lrp-gamma.ipynb

{
 "cells": [
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   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#LRP-gamma\" data-toc-modified-id=\"LRP-gamma-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>LRP-gamma</a></span><ul class=\"toc-item\"><li><span><a href=\"#Loading-the-trained-net-from-the-tutorial\" data-toc-modified-id=\"Loading-the-trained-net-from-the-tutorial-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Loading the trained net from the tutorial</a></span></li><li><span><a href=\"#Implementing-LRP-gamma-and-DTD\" data-toc-modified-id=\"Implementing-LRP-gamma-and-DTD-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Implementing LRP gamma and DTD</a></span></li><li><span><a href=\"#Transform-the-existing-model-to-an-instance-of-ExplainableModel\" data-toc-modified-id=\"Transform-the-existing-model-to-an-instance-of-ExplainableModel-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Transform the existing model to an instance of <code>ExplainableModel</code></a></span></li><li><span><a href=\"#Plotting-the-Heatmaps\" data-toc-modified-id=\"Plotting-the-Heatmaps-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Plotting the Heatmaps</a></span></li><li><span><a href=\"#Layerwise-comparison-of-LRP-gamma=0-and-DTD\" data-toc-modified-id=\"Layerwise-comparison-of-LRP-gamma=0-and-DTD-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Layerwise comparison of LRP gamma=0 and DTD</a></span></li><li><span><a href=\"#Effect-of-gamma\" data-toc-modified-id=\"Effect-of-gamma-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>Effect of gamma</a></span></li><li><span><a href=\"#Comparison-of-LRP-gamma=1-and-Gradient-Times-Input\" data-toc-modified-id=\"Comparison-of-LRP-gamma=1-and-Gradient-Times-Input-1.7\"><span class=\"toc-item-num\">1.7&nbsp;&nbsp;</span>Comparison of LRP gamma=1 and Gradient Times Input</a></span></li><li><span><a href=\"#Usage-example:-direct-rebuilt-of-the-tutorial-net-as-explainable-net\" data-toc-modified-id=\"Usage-example:-direct-rebuilt-of-the-tutorial-net-as-explainable-net-1.8\"><span class=\"toc-item-num\">1.8&nbsp;&nbsp;</span>Usage example: direct rebuilt of the tutorial net as explainable net</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LRP-gamma\n",
    "\n",
    "In this notebook, I try to implement LRP gamma without using gradients.\n",
    "\n",
    "The advantage of not using gradients is, that it is possible to compute gradients of functions that depend on heatmaps. Since pytorch does not allow to compute gradients of gradients, it's the only way to do it.\n",
    "\n",
    "`cifar10_utils.py` contains the net and dataloaders of the pytorch [cifar10 tutorial](https://pytorch.org/tutorials/beginner/blitz/cifar10_tutorial.html#sphx-glr-download-beginner-blitz-cifar10-tutorial-py). Additionally, it contains `input_times_gradient(net, images, target_pattern)` and `plot_heatmaps(images, heatmaps)`. The idea is to analyze their trained network using LRP-gamma.\n",
    "\n",
    "Let's prepare the implementation and load a trained network."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the trained net from the tutorial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n",
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "\n",
    "from cifar10_utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5268\n",
      "{   'bird': 0.31690140845070425,\n",
      "    'car': 0.7869674185463659,\n",
      "    'cat': 0.2810126582278481,\n",
      "    'deer': 0.33164556962025316,\n",
      "    'dog': 0.5699208443271768,\n",
      "    'frog': 0.517162471395881,\n",
      "    'horse': 0.5382585751978892,\n",
      "    'plane': 0.5869017632241813,\n",
      "    'ship': 0.7306733167082294,\n",
      "    'truck': 0.5280612244897959}\n"
     ]
    }
   ],
   "source": [
    "net = Net()\n",
    "net.load(\"cifar10net\")\n",
    "accuracy = net.accuracy(testloader)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "import pprint\n",
    "pp = pprint.PrettyPrinter(indent=4)\n",
    "pp.pprint(net.class_accuracy(testloader))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be dealing with different heatmapping techniques. The functions that generate them have the regular name, and objects that contain heatmaps will be called `h_<shortname>`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing LRP gamma and DTD\n",
    "\n",
    "Now, let's implement deeptaylor and LRP gamma.\n",
    "We need the forward activations, so let's rewrite the network class a bit.\n",
    "\n",
    "We want to add rules to layers that depend on their actications during the forwarding.\n",
    "So we wrap the forwarding and create a number of `ExplainableLayer`s. Then we create a class for models that support deep taylor decompositions. `layerized_net.py` contains a layer and a network class that easily layerize any given pytorch model. We can add all the LRP functionality into some new `ExplainableLayer`s and an `ExplainableModel` and convert the pretrained net."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
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   "outputs": [],
   "source": [
    "import torch.nn.functional as F\n",
    "from torch import nn\n",
    "import numpy as np\n",
    "import copy\n",
