diff --git a/conv-nn-bigmax-2019/bigmax_mnist_example_tutorial.ipynb b/conv-nn-bigmax-2019/bigmax_mnist_example_tutorial.ipynb
index 7711a524373f33e714b5e5fc67692b628a1ab03c..0552178c40ebd67c740a0295119419dd2b62d637 100644
--- a/conv-nn-bigmax-2019/bigmax_mnist_example_tutorial.ipynb
+++ b/conv-nn-bigmax-2019/bigmax_mnist_example_tutorial.ipynb
@@ -9,7 +9,7 @@
     "\n",
     "##### Authors: Angelo Ziletti, Andreas Leitherer, and Luca M. Ghiringhelli - Fritz Haber Institute of the Max Planck Society, Berlin\n",
     "\n",
-    "In this tutorial, we briefly introduce the main concepts of convolutional neural network, build a neural network model, and finally explain the classification decision process using attentive response maps."
+    "In this tutorial, we briefly introduce the main ideas behind convolutional neural networks, build a neural network model, and finally explain the classification decision process using attentive response maps."
    ]
   },
   {
@@ -23,7 +23,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We first install the packages that we will need to perform this tutorial, and then we load the necessary Python libraries."
+    "We first install the packages that we will need to perform this tutorial, and then we load the necessary Python libraries. This tutorial has been tested on Python 3.5."
    ]
   },
   {
@@ -38,91 +38,67 @@
      "output_type": "stream",
      "text": [
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       "\u001b[31mtwisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.\u001b[0m\n",
       "\u001b[33mYou are using pip version 10.0.1, however version 19.0.2 is available.\n",
       "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
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       "\u001b[33mYou are using pip version 10.0.1, however version 19.0.2 is available.\n",
       "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
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+      "\u001b[31mtwisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.\u001b[0m\n",
+      "\u001b[33mYou are using pip version 10.0.1, however version 19.0.2 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
+      "Requirement already satisfied: scipy in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (1.1.0)\n",
+      "\u001b[31mtwisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.\u001b[0m\n",
+      "\u001b[33mYou are using pip version 10.0.1, however version 19.0.2 is available.\n",
+      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
+      "Requirement already satisfied: numpy in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (1.15.1)\n",
       "\u001b[31mtwisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.\u001b[0m\n",
       "\u001b[33mYou are using pip version 10.0.1, however version 19.0.2 is available.\n",
       "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n",
       "Collecting git+https://github.com/raghakot/keras-vis.git\n",
-      "  Cloning https://github.com/raghakot/keras-vis.git to /tmp/pip-req-build-z_jmzf3p\n",
+      "  Cloning https://github.com/raghakot/keras-vis.git to /tmp/pip-req-build-epbr_4s0\n",
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+      "Requirement not upgraded as not directly required: numpy>=1.9.1 in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from keras->keras-vis==0.4.1) (1.15.1)\n",
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-      "Requirement not upgraded as not directly required: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from matplotlib->keras-vis==0.4.1) (2.2.0)\n",
+      "Requirement not upgraded as not directly required: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from matplotlib->keras-vis==0.4.1) (2.2.0)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
       "Requirement not upgraded as not directly required: python-dateutil>=2.1 in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from matplotlib->keras-vis==0.4.1) (2.7.3)\n",
       "Requirement not upgraded as not directly required: pytz in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from matplotlib->keras-vis==0.4.1) (2018.5)\n",
       "Requirement not upgraded as not directly required: kiwisolver>=1.0.1 in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from matplotlib->keras-vis==0.4.1) (1.0.1)\n",
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       "Requirement not upgraded as not directly required: setuptools in /home/ziletti/anaconda2/envs/py36/lib/python3.6/site-packages (from kiwisolver>=1.0.1->matplotlib->keras-vis==0.4.1) (40.2.0)\n",
       "Building wheels for collected packages: keras-vis\n",
       "  Running setup.py bdist_wheel for keras-vis ... \u001b[?25ldone\n",
-      "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-nwbaidz1/wheels/c5/ae/e7/b34d1cb48b1898f606a5cce08ebc9521fa0588f37f1e590d9f\n",
+      "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-3ikp3hx6/wheels/c5/ae/e7/b34d1cb48b1898f606a5cce08ebc9521fa0588f37f1e590d9f\n",
       "Successfully built keras-vis\n",
       "\u001b[31mtwisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.\u001b[0m\n",
       "Installing collected packages: keras-vis\n",
@@ -157,6 +139,13 @@
     "! pip install --user tensorflow\n",
     "! pip install --user keras\n",
     "\n",
+    "# to visualize images\n",
+    "! pip install matplotlib\n",
+    "\n",
+    "# to calculate convolution\n",
+    "! pip install scipy\n",
+    "! pip install numpy\n",
+    "\n",
     "# package for neural network attention map visualization\n",
     "! pip install git+https://github.com/raghakot/keras-vis.git -U"
    ]
@@ -207,8 +196,9 @@
    "source": [
     "Convolutional networks are a specialized kind of neural network for processing data that has a known **grid-like topology**; they are networks that use convolution in place of general matrix multiplication in at least one of their layers.\n",
     "\n",
-    "Examples include time-series data (1-D grid with samples at regular time intervals) and image data (2-D grid of pixels).   \n",
-    "Convolutional networks have been tremendously successful in practical applications. \n",
+    "Examples of such data include time-series data (1-D grid with samples at regular time intervals) and image data (2-D grid of pixels).   \n",
+    "Convolutional networks have been tremendously successful in practical applications, especially in computer vision. \n",
+    "\n",
     "The name \"convolutional neural network\" indicates that the network employs a mathematical operation called convolution. Convolution is a specialized kind of linear operation. \n",
     "\n",
     "\n",
@@ -218,7 +208,7 @@
     "2. **Detector** stage: each linear activation is run through a nonlinear activation function (e.g. rectified linear \n",
     "activation function)\n",
     "\n",
-    "3. **Pooling** stage: a pooling function is used to modify (downsample) the output of the layer. A pooling function replaces the output of the net at a certain location with a summary statistic of the nearby outputs. For example, the max pooling operation reports the maximum output within a rectangular neighborhood. Other popular pooling functions include the average of a rectangular neighborhood, the $L^2$ norm of a rectangular neighborhood, or a weighted average based on the distance from the central pixel.\n",
+    "3. **Pooling** stage: a pooling function is used to modify (downsample) the output of the layer. A pooling function replaces the output of the network at a certain location with a summary statistic of the nearby outputs. For example, the max pooling operation reports the maximum output within a rectangular neighborhood. Other popular pooling functions include the average of a rectangular neighborhood, the $L^2$ norm of a rectangular neighborhood, or a weighted average based on the distance from the central pixel.\n",
     "\n",
     "#### Max pooling example\n",
     "\n",
@@ -244,14 +234,15 @@
     "- **sparse interactions**\n",
     "- **parameter sharing**\n",
     "- **equivariant representations**   \n",
-    "Moreover, convolution provides a means for working with inputs of variable size.\n",
+    "\n",
+    "Moreover, convolution provides a means for working with inputs of variable size - while this is not possible with fully connected neural networks (also called multi-layer perceptrons).\n",
     "\n",
     "#### 2.1 Sparse interactions\n",
     "##### Fully connected NN\n",
     "It uses matrix multiplication by a matrix of parameters with a separate parameter describing the interaction between each input unit and each output unit. This means that every output unit interacts with every input unit. This do not scale well to full images. For example, an image of 200x200x3 would lead to neurons that have 200x200x3 = 120,000 weights. Moreover, we would almost certainly want to have several such neurons. Clearly, this full connectivity is wasteful and the huge number of parameters would quickly lead to overfitting.\n",
     "##### CNN\n",
-    "It achieves sparse interactions (sparse connectivity) by making the kernel smaller than the input. When processing an image, we can detect small, meaningful features such as edges with kernels that occupy only tens or hundreds of pixels. This means that we need to store fewer parameters, which both reduces the memory requirements of the model and improves its statistical efficiency. It also means that computing the output requires fewer operations. If there are m inputs and n outputs, then matrix multiplication requires m×n parameters, and the algorithms used in practice have O(m×n) runtime (per example). If we limit the number of connections each output may have to k, then the sparsely connected approach requires only k×n parameters and O(k×n) runtime. For many practical applications, k is several orders of magnitude smaller\n",
-    "than m.\n",
+    "It achieves sparse interactions (sparse connectivity) by making the kernel smaller than the input. When processing an image, we can detect small, meaningful features such as edges with kernels that occupy only tens or hundreds of pixels. (*see Sec. 3.3.2 for two concrete examples*).   \n",
+    "This means that we need to store fewer parameters, which both reduces the memory requirements of the model and improves its statistical efficiency. It also means that computing the output requires fewer operations. If there are $m$ inputs and $n$ outputs, then matrix multiplication requires $m \\times n$ parameters, and the algorithms used in practice have $O(m \\times n)$ runtime (per example). If we limit the number of connections each output may have to $k$, then the sparsely connected approach requires only $k \\times n$ parameters and $O(k \\times n)$ runtime. For many practical applications, $k$ is several orders of magnitude smaller than $m$.\n",
     "\n",
     "#### 2.2 Parameter sharing\n",
     "It refers to using the same parameter for more than one function in a model. \n",
@@ -260,15 +251,14 @@
     "Each element of the weight matrix is used exactly once when computing the output of a layer.\n",
     "\n",
     "##### CNN\n",
-    "Each member of the kernel is used at every position of the input. The parameter sharing used by the convolution operation means that rather than learning a separate set of parameters for every location, we learn only one set. This further reduce the storage requirements of the model to k parameters. Recall that k is usually several orders of magnitude smaller than m. Since m and n are usually roughly the same size, k is practically insignificant compared to m × n . Convolution is thus dramatically more efficient than dense matrix multiplication in terms of the memory requirements and statistical efficiency.  \n",
+    "Each member of the kernel is used at every position of the input. The parameter sharing used by the convolution operation means that rather than learning a separate set of parameters for every location, we learn only one set. This further reduce the storage requirements of the model to $k$ parameters. Recall that $k$ is usually several orders of magnitude smaller than $m$. Since $m$ and $n$ are usually roughly the same size, $k$ is practically insignificant compared to $m \\times n$. Convolution is thus dramatically more efficient than dense matrix multiplication in terms of the memory requirements and statistical efficiency.  \n",
     "\n",
     "\n",
     "#### 2.3 Equivariant representations\n",
     "\n",
     "Parameter sharing causes the layer to have **equivariance to translation**. To say a function is equivariant means that if the input changes, the output changes in the same way.\n",
     "\n",
-    "When processing time-series data, this means that convolution produces a sort of timeline that shows when different features appear in the input. If we move an event later in time in the input, the exact same representation of it will appear in the output, just later. Similarly with images, convolution creates a 2-D map of where certain features appear in the input. If we move the object in the input, its representation will move the same amount in the output. This is useful\n",
-    "for when we know that some function of a small number of neighboring pixels is useful when applied to multiple input locations."
