968184, 0.5127427 , 0.79997593,\n",
       "        0.6534909 ]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 153
    }
   ],
   "source": [
    "hidden_layer_output_A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Dense at 0x1dd986daa30>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99bd00a0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99bd02b0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99bd0bb0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd986d57f0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99bd8ca0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd9570daf0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd9570d700>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99c0e9a0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99c0e100>]"
      ]
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(0.3448198, 0.6276533)"
      ]
     },
     "metadata": {},
     "execution_count": 154
    }
   ],
   "source": [
    "# make some fake input!\n",
    "h = np.ndarray(shape=(150,2))\n",
    "h[:,0] = np.random.normal(loc=hidden_layer_output_B[8,0], scale=2, size=150)\n",
    "h[:,1] = np.random.normal(loc=hidden_layer_output_B[8,1], scale=2, size=150)\n",
    "#h[:,0].fill(hidden_layer_output_B[8,0])\n",
    "#h[:,1].fill(hidden_layer_output_B[8,1])\n",
    "hidden_layer_output_B[8,0], hidden_layer_output_B[8,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:Functional inputs must come from `tf.keras.Input` (thus holding past layer metadata), they cannot be the output of a previous non-Input layer. Here, a tensor specified as input to \"functional_67\" was not an Input tensor, it was generated by layer dense_27.\nNote that input tensors are instantiated via `tensor = tf.keras.Input(shape)`.\nThe tensor that caused the issue was: dense_27/BiasAdd:0\n"
     ]
    },
    {
     "output_type": "error",
     "ename": "ValueError",
     "evalue": "Graph disconnected: cannot obtain value for tensor Tensor(\"dense_25_input:0\", shape=(None, 36), dtype=float32) at layer \"dense_25\". The following previous layers were accessed without issue: []",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-158-db682d22ef92>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m inject_fake_hidden = K.function([model.layers[5].input],\n\u001b[0m\u001b[0;32m      2\u001b[0m                                 [model.layers[-1].output])\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#X = inject_fake_hidden([hidden_layer_output])[0].transpose()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minject_fake_hidden\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mh\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\backend.py\u001b[0m in \u001b[0;36mfunction\u001b[1;34m(inputs, outputs, updates, name, **kwargs)\u001b[0m\n\u001b[0;32m   3934\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmodels\u001b[0m  \u001b[1;31m# pylint: disable=g-import-not-at-top\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3935\u001b[0m     \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mutils\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtf_utils\u001b[0m  \u001b[1;31m# pylint: disable=g-import-not-at-top\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3936\u001b[1;33m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3937\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3938\u001b[0m     \u001b[0mwrap_outputs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\training.py\u001b[0m in \u001b[0;36m__new__\u001b[1;34m(cls, *args, **kwargs)\u001b[0m\n\u001b[0;32m    240\u001b[0m       \u001b[1;31m# Functional model\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    241\u001b[0m       \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfunctional\u001b[0m  \u001b[1;31m# pylint: disable=g-import-not-at-top\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 242\u001b[1;33m       \u001b[1;32mreturn\u001b[0m \u001b[0mfunctional\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFunctional\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    243\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    244\u001b[0m       \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mModel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    455\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, inputs, outputs, name, trainable)\u001b[0m\n\u001b[0;32m    113\u001b[0m     \u001b[1;31m#     'arguments during initialization. Got an unexpected argument:')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m     \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFunctional\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtrainable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtrainable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 115\u001b[1;33m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_graph_network\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moutputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m   \u001b[1;33m@\u001b[0m\u001b[0mtrackable\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mno_automatic_dependency_tracking\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\training\\tracking\\base.py\u001b[0m in \u001b[0;36m_method_wrapper\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    455\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    456\u001b[0m     \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m       \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    458\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    459\u001b[0m       \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_self_setattr_tracking\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprevious_value\u001b[0m  \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m_init_graph_network\u001b[1;34m(self, inputs, outputs)\u001b[0m\n\u001b[0;32m    188\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    189\u001b[0m     \u001b[1;31m# Keep track of the network's nodes and layers.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 190\u001b[1;33m     nodes, nodes_by_depth, layers, _ = _map_graph_network(\n\u001b[0m\u001b[0;32m    191\u001b[0m         self.inputs, self.outputs)\n\u001b[0;32m    192\u001b[0m     \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_network_nodes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnodes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\Users\\HP\\anaconda3\\lib\\site-packages\\tensorflow\\python\\keras\\engine\\functional.py\u001b[0m in \u001b[0;36m_map_graph_network\u001b[1;34m(inputs, outputs)\u001b[0m\n\u001b[0;32m    924\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkeras_inputs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    925\u001b[0m           \u001b[1;32mif\u001b[0m \u001b[0mid\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mcomputable_tensors\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 926\u001b[1;33m             raise ValueError('Graph disconnected: '\n\u001b[0m\u001b[0;32m    927\u001b[0m                              \u001b[1;34m'cannot obtain value for tensor '\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    928\u001b[0m                              \u001b[1;34m' at layer \"'\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mlayer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m'\". '\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Graph disconnected: cannot obtain value for tensor Tensor(\"dense_25_input:0\", shape=(None, 36), dtype=float32) at layer \"dense_25\". The following previous layers were accessed without issue: []"
     ]
    }
   ],
   "source": [
    "inject_fake_hidden = K.function([model.layers[5].input],\n",
    "                                [model.layers[-1].output])\n",
    "\n",
    "#X = inject_fake_hidden([hidden_layer_output])[0].transpose()\n",
    "X = inject_fake_hidden([h])[0].transpose()\n",
    "\n",
    "#th = 0.1\n",
    "#X[ X <= th] = 0\n",
    "#X[ X > th] = 1\n",
    "\n",
    "plt.imshow(X[:14,:], cmap=\"gray\", interpolation=\"nearest\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,figsize=(7,5))\n",
    "#inject_fake_hidden = K.function([model.layers[5].input],\n",
    "#                                [model.layers[-1].output])\n",
    "\n",
    "#inject_fake_hidden([hidden_layer_output])\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 0, 1)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], alpha=1)\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 1, 2)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"green\", alpha=1)\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 2, 3)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"red\", alpha=1)\n",
    "\n",
    "if False:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 3, 4)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"orange\", alpha=.5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1dd9ebb9b50>"
      ]
     },
     "metadata": {},
     "execution_count": 174
    },
    {
     "output_type": "display_data",
     "data": {