    "from layerized_net import Layer, LayerizedNet\n",
    "\n",
    "\n",
    "na = None\n",
    "\n",
    "\n",
    "class ExplainableLayer(Layer):\n",
    "    \"\"\"\n",
    "    Base class for all layers that support deeptaylor\n",
    "    \"\"\"\n",
    "    pass\n",
    "\n",
    "\n",
    "class ReluLayer(ExplainableLayer, nn.Module):\n",
    "    \"\"\"\n",
    "    Base class for layers that use Relus as nonlinearity.\n",
    "    \"\"\"\n",
    "    def forward(self, x):\n",
    "        self.x = x\n",
    "        self.a = super().forward(x) # Super is called from a child class and via dependency injection this will be eg nn.Linear's forward layer \n",
    "        self.z = F.relu(self.a)\n",
    "        return self.z\n",
    "        \n",
    "    \n",
    "class Linear(ExplainableLayer, nn.Linear):\n",
    "    \"\"\"\n",
    "    For linear layers without Relu activation, usually the last\n",
    "    \"\"\"\n",
    "    def forward(self, x):\n",
    "        D = np.prod(x.shape[1:])\n",
    "        self.x = x\n",
    "        x = x.view(-1, D)\n",
    "        self.a = super().forward(x) # Super is called from a child class and via dependency injection this will be eg nn.Linear's forward layer \n",
    "        self.z = self.a\n",
    "        return self.z\n",
    "    \n",
    "    def zplus(self, R, eps=1e-9):\n",
    "        w_plus = F.relu(self.weight) # (D, d)\n",
    "        z_plus = F.linear(self.x, w_plus, None)\n",
    "        R_norm = R/(z_plus+eps) # (N, d)\n",
    "        return F.linear(R_norm, w_plus.transpose(0, 1))*self.x\n",
    "    \n",
    "    def zb(self, R, l, h, eps=1e-9):\n",
    "        w_plus  = F.relu(self.weight)\n",
    "        w_minus = -F.relu(-self.weight)\n",
    "        norm = self.z - l*F.linear(self.x, w_plus) - h*F.linear(self.x, w_minus)\n",
    "        R_norm = R/norm\n",
    "        return F.linear(R_norm, self.weight.transpose(0, 1))  \\\n",
    "            - l*F.linear(R_norm, self.w_plus.transpose(0, 1)) \\\n",
    "            - h*F.linear(R_norm, self.w_minus.transpose(0, 1))\n",
    "    \n",
    "    def lrp_gamma(self, R, gamma, bias_relevance=False):\n",
    "        def leaky_relu(a):\n",
    "            return F.relu(a) - gamma*F.relu(-a)\n",
    "        w = leaky_relu(self.weight)\n",
    "        b = leaky_relu(self.bias) if bias_relevance else None\n",
    "        z = F.linear(self.x, w, b)\n",
    "        R_norm = R/z\n",
    "        return F.linear(R_norm, w.transpose(0, 1))*self.x\n",
    "    \n",
    "\n",
    "\n",
    "class LinearRelu(ReluLayer, Linear):\n",
    "    \"\"\"\n",
    "    Combines the forwarding behaviour of ReluLayer with the\n",
    "    relevance propagation rule of Linear\n",
    "    \"\"\"\n",
    "    def forward(self, x):\n",
    "        D = np.prod(x.shape[1:])\n",
    "        return super().forward(x.view(-1, D))\n",
    "    \n",
    "    \n",
    "class ConvRelu(ReluLayer, nn.Conv2d):\n",
    "    \"\"\"\n",
    "    Implements deep taylor for conv2d layers\n",
    "    \"\"\"\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.conv_args = {\n",
    "            'padding': self.padding,\n",
    "            'stride': self.stride\n",
    "        }\n",
    "        \n",
    "    def zplus(self, R, eps=1e-9):\n",
    "        w_plus = F.relu(self.weight) # (D, d)\n",
    "        z_plus = F.conv2d(self.x, w_plus, **self.conv_args)\n",
    "        R_norm = R/(z_plus+eps) # (N, d)\n",
    "        R_out_norm = F.conv_transpose2d(R_norm, w_plus, **self.conv_args)\n",
    "        return R_out_norm*self.x\n",
    "    \n",
    "    def zb(self, R, l, h, eps=1e-9):\n",
    "        w_plus  = F.relu(self.weight)\n",
    "        w_minus = -F.relu(-self.weight)\n",
    "        l = self.x*0 + l\n",
    "        h = self.x*0 + h\n",
    "        norm = F.conv2d(self.x, self.weight, **self.conv_args) \\\n",
    "            - F.conv2d(l, w_plus, **self.conv_args) \\\n",
    "            - F.conv2d(h, w_minus, **self.conv_args)\n",
    "        R_norm = R/norm\n",
    "        return self.x*F.conv_transpose2d(R_norm, self.weight, **self.conv_args)  \\\n",
    "            - l*F.conv_transpose2d(R_norm, w_plus, **self.conv_args) \\\n",
    "            - h*F.conv_transpose2d(R_norm, w_minus, **self.conv_args)\n",
    "    \n",
    "    def lrp_gamma(self, R, gamma, bias_relevance=False):\n",
    "        def leaky_relu(a):\n",
    "            return F.relu(a) - gamma*F.relu(-a)\n",
    "        w = leaky_relu(self.weight)\n",
    "        b = leaky_relu(self.bias) if bias_relevance else None\n",
    "        z = F.conv2d(self.x, w, b, **self.conv_args)\n",
    "        R_norm = R/z\n",
    "        return F.conv_transpose2d(R_norm, w, **self.conv_args)*self.x\n",
    "    \n",
    "    def zb_gamma(self, R, l, h, gamma=0.9, bias_relevance=False):\n",
    "        # I am not sure if the first layer's rule should be\n",
    "        # the normal zB rule, but this is an interpolation of\n",
    "        # the zB rule and input*gradient\n",
    "        \n",
    "        w_plus  = F.relu(self.weight)\n",
    "        w_minus = -F.relu(-self.weight)\n",
    "        l = self.x*0 + l\n",
    "        h = self.x*0 + h\n",
    "        norm = F.conv2d(self.x, self.weight, **self.conv_args) \\\n",