+    "When processing time-series data, this means that convolution produces a sort of timeline that shows when different features appear in the input. If we move an event later in time in the input, the exact same representation of it will appear in the output, just later. Similarly with images, convolution creates a 2-D map of where certain features appear in the input. If we move the object in the input, its representation will move the same amount in the output. This is useful for when we know that some function of a small number of neighboring pixels is useful when applied to multiple input locations."
    ]
   },
   {
@@ -278,15 +268,15 @@
     "## 3. The convolution operation\n",
     "\n",
     "### 3.1 Summary and intuition\n",
-    "The CONV layer's parameters consist of a set of learnable filters. Every filter is small spatially (along width and height), but extends through the full depth of the input volume. For example, a typical filter on a first layer of a ConvNet might have size 5x5x3 (i.e. 5 pixels width and height, and 3 because images have depth 3, the color channels). \n",
+    "The convolutional layer's parameters consist of a set of learnable filters. Every filter is small spatially (along width and height), but extends through the full depth of the input volume. For example, a typical filter on a first layer of a ConvNet might have size 5x5x3 (i.e. 5 pixels width and height, and 3 because images have depth 3, the color channels). \n",
     "\n",
     "* During the forward pass, we slide (more precisely, convolve) each filter across the width and height of the input volume and compute dot products between the entries of the filter and the input at any position. Intuitively, a convolution can be thought as a sliding (weigthed) average.   \n",
     "\n",
     "* As we slide the filter over the width and height of the input volume we will produce a 2-dimensional activation map that gives the responses of that filter at every spatial position. Intuitively, the network will learn filters that activate when they see some type of visual feature such as an edge of some orientation or a blotch of some color on the first layer, or eventually entire honeycomb or wheel-like patterns on higher layers of the network. \n",
     "\n",
-    "* Now, we will have an entire set of filters in each CONV layer (e.g. 12 filters), and each of them will produce a separate 2-dimensional activation map. We will stack these activation maps along the depth dimension and produce the output volume.\n",
+    "* At this stage, we have an entire set of filters in each convolutional layer (e.g. 12 filters), and each of them produce a separate 2-dimensional activation map. We stack these activation maps along the depth dimension and produce the output volume.\n",
     "\n",
-    "Below, you can see a representation on how the convolution is performed.\n",
+    "Below, you can see a representation on how the convolution operation is performed.\n",
     "![AnimationConvolution](padding_strides.gif \"convolution\")\n",
     "\n",
     "Animation from: https://github.com/vdumoulin/conv_arithmetic/blob/master/gif/padding_strides.gif"
@@ -297,9 +287,9 @@
    "metadata": {},
    "source": [
     "### 3.2 Mathematical formulation - from Ref. [1]\n",
-    "Suppose we are tracking the location of a spaceship with a laser sensor. Our laser sensor provides a single output $x(t)$, the position of the spaceship at time $t$.\n",
     "\n",
-    "Now suppose that our laser sensor is somewhat noisy. To obtain a less noisy estimate of the spaceship’s position, we would like to average several measurements. Of course, more recent measurements are more relevant, so we will want this to be a weighted average that gives more weight to recent measurements. We can do this with a weighting function $w(a)$, where a is the age of a measurement.    \n",
+    "#### Main idea\n",
+    "Suppose we are tracking the location of a spaceship with a laser sensor. Our laser sensor provides a single output $x(t)$, the position of the spaceship at time $t$. Now suppose that our laser sensor is somewhat noisy. To obtain a less noisy estimate of the spaceship’s position, we would like to average several measurements. Of course, more recent measurements are more relevant, so we will want this to be a weighted average that gives more weight to recent measurements. We can do this with a weighting function $w(a)$, where $a$ is the age of a measurement.    \n",
     "If we apply such a weighted average operation at every moment, we obtain a new function $s$ providing a smoothed estimate of the position of the spaceship:    \n",
     "\n",
     "$s(t) = \\int x(a)w(t− a)da$\n",
@@ -340,7 +330,7 @@
     "## 3.3 Examples\n",
     "\n",
     "### 3.3.1 Example: computing output value of a discrete convolution (from Ref. [3])\n",
-    "We present below the calculation of the discrete convolution of a 3x3 kernel $K_{\\rm ex}$ with no padding and stride 1:   \n",
+    "We present below the calculation of the discrete convolution of a 3x3 kernel $K_{\\rm ex}$ (with no padding and stride 1):   \n",
     "$K_{\\rm ex} = \\begin{pmatrix}\n",
     "0 & 1 & 2 \\\\  \n",
     "2 & 2 & 0 \\\\  \n",
@@ -366,34 +356,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Retrieving picture of Max Planck. Saving image to './img_max_planck.jpg'.\n",
-      "Retrieving picture of Berlin landscape. Saving image to './img_berlin_landscape.jpg'.\n",
-      "Done.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
+    "# this can be skipped because the images are already saved on the server\n",
+    "\n",
     "# retrieve image of Max Planck from wikipedia\n",
-    "print(\"Retrieving picture of Max Planck. Saving image to './img_max_planck.jpg'.\")\n",
-    "urllib.request.urlretrieve(\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c7/Max_Planck_1933.jpg/220px-Max_Planck_1933.jpg\", \"./img_max_planck.jpg\")\n",
+    "#print(\"Retrieving picture of Max Planck. Saving image to './img_max_planck.jpg'.\")\n",
+    "#urllib.request.urlretrieve(\"https://upload.wikimedia.org/wikipedia/commons/thumb/c/c7/Max_Planck_1933.jpg/220px-Max_Planck_1933.jpg\", \"./img_max_planck.jpg\")\n",
     "\n",
     "# retrive a picture of Berlin\n",
-    "print(\"Retrieving picture of Berlin landscape. Saving image to './img_berlin_landscape.jpg'.\")\n",
-    "urllib.request.urlretrieve(\"http://vivalifestyleandtravel.com/images/cache/c-1509326560-44562570.jpg\", \"./img_berlin_landscape.jpg\")\n",
+    "#print(\"Retrieving picture of Berlin landscape. Saving image to './img_berlin_landscape.jpg'.\")\n",
+    "#urllib.request.urlretrieve(\"http://vivalifestyleandtravel.com/images/cache/c-1509326560-44562570.jpg\", \"./img_berlin_landscape.jpg\")\n",
     "\n",
-    "print(\"Done.\")"
+    "#print(\"Done.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We define a function to display images in a single figure; it is not important for the purpose of this tutorial to understand this function implementation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "code_folding": [
      7
@@ -438,7 +427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -521,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -568,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -585,7 +574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -616,7 +605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -656,7 +645,9 @@
     "4. highlighting edges\n",
     "5. embossing (i.e. raising the pattern against the background)\n",
     "\n",
-    "As you can see above, the effect are similar for both pictures, and it is defined by the kernel with which the image is convolved."