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y89Kiloyzc6ssfOJyudkjk9TxfOJyubVdj8wbtpLma8e2DezdfxwoemJnz1+kNjHJJz6wqcUtUzPaqkfmDVtJC+E93XJrqx6ZX8KVtFDe0y2vtuqR+SVcSeo8DYMsIvoi4oWIqEbEsYjYNUudSkR8LyJeqdd5aMb6myPi5xHx/cVs/EzesJWkztNMj+wi8GhmDgB3Aw9HxNYZdR4GXs3MO4D7gC9HxMop63cB1UVo7zX5JVxJ6jwNgywzxzLzYH35LEUg9c6sBqyNiADWAG9RBCARsQn4p8DXFrHds/KGrSR1nnkN9oiIzcCdwEszVj0JPAecBNYCn8jMS/V1fwr823r5kvOGrSR1lqYHe0TEGuBZ4JHMfHvG6o8Ah4D3AO8HnoyId0XEPwP+NjNfbuLzd0bEgYg4cPr06eb3QJLU0ZrqkUVEF0WIPZOZw7NUeQj4UmYm8FpEHAduB+4B/nlEfBRYBbwrIv48Mz818wMycy+wF2BwcDAXtDeSpOVh/DCMDsO5EVjdD31DS/YMyGZGLQbwNFDNzCfmqDYC3F+vvwG4DXg9M/9dZm7KzM3AA8BfzRZikqQ2Mn4Yqnvgwjh0byreq3uK8iXQTI/sHuBB4EhEHKqXPQ70A2TmU8AXgW9ExBEggMcy880laK8kabkbHYaVPcULrr6PDi9Jr6xhkGXmixThdK06J4EPN6jzY+DH82ibJKmMzo0UPbGpuipF+RJoq5k9JEnLwOp+mJwxx+1krShfAgaZJGlx9Q0V98UujENeurrcN7QkP84gkyQtrp7tMLC7uDc28UbxPrB7yUYtttXs95KkZaJn+5IF10z2yCRJpWaQSZJKzSCTJJWaQSZJKjWDTJJUagaZJKnUDDJJUqkZZJKkUjPIJEmlZpBJkkrNIJMklZpBJkkqNYNMklRqBpkkqdQMMklSqRlkkqRSM8gkSaVmkEmSSs0gkySVmkEmSSq1Fa1ugHRdxg/D6DCcG4HV/dA3BD3bW90qSTeQPTKV1/hhqO6BC+PQval4r+4pyiV1DHtkKq/RYVjZU7zg6vvosL2yZtmjVRuwR6byOjcCXZXpZV2VolyN2aNVm2gYZBHRFxEvREQ1Io5FxK5Z6lQi4nsR8Uq9zkPNbist2Op+mKxNL5usFeVqbGqPNm66ujw63OqWSfPSTI/sIvBoZg4AdwMPR8TWGXUeBl7NzDuA+4AvR8TKJreVFqZvqOhFXBiHvHR1uW+o1S0rB3u0ahMNgywzxzLzYH35LFAFemdWA9ZGRABrgLeAi01uKy1Mz3YY2F30IibeKN4HdnuPp1n2aNUm5jXYIyI2A3cCL81Y9STwHHASWAt8IjMvNbmttHA92w2uheobKu6JQdETm6wVPdr3fbq17ZLmqenBHhGxBngWeCQz356x+iPAIeA9wPuBJyPiXU1ue7nOzog4EBEHTp8+Pc/dkDRv9mjVJprqkUVEF0UQPZOZs90Jfgj4UmYm8FpEHAduB37axLYAZOZeYC/A4OBgzm83JC2IPVq1gWZGLQbwNFDNzCfmqDYC3F+vvwG4DXi9yW0lSVqwZnpk9wAPAkci4lC97HGgHyAznwK+CHwjIo4AATyWmW9GxD+abdvMfH4xd0KS1LkaBllmvkgRTteqcxL48EK2lSTperTtFFXVsRr7jp7ixJkJetd1s2PbBgY2VhpvKEkqlbacoqo6VmPv/uPUJibZWFlFbWKSvfuPUx2rNd5YklQqbRlk+46eotLdRaW7i5sirizvO3qq1U2TJC2ytgyyE2cmWLtq+lXTtatWcOLMRItaJElaKm0ZZL3rujl7/uK0srPnL9K7rrtFLZIkLZW2DLId2zZQm5ikNjHJpcwryzu2bWh10yRJi6wtg2xgY4Wd926h0t3FWO08le4udt67xVGLktSG2nb4/cDGisElSR2gLXtkkqTOYZBJkkrNIJMklZpBJkkqNYNMklRqBpkkqdQMMklSqRlkkqRSM8gkSaVmkEmSSs0gkySVmkEmSSo1g0ySVGoGmSSp1AwySVKpGWSSpFIzyCRJpWaQSZJKzSCTJJWaQSZJKrWGQRYRfRHxQkRUI+JYROyapU4lIr4XEa/U6zw0Zd2OiPhlRLwWEV9Y7B2QJHW2ZnpkF4FHM3MAuBt4OCK2zqjzMPBqZt4B3Ad8OSJWRsTNwFeB3we2Ap+cZVtJkhasYZBl5lhmHqwvnwWqQO/MasDaiAhgDfAWRQDeBbyWma9n5gXgW8DHFrH9kqQON697ZBGxGbgTeGnGqieBAeAkcATYlZmXKAJvdEq9N3hnCEqStGArmq0YEWuAZ4FHMvPtGas/AhwCPgS8D/jLiPhrIGb5qJzj83cCOwH6+/ubbZZ0fcYPw+gwnBuB1f3QNwQ921vdKknz0FSPLCK6KELsmcwcnqXKQ8BwFl4DjgO3U/TA+qbU20TRa3uHzNybmYOZObh+/fr57IO0MOOHoboHLoxD96bivbqnKJdUGs2MWgzgaaCamU/MUW0EuL9efwNwG/A68DPg1ojYEhErgQeA5xaj4dJ1Gx2GlT3FK266ujw62/+rSVqumrm0eA/wIHAkIg7Vyx4H+gEy8yngi8A3IuIIxeXExzLzTYCI+CzwA+Bm4OuZeWxxd0FaoHMjRU9sqq5KUS6pNBoGWWa+yOz3uqbWOQl8eI51zwPPL6h10lJa3V9cTlzZc7VsslaUSyoNZ/ZQ5+obKoLswjjkpavLfUOtbpmkeTDI1Ll6tsPA7qJHNvFG8T6w21GLUsk0Pfxeaks92w0uqeTskUmSSs0gkySVmkEmSSo1g0ySVGoGmSSp1AwySVKpGWSSpFIzyCRJpWaQSZJKzSCTJJWaQSZJKjWDTJJUagaZJKnUDDJJUqkZZJKkUjPIJEmlZpBJkkrNIJMklZpBJkkqtRWtboAktdz4YRgdhnMjsLof+oagZ3urW6Um2SOT1NnGD0N1D1wYh+5NxXt1T1GuUjDIJHW20WFY2VO84qary6PDrW6ZmmSQSeps50agqzK9rKtSlKsUDDJJnW11P0zWppdN1opylYJBJqmz9Q0V98UujENeurrcN9TqlqlJDYMsIvoi4oWIqEbEsYjYNUudfxMRh+qvoxHx64j4e/V1n69vdzQivhkRq5ZiRyRpQXq2w8Du4r7YxBvF+8BuRy2WSGTmtStEbAQ2ZubBiFgLvAz8YWa+Okf9PwA+n5kfiohe4EVga2ZORMR/A57PzG9c62cODg7mgQMHFrA7kqR2FBEvZ+bgbOsafo8sM8eAsfry2YioAr3ArEEGfBL45oyf0R0Rk8Bq4OQ82i5pDtWxGvuOnuLEmQl613WzY9sGBjZWGm8otZl53SOLiM3AncBLc6xfDewAngXIzBPAHmCEIgxrmfnDhTdXEhQhtnf/cWoTk2ysrKI2Mcne/cepjtUabyy1maaDLCLWUATUI5n59hzV/gD4n5n5Vn2bHuBjwBbgPcAtEfGpOT5/Z0QciIgDp0+fns8+SB1n39FTVLq7qHR3cVPEleV9R0+1umnSDddUkEVEF0WIPZOZ1/qW4ANMv6z4e8DxzDydmZPAMPDB2TbMzL2ZOZiZg+vXr2+u9VKHOnFmgrWrpt8ZWLtqBSfOTLSoRVLrNDNqMYCngWpmPnGNehXgnwB/MaV4BLg7IlbXP+d+oHp9TZbUu66bs+cvTis7e/4iveu6W9QiqXWa6ZHdAzwIfGjKEPuPRsRnIuIzU+r9EfDDzPzV5YLMfAn4NnAQOFL/eXsXr/lSZ9qxbQO1iUlqE5NcyryyvGPbhlY3TbrhGg6/bwWH30uNOWpRneS6ht9LWp4GNlYMLgmnqJIklZxBJkkqNYNMklRqBpkkqdQMMklSqRlkkqRSM8gkSaVmkEmSSm1ZzuwREaeB/9vqdiwz7wbebHUjljmPUWMeo2vz+DTWqmP03sycdUb5ZRlkeqeIODDX9CwqeIwa8xhdm8enseV4jLy0KEkqNYNMklRqBll5+PibxjxGjXmMrs3j09iyO0beI5MklZo9MklSqRlkLRYRfRHxQkRUI+JYROy6Rt0PRMSvI+LjU8p2RMQvI+K1iPjCjWn1jbUIx+hvIuJI/enmbffE1maOT0TcFxG1KU95//dT1nkO0fAYdfw5VK93X/0YHIuI/zGlvLXnUGb6auEL2Aj8Vn15LfC/ga2z1LsZ+CvgeeDjU8r+D/CbwErgldm2Lfvreo5RvfxvgHe3ej9aeXyA+4Dvz3HMPIeucYw8h67UWQe8CvTX//0by+UcskfWYpk5lpkH68tngSrQO0vVzwHPAn87pewu4LXMfD0zLwDfAj62xE2+4a7zGLW9eRyf2XgOqdnj8y+A4cwcqde7/HvW8nPIIFtGImIzcCfw0ozyXuCPgKdmbNILjE759xu0+S/nAo4RQAI/jIiXI2LnUrexleY6PnW/GxGvRMR/j4h/WC/zHJputmMEnkMA/wDoiYgf14/DH9fLW34OrbiRP0xzi4g1FL2JRzLz7Rmr/xR4LDN/HRHTNpvlo9p2GOoCjxHAPZl5MiJ+A/jLiPhFZu6/AU2+oRocn4MUU/z8XUR8FPgucCueQ1PNdYzAcwiKvPht4H6gG/hJRPwvlsE5ZJAtAxHRRXHyPJOZw7NUGQS+Vf8D/W7goxFxkeL/fPqm1NsEnFzi5rbEQo9RZn43M09CcSkkIr5DcSmkrf4INTo+U/8oZebzEfGfIuLdeA5dMdcxysw3PYeA4lx5MzN/BfwqIvYDd7AMziEvLbZYFH95nwaqmfnEbHUyc0tmbs7MzcC3gX+Vmd8FfgbcGhFbImIl8ADw3A1q+g1zPccoIm6JiLX1z7kF+DBw9AY1/YZo5vhExN+v1yMi7qL43f9/eA5NrTPrMfIcuuIvgH8cESsiYjXwOxT30lp+Dtkja717gAeBIxFxqF72ONAPkJmz3fOhvu5iRHwW+AHFyKGvZ+axJW5vKyz4GAEbgO/U/z6tAP5rZu5bwra2QjPH5+PAv6z35CeAB7IYcuY5xLWPUUR4DmU+lZnViNgHHAYuAV/LzKMArT6HnNlDklRqXlqUJJWaQSZJKjWDTJJUagaZJKnUDDJJUqkZZJKkUjPIJEmlZpBJkkrt/wPorFFoLVcKwQAAAABJRU5ErkJggg==\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,figsize=(7,5))\n",