    "            - F.conv2d(l, w_plus, **self.conv_args)*(1-gamma) \\\n",
    "            - F.conv2d(h, w_minus, **self.conv_args)*(1-gamma)\n",
    "        R_norm = R/norm\n",
    "        return self.x*F.conv_transpose2d(R_norm, self.weight, **self.conv_args) \\\n",
    "            - l*F.conv_transpose2d(R_norm, w_plus, **self.conv_args)*(1-gamma) \\\n",
    "            - h*F.conv_transpose2d(R_norm, w_minus, **self.conv_args)*(1-gamma)\n",
    "    \n",
    "    @classmethod\n",
    "    def from_module(cls, src):\n",
    "        layer = copy.deepcopy(src)\n",
    "        layer.__class__ = cls\n",
    "        layer.conv_args = {\n",
    "            'padding': layer.padding,\n",
    "            'stride': layer.stride\n",
    "        }\n",
    "        return layer\n",
    "    \n",
    "\n",
    "class MaxPool(ReluLayer, nn.MaxPool2d):\n",
    "    \"\"\"\n",
    "    Maxpool2d layer with the interface of the explainable ReluLayer\n",
    "    \"\"\"\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        kwargs['return_indices'] = True\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self._unpool = nn.MaxUnpool2d(kernel_size=self.kernel_size, stride=self.stride)\n",
    "        \n",
    "    def forward(self, x):\n",
    "        self.x = x\n",
    "        self.a, self.indices = nn.MaxPool2d.forward(self, x) \n",
    "        self.z = F.relu(self.a)\n",
    "        return self.z\n",
    "    \n",
    "    def unpool(self, y):\n",
    "        return self._unpool(y, self.indices, output_size=self.x.size())\n",
    "    \n",
    "    def zplus(self, R, *args, **kwargs):\n",
    "        return self.unpool(R)\n",
    "    \n",
    "    def lrp_gamma(self, R, *args, **kwargs):\n",
    "        return self.unpool(R)\n",
    "    \n",
    "    @staticmethod\n",
    "    def from_module(src):\n",
    "        return MaxPool(kernel_size=src.kernel_size,\n",
    "                      stride=src.stride,\n",
    "                      padding=src.padding,\n",
    "                      dilation=src.dilation,\n",
    "                      return_indices=True,\n",
    "                      ceil_mode=src.ceil_mode\n",
    "                      )\n",
    "    \n",
    "    \n",
    "class ExplainableModel(LayerizedNet):\n",
    "    \"\"\"\n",
    "    Base class for networks that implement deep taylor decomposition.\n",
    "    Network architecture is not specified here\n",
    "    \n",
    "    Subclasses can do some of the following:\n",
    "        - Add an architecture to `__init__`\n",
    "        - Overwride `deeptaylor,` if the architecture and input domains require a\n",
    "        different set of rules\n",
    "        - Overwride `from_model` if the architecture requires it.\n",
    "    \"\"\"\n",
    "    def __init__(self, layers, min_x=None, max_x=None):\n",
    "        \"\"\"\n",
    "        Layers: Instances of subclasses of ReluLayer\n",
    "        min_x, max_x: box constraints of the input domain, zB rule will be used there\n",
    "            if not set, ww rule will be used in the first layer\n",
    "        \"\"\"\n",
    "        super().__init__(layers)\n",
    "        self.min_x, self.max_x = min_x, max_x\n",
    "    \n",
    "    def debug_info(self, layer, R, R_total):\n",
    "        print(f\"({layer.__class__.__name__}) ->\", R.shape)\n",
    "        print(f\"({layer.__class__.__name__})     Conservation quotient: {R.sum()/R_total}\")\n",
    "        if R.min() < 0:\n",
    "            print(f\"({layer.__class__.__name__})     encountered negative values\")\n",
    "        print(\"\")\n",
    "    \n",
    "    def deeptaylor(self, x, pattern, debug=False):\n",
    "        \"\"\"\n",
    "        Deep Taylor Decomposition, where relevance is the outputlayer is predictions*pattern\n",
    "        Pattern: eg one_hot(labels) if the heatmap for the correct class should be used\n",
    "        \"\"\"\n",
    "        out = self.forward(x)\n",
    "        R = F.relu(out*pattern)\n",
    "        R_total = R.sum()\n",
    "            \n",
    "        for layer in self.layers[1:][::-1]:\n",
    "            R = R.view(layer.z.shape)\n",
    "            R = layer.zplus(R)\n",
    "            if debug:\n",
    "                self.debug_info(layer, R, R_total)\n",
    "            \n",
    "        # Select the input layer rule\n",
    "        if self.min_x is None and self.max_x is None:\n",
    "            R = self.layers[0].ww(R)\n",
    "        elif self.min_x == 0 and self.max_x is None:\n",
    "            R = self.layers[0].zplus(R)\n",
    "        else:\n",
    "            R = self.layers[0].zb(R, self.min_x, self.max_x)\n",
    "            \n",
    "        if debug:    \n",
    "            self.debug_info(self.layers[0], R, R_total)\n",
    "            \n",
    "        return R\n",
    "    \n",
    "    def lrp_gamma(self, x, pattern, gamma=0.1, bias_relevance=False, debug=False):\n",
    "        # Output layer relevance\n",
    "        out = self.forward(x)\n",
    "        R = out*pattern\n",
    "        R_total = R.sum()\n",
    "        \n",
    "        # Backward pass\n",
    "        for layer in self.layers[1:][::-1]:\n",
    "            R = R.view(layer.z.shape)\n",
    "            R = layer.lrp_gamma(R, gamma, bias_relevance=bias_relevance)\n",