+    "As you can see above, the effect are similar for both pictures, and it is defined by the kernel with which the image is convolved.\n",
+    "\n",
+    "In the case of **convolutional neural network**, the **kernels** will not be the one reported above, but they are going to be **learned by the network** from the data (by minimizing the classification error). "
    ]
   },
   {
@@ -697,7 +688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 10,
    "metadata": {
     "code_folding": []
    },
@@ -756,7 +747,7 @@
     "\n",
     "For more information on Keras, please visit https://keras.io/\n",
     "\n",
-    "We start with defining the architecture (i.e. the shape) of the network. We use two convolutional layers, one max pooling, and one fully connected layer. There is no particular reason behind this choice, and other - better performing - choices are possible."
+    "We start by defining the architecture (i.e. the shape) of the network. We use two convolutional layers, one max pooling, and one fully connected layer. There is no particular reason behind this choice, and other - better performing - choices are possible."
    ]
   },
   {
@@ -818,7 +809,7 @@
     "\n",
     "An **epoch** is a single step in training a neural network; one epoch is completed when the neural network has seen every training sample once. \n",
     "\n",
-    "<span style=\"color:red\"> **Run the cell below to start training your first convolutional neural network. The full optimization should take approximately 7 minutes; please take this time to read carefully the materials above, and maybe check out some external references.**</span>."
+    "<span style=\"color:red\"> **Run the cell below to start training your first convolutional neural network. The full optimization should take approximately 7 minutes (~1 min per epoch); please take this time to read carefully the materials above, and maybe check out some external references.**</span>."
    ]
   },
   {
@@ -832,21 +823,21 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/7\n",
-      "60000/60000 [==============================] - 67s 1ms/step - loss: 0.1929 - acc: 0.9408 - val_loss: 0.0693 - val_acc: 0.9759\n",
+      "60000/60000 [==============================] - 70s 1ms/step - loss: 0.2028 - acc: 0.9379 - val_loss: 0.0561 - val_acc: 0.9829\n",
       "Epoch 2/7\n",
-      "60000/60000 [==============================] - 69s 1ms/step - loss: 0.0480 - acc: 0.9855 - val_loss: 0.0421 - val_acc: 0.9870\n",
+      "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0472 - acc: 0.9859 - val_loss: 0.0360 - val_acc: 0.9881\n",
       "Epoch 3/7\n",
-      "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0294 - acc: 0.9907 - val_loss: 0.0298 - val_acc: 0.9886\n",
+      "60000/60000 [==============================] - 66s 1ms/step - loss: 0.0306 - acc: 0.9904 - val_loss: 0.0328 - val_acc: 0.9891\n",
       "Epoch 4/7\n",
-      "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0203 - acc: 0.9941 - val_loss: 0.0309 - val_acc: 0.9893\n",
+      "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0207 - acc: 0.9936 - val_loss: 0.0304 - val_acc: 0.9904\n",
       "Epoch 5/7\n",
-      "60000/60000 [==============================] - 73s 1ms/step - loss: 0.0140 - acc: 0.9959 - val_loss: 0.0302 - val_acc: 0.9894\n",
+      "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0147 - acc: 0.9953 - val_loss: 0.0298 - val_acc: 0.9892\n",
       "Epoch 6/7\n",
-      "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0091 - acc: 0.9974 - val_loss: 0.0294 - val_acc: 0.9912\n",
+      "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0097 - acc: 0.9973 - val_loss: 0.0317 - val_acc: 0.9913\n",
       "Epoch 7/7\n",
-      "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0061 - acc: 0.9984 - val_loss: 0.0379 - val_acc: 0.9905\n",
-      "Test loss: 0.03785699036198257\n",
-      "Test accuracy: 0.9905\n"
+      "60000/60000 [==============================] - 67s 1ms/step - loss: 0.0066 - acc: 0.9983 - val_loss: 0.0328 - val_acc: 0.9911\n",
+      "Test loss: 0.03277341276791031\n",
+      "Test accuracy: 0.9911\n"
      ]
     }
    ],