    "\n",
    "get_hidden_layer_output = K.function([model.layers[0].input],\n",
    "                                  [model.layers[4].output])\n",
    "\n",
    "test_a = gm.getMapsRangeWindowed(4, 2, 3)\n",
    "test_at = test_a.transpose()\n",
    "hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], alpha=.5)\n",
    "\n",
    "if False:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 1, 2)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"green\", alpha=.5)\n",
    "\n",
    "if False:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 2, 3)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"red\", alpha=.5)\n",
    "\n",
    "\n",
    "test_a = gm.getMapsRangeWindowed(4, 3, 4)\n",
    "test_at = test_a.transpose()\n",
    "hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"orange\", alpha=.5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,figsize=(7,5))\n",
    "\n",
    "get_hidden_layer_output = K.function([model.layers[0].input],\n",
    "                                  [model.layers[1].output])\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 1, 2)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], alpha=.5)\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 0, 1)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"green\", alpha=.5)\n",
    "\n",
    "if True:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 3, 4)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"red\", alpha=.5)\n",
    "\n",
    "if False:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 7, 8)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"orange\", alpha=.5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
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     "metadata": {},
     "execution_count": 164
    },
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1,figsize=(7,5))\n",
    "\n",
    "get_hidden_layer_output = K.function([model.layers[0].input],\n",
    "                                  [model.layers[1].output])\n",
    "\n",
    "test_a = gm.getMapsRangeWindowed(4, 2, 3)\n",
    "test_at = test_a.transpose()\n",
    "hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], alpha=.5)\n",
    "\n",
    "if False:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 1, 2)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"green\", alpha=.5)\n",
    "\n",
    "if False:\n",
    "    test_a = gm.getMapsRangeWindowed(4, 2, 3)\n",
    "    test_at = test_a.transpose()\n",
    "    hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "    plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"red\", alpha=.5)\n",
    "\n",
    "\n",
    "test_a = gm.getMapsRangeWindowed(4, 3, 4)\n",
    "test_at = test_a.transpose()\n",
    "hidden_layer_output = get_hidden_layer_output([test_at])[0]\n",
    "plt.scatter(hidden_layer_output[:,0], hidden_layer_output[:,1], color=\"orange\", alpha=.5)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "IndexError",
     "evalue": "list index out of range",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-202-35d65e0723a8>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# classify each window\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mtest_a\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetMapsRangeWindowed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mtest_at\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtest_a\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0md\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m{\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-66-49749e0b7dc8>\u001b[0m in \u001b[0;36mgetMapsRangeWindowed\u001b[1;34m(self, windowsize, from_level, to_level)\u001b[0m\n\u001b[0;32m     47\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mto_level\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     48\u001b[0m             \u001b[0mto_level\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfrom_level\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 49\u001b[1;33m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetMapsRange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrom_level\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     50\u001b[0m         \u001b[0mwindows\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mroll\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mshift\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mshift\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mwindowsize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     51\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mwindows\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-66-49749e0b7dc8>\u001b[0m in \u001b[0;36mgetMapsRange\u001b[1;34m(self, from_level, to_level)\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mto_level\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[0mto_level\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfrom_level\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetMaps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrom_level\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetMapsRangeWindowed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwindowsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrom_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-66-49749e0b7dc8>\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m     42\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mto_level\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     43\u001b[0m             \u001b[0mto_level\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfrom_level\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mconcatenate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetMaps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfrom_level\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_level\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     45\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     46\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetMapsRangeWindowed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwindowsize\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfrom_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mto_level\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-66-49749e0b7dc8>\u001b[0m in \u001b[0;36mgetMaps\u001b[1;34m(self, level)\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgetMaps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m         \u001b[0mlayers\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheights\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwidths\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNLAYERS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbool\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m         \u001b[0mlayers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfill\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     28\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[1;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlevels\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlevel\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# classify each window\n",
    "test_a = gm.getMapsRangeWindowed(4, 0, 14)\n",
    "test_at = test_a.transpose()\n",
    "\n",
    "d = {}\n",
    "\n",
    "get_hidden_layer_output = K.function([model.layers[0].input,  K.learning_phase()], [model.layers[6].output])\n",
    "h = get_hidden_layer_output([test_at,0])[0]\n",
    "h[ h < .5] = 0\n",
    "h[ h >= .5] = 1\n",
    "h[:,0] *= 8\n",
    "h[:,1] *= 4\n",
    "h[:,2] *= 2\n",
    "classes = h.sum(axis=1, dtype=\"int\")\n",
    "print(np.unique(classes))\n",
    "\n",
    "for i, c in enumerate(classes):            \n",
    "    key = tuple(test_at[i])\n",
    "    if not d.has_key(c):\n",
    "        d[c] = defaultdict(int)        \n",
    "    d[c][key] += 1\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def displayclass(d, class_id, nshow=10):\n",
    "    fig, axes = plt.subplots(1, nshow,figsize=(8,2))\n",
    "    i =0\n",
    "    bin_class = bin(class_id)[2:]\n",
    "    bin_class = \"0\"*(4-len(bin_class))+bin_class\n",
    "    axes[i].set_ylabel(\"%d = %0.4s\" % (class_id, bin_class))\n",
    "    for key, value in sorted(d[class_id].items(), key=lambda kv: kv[1], reverse=True):\n",
    "        axes[i].imshow(np.reshape(key, (4,14)).transpose(), cmap=\"gray\", interpolation=\"nearest\")\n",
    "        axes[i].set_title(value)\n",
    "\n",
    "        axes[i].get_xaxis().set_visible(False)\n",
    "        axes[i].get_yaxis().set_ticks([])\n",
    "        i += 1\n",
    "        if i == nshow:\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'iterkeys'",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-189-bec7da443fbc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;32min\u001b[0m \u001b[0md\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miterkeys\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m     \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'iterkeys'"