    "            if debug:\n",
    "                self.debug_info(layer, R, R_total)\n",
    "            \n",
    "        # Input layer relevance depending on domain\n",
    "        if self.min_x is None and self.max_x is None:\n",
    "            R = self.layers[0].ww(R)\n",
    "        elif self.min_x == 0 and self.max_x is None:\n",
    "            R = self.layers[0].lrp_gamma(R, gamma)\n",
    "        else:\n",
    "            R = self.layers[0].zb_gamma(R, self.min_x, self.max_x, gamma=gamma)\n",
    "            \n",
    "        if debug:    \n",
    "            self.debug_info(self.layers[0], R, R_total)\n",
    "                \n",
    "        return R "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform the existing model to an instance of `ExplainableModel`\n",
    "\n",
    "Let's check if the `from_model` works:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.5268\n",
      "{   'bird': 0.31690140845070425,\n",
      "    'car': 0.7869674185463659,\n",
      "    'cat': 0.2810126582278481,\n",
      "    'deer': 0.33164556962025316,\n",
      "    'dog': 0.5699208443271768,\n",
      "    'frog': 0.517162471395881,\n",
      "    'horse': 0.5382585751978892,\n",
      "    'plane': 0.5869017632241813,\n",
      "    'ship': 0.7306733167082294,\n",
      "    'truck': 0.5280612244897959}\n"
     ]
    }
   ],
   "source": [
    "enet = ExplainableModel.from_model(\n",
    "    model=net,\n",
    "    layer_names=['conv1', 'pool', 'conv2', 'pool', 'fc1', 'fc2', 'fc3'],\n",
    "    layer_base_type=ReluLayer, last_layer_type=Linear,\n",
    "    min_x=-1, max_x=1)\n",
    "\n",
    "accuracy = enet.accuracy(testloader)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "pp.pprint(net.class_accuracy(testloader))\n",
    "\n",
    "# Also get some images and labels that are used in the cells below\n",
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great!\n",
    "\n",
    "Now, let's alsp generate some heatmaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Heatmaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "   Deep Taylor Decomposition\n",
      "(5, 3, 32, 32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--------------------\n",
      "   LRP-gamma 0.5\n",
      "(5, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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//qXEMxTNF6FJGistTbBYzQjyTz6tTQHObqohxeYFNZE/fUaTTTrLKsDv/kJN6V/7hZoi9DNJHchLiAYq5tewj+tXNPHr97/wso55WZMAltGUep4R+EcjLfSfTDVho7enCRqJATmaUG9saALc3Tt3Jd7e3knGsMTkhvM6192uJmScWYNBxaOACVGMmcTD5LIV2sVHmthVFtPkAsYBcXio8d/8jcY//nE6hjKDESYsfelLGl9CMw0aebyDhh9/+7fpGJhkxfvzy19OP3Pc52lQwjEywSoiTWArS4hbEL9445cSz3BNFKHX2RISptoZ45PN0zq/K109x0urmmS11NV7eMxEoftqvnN9W+MXMvlrqT8I3oR9vL2Ohh1I5lxr4ThxTZzLnT4kn52BgcjuUI02aFAyxxjPIjHsBu7HXRhGRURES89Xd0XnumierOmHvwEbY4wxFeAF2BhjjKkAL8DGGGNMBSxUA/7Rj38m8VJTf3sfDlUDarX0/4MvfunzyTbfu60a7YMP9fUX0My8BVOM3lB15yZMNF5+WU0TBv3UkKAFTeHpp65K/Pyzn5H44hnVRdZQfD4b6Jhu3VGThXs0QIiID7f1PUeHarSwi6YCo7EeRxONLlptnafpRLWeMczlIyK6G6rpvhA69+swHHnq/NlkG584GcMQgXot359pjJ7ojNR4GXOb1HzZ+OBP/kTjP/iDdAw0xuA4339f43v3NP6rv9KYuvTLmpcQT6TmL3H1avq348ZUZrTB93OeM+YUpZ+pSAO+e0fnu1FgXMjRaNZ1nBcyZjsPDlSjHeEyunwWOReYrzauwx0kr7xyQU0xXh+n9w4b0W+c0mfZ586ckfg6ckuoEceE94Y+t97pq54bEfFaT98zQqOZ/kA/czTT+3GKY6ghxwYeGzHImPk0oBufDp37djtjGpPB34CNMcaYCvACbIwxxlSAF2BjjDGmAhaqAd+/pTW2p0+rQfylS/o7+nMvPi1xs53qOT9/9YcSb0HLWqmpxnBvW0Xi5TWtndtc089/6xt/KHGRMShfX9dtnNnclHhnR5uh33hPzfX3dlX73odJ+sF+T+JdGulHxM6+6kMT1Fg3UUPZamtcQBdZX9O53thQrefUubSGt42a2taSxocZPWfhUJ+lhsj62pyGXKYrs7YYmlTSGIFaKLVSNjnIjYEa76c/rfFLL2nMxvWEjRMyeQfx2msa8zipbbP5AuuG+X7OQ06PJ9TrqAkviB7ux1U8l5bXtG70wpY+M4rMk/nhndsSs/FBHTW5qz19jjCvY9LWz29cV3+CVqajwDKO4wmcs/f7WjN7b0+ffTM0hRki32U+1OuyM8Z1GBHryH9o4V4Y4pzfx7PtoNDjmmJdaeMY27kmIHieNhoaD/mc+TX4G7AxxhhTAV6AjTHGmArwAmyMMcZUwEI14Ntv/lzi/TX12f3HX/u3En/jG38s8X/7H+olHRFxDrWn5+B/utRAI+yaakRb66pDrSLuoI5tQu/TyGgr8Cq984ZqNzfvqa/yaIzm3R09htVV1QDPdaAxRsR4dLym14R3aR26CONVNKVeg29zvZ7qQ4dHqlXfvbst8WCgr8fvQZd8FFCXpDZDvZZaJ+oaT0QPx0ld8uZNjS9e1PhHP9KYnsgREd//vsbUU6m3buu5SI7rgWp1J9JOUVueaLj3tTY90Z2RO5FovFtal5rVramPl3l/L4jRAz32AepCr1/7PYl//7rWVP/whnpJR0R04FHQgg65yrLrmj5XXsDnt6Gxfwk1urcyz7rLrMWe6XtuPNB8lqvIV7k71fc/Ce002noNUVuNiFiF5kun5lO4du/gGtiHBtyExruLeTnIfE29g+ftAZ59w0lJjsX/xd+AjTHGmArwAmyMMcZUgBdgY4wxpgIWqgEP4OH52Zc+K/FX/vgrEm9uaG3cP/qi1uRGRBQFat+a+vv9Gvo01luq1zZaqjnMsb0ZehTvPYRWFhFrDd3nLFSDeOozL0h87pLWaO48VN1kFTW3Y+gmtXn6f1MTXrPsPzqAFnp4pEayc/TMPOzp67c+1PrpQR86Z0SMe7qPKbSa7nLa4/SRQ/0VdYpJDe6B1k5mtVDqiuxTS42XmvDf/Z3G1IB/8AONcz7M3Mct9USPZ5/VmDW7X//68fukzzNrlyMiXn9d48uXNX73XY2fe05j1iZzHnncGU/eUj/pipihfvXClnoc0C9+C3kdL13SmtyItA94gXv+aczFCjyONxBfT+RxvV8vM2cjIlOgrPN9dVOPM9b0GX6ZXvr0IKfsnGt+jvtvnbX7yPP4FM7F23j/AV5/9RC1y5ncg4fwwp/M9dps0vP61/B4XK3GGGPMbxlegI0xxpgK8AJsjDHGVMBCNeCnnvkdif/lv/43EvemqgG98bbWy85qaU1YB7XEY2gGO7vwzJ2prjGdqiZYY5lbqGZxsA+NMCLqd1Uj+AC+vEP4m84Gqh8so3b5nbe0l+sN6H21TG3c6TOqtYzgl7q3p960D1AXOodeWxSqadQQL7PmMyI2UL/MusX+ISv2FgA1wWUdY2Begj7bOzvl22Q9LOtXuY8vfvH4ffzpn2r8lnqHR0TEF76g8Z07GiOPIL76VY2pY3NM9JbOaasvaq/sROv+2tc0pr80a5dZF8ztUS+MSOu6c769FbB+5rzET76k/ZW7M53P3o7mXDxRS3MPCtQS4wzHvYFqm525PnfeRryJU3ofGvCVYerDfOsQzwHcL9t4jown1Eb1GHZ20AMe98qMfZQjooH+6R30FL451FyUCa6jfdQu76FeeoB4mtFz5w09jgK9lct8Gf7f5070LmOMMcZ8ongBNsYYYyrAC7AxxhhTAQvVgP/5t/+VxKfOq8ftT36m2ucIv6OPMnWAU9TczqGt1NHTsoZCsyl8m+d4PZW+Un9U6hzbD1S7nkxU+4ScGhtrquaMRqrf7jyALpmpTd3eVt1jONZ9TtCLdwo9rt7SS6HbUY2jTe/oSTqG0YC6h2ozS8sZDe9Rw9pS1vSx7hce2Il+G5H6LO+rjhWHqucl9as3bmjM3r7Uc+nTHJFq1fwMelInx/kp1JmizjvRY6kJR0Rcu6bxm29qzPpmXrfUynmu6NOdo8zrmTWiC/KGfvqz6nHQWlG9++ieXjNv4Tl0geOOiA/wLLuEfJchXh/hWbWGbfLKXsXUsL9wRMQa9NM1eDtMZnp/rWITd+Gb/yKu/bt9/fy5WtpX95c9/dulqX6mhryAMfZxD8+yCfTbEbyi57P0mhlDd56Fnr+G64CNMcaYxxcvwMYYY0wFeAE2xhhjKmChGvCPX/17iX/62qsS10Lru+p11YQazdRLuN6grsi+t/r7fqOl/3N0UFvYhA7VQm/IopXWv9bn+pm1lvrmFm3UKtdVPxhM6Suq229BCxv34KcaEb0j1ZRGE31PjdonxO0R/Kan6G95dKDb67bSS+fsuh5nA72UW2n58qOHIj5rUelxzJres2fTbVIXpm5MfbasT+7KyvGvU4+NSDVbwn2+qvdaogGzXzB7ELMmNyKtNX7mmeO3wV6yZXXArNnO1SJzmxVpvuT2nQ8kPryrGn0Dj95TqHd9kKl/XYEP8w3kv6xCu9xHz+5rmKs+9nEVWujdenrDPkEf+jqev6iPPYX+67fhmYxuznEDz9/DcaoBbw/0WdSb6XtuQ0+f4BrYxSWxj1yjw7luj73SIyJqyJFpQfPNnL4s/gZsjDHGVIAXYGOMMaYCvAAbY4wxFeAF2BhjjKmAhSZh/fX//K8S9/Y14aXV1GSjpS6SWzLDrc/1b3P8T1E0mYSlCnynDWNvNBBooVF2YxkGBxHRaWnySLtA8hj+zal1YA4C8+8xTNAHMNEYj1OT9BmSHQLbbLConlkCbR3zxrLG68s6zytLqel9uwnj9RoK5Kdp8tgjh8lDTDaiyQUTqPj+iLQBAI03yhoCsNk9G9f/7u9qnGsI8Tva2CT6aHRBU4srVzRmAhXNRfj6pz+djoFGGdwnk6zYTIEJVGXzljGnSJKsaGqSS9xaANvvvSPxDA0CzuEZMUeTgm7mu1Edf1uB8cYYx8pb/HuYbyZl/QOavJxppgmn/xlJVys13ecUp6Pb0D/s4Tn0U5yvbSRdzWY4nxFxhG2Msc/DYHMFGG8gqapo6US1m/p6t5km8tULPl/12Vdkxp3D34CNMcaYCvACbIwxxlSAF2BjjDGmAhaqAW+dVU3ow74aDkynqgmvndYy7UYtLQzf334o8cG+anjjqeqlMxhUJEb5BFpNc+lc8pZ5U49rUkNRNkTgbku1s+UljacsPocBerQzheHUtmGUsQRt+/SqmhxcXlG9/dKFMzpmyHfDAcz9I6KYq87VgBHAxlqqKT1yaHJBjZcNAmjMkTOgoM5Ic48f/Uhjasbf+Y7G1EpfeUXjnJkETSt+9jONce/EQM9NnMN1XGZQkmuMQC2bhiTPP68xj4OaMTVenpvcvVpmvFGREUd09X4bjvW6uzfXa+h9GKdQW42ImPb0HM6RK/IydMcG4n+CuUILkfhUjfkzMEKJiCcKPa7n8JkdmIGswszjIXTmJs7pQ4yxX0/P3w38rQ9N9w0YirSRW9BFvLSq13Ydq+JskubcROg9XRTMLXIzBmOMMeaxxQuwMcYYUwFegI0xxpgKWKgGPB+rnra+rL/FH0CnGk+1vvKZZ19It3lR63Lv3de6zXsPND7cVV2kB41visYI86mOabmRGuM/89J1iT/YU330/r7q1P2RHld/oGOoo76vjSYUy9TOImJjWTWks6e0jvPCxfMSX39Cm6Gfa6tucojmDjs7qtfXW+n/bt1l1Q1XVnVMm5vQFRcBdUM014ghcgKolWbmOtFsb97U+DOf0ZgNHr761eNfp67JmtyItFHBtWvHb5PHxbphNmfgGDbT+ve4ePH4cVJHph5LjZevUzvP1QGzzpf6PD+zoLrgGhrTB2pNeWytueqMq5tpE5D+Cu6nnp7D2319jizjnH8PDVnWeG8gHBZsdBOxtKW5Bd/D/dNCvXMTOThLEx3DLTzrGrgm9jONEHbQ1WWM+vL2quZ9bCHfpYacnMFIj2HYV72+ntGh66iRZs7NSvdk+S7+BmyMMcZUgBdgY4wxpgK8ABtjjDEVsFAN+MEH70s8Hate0IeHZ++WamunMw2iz3b09/3mUHWQpUKFjX5d9zGfs+EzPTwxpn7qDfyHn9d6x+ef/azEN2++J/GDXdWEh6jnY91vA6auS0WqhZ1Bne8GNMIpjuvOts7tG9va4J0Np9fOqQa4tEaf7ogutJbTZ/QzK7nG8o8a+gtT+yQHqG9mvWxEqguzHpa68q1bGu+jApP66o0bGudqcC9fPv4zX/uaxvRd5rngvHDeoB9GRKqnU1PkNqj5ckzUgMtqfHNwDNzngtg70HO8itySCZ4rzb09iR9k6oALNLu/OdVzMoP/+ybizlzn5h5E36uY3t1xWgO/9IRq018+o7kk/2tPcw+Y17MDjb6DczzBcS/V0mfdaVw3zIl5iLnd7encPuxpDs4cdcPLyKdp8jqPiDq885dxj+Y+k8PfgI0xxpgK8AJsjDHGVIAXYGOMMaYCFqoBn7+gNWTv34QmPIQeW9P4xptvJNvcg68y/6M4Qj3eEerQZlNqwKofFNCdRsPUA/kf/vdfSvxHy1qH9gJqD/vrqp/OJqqL1FAjOBipjrKX6avLeuf3Xr8r8XZfNakBelwundNzc+q81nS21+CXmukH3F3X+th2F/V3NFldBNQNqbdS86XWmesHTH9p+ijTE5m9drlN6pavv67xz3+ejuHP/kzjs6gbvX1b42eeOX6f1Gs5b7n6WdZDswaXUDsv691LDfgk/YBP8pkFsIya3ft7ev91pjr/69Bjm+xTHREz5MCw0nQH/tI3cY5XoQHX8Kx7FjW5NfrmR8T3b/5S4iP0Mf4jnI/XkEtyCfktNYxxTK0801f3Zg8eBT2t2z0Y67gPkQbQgsbbXkEeEfTdejPNI2hD423i2i5OWG/ub8DGGGNMBXgBNsYYYyrAC7AxxhhTAQsV5a58+orE+/AbPnqfepvqCYOMxrQzUQ2hhV68I9T5TufH1/mS2gk0pLd++kOJbx2oJni2UP10jm1OoRcWpxeoAAAHhElEQVQconb5zlxrNN8epvV570Ov6XV1HlavqG/v1lX1/u1sQM8rcGnAk3WFOmhEdFEbXMDDep6pbVw41GboX8z+vzldkx7F9F1mHfB99dGOLa2djA8+0PgLX9D4zTfTMbzzjsZf+crxY6JOzeuaNbn0ms7B46LGS12Z+8j5bP8qZfpujorqfsnapt5Ph9Al9/b1OmOld+pPEDGDfrqB+2kdGu8p6Mp3MX9w8g50sY7rmbLrU3f1Wv3BSPfxLJ6/LUryOKc91PneQZ/dh5O0/ryYIUemqfPQRI7N+gZq3jvHP5fm6O3b4nUcaZ3vHPkt8zhZH+rH4IlojDHG/PbhBdgYY4ypAC/AxhhjTAUsVANeO6W1pme31Gf3Q2jA/BUdlYsRETGEpjuG5kAP5GmJ5kuSd2d+2h/DR/doW7Wxoq06Yx09Mz/AGF8N1YvebuiRH62k2tnyZe29evbiExJvnlXdsb2sqtMIRzqHntSGX2q9kWpt9Trfo5dXUYU+R92QHq3UGVnbmvNA5nGwtpg+y6y5LasjZnxFcyciImIJVaCH6m+beFiz3vm89oeOu1o3HvfuaZzR/JN6Z/RlTeaa+nuZt3NZ/+Acj4kG3Ono+elDUx9AA4Y7eKKdRkTManodTfGeXdyzfNbhCgk+RVp4f6p8Roygya6iBvd+Xa+BJdT1/pLPGWi+R/C5P2Af5YiYr+k+argO6/BhqCHXIHV+gC83rtNZkV6nBf7GPJ6664CNMcaYxxcvwMYYY0wFeAE2xhhjKmChGvASeve2UY/VbOn/A9Oxaho59XaS9IuE3saXP6I1bKI7Z3SrQ9TnvT5SfWe9pXrQ6wPV234+UR3lAXyXNy9rv9kLV1XfjYjYgM92GzpIMdNxj6EX1dFrtI4a3gZq4WoZXWSKmtka5qqoog4YWkyZVXBBDTgHdUb0PE3qW6nHsk74ySc1prb68svpGFiDe0pzAJL+vvQWpsZLTZn10Yxzn6HW3Ti+NrI2z2V1/Ibw3FTkBc37qYaciRrq6g/gDZ3LNZnib208zJitcBcbaWMutEt5xAHu17/PjOEytrGJnsTX4Fd9iGdbMdP8hh34Lg/X9DqebKR9x2NVr7sG/KhHySmHvz/6qye5KYhZuxwRMWHPdrw+cx2wMcYY8/jiBdgYY4ypAC/AxhhjTAV4ATbGGGMqYKFJWGy2fNRXc4DVDS2wHhxpssqUSR4RMUViD4vT+YdaSc9wMp+rmE7T7YiIo0KP669HexK/19PXH3R1zI2tyxJfuKTN1a+i2frmOowfIqJA0tUREg8GSFZrICmkg4S4TlcT5hotPTedJdrHR7RhxMAm1Y8DbEKemKZzzLkkHv6trAkBGxuwSL8szplLnNaku6TRAa6Z5P0cY5lJBrcfETP+/45xlqahlDVbOImZAbaRJHqdLBfmE2eGBLPxRJOP2h29ZqZJs4x0mwUOZoL30ISox3zUZK6O3yWfrRERd2EGsjvVZ/QtJM6OmhjzsprUdJFwut7VuN1JnzNzJIiOMfIJzjmTP/nsayKJq4ZnfNFIn2O1xGTo4y2l/gZsjDHGVIAXYGOMMaYCvAAbY4wxFbBgDVj1gjocx0+dVa1svKK/zU/GqQbMP42hE8+hARdJvTs1I+gkiRaW6gGNBkwulnTcw3XV365taGOEU6fV/GFlTU/LSlc1i3YnPW2DiYrbIzR4mEOnrDexDR4n4iY0w1wzhia2yeYMND1/HEhkSP5PmtEQEx0Z+k/yOvTyGg0sqH3SDGSSNmcvazKRaKEY02y+eHGUQ07mHlodx5zdJo4zMVrB6xn/mEfCFE3ja3UdWAf39Kyl52+WJLNEwPshyXdpQXfm+3ucC2z/Nk5II6PBjziBeA6sognFaluf6ctLmifSaus+iibizHNmMju+6UTA5KSoH5/fwFshMebIXDQ0UqE+f9Innb8BG2OMMRXgBdgYY4ypAC/AxhhjTAUsVAOuoyZsY1NrV1eWUdM7RH1XRgOeTNmwATpHQX3u+N/uE6PuBmrIMp2yu9RBVlX32FpRI/uVtuoky2jW0IJB+Qiy82Er/b+pjxpr1vB1oF23oFtS46UOUkuaGqTzMBqpMXurhbj5GDRLr1Eb/VgbOXYbrB0nSe0xx8QGI6zZPck2S14vq48t02sjTlBTzfeX7PMkmm/ZZ1JNv5pC4KKu++0gL6SFZ+Ec/gR5DZjzrdQSDZ3X6fH5LmywUmRu1wbe08a1uQwtu4WmFHzu1KGlTjFvo8wYxjOdrFmJdl3nM57PNtY7n6B4nI1npnWN6ydMNvA3YGOMMaYCvAAbY4wxFeAF2BhjjKmAWk7LM8YYY8yjxd+AjTHGmArwAmyMMcZUgBdgY4wxpgK8ABtjjDEV4AXYGGOMqQAvwMYYY0wFeAE2xhhjKsALsDHGGFMBXoCNMcaYCvACbIwxxlSAF2BjjDGmArwAG2OMMRXgBdgYY4ypAC/AxhhjTAV4ATbGGGMqwAuwMcYYUwFegI0xxpgK8AJsjDHGVIAXYGOMMaYCvAAbY4wxFeAF2BhjjKkAL8DGGGNMBXgBNsYYYyrg/wDxNjUcDVdPaAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"--------------------\")\n",