@@ -881,7 +872,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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tOQhAeqpx8qRcrvnwNBZPG8eiqeMozs2Mc8QiiUVDlCehkXDe+5s/6L762vv7eO39+kNDYRSMDbqvdt1rmDs5l8x0dV8V6YmGKJek4e68W3fgUKvhle372LK7CfdgoLyTinO4bOGkIDlMHc+U8VnqvirgDk27Yf8OqH8//Lvjg/mDe2HUaBg1BkaNDf92nx4Do7J7WRdOZ4yF9DGQktxlTCULGXbcnY1VDTzzdu2hITO6XriTk5nGomnjuHj+pEPdV/VCnRGqswMaqz+4+NfvgP3vRyWEHdDZevg+mbmQOxXGTYfJi6GjBdoOQFsTNO+D/ZUfzLc2QeTIFz31Kn30UZLKUZJRRi/JKH0MpA6f/2/HNBIzWwL8EEgFfu7ud3ZbPw1YCRQCe4Fr3L0yXPdtYHm46bfc/T8HEkNX/X+kSOSy4vt7DvK79Tt5dP1O3qkNhsuYVTiGC0qLDpWVZhWOJUXdV0eGjtbg4h3dKoiebtgJ3u3VrWMKIW8qFM2FE5cGiSFvCuROCf5m5h5jDG1B4mg7EPWJnm/sZXk439IADdWHr++ewI4mLTMqgXRv8UTNj58JZdce27kdo5glCzNLBe4BLgAqgZfN7DF33xS12V3AA+5+v5mdC9wB/K2ZLQcWAQuADOAZM1vl7g3HEkNmZiZ79uwhPz9/RCQMd2fPnj1kZibOzdu9B9p4fEMVj66v4pXt+wA4bfp4vnDmDJacXKzuq8mstfHwslD3MlHTrsO3txTInhRc9Kd+OPibNzVMBFMhtwTSs4Y2xrRRkDYeRo8fumN2tndLKo1HSUZhC6f78qbdh89PnJ+4yQI4Ddjq7tsAzOxB4FIgOlmUAl8Np9cAj0Ytf8bdO4AOM3sdWAL85lgCKCkpobKyktra2oGfRYLJzMykpKQk3mEcVXNbJ09squF366t49u1aOiLOh4rG8k9LTuSSUyZRMm50vEOUwXIPyjqHksD7RyaG5n2H75OSHlzw86bC7AuObBXkTIbUJHhiPjUdsvKCz1CJRPreZpBimSwmAzui5iuB07tt8zpwJUGp6nIg28zyw+W3mtn3gdHAORyeZAAwsxXACoCpU6ceEUB6ejozZswY9InI4HV0RvjzO3v43Ws7Wb2xhgNtnRTnZHLdmTO4dMFk5kzMHhGtv6QRiQS//Lu3CqKn2w8cvk/6mA8u/iVlH7QIuloHY4uS/iZxzByH/91imSx6+pffvaB+E3C3mX0eeBbYCXS4+xNmdirwAlAL/AXo6LYv7n4vcC8EXWeHLnQZCu7Ohsr9PLp+J79/vZq6playM9O4eP4kLls4mdNnjE+O+w+RCLQf7L1u3XYAOtvAI4AHv7o9EvU3ErUuat7pY33UcXpdF+nhe7utp/vyo8TV2QGNVcG9hM7DX+FKZl6QDPJPgJnnHN4qyJ0alHL0gyBhxTJZVAJTouZLgKroDdy9CrgCwMzGAle6+/5w3e3A7eG6/wC2xDBWGULv1R3g0fU7+d36Kt6tO8Co1BTOPWkCly2cxNknTojvMw+RSPCLt6c68BHTR1sXTrc2HfkLOlYsJfhgH0xbSnABjv7b6/quddbzusP27f43XJeSChNPgZMuPrxVkDcl6NUjSSuWyeJlYLaZzSBoMXwG+Gz0BmZWAOx19whwC0HPqK6b43nuvsfM5gPzgSdiGKsMUl1TK394PbhRvX5HPWZw+ozx3HDWTJbMnUhu1gBqzZ0dwYW4+0W6tfuF/Bgu9O0H+//9KWk9dHscE1wce+2TP7aH3iujIXVUDxfl/ly0u9bpF7nEV8yShbt3mNmXgdUEXWdXuvtGM7sNWOfujwFnA3eYmROUob4U7p4OPBfWsBsIutQeUYaS+DrQ2sGTm3bxyGs7eX5rHZ0RZ87EHG5ZehKXLJjExNxeeqa4Q8t+aKgKPo3h34ad4d/qYLqlvv/BpGb0fPEeXRDVn/1o/d27X+DHBj1hRARI8uE+ZOi1d0Z4fksdj67fyRMbd9Hc3snkvCwuWTCJyxZM5sQJY+Bg3QeJoCsBNFZHJYOqHn7hW9BHPmdS0OslZxKMKQgu2kdc5Ht4eEkXdpEB0XAfMmTcndd21PP7V7fz0oY3yWyuYWZGA9+d0sHivIMU215sZxW8FbYKuj/1mpIG2RODBFA0F2ZfFCaFqM/YYl3wRYYxJQsJtDdHtQCCFsH+XdvZvfNdOup3Mrmzlq+zn1Tz4DFJCLor7M6CnIlBa2DqR4ILf3a3RDCmMLgxKiIJS8liJGhpOEpJKJxu3nvEbuajifh42rOKOTh5Lh1TZ5E6Pnw4qqulkDVON19FRgAli2TSUA2v/N/goajo+wNtTUduO6YwuODnltA+qYzNzTn8eXc6z9WMosrHM37iNJYsPIFPnDKJopzEGT5ERGJDySJZbH0KfrsiaCF0/eqfMAdmnXfk/YHsibSRzrNv1/Lo+p38ad0uWtojTBmfxWVnT+afF0zihAnqMy8iH1CySHSdHbD2Dnjue0FyuLYcCk/scVN355Xt+3j0mbd5fEM1+w62M250Op9aPIXLFk5i0dRxGnJDRHqkZJHIGqrh4etg+59h4d/C0u8ED4B1s2VX46Enqiv3NZOZnsIFpcVcvnASH5tdqHdPi0iflCwSVVfZqf0gXPZTWHD1Yatr9rfw+9ereHT9TjZWNZBicObsQr52wYe48ORivTBIRI6JrhiJprMDnrkTnr0LCk+CT99/qOwUiTgPv1rJI6/t5C/b9uAOp5Tk8o2LS7n4lIlMyNaNahEZGCWLRNJQDQ9fD9ufh4XXwNLvHlZ2+s91O7jltxVMzx/NV86dzaULJjGzcGwcAxaRZKFkkSjeeRoe/mKvZSeAx9ZXMatwDH/62lm6US0iQ0p3Noe7SCc8fTv88opgrKQvrukxUdQ1tfLSu3tYPm+iEoWIDDm1LIazxpqg7PTec7DgGlj2nWDgvB6s3lhDxGHpvInHOUgRGQmULIard9bAb78YvIPhsp/Ags8edfPyimpmFozhpGI9TCciQ09lqOEm0glr/hV+eTmMzg/LTkdPFHuaWnlx216WqQQlIjGilsVwEl12OuWzsPyuXstO0Z7YtIvOiLN0XvFxCFJERiIli+Fi29qgt1NrI1z6/8HCv+n3ruUV1UzPH03pxJzYxSciI5rKUPEW6YQ1d8ADlwXDfa9Yc0yJYt+BNl54Z49KUCISU2pZxFPjrmBsp/eeg1OuhuXf61fZKdoTm2rojDjL1AtKRGJIySJeBlF2ivZ4RQ1Tx4/m5EkqQYlI7KgMdbxFOmHtnWHZKQ+++PSAE0X9wTZe2FrH0nnFKkGJSEypZXE8Ne6C314P7z4L8z8TlJ0yBj520xObdtERcZarBCUiMaZkcbxseyboFtvaCJfeAwv+ZtDvrl5VUU3JuCzmTc4doiBFRHqmMlSsRTph7bfhl9Flp2sGnSj2N7fz/NY69YISkeNCLYtYatodtCbefQbmXwXLvz+oslO0Jzftor1TvaBE5PiIacvCzJaY2WYz22pmN/ewfpqZPWVmG8xsrZmVRK37jpltNLM3zexHlmg/n999Fn56Jux4CS65Gy7/P0OWKCAoQU3Oy+KUEpWgRCT2YpYszCwVuAdYCpQCV5tZabfN7gIecPf5wG3AHeG+HwXOAOYDc4FTgbNiFeuQinTCM9+BBy6FjJyg7LTobwdddorW0NLOc1vqWDpXvaBE5PiIZRnqNGCru28DMLMHgUuBTVHblAJfDafXAI+G0w5kAqMAA9KBXTGMdWg07Q5Git22FuZ9Gi7+tyFtTXT506ZdtHVGWDZfJSgROT5iWYaaDOyImq8Ml0V7HbgynL4cyDazfHf/C0HyqA4/q939ze5fYGYrzGydma2rra0d8hM4Ju8+F5Sd3n8RLvkxXHFvTBIFQHlFDRNzM1lQkheT44uIdBfLZNFTfcS7zd8EnGVmrxGUmXYCHWZ2AjAHKCFIMOea2cePOJj7ve5e5u5lhYWFQxt9f0U64ZnvwgOXQEY2XP8ULPrckJadojW2tPPsllqWzp1ISopKUCJyfMSyDFUJTImaLwGqojdw9yrgCgAzGwtc6e77zWwF8KK7N4XrVgEfBp6NYbzHrqk2LDutgXmfCstOsX350NNv7aatI8Ly+RqOXESOn1i2LF4GZpvZDDMbBXwGeCx6AzMrMLOuGG4BVobT7xO0ONLMLJ2g1XFEGSquDpWd/gKf+BFc8bOYJwqAxzdUU5yTycIp42L+XSIiXWKWLNy9A/gysJrgQv8bd99oZreZ2SXhZmcDm83sbaAIuD1c/hDwDlBBcF/jdXf/faxiPSaRSFTZaWxQdlr8dzErO0Vrau1g7du1LJlbrBKUiBxXMX0oz93LgfJuy74RNf0QQWLovl8n8PexjG1AostOcz8Jn/jBcWlNdPmgBKVeUCJyfOkJ7v5673l46Dpo3gef+CEsOj6tiWjlG6qZkJ3B4qkqQYnI8aWxofoSicCzd8H9nwjKTl98ChZ//rgnigOtHazZvJulKkGJSByoZXE0B+qCstM7T8el7BRtzebdtHZEWKqxoEQkDpQsevPen4NXnh7cCxf/IC6tiWjlFdUUjM3g1Onj4xaDiIxcKkN1F4nAc9+D+y+G9NFw/Z+g7Nq4JoqDbR2seauWpXOLSVUJSkTiQC2LaAfq4Lcr4J2n4OQrghvZmfF/t/XazbU0t3eydJ4exBOR+FCy6LL9BXjoC2HZ6d9gcXxbE9Eer6gmf8woTp+RH+9QRGSEUrKIRODP/wZP3w7jpgVlp4nz4x3VIc1tnax5azeXLZysEpSIxI2Sxd5twWtPSy8dNmWnaM+8vZuDbZ0sVy8oEYkjJYuCE+Dvn4XCE4dN2Sna4xU1jB8zitNnqBeUiMRPn72hzOzLZpbcjwxPOGlYJoqW9k6efnMXF51cRFqqOq6JSPz05wpUDLxsZr8J36k9/K6qSeqZt2s50NbJMpWgRCTO+kwW7v51YDZwH/B5YIuZ/auZzYpxbCPeqopqxo1O58Mz1QtKROKrX7UNd3egJvx0AOOAh8zsOzGMbURrae/kT2/u5sLSYtJVghKROOvzBreZfQX4O6AO+Dnwj+7eHr60aAvwT7ENcWR6bksdTa0dLNNw5CIyDPSnN1QBcIW7b49e6O4RM7s4NmHJqopqcrPS+egslaBEJP76U98oB/Z2zZhZtpmdDuDuw+tVp0mitaOTJzft4sLSIpWgRGRY6M+V6CdAU9T8gXCZxMjzW+poVAlKRIaR/iQLC29wA0H5CT3MF1PlFTXkZKZxxqyCeIciIgL0L1lsM7OvmFl6+LkR2BbrwEaqto4IT26q4YLSYkalqQQlIsNDf65GNwAfBXYClcDpwIpYBjWS/fmdOhpaOlg+X8ORi8jw0Wc5yd13A585DrEIUL6hmuyMNM44QSUoERk++vOcRSZwHXAykNm13N2/EMO4RqT2zghPbNrFBaVFZKSlxjscEZFD+lOG+iXB+FAXAc8AJUBjLIMaqV54Zw/7m9tZqrGgRGSY6U+yOMHd/1/ggLvfDywH5sU2rJGpfEM1YzPS+NhslaBEZHjpT7JoD//Wm9lcIBeY3p+Dh6PUbjazrWZ2cw/rp5nZU2a2wczWmllJuPwcM1sf9Wkxs8v6eU4Jqb0zwupNNZw/ZwKZ6SpBicjw0p9kcW/4PouvA48Bm4Bv97WTmaUC9wBLgVLgajMr7bbZXcAD7j4fuA24A8Dd17j7AndfAJwLHASe6N8pJaYXt+2h/qBKUCIyPB31Bnc4WGCDu+8DngVmHsOxTwO2uvu28FgPApcSJJsupcBXw+k1wKM9HOeTwCp3P3gM351wyiuqGTMqlbM+VBjvUEREjnDUlkX4tPaXB3jsycCOqPnKcFm014Erw+nLgWwz6z5y3meAXw8whoTQ0Rlh9cZdnDenSCUoERmW+lOGetLMbjKzKWY2vuvTj/16eqOed5u/CTjLzF4DziJ48K/j0AHMJhLcTF/d4xeYrTCzdWa2rra2th8hDU8vvbuXvQfaWDZPD+KJyPDUnzGeup6n+FLUMqfvklQlMCVqvgSoit7A3auAKwDMbCxwpbvvj9rk08Aj7t5OD9z9XuBegLKysu6JKGE8XlHN6FGpnH3ihHiHIiLSo/5cuBvgAAARmElEQVQ8wT1jgMd+GZhtZjMIWgyfAT4bvYGZFQB7w3LXLcDKbse4OlyetDojzuo3ajjnJPWCEpHhqz9PcH+up+Xu/sDR9nP3DjP7MkEJKRVY6e4bzew2YJ27PwacDdxhZk5wA/1Q68XMphO0TJ7p15kkqJfe3cOeA20sVy8oERnG+lOGOjVqOhM4D3gVOGqyAHD3coKXJ0Uv+0bU9EPAQ73s+x5H3hBPOuUV1WSlp3KOSlAiMoz1pwz1D9HzZpZLMASIDFJnxPnjG7s456RCskapBCUiw9dAXphwEJg91IGMRC+/t5e6plaWqQQlIsNcf+5Z/J4PurymEDxI95tYBjVSrKqoJjM9RSUoERn2+nPP4q6o6Q5gu7tXxiieESMScVa9UcPZH5rAmAy9pVZEhrf+XKXeB6rdvQXAzLLMbHp4A1oGaN32fexubGXZfJWgRGT46889i/8CIlHzneEyGYTyimpGpaVw7kkqQYnI8NefZJHm7m1dM+H0qNiFlPyCElQ1Z3+okLEqQYlIAuhPsqg1s0u6ZszsUqAudiElv1ff38euhlaWqwQlIgmiPz9rbwB+ZWZ3h/OVQI9PdUv/lFfUqAQlIgmlPw/lvQN8OBzoz9xd798ehK4S1MdnF5KdmR7vcERE+qXPMpSZ/auZ5bl7k7s3mtk4M/uX4xFcMnptRz3V+1tYPl/DkYtI4ujPPYul7l7fNRO+NW9Z7EJKbqsqqhmVmsJ5c4riHYqISL/1J1mkmllG14yZZQEZR9leeuEePIj3sdkF5KgEJSIJpD/J4t+Bp8zsOjO7DngSuD+2YSWn1yv3s7O+maUaC0pEEkx/bnB/x8w2AOcTvCr1j8C0WAeWjMorqklPNS4oVQlKRBJLf0edrSF4ivtKgvdZvBmziJKUu/P4hmrOPKGA3CyVoEQksfTasjCzDxG8CvVqYA/wnwRdZ885TrEllYqdQQnqxvM1uruIJJ6jlaHeAp4DPuHuWwHM7KvHJaok9HhFNWkpxoUqQYlIAjpaGepKgvLTGjP7mZmdR3DPQo6Ru1NeUc0ZJxSQN1rDaolI4uk1Wbj7I+5+FXASsBb4KlBkZj8xswuPU3xJYWNVAzv2NrNsnh7EE5HE1OcNbnc/4O6/cveLgRJgPXBzzCNLIo9XVJOaYlxYqmQhIonpmN7B7e573f3/uPu5sQoo2bg7qyqq+eisfMaNUQlKRBLTMSULOXabqht4b89BlulBPBFJYEoWMVYelqAuOlklKBFJXEoWMRT0gqrhwzPHM14lKBFJYEoWMfRWTSPv1h1QCUpEEl5Mk4WZLTGzzWa21cyO6EFlZtPM7Ckz22Bma82sJGrdVDN7wszeNLNNZjY9lrHGQnlFNSmGSlAikvBilizMLBW4B1gKlAJXm1lpt83uAh5w9/nAbcAdUeseAL7r7nOA04DdsYo1FtydxyuqOX1GPgVjNaK7iCS2WLYsTgO2uvs2d28DHgQu7bZNKfBUOL2ma32YVNLc/UmA8C19B2MY65B7e1cT22oPsGy+SlAikvhimSwmAzui5ivDZdFeJxhWBOByINvM8oEPAfVm9lsze83Mvhu2VA5jZivMbJ2ZrautrY3BKQxceUU1ZnDRyRoLSkQSXyyTRU/jSHm3+ZuAs8zsNeAsYCfQQTDA4cfC9acCM4HPH3Ew93vdvczdywoLC4cw9MErr6jmtOnjmZCdGe9QREQGLZbJohKYEjVfAlRFb+DuVe5+hbsvBP6fcNn+cN/XwhJWB/AosCiGsQ6pLbsa2bK7ieUqQYlIkohlsngZmG1mM8xsFMG7MR6L3sDMCsysK4ZbgJVR+44zs67mwrnAphjGOqTKK2owgyXqBSUiSSJmySJsEXwZWE3wZr3fuPtGM7vNzC4JNzsb2GxmbwNFwO3hvp0EJainzKyCoKT1s1jFOtTKK6o5ddp4JuSoBCUiyaHPd3APhruXA+Xdln0javoh4KFe9n0SmB/L+GJh6+4mNu9q5NZPdO8lLCKSuPQE9xBbVVENwNK5ul8hIslDyWKIPV5RTdm0cRTnqgQlIslDyWIIbatt4q2aRpZqLCgRSTJKFkNo1Rs1AHp9qogkHSWLIfT4hmoWTc1jYm5WvEMRERlSShZD5L26A2yqbtBw5CKSlJQshkj5G2EvKCULEUlCShZDpLyimlOm5DE5TyUoEUk+ShZD4P09B3ljZwPLdWNbRJKUksUQOFSC0oN4IpKklCyGwKqKauaX5DJl/Oh4hyIiEhNKFoO0Y+9BXq/cr15QIpLUlCwGaVVYglqmEpSIJDEli0Eqr6hh7uQcpuarBCUiyUvJYhB21jezfke9SlAikvSULAahazhylaBEJNkpWQxCeUU1pRNzmF4wJt6hiIjElJLFAFXVN/Pq+/Usn69WhYgkPyWLAfpjOBz50rl6altEkp+SxQCVV1RzUnE2MwvHxjsUEZGYU7IYgJr9Lazbvo/l6gUlIiOEksUA/FHDkYvICKNkMQDlFTWcWJTNCRNUghKRkUHJ4hjtbmjh5e17WarhyEVkBFGyOEZ/3FiDO7pfISIjSkyThZktMbPNZrbVzG7uYf00M3vKzDaY2VozK4la12lm68PPY7GM81g8vqGa2RPGMrsoO96hiIgcNzFLFmaWCtwDLAVKgavNrLTbZncBD7j7fOA24I6odc3uviD8XBKrOI9FbWMrf31vr25si8iIE8uWxWnAVnff5u5twIPApd22KQWeCqfX9LB+WFEJSkRGqlgmi8nAjqj5ynBZtNeBK8Ppy4FsM8sP5zPNbJ2ZvWhml/X0BWa2ItxmXW1t7VDG3qPyDdXMKhzDh4rUC0pERpZYJgvrYZl3m78JOMvMXgPOAnYCHeG6qe5eBnwW+IGZzTriYO73unuZu5cVFhYOYehHqmtq5aV397Bs3kTMejo1EZHklRbDY1cCU6LmS4Cq6A3cvQq4AsDMxgJXuvv+qHW4+zYzWwssBN6JYbxHtXpjDRFH764QkREpli2Ll4HZZjbDzEYBnwEO69VkZgVm1hXDLcDKcPk4M8vo2gY4A9gUw1j7VF5RzYyCMZxUrF5QIjLyxCxZuHsH8GVgNfAm8Bt332hmt5lZV++ms4HNZvY2UATcHi6fA6wzs9cJbnzf6e5xSxZ7mlp5cdtels0rVglKREakWJahcPdyoLzbsm9ETT8EPNTDfi8A82IZ27F4YtMuOiOuEpSIjFh6grsfyiuqmZY/mtKJOfEORUQkLpQs+rDvQBsvvKNeUCIysilZ9OGJTTV0RlwP4onIiKZk0YfyihqmjM/i5EkqQYnIyKVkcRT1B9v489Y6laBEZMRTsjiKJzbtoiPiLJurEpSIjGxKFkexqqKaknFZzC/JjXcoIiJxpWTRi/3N7TyvEpSICKBk0as/bdpFe6ezdK5enyoiomTRi/KKaiblZrJgSl68QxERiTslix40tLTz3JY6lqoEJSICKFn06Kk3d9HWGdFYUCIiISWLHjy+oYaJuZksVAlKRARQsjhCY0s7z26pZcncYlJSVIISEQEliyM8/dZu2joiGgtKRCSKkkU3j2+opigng0VTx8U7FBGRYUPJIkpTawdr365l6dyJKkGJiERRsojSVYJSLygRkcMpWUQp31BNYXYGi6epBCUiEk3JInSgtYM1m3ezdG4xqSpBiYgcRskitGbzblpVghIR6ZGSRai8opqCsRmcOn18vEMRERl2lCyAg20drHmrliVzi1SCEhHpgZIFsHZzLc3tnSpBiYj0QsmCoASVP2YUp6kEJSLSo5gmCzNbYmabzWyrmd3cw/ppZvaUmW0ws7VmVtJtfY6Z7TSzu2MVY0t7J0+/tZuL5haTlqrcKSLSk5hdHc0sFbgHWAqUAlebWWm3ze4CHnD3+cBtwB3d1n8LeCZWMULw+tTz5xRxySmTYvk1IiIJLZY/pU8Dtrr7NndvAx4ELu22TSnwVDi9Jnq9mS0GioAnYhgjRTmZ/OjqhXx4Zn4sv0ZEJKHFMllMBnZEzVeGy6K9DlwZTl8OZJtZvpmlAN8D/jGG8YmISD/FMln01AfVu83fBJxlZq8BZwE7gQ7gvwPl7r6DozCzFWa2zszW1dbWDkXMIiLSg7QYHrsSmBI1XwJURW/g7lXAFQBmNha40t33m9lHgI+Z2X8HxgKjzKzJ3W/utv+9wL0AZWVl3RORiIgMkVgmi5eB2WY2g6DF8Bngs9EbmFkBsNfdI8AtwEoAd/+bqG0+D5R1TxQiInL8xKwM5e4dwJeB1cCbwG/cfaOZ3WZml4SbnQ1sNrO3CW5m3x6reEREZODMPTmqN2VlZb5u3bp4hyEiklDM7BV3L+trOz2FJiIifVKyEBGRPiVNGcrMaoHtgzhEAVA3ROHEU7KcB+hchqtkOZdkOQ8Y3LlMc/fCvjZKmmQxWGa2rj91u+EuWc4DdC7DVbKcS7KcBxyfc1EZSkRE+qRkISIifVKy+MC98Q5giCTLeYDOZbhKlnNJlvOA43AuumchIiJ9UstCRET6pGQhIiJ9GvHJoq9XvyYKM1tpZrvN7I14xzJYZjbFzNaY2ZtmttHMbox3TANhZplm9lczez08j2/GO6bBMrNUM3vNzP4Q71gGw8zeM7MKM1tvZgk9TpCZ5ZnZQ2b2Vvhv5iMx+Z6RfM8ifPXr28AFBEOqvwxc7e6b4hrYAJjZx4EmgtfUzo13PINhZhOBie7+qpllA68AlyXafxczM2CMuzeZWTrwPHCju78Y59AGzMy+BpQBOe5+cbzjGSgze49gNOuEfyjPzO4HnnP3n5vZKGC0u9cP9feM9JZFf179mhDc/Vlgb7zjGAruXu3ur4bTjQSjFnd/y+Kw54GmcDY9/CTsrzMzKwGWAz+PdywSMLMc4OPAfQDu3haLRAFKFv159avEkZlNBxYCL8U3koEJyzbrgd3Ak+6ekOcR+gHwT0Ak3oEMAQeeMLNXzGxFvIMZhJlALfCLsDz4czMbE4svGunJoj+vfpU4Cd+e+DDwP9y9Id7xDIS7d7r7AoI3RZ5mZglZIjSzi4Hd7v5KvGMZIme4+yJgKfClsIybiNKARcBP3H0hcACIyb3XkZ4s+nz1q8RHWON/GPiVu/823vEMVlgaWAssiXMoA3UGcElY638QONfM/j2+IQ1c+Epn3H038AhBSToRVQKVUS3WhwiSx5Ab6cni0KtfwxtDnwEei3NMI154Y/g+4E13/3684xkoMys0s7xwOgs4H3grvlENjLvf4u4l7j6d4N/J0+5+TZzDGhAzGxN2nCAs2VwIJGQvQnevAXaY2YnhovOAmHQEieU7uIc9d+8ws65Xv6YCK919Y5zDGhAz+zXBa2oLzKwSuNXd74tvVAN2BvC3QEVY7wf43+5eHseYBmIicH/Y6y6F4NXCCd3lNEkUAY8Ev0lIA/7D3f8Y35AG5R+AX4U/eLcB18biS0Z011kREemfkV6GEhGRflCyEBGRPilZiIhIn5QsRESkT0oWIiLSJyULkWNgZp3hSKVdnyF7WtbMpifDqMGSnEb0cxYiA9AcDt8hMqKoZSEyBML3I3w7fH/FX83shHD5NDN7ysw2hH+nhsuLzOyR8F0Xr5vZR8NDpZrZz8L3XzwRPvktEndKFiLHJqtbGeqqqHUN7n4acDfBCK2E0w+4+3zgV8CPwuU/Ap5x91MIxvLpGjlgNnCPu58M1ANXxvh8RPpFT3CLHAMza3L3sT0sfw841923hYMg1rh7vpnVEbzIqT1cXu3uBWZWC5S4e2vUMaYTDGM+O5z/X0C6u/9L7M9M5OjUshAZOt7LdG/b9KQ1aroT3VeUYULJQmToXBX19y/h9AsEo7QC/A3Bq1UBngL+Gxx6QVLO8QpSZCD0q0Xk2GRFjYQL8Ed37+o+m2FmLxH8CLs6XPYVYKWZ/SPBG826RgS9EbjXzK4jaEH8N6A65tGLDJDuWYgMgfCeRZm718U7FpFYUBlKRET6pJaFiIj0SS0LERHpk5KFiIj0SclCRET6pGQhIiJ9UrIQEZE+/f9T1SLJCDMX3QAAAABJRU5ErkJggg==\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -910,7 +901,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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7+1qgpTcHtODB6BcDT4dFjwFXDl6VB8fC4gIAlq3T6C0RGfliGSRTgLKo5fKwrLfSzazUzN4ys9awGA/sd/emfh4zLgpzMzh9Wp7ucheRUSGWQWKdlPVlRsNp7l4CfBG418xO6MsxzWxxGESlA30KYn8sLC5kQ0UN26sOx/27RUTiKZZBUg5MjVouAnb2dmd33xm+bwNeAU4DqoA8M2t9smOXx3T3h9y9xN1L8vPz+177AVpQHIzeUqe7iIx0sQySlcCscJRVKnAtsLSHfQAws7FmlhZ+ngCcC2zwYI72PwGtI7xuBH4/6DUfBFPyMpg3NY9l6xQkIjKyxSxIwn6MrwLLgY3AU+6+3szuNLPPApjZmWZWDlwN/KeZrQ93PwUoNbO/EgTH3e6+IVz3beB2M9tC0GfycKzOYaAWFRey7qMaduytTXRVRERixkbDg5hKSkq8tLQ07t9bVl3Lef/nT3x7/sncduEJcf9+EZGBMLNVYV91t3RnewxNHTeGjxXlqnlLREY0BUmMLSwuZG35Acqq1bwlIiOTgiTGFoajtzQjsIiMVAqSGJs6bgzFU3IVJCIyYilI4mBhcSF/LT9A+T41b4nIyKMgiYO2ubf05EQRGYEUJHFw3PhM5kzO0V3uIjIiKUjiZGFxIWvK9vPR/iOJroqIyKBSkMTJomI9OVFERiYFSZxMn5DJ7MIcjd4SkRFHQRJHC4sLWL1jPzvVvCUiI4iCJI5ab078g56cKCIjiIIkjo7Pz+Lkgmw1b4nIiKIgibOFxYWUfriPXQfqEl0VEZFBoSCJs9bmLc0ILCIjhYIkzmZOzOKkSdm6y11ERgwFSQIsLC5k5YfV7KlR85aIDH8KkgRYWFyAOyzT6C0RGQFiGiRmNt/MNpnZFjO7o5P155vZajNrMrOrosrnmdmbZrbezNaa2TVR635hZh+Y2ZrwNS+W5xALsyZlM2tilkZviciIELMgMbMI8ACwAJgNXGdmsztstgO4CXiiQ3ktcIO7zwHmA/eaWV7U+m+5+7zwtSYmJxBjC4sLWbG9mj0H1bwlIsNbLK9IzgK2uPs2d28AlgBXRG/g7tvdfS3Q0qH8fXffHH7eCewB8mNY17hbdGoh7rBczVsiMszFMkimAGVRy+VhWZ+Y2VlAKrA1qviusMnrR2aW1sV+i82s1MxKKysr+/q1MTdrYhYn5GdqankRGfZiGSTWSZn36QBmhcAvgZvdvfWq5TvAycCZwDjg253t6+4PuXuJu5fk5w+9ixkzY1FxISs+qKbyYH2iqyMi0m+xDJJyYGrUchGws7c7m1kO8BzwXXd/q7Xc3Ss8UA88StCENiwtPLWQFofl69W8JSLDVyyDZCUwy8xmmFkqcC2wtDc7hts/Czzu7r/tsK4wfDfgSmDdoNY6jk6alM3xEzI1ektEhrWYBYm7NwFfBZYDG4Gn3H29md1pZp8FMLMzzawcuBr4TzNbH+7+BeB84KZOhvn+2szeBd4FJgA/jNU5xJqZsbC4kLe27WXvITVvicjwZO596rYYlkpKSry0tDTR1ejUhp01LLzvNf7lb4r54senJbo6IiJtzGyVu5f0tJ3ubE+wUwqzmaHmLREZxhQkCWZmLJhbwJvb9lJ9uCHR1RER6TMFyRCwsLiQ5hbnBY3eEpFhSEEyBMyZnMNx48fo5kQRGZYUJENA0LxVyBtb97JPzVsiMswoSIaIRa3NWxvUvCUiw4uCZIiYOyWHqeMyeF5PThSRYUZBMkS03pz4ly1V7K9V85aIDB8KkiFk4dxCmlqcFzbsTnRVRER6TUEyhJxalEvR2AyWafSWiAwjCpIhpLV56/UtVRw40pjo6oiI9IqCZIhZWFxIY7Pzopq3RGSYUJAMMR8rymVKXobm3hKRYaNXQWJmJ7Q+0tbMLjSzr5tZXmyrNjq1zr312uZKaurUvCUiQ19vr0ieAZrNbCbwMDADeCJmtRrlFp4aNG+9pOYtERkGehskLeGDqv4GuNfd/wEojF21Rrd5RXkU5qareUtEhoXeBkmjmV0H3Aj8d1iWEpsqSVJSMPfWn9+v4qCat0RkiOttkNwMnAPc5e4fmNkM4Fc97WRm881sk5ltMbM7Oll/vpmtNrMmM7uqw7obzWxz+LoxqvwMM3s3POZ94bPbR5xFpxbQ0NzCHzfuSXRVRES61asgcfcN7v51d/+NmY0Fst397u72MbMI8ACwAJgNXGdmsztstgO4iQ79LWY2Dvge8HHgLOB74fcC/AxYDMwKX/N7cw7DzWlTx1KQk66p5UVkyOvtqK1XzCwn/AH/V+BRM/uPHnY7C9ji7tvcvQFYAlwRvYG7b3f3tUBLh30vB15092p33we8CMw3s0Igx93f9OBh848DV/bmHIabpCRj/twCXn2/Us1bIjKk9bZpK9fda4DPAY+6+xnAJT3sMwUoi1ouD8t6o6t9p4Sf+3PMYWfRqYU0NLXw8ntq3hKRoau3QZIcXg18gfbO9p501nfhA9y318c0s8VmVmpmpZWVlb382qHljGljmZidptFbIjKk9TZI7gSWA1vdfaWZHQ9s7mGfcmBq1HIRsLOX39fVvuXh5x6P6e4PuXuJu5fk5+f38muHlmD0VgGvbKrkcH1ToqsjItKp3na2/9bdT3X328Llbe7++R52WwnMMrMZZpYKXAss7WW9lgOXmdnYsJP9MmC5u1cAB83s7HC01g3A73t5zGFpYXEh9WreEpEhrLed7UVm9qyZ7TGz3Wb2jJkVdbdPeAPjVwlCYSPwlLuvN7M7zeyz4XHPNLNy4GrgP81sfbhvNfADgjBaCdwZlgHcBvwc2AJsBZb18ZyHlZLp48hX85aIDGHJvdzuUYIhuleHy9eHZZd2t5O7Pw8836Hsn6I+r+Topqro7R4BHumkvBSY28t6D3uRsHnrqdIyahuaGJPa278yEZH46G0fSb67P+ruTeHrF8Dw7HgYhhbMLaSuUc1bIjI09TZIqszsejOLhK/rgb2xrJi0O2vGOCZkpbHs3V2JroqIyDF6GyRfIRj6uwuoAK4imDZF4iCSZMyfO4mX39vDkYbmRFdHROQovR21tcPdP+vu+e4+0d2vJLg5UeJk4dxCjjQ286dNat4SkaFlIE9IvH3QaiE9OmvGOMZnpmr0logMOQMJkhE56+5QlRxJ4vK5Bbz83h7qGtW8JSJDx0CCpLfTncggWVRcSG1DM6+oeUtEhpBug8TMDppZTSevg8DkONVRQh+fMY5xmak8p9FbIjKEdHt3m7tnx6si0rPkSBKXz5nE0jU7qWtsJj0lkugqiYgMqGlLEmBhcSGHG5p59f3hOaOxiIw8CpJh5uzjxzN2TIpGb4nIkKEgGWZSIklcNruAP27U6C0RGRoUJMPQwlMLOVTfxGubqxJdFRERBclw9IkTxpOboeYtERkaFCTDUNC8NYmXNuymvknNWyKSWAqSYWrhqYUcrG/idTVviUiCKUiGqXNPmEBOejLPqXlLRBJMQTJMpSYncdmcAl5U85aIJFhMg8TM5pvZJjPbYmZ3dLI+zcyeDNe/bWbTw/IvmdmaqFeLmc0L170SHrN13cRYnsNQtrC4gIN1Tfxli5q3RCRxYhYkZhYBHgAWALOB68xsdofNbgH2uftM4EfAPQDu/mt3n+fu84AvA9vdfU3Ufl9qXe/uo3YGw0/OzCc7PZnnNfeWiCRQLK9IzgK2uPs2d28AlgBXdNjmCuCx8PPTwKfMrOP09NcBv4lhPYet1OQkLp09iRfW76KhqSXR1RGRUSqWQTIFKItaLg/LOt3G3ZuAA8D4Dttcw7FB8mjYrPX/dBI8AJjZYjMrNbPSysqROy/VwrmF1NQ18Zetat4SkcSIZZB09gO+4zNMut3GzD4O1Lr7uqj1X3L3YuC88PXlzr7c3R9y9xJ3L8nPz+9bzYeR806cQHZaMss0ektEEiSWQVIOTI1aLgJ2drWNmSUDuUB11Ppr6XA14u4fhe8HgScImtBGrbTkCJfMnsQLG3bT2KzmLRGJv1gGyUpglpnNMLNUglBY2mGbpcCN4eergJfd3QHMLAm4mqBvhbAs2cwmhJ9TgE8D6xjlFhYXsr+2kTe27k10VURkFIpZkIR9Hl8FlgMbgafcfb2Z3Wlmnw03exgYb2ZbgNuB6CHC5wPl7r4tqiwNWG5ma4E1wEfAf8XqHIaL82ZNICstmefXqnlLROKv2yckDpS7Pw8836Hsn6I+1xFcdXS27yvA2R3KDgNnDHpFu/LyXXBkH1z4HcjsOAZg6EhPifCpUyayfMMuftg8l5SI7jMVkfjRT5zuNNZC6SNw32nw5gPQ1JDoGnWptXnrrW1q3hKR+FKQdOfyu+C2v0BRCSz/3/DTs+G958A7Dj5LvAtOzCczNaKp5UUk7hQkPZl4Cnz5d/ClpyEpGZZ8ER7/LOx6N9E1O0p6SoSLT5nE8vW7adLoLRGJIwVJb826NLg6WfBvQYg8eB4s/RocGjoztCwqLqD6cANvf1Dd88YiIoNEQdIXkRT4+GL4+jtw9m2w5gm473R47T+gsS7RtePCkyYyJjWiqeVFJK4UJP2RMRbm/yv8j7dh+ifhj9+HB86E9c8mtP8kPSXCRSdPZPm6XWreEpG4UZAMxISZ8MUlcMPvITUbfnsTPLoAPlqdsCotKi5k7+EGVmxX85aIxIeCZDAcfyHc+hp8+l6o2gz/dRE8eyvUdJwRJvYuOmkiGSkavSUi8aMgGSxJESi5Gb6+Gs79Bqx7Bn5yBrxyDzTUxq0aGakRLj55In9Yt5vmlqE3TFlERh4FyWBLz4VL74S/XxGM9HrlX+D+Elj7FLTEp99iQXEBVYfqWaHRWyISBwqSWBk3A77wONz0PGROgN/9HTx8CZStiPlXX3zyRNJTkli2Ts1bIhJ7CpJYm34u/N0rcOXP4MBH8PCl8PRXYP+OmH3lmNRkLjppIsvW7VLzlojEnIIkHpKSYN4X4Wur4Pz/FUyzcv+Z8Mc7of5gTL5yQXEhlQfrKdXoLRGJMQVJPKVlwcX/CF8thVM+A6/9e9Ahv/qX0NI8qF/1qZMnkpacxLJ1uwb1uCIiHSlIEiFvKnz+53DLS5A3DZZ+FR66ELa/PmhfkZmWzIUn5bNsXQUtat4SkRhSkCTS1DPhlhfh8w9DbTX8YhE8eT1Ub+t5315YWFzI7pp6Vu3YNyjHExHpjIIk0cyg+Cr4Wilc9F3Y8jI88HF44btQd2BAh/7UKZNITU7iOT05UURiKKZBYmbzzWyTmW0xszs6WZ9mZk+G6982s+lh+XQzO2Jma8LXg1H7nGFm74b73GdmFstziJuUDLjgW0GHfPHV8Mb9wYSQKx+G5qZ+HTIrLZkLTsznD+t2qXlLRGImZkFiZhHgAWABMBu4zsxmd9jsFmCfu88EfgTcE7Vuq7vPC1+3RpX/DFgMzApf82N1DgmRUwhX/hQWvwL5J8Fzt8ODn4Qtf+zX4RYVF7Krpo53ytS8JSKxEcsrkrOALe6+zd0bgCXAFR22uQJ4LPz8NPCp7q4wzKwQyHH3N93dgceBKwe/6kPA5Hlw03PBTY2NtfCrz8GvvwCV7/fpMBefMpHUSBLPrdXoLRGJjVgGyRSgLGq5PCzrdBt3bwIOAOPDdTPM7B0ze9XMzovavryHYwJgZovNrNTMSisrKwd2JoliBrOvgK+uDKZd+fAN+Nk5sOzbQed8L+Skp3D+iRM0ektEYiaWQdLZlUXHn2RdbVMBTHP304DbgSfMLKeXxwwK3R9y9xJ3L8nPz+9DtYeg5LRgIsivr4bTrocVD8F9p8FbD0JzY4+7LywupOJAHe+U7Y9DZUVktIllkJQDU6OWi4CO86q3bWNmyUAuUO3u9e6+F8DdVwFbgRPD7Yt6OObIlTURPvNjuPX1oOnrD9+Gn54Dm/7Q7QO1Lpk9iYyUCDc8/DZ3PLOW1Tv24Ql8AJeIjCyxDJKVwCwzm2FmqcC1wNIO2ywFbgw/XwW87O5uZvlhZz1mdjxBp/o2d68ADprZ2WFfyg3A72N4DkPTpDnw5f8PrnsScPjNNfDLv4HdGzrdPCc9hadvO4dFpxby+zU7+dxP3+Dye//Mw69/QPXhhvjWXURGHIvlb6ZmthC4F4gAj7j7XWZ2J1Cdol+vAAAT4ElEQVTq7kvNLB34JXAaUA1c6+7bzOzzwJ1AE9AMfM/d/294zBLgF0AGsAz4mvdwEiUlJV5aWhqTc0y4pgYofRhe+ddg3q4zboKL/jGYcbgTB+sa+e+1FTy5sow1ZftJjSRx6ZxJXFMylU/OnEBS0sgYTS0iA2dmq9y9pMftRkMTx4gOkla11fDK3bDy55CaCed/Ez5+a9C/0oX3dtXw5Moynn3nI/bXNjIlL4OrS4q4umQqU/Iy4lh5ERmKFCRRRkWQtKrcFNwVv/kFGDsdLv1BMEFkN/dt1jc18+KG3Ty5sozXNldhBufNyufaM6dySXh3vIiMPgqSKKMqSFpteQmW/yNUvgfHfRIuvyvooO9BWXUtv11Vzm9Ly6g4UMe4zFQ+d9oUrjlzKrMmZceh4iIyVChIoozKIIFgapXVv4CX74Ij++C4T8CM82H6eVBU0m2zV3OL89rmSp5cWcaLG3bT1OKcPi2Pa8+cxqJTC8lMS47feYhIQihIoozaIGl1ZD+89VN4fzlU/BVwSM6AaWcHwTLjfCicB5HOw6HqUD3Prv6IJSt3sLXyMJmpET7zsclcc+ZU5k3NY6RMdyYiR1OQRBn1QRLtyD7Y/hfY/hp88GfYEw4ZTs0OHgvcGiwT5wRPdozi7qzesY8lK8r477UVHGls5sRJWVxz5jT+5rQpjMtMTcAJiUisKEiiKEi6cWhPe6h88BpUbw3KM8bB9E+GwXIBTJh1VId96zDiJSvL+KuGEYuMSAqSKAqSPjhQHgTKB38OXjXh1GZZBTDjvPYrlrHT23bpbBjxF0qmcnVJEZM1jFgkvlpa4MCO4AblPevh7L+H1DH9OpSCJIqCpJ/cYd8H7aHywZ/hcDgBZt60sOP+/CBgciZT19g+jPj1LcEw4vNn5XONhhGLxEZtNexeHzRRt77v2QgNh9q3ufV1KCju1+EVJFEUJIPEPbhP5YM/wwevBs+Yrwsnghw/K7xaOQ+mn0dZ/Rh+W1rGU6Xl7KqpY3xmKp87PRhGPHOihhGL9EljXTCUPzowdm+AQ1GPh8gYG/RtTpoNE2cHUylNPAXS+v//TUESRUESIy3NsHtd+9XKh2+0/yY0aS7MOJ/m487jL00n8cSa/by0MRhGfMZxY7mmZKqGEYt01NIStAK0BsWe9cF79VbwlmCbSFrw0LtJc8LAmB0ESHZBtzce94eCJIqCJE6aG2HnO+3BUvY2NNWBJUHhPGqnnMufGk7mga0T2FDVrGHEMrodqmwPitb3yveCB9kBYEFfZMfAGHd8l0P1B5uCJIqCJEEa66B8ZfuosPKV0NKEJ6VwaMLHeNPn8qtdx/FW4/FMnzRWw4hlZGqohcqNYWBENU0djnrg3pgJ7UHR+j7x5GDevARSkERRkAwR9Yeg7K32ocYVa8BbaEpKY13kFF6oPZGVVkzBKefwhbOmc+4JGkYsw0hLM1RvO7bzu/oD2p6/l5wRBETHvoysiQmtelcUJFEUJEPUkf1Bv0prU9ie9QAcIoO3m09mQ9rHGD/3Ei684GImj03sb2aDpqUZmhvCV2OwnJQMSZHwPbl9WU19Q5M7HNp9dKf3nvXBQJSmumAbSwqaoNo6vcP3sdODv9thQkESRUEyTByugu2v0bT1Vere/xNZh7YDsM+z2DJmHmNOvJBZZy8iteCUY3/ItrRAS2P7D+jWH9ZNDUf/4D7qc30X5X3dr6fva32vb+8w7Q3rJFz6shxJHtj+xyx3VRb9Sup6n7bz6eq4HcqHQpDWHwqG07b1ZYRXGkeq27fJmtQhMGZD/smQMvzvoVKQRFGQDFM1O6l69yV2/fUFxu95k0KqADgSySYpOY1kmkhqacSa66GlKTZ1iKQGo2QiKeHn1PbPyanHlrV97mKfjvuZhSHYFPVq7rDcWVl3y4193L6T5b4EXqxYh1DquHxU8HQSRL0KsU72g/Ymqv0fttcnJTMYTtuxLyNzfGL+fOJAQRJFQTL8NTe3sOKd1Wx+exkpu97B3aknBUtOITczk3E5WYzPzSI/L4sJuTlEUrr6Id9ZeUowE3LHbYfKb8WJ0NIC3kXYNDd2EV5hIB61X2fH6CbE2vbtab9Owta7C+RuvveY+jYHN9weFRizIe+4Y+afG+l6GyQaxC/DQiSSxDklJZxTUsKRhmY27T7I+p0H2LCzhvU7a3ivvIa6D4LfolMjSZxYkMWcwlxmT85hzuQcTi7MIUv3rPReUhKQFASrSA9i+j/LzOYDPyZ4ZvvP3f3uDuvTgMeBM4C9wDXuvt3MLgXuBlKBBuBb7v5yuM8rQCFwJDzMZe6+J5bnIUNLRmqEeVPzmDc1r62sucX5oOoQ63fWtIXLCxt28WRpGRBcWEwfn8nswpy2cJk9OYeJ2emJOg2RESNmQWJmEeAB4FKgHFhpZkvdfUPUZrcA+9x9ppldC9wDXANUAZ9x951mNhdYDkyJ2u9L7q62KmkTSTJmTsxm5sRsrpgX/FNxd3bV1LUFy4adNaz9aD/PvVvRtl9+dhqzC9uDZc7kXI4bN0bDjkX6IJZXJGcBW9x9G4CZLQGuAKKD5Argn8PPTwP3m