     ]
    }
   ],
   "source": [
    "for key in d.iterkeys():\n",
    "    displayclass(d, key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "1",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-190-08c5fe3052c7>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-188-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 1"
     ]
    }
   ],
   "source": [
    "displayclass(d,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "3",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-88-6aeac3ef993b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 3"
     ]
    }
   ],
   "source": [
    "displayclass(d, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "8",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-89-af66d8de5266>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 8"
     ]
    }
   ],
   "source": [
    "displayclass(d, 8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "9",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-90-266e434f7b60>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m9\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 9"
     ]
    }
   ],
   "source": [
    "displayclass(d, 9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "11",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-91-f3a587fc3a3d>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m11\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 11"
     ]
    }
   ],
   "source": [
    "displayclass(d, 11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "12",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-92-dd649ef95636>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 12"
     ]
    }
   ],
   "source": [
    "displayclass(d, 12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "13",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-93-3139f68e60db>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m13\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 13"
     ]
    }
   ],
   "source": [
    "displayclass(d, 13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "error",
     "ename": "KeyError",
     "evalue": "15",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-94-97867d7cd8e2>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdisplayclass\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m15\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-85-0e03206b5edf>\u001b[0m in \u001b[0;36mdisplayclass\u001b[1;34m(d, class_id, nshow)\u001b[0m\n\u001b[0;32m      5\u001b[0m     \u001b[0mbin_class\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"0\"\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m-\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mbin_class\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_ylabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"%d = %0.4s\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbin_class\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m     \u001b[1;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0md\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mclass_id\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mkv\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreverse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      8\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m14\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"gray\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"nearest\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      9\u001b[0m         \u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 15"
     ]
    }
   ],
   "source": [
    "displayclass(d, 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[<tensorflow.python.keras.layers.core.Dense at 0x1dd986daa30>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99bd00a0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99bd02b0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99bd0bb0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd986d57f0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99bd8ca0>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd9570daf0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd9570d700>,\n",
       " <tensorflow.python.keras.layers.core.Dense at 0x1dd99c0e9a0>,\n",
       " <tensorflow.python.keras.layers.core.Activation at 0x1dd99c0e100>]"
      ]
     },
     "metadata": {},
     "execution_count": 95
    }
   ],
   "source": [
    "model.layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3.8.3 64-bit ('base': conda)"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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