    "print(\"   Deep Taylor Decomposition\")\n",
    "h_dtd = enet.deeptaylor(images[:5], one_hot(labels[:5]))\n",
    "plot_heatmaps(images[:5], h_dtd.sum(1)*8)\n",
    "\n",
    "print(\"--------------------\")\n",
    "print(\"   LRP-gamma 0.5\")\n",
    "h_gamma = enet.lrp_gamma(images[:5], one_hot(labels[:5]), gamma=0.5)\n",
    "plot_heatmaps(images[:5], h_gamma.sum(1)*8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Layerwise comparison of LRP gamma=0 and DTD\n",
    "We can test that LRP gamma with gamma=0 is equal to the deep taylor decomposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Linear Max relative error: 0.0\n",
      "LinearRelu Max relative error: 0.0\n",
      "LinearRelu Max relative error: 0.0\n",
      "MaxPool Max relative error: 0.0\n",
      "ConvRelu Max relative error: 0.0\n",
      "MaxPool Max relative error: 0.0\n",
      "ConvRelu Max relative error: 0.0\n"
     ]
    }
   ],
   "source": [
    "# DEBUG\n",
    "self = enet\n",
    "x = images\n",
    "pattern = one_hot(labels)\n",
    "gamma = 0\n",
    "\n",
    "out = self.forward(x)\n",
    "R = out*pattern\n",
    "R_total = R.sum()\n",
    "\n",
    "R_g = R\n",
    "R_d = R\n",
    "# Backward pass\n",
    "for layer in self.layers[1:][::-1]:\n",
    "    R_g = R_g.view(layer.z.shape)\n",
    "    R_g = layer.lrp_gamma(R_g, gamma)\n",
    "    \n",
    "    R_d = R_d.view(layer.z.shape)\n",
    "    R_d = layer.zplus(R_d, gamma)\n",
    "    \n",
    "    print(f\"{layer.__class__.__name__} Max relative error: {((R_g-R_d)/(R_d+1e-7)).abs().max()}\")\n",
    "\n",
    "R_g = self.layers[0].zb_gamma(R_g, self.min_x, self.max_x, gamma=gamma)\n",
    "R_d = self.layers[0].zb_gamma(R_d, self.min_x, self.max_x, gamma=gamma)    \n",
    "print(f\"{self.layers[0].__class__.__name__} Max relative error: {((R_g-R_d)/(R_d+1e-7)).abs().max()}\")\n",
    "\n",
    "lrp_gamma = enet.lrp_gamma(images, pattern, 0).detach()\n",
    "deeptaylor = enet.deeptaylor(images, pattern).detach()\n",
    "assert np.allclose(R_g.detach(), lrp_gamma), \"R_g is wrong\"\n",
    "assert np.allclose(R_d.detach(), deeptaylor), \"R_d is wrong\"\n",
    "assert np.allclose(lrp_gamma, deeptaylor), \"R_d != R_g\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Effect of gamma\n",
    "\n",
    "Let's compare heatmaps depending on their gamma value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.0\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.1\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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KfPqp2h99lH6H56RDFBNnvKlFS+KqJvaPnR216XTFRB8RqdMUn8/33lO7KLEGizPAGSa5VxGpA9uchdAvmo8//rXYMyRraYT2RQsOU92cBCIb69q/Kx3tj9ayOlltdPQZrsNRaPZMk6+s7Ki9mZNM4gbzbGCu6+IcT1fVMa8BZ85o4Dr57OTdTjifrSCByPGpJtqYYfsYbVyDY9hXSFKzj4RRERHTht6vzpL2daWekzQmB/8DNsYYY0rAC7AxxhhTAl6AjTHGmBJYqAb8f/7uZ2K36/ru/fRUkwM0Gvr74Ht/8p3kmPcfqEa7+0i3v/vOO3pMJMXonapWVkcSjfff16QYA1bOjogGNIW7r90W+523viH2jauqi6wg+Hw60DZ9+Vj1t6csrh4Rj3Z0n5NjTfC/j6ICw5FeRx2FLhpN7afJGAWnWXA8Ijprqtm9G9r3q0g48to1JGK4CKg7snACbWqreRQVADjvOaidfu97alO/jcgvjvCbPHigNnVoJveg9vYNHbOxpXp+RES8+qravC4myaCuyX6k5kuNeJ4kG/xOSZrw48dPxW5U0C74aDSr2s6XcpLtPD1SjXaEehrXNvUeMSnGDM/CKZxXHl/XpBg7ozRJTRtFIjau6Fz2R/AteBW+JUnCCiTNCMxbD/qq50ZEfNjTfaYoNLM/0O/0p/CPYaEL+NhAYo4en9+IyKAbb4T2faOZ46+Qg/8BG2OMMSXgBdgYY4wpAS/AxhhjTAksVAN+9qXG2K6va5GBmzf1Pfrb37wrdr2Z6jk///EHYm9Dd1rKVGN4uqMicXcFsXMr+v1/8gNNjF/JSVC+uqrHuLqxIfbensZUfnb/V2If7Kv2fYgk6UeHqt/ts4B7ROwdqj40hi5ZRyL7RlPtCnSR1RXt6zUk2r+ylcbwNhEz22irfZyj51w4jNumTplX5L0Iar4kL171PPuzqAG10Yj0OvYQl33njtrUYxl7zOPxGhEbGRFpwYaiGGo8F8l1cf95ijEU6cIlacB9PI819H93ReNGb25r31RyLuv5Y9X1M1w7Y3JPejqPrMGvo9rU72/f0fwEtZwg3CVcx00UXHmIWO5nBzr3zVAUZgJ/l9mpjrvaKPV1aGHsVjDuanimDzC3HVX0uqZYV3ivqjnP8wRjsVpT+3QeX5LwP2BjjDGmFLwAG2OMMSXgBdgYY4wpgYVqwA8++bnYhyuat/kf/6N/J/YPfvAXYv/3/6m5pCMithB7uoX8p+2avt9vZRrTtb2qMZjLsFuIYxtHmiCVMbMslP34Y9VuvniqeZWHIz1mraXXsLy8LvZWK81PPBqerUvWkbu0Cl2E9jL0uRXkba5WU33o+ES16idPNL/wYIBY1G9/67c3+EVBrZPxs9QdD1S7i3Xt+4hIdUVqRIy5pR708KHazMt8757aXR0PEZHqr9yHscWIAw/4LcSR6oWJBpyntfK6oAdGjq/CmeegzkzNOE/PZd8nuYTL0YBPdzUun3Ghd1//ttjfvaO5A/7mM80lHRHRRo4C+nUs49Jbmc4rd/H9I8Rlv4EY3aOcuW6F42Cq+zzeVX+WVzAG9ia6/2qNvik6hrJaqvtn0HyZqbkJDXgXY4AaMMfQMfqln/M39Rnm2yPMfcNxgZ/I/8f/gI0xxpgS8AJsjDHGlIAXYGOMMaYEFqoBD5DD871vab3QP/+LPxd7Y001oD/9nsbkRkRUKqopLNf1/f0K6jRWG6rX1hqqOcxwvClqFB88R53UiFip6TmnoRrEa994V+ytm2+IvfdcdZNlxIGOoJtks/R3Ux25ZqfIXzpAftTjE9XbWDPzuKfbv3yk8dODflrjdtTTc0yg1XS6aY3TC4dxwNSAqSFSt2Rsa953qDuytu6h3t/45BO1mZeZGnCeDk299Be/UPv119X+UnOmJ7V672uMfqIp58Ui85ivaBxpojszjpfXxZhsasA5OXkTbfqS1AOeIH51a1tzHDBf/FX4dbx/E30ZESNcGp/5lzEu15DjeKXKvuIZdKyv0GcjIidAWcf+tQ3kDF/Re7iOXPoNxqdTds4rfs57XBDrz1jiE+zfw/aPjnWOZ936iIgD5MIfz3Rs1pjz+rfgf8DGGGNMCXgBNsYYY0rAC7AxxhhTAgvVgF9784/E/pf/5t+K3ZuoRvTxpxovO83SmLAWYolH0Az29lkPVnWNyUSjyDKGuYVqFkeHiJeMiOoT1QgePtVaoKfIbzodqH7QRezyvV99JfZnX3yBNqb9sH5VtZYh4l8PEN+6C52StUIrFdU0MthdxnxGxBril1uIO+wfM2JvAVAvoubEuF9o5YWxrBERyH+bxBYzxvbWLbWZx5n1gD/9ND3ntzWONDDmkjjfu5pXPYnhvXlT7V34OuTlt75xQ2323dtvn31O9hN1Z8ZT895FpBo99fiSWLt6TezXv/W+2J2ptvNkTzX95SzNUV5DLDGfwOOBapvdmc47j2Cvo6v2oQFvn6Z5mO8f6zzQxvOxi/sxHuv+HdSAf76n/hFVPI8j1lGOiBrqpzdQU/jeqY7DEcbRLmKXe4iX7sOe5ui505peR4baykV5Gf6eyzFajTHGmD8wvAAbY4wxJeAF2BhjjCmBhWrA//xf/yuxr1xT3eknP1Ptc4j36MOcOMAJYm5n0FaqCHbLEGg2Qd7mGbanklKaH3UEnWNnV7Xr8Vg1Qsipsbaicb/Doeq3e7vQIXNq2O7sqO5xOtJzjlGLd4J42GpDh0KnpRpHk7mjx2kbhgPqHqrNtLs5Gt5Fw9hTaobULalDIv45ItLczYzzpa7M2OMHmhs8yVfNXNHUd/OOyX1eeunsNl6/rvaHH6q9uan248dpG958U+3PPlP7vPos79XSUv5+v8klifsldxFn3VzS3Nz7T/V+7GEe2mJsa0Q8w1y2BX+XCrZPMFdt4Jis8NxJujJtwxr002XkduhPdQ5YxSGeI2/+mxgjX/T1+69kaV3dX/b0s80JzglfA+YjGGMuG0O/HSBXdGWajrHhmPkB9P5VHQdsjDHGXF68ABtjjDEl4AXYGGOMKYGFasB/9+O/EfunH/5Y7AyRbdWqakK1eppLuFqjrsi6t/p+v9bQ3xwtxBayxmYDtSErjTT+tTrT76w0ruh3mohVrqp+MJgwr6gev4F4yVEPmmFE9E5UUxqOdZ+M+Uyhxw2Rb3qC+pYnR3q8TiMdOpurep011FJupOHLFw91R2prrJvLuF9qoRHFujHzNDPelW1gnmXqrdScI9J809RC6SfA/NLXNE41iUWmhpwXg8tYcMY3M7aYeZuR8zzpB54zT0PmdbJvS9KIHzxWHf/gid7TJqbeJcS7Ps2Jf20jD3Mf/i8r0C4PUbO7hf7fxzlegha6V83JN8A89FW9R03Ex66h/voOciZT5Z9g/h2MUg34aKBz0b2p7vMl9PQxxsAehsQJfI2OZ3o81kqPiMjgI9OA5pvjppOL/wEbY4wxJeAF2BhjjCkBL8DGGGNMCXgBNsYYY0pgoU5Yf/2//pvYvUMt2N2oq7NKu8Mi4GlzqzP9bIbfFJU6nbDgmNBURxIWEGigUHatiyLhEdFqaOL7ZgXOY/iZk7WQHATJv0dIgj5AEo3RKE2SPoWzQ+CYNQbV08mjqW1e66q92oXTSDtNzt+saxvqmTo3ZJPUeezCYcIKJvhnggo6YdGhKqI4mQe3sw10VvpKE9AkCS5Y2D4i4p13zm4nHZroTEbHrq2ts+3XXkvbkOcc9pvQwY0FHZh4Aw6PidNVnkMVP2OyHtrzesf8jjy+r05vHRQIuII5Iup8ntL/RkN81kTijQH6q4VL/RJOWLdhf4giL+v11OF0F05XqxmKSuB2rNb0gx3MQz/Hs/IQTlfVKRNeRDzjXIZzHmP7EG0c0amqoR3VqOv2ej0dd5WKnqOKRByVnHbn4X/AxhhjTAl4ATbGGGNKwAuwMcYYUwIL1YC3N1UTetR/JvZkolrXyvq62LUsDQw/3Hku9tGhanijiepvUySoSDQiAq2m3t5KdpnV9brGmXZrBSJwp6G6cret9oTB50iAHs2cwHBq20iU0Ya2vb6sySNeXlLN8OZ11feQUyNOBygyHxGVmepcNSQCWFtJNaULh7oikzuwSDx1TRZKiEiTPfAYRUUJPvhAbSao+OILtZlEJSLiiRb8iPv31b6iyWASnfrGDbWLCkbkaadM1sG+puZLvZYaMCkq3hBRnHijpEQc9Y72xWSk89LJDMl4kNSkneVowD0UVIGvyC3ojtRPv4O+YoqLGzhnu44EMxERFb2um/jOPpKBLCOZRx06cw3zb41trKb3b4LPWFzhCAlF6hiHbdjNZZ1/ke8kRuPU54a9V8F1N5suxmCMMcZcWrwAG2OMMSXgBdgYY4wpgYVqwLORxmCudvVd/BF0qtFEYxvffOvd9Jg3NC736TNNUv90V+3jfdVFeogLnaAwwmyiberWNOY3IuLNb90R++GB6qPPDlWn7g/1uvoDbUMVgW1NFKHo5mhna13VkDavaKL76zc0+f6dl7bF3mqqbnKM4g57e6rXVxvpb7dOV3XHpWVt08YGdMkyYN/Rpl5LXTMi1SYZp0t99Qh6+dtvq/1cx0f0+2rn6a/UsjcQn07dmBowr/v69bOPz8ILeedkUYolpNpnv1Ej5vYi/4yIVAMu0oTn0ZVfADMUpq8g1nRGv4GZ6oydjbQISGtJ70Gjp+PksK/zSBP3/COMiTX0bxPd3aikBTiWttUv5x58JIaId96FD85orG3Yx1w3wlinvhsRcYKqLjOM1aVlHXdr8HdhYob+UK+h31e9vpqjQ1cRI02fm25nPn8X/wM2xhhjSsALsDHGGFMCXoCNMcaYElioBrz7UHPeTkaqF/SRw7P3pcZDrucUiN5s6fv9+qnqIO2KChv9qp5jNmM0HHN4ok19FEKPiO9/R/PyvvPWe2J/8YXGaO7uq+Z3ing+xv3WkLe5jTykERFXEee7Bj1ugut6vKN9+/HOI7FZcHplS/W+9grzdEd0oLWsX9XvLK2m+vmFwxhbxrsW5W2ep83Ms0z9lRou808zb/Ovf6029diIVGdmXPB7OgYTzRcx9olGTO07LxaZ7aL+So2X/VCU67lIz82D31mQ5kuOjvQeX4FvyRTzyvjgQOznOXHAHRS7P5zoPRkj/3sHdnumc+Eh8hffRvcO4LMTEbH2ko71P72qviQfHKg/xAHG1TM8b03crymueylL57oVPNNtjO0D9O1BD33bUx+cGeKGm/Cnqef4gVSQO7/T0VjiRp7vSA7+B2yMMcaUgBdgY4wxpgS8ABtjjDElsFAN+Np11Z2++gKa8Cn02Eztzz75ODnmAfIq8xfFCeLxThCHNp1QA1b9oALdaXia5kD+2//9l2L/w67Gob0LHaq/qprfdKy6SIYYwcFQdZSDnLq6jHe+/5Fqgjt91aQGqHHZ3tJ7c+WaxhE3V7Sfqzn1gDurmhO72VFNOKsudLj9P6gJMjaV2iY1YdYHjkj1UeaLpt5KjZd1ctmGHfgZQB+MiIjXX1d7Te9XElt8967aRXmbSZ7+yutibGtR7ufzxvDmxQXzs6JjLohlxOweHejz151ou1vQY0e7u8kxj+ADw+jwU+SX7qNvWtCA65jrthCTu8K8+RHxsy/UP+EN1DH+Lu7ZL+BLsgb/lgxtHFMrz6mr+6inffm8p8/o4UjbfYKOqkPjbS7pPFWDvlutp3H41HhrGNvZnL4H/gdsjDHGlIAXYGOMMaYEvAAbY4wxJbBQUe7WG7fEPkS+4ZOvGGOresKA+lxE7I1VQ2igFu8Qcb6T2dlxviSbQ0P61U+1vuuXRxpLullR/XSGY06gFxwjdvnxTHO+fnqaxud9Bb2m19F+WL6lcaPbt18Ru7UGXZJFMZGTdYlaakR0EBtcQQ7rWU5s44XD2FPqiozzpXY6j5ZTFFvMuF/G4D7TPNtJruhPPknP+fCh2u+/rzavg/os803zOhHXmFu7l9o0j0GdmccoujfzUBT3W1I94O6GPk9T6JKHh8z/DpL8BJHkB1jC81SDxtuBrryHvmK26V/CvpPTdc0nOu4+GOo53sb8i7QL0cL96CPOdxd1dnfHafz5eKr7nNa1HzL42Cyv6TOeIWcCY48U4LejAAAGaElEQVRnqO3LesIRaWzwDP4ts5hv3PkfsDHGGFMCXoCNMcaYEvACbIwxxpTAQjXglSuqfW1ub4n9CBow36LnVQdl7NsImgNzIE8KNF+S7J3zan8EPe1kRzW9SlNjNKuomfkQbfxxqF70aU2v/GQp1eO6L2ut3c0bL4m9sak5W5td1fiGuNIZ9KQm8qVWa2lsXLXKfXR4VfLq2l40jBMtynFMbZvaaUSqOzInMrXOorq2jKdlXPG23ruIKG4n6/tSl756Ve29PbUZ/5yXj5qfUSvjdt7/8+Z6nmf8lJT7mbRbGmvaQ272ETRgRpvXcqapSoZczthniGd2XDDXwTMhmtg/zfYe0Ycm20EM7uMq7jniej/HOYbQfPeR5/6wkd7z2oqeo4rnp4I8DHwe+TRyTahyDFVy6gHzM3zHccDGGGPMJcYLsDHGGFMCXoCNMcaYElioBtxG7d4m4rHqDf09MBnp2/o8RYM1MJM3/Nx8ztSwiXqXo1MdIz7vo6HqO6sN1YM+Gmie5p+PVUfZRd7ljZdvi339tuq7ERFryLPdhA5SmWq7R9CLqqg1WkUMbw36Xpaji0ygM2boq0oZccCsB0zdcc66nWdC/bUovpX7UwOGXhivvZae81hrmiaxxdS2Gfd7757a1KwYH8244Iji/NHUwnkOauNfpx7w16kZvAAyPE8ZfCbGVW1nbwIfjLzLwGc1TGbMcDDBF2roK2a1P0Hf/TqnDVdxDNYkvo181QPMbYdTjZk/Qd7l4Yr6slTWUiU6W9b5NEM+6iGGBGsvV1FfPXleYU9yxtQIc37qleM4YGOMMebS4gXYGGOMKQEvwMYYY0wJeAE2xhhjSmChTlgjBGWf9NUNYHlNA6wHJ5qQYJKT0GACx54JnazwQZbWcziTGbwhmHQ7IuKkotf110NNUn+/hwTjHSRR335Z7Os3NU367U21N1Y3kjYw+PwEjgcDOKvV4BTSgkNcq4Mi1Q29N6126pTTROKFel4C/5JhkvSMXnl0LMpLokEnKjp60dnovM5I3D+vH+k4wjbQuYyOXUVFC3j8vDbwnEXwHEXJCuZxqOI+l8QJawonx9FYnY8aLe27yVDHVOJbGhEVXBvnOvgFxZj+qHSKxPGZcmaY4zT5DMlAehOdo5/AcXZQx/3oqnNfFw6nK3D2a7TSeWZCpys8w+NkSMAZDXNfDcfLMMdXa+nYrxQkGZrX19f/gI0xxpgS8AJsjDHGlIAXYGOMMaYEFqwBq15Qbeib8iubSFi+pO/mx6NUj+NHI+hpMwglFcb+UxOEXkDdJHL0gFoNSS7a2u7TVU2S8PqaJte/sq7Fu5dW9LYsdVRfaLbS2zYYq7g9RFj+DBpetY5jFGhpLEqdV4yhjmOyOMPsvFlQFkCiCVNCzCsAcN6iEkXJQMiaFu9ICim8iHNQf70MCS2KEnH8HjFB0fgMlelbHRQIQNGBaeLMkmq+M/RPBXZamObsezpA/ydFCSJigAQ8J5gHVlCEYqmJ5EttJPhp6jlG0IyzWtqGMeb4pJBMFT42sDmnM+lJUjQmJ+lQhmMm68acc53/ARtjjDEl4AXYGGOMKQEvwMYYY0wJLFQDruL9/tqGxq4udRHTe4r4rhwNeDxhwQbEulX0EjP85mBsXQWJuivQIGqN9N1+BzrI8rLqHttLquktNVUn6aJYQ4MJyiE7HzfS3019xFgzProF7bqBWDdqvNRBWGCa+lNExHCoidkbDdj1c2qnF0CxtPkCtM8CPXVWUJSCsckzJo+fgwyBpHwucr5w/nP8rpr+ec95SWJ856GCYgst+IU0MBfOIPPnacDTGe+pkmFcJf4tBf4uLLCSN+xq2KeJeaPbUI23UTDv0E9kgnOeVtN7Tj+fKeN80fAq+iWZ2/g8JnpuCgvPVKooblMU4/7335trL2OMMca8ULwAG2OMMSXgBdgYY4wpgSxPyzPGGGPMxeJ/wMYYY0wJeAE2xhhjSsALsDHGGFMCXoCNMcaYEvACbIwxxpSAF2BjjDGmBLwAG2OMMSXgBdgYY4wpAS/AxhhjTAl4ATbGGGNKwAuwMcYYUwJegI0xxpgS8AJsjDHGlIAXYGOMMaYEvAAbY4wxJeAF2BhjjCkBL8DGGGNMCXgBNsYYY0rAC7AxxhhTAl6AjTHGmBLwAmyMMcaUgBdgY4wxpgS8ABtjjDEl8H8Bk1DrGbPTyvUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.2\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.30000000000000004\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.5\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.6000000000000001\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.7000000000000001\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.8\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 0.9\n",
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=============\n",
      "LRP-gamma 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "image, label = images[1][na], labels[1][na]\n",
    "heatmaps = []\n",
    "for gamma in np.arange(0,1.1,0.1):\n",
    "    print(\"\\n=============\")\n",
    "    print(f\"LRP-gamma {gamma}\")\n",
    "    h = enet.lrp_gamma(image, one_hot(label), gamma).detach()\n",
    "    plot_heatmaps(image, h.sum(1)*8)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of LRP gamma=1 and Gradient Times Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deep Taylor == LRP gamma 0\n",
      "Input times Gradient != LRP gamma 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nielswarncke/Documents/pytorch-tuts/cifar10_experiments/trainable_net.py:78: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
      "  img = torch.tensor(images, requires_grad=True)\n"
     ]
    }
   ],
   "source": [
    "# Check if LRP-gamma 0 == deep taylor\n",
    "if np.allclose(\n",
    "        enet.lrp_gamma(images, one_hot(labels), 0).detach(),\n",
    "        enet.deeptaylor(images, one_hot(labels)).detach()):\n",
    "    print(\"Deep Taylor == LRP gamma 0\")\n",
    "else:\n",
    "    print(\"Deep Taylor == LRP gamma 0\")\n",
    "\n",
    "if np.allclose(\n",
    "        enet.lrp_gamma(images, one_hot(labels), 1).detach(),\n",
    "        input_times_gradient(enet, images, one_hot(labels)).detach()):\n",
    "    print(\"Input times Gradient == LRP gamma 1\")\n",
    "else:\n",
    "    print(\"Input times Gradient != LRP gamma 1\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I think LRP-1 is equal to LRP-epsilon, which I haven't implemented here. LRP-epsilon is like input times gradient, but for every neuron, it is normalized such that it is conservative. The connection is explained nicely [here](https://arxiv.org/abs/1711.06104)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Usage example: direct rebuilt of the tutorial net as explainable net\n",
    "\n",
    "And here is an example how to rebuild Net directly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1,  2000] loss: 2.298\n",
      "[1,  4000] loss: 2.085\n",
      "Accuracy: 0.3522\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 3, 32, 32)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class ExplainNet(ExplainableModel):\n",
    "    \"\"\"\n",
    "    Rebuilt of cifar10utils.Net with deep taylor support\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        layers = [\n",
    "            ConvRelu(3, 6, 5),\n",
    "            MaxPool(2, 2),\n",
    "            ConvRelu(6, 16, 5),\n",
    "            MaxPool(2, 2),\n",
    "            LinearRelu(16 * 5 * 5, 120),\n",
    "            LinearRelu(120, 84),\n",
    "            LinearRelu(84, 10)\n",
    "        ]\n",
    "        super().__init__(layers, -1, 1)\n",
    "        \n",
    "        # Because pytorch is weird, the submodels cannot be stored in a list\n",
    "        self.l1 = layers[0]\n",
    "        self.l2 = layers[1]\n",
    "        self.l3 = layers[2]\n",
    "        self.l4 = layers[3]\n",
    "        self.l5 = layers[4]\n",
    "        self.l6 = layers[5]\n",
    "            \n",
    "        \n",
    "\n",
    "enet_sister = ExplainNet()\n",
    "enet_sister.train(1, trainloader)\n",
    "\n",
    "accuracy = enet_sister.accuracy(testloader)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "dataiter = iter(testloader)\n",
    "images, labels = dataiter.next()\n",
    "h_dtd = enet_sister.deeptaylor(images, one_hot(labels))\n",
    "plot_heatmaps(images, h_dtd.sum(1)*10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is trained for only one epoch, and the heatmaps look rather blurry. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "hotkeys": {
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  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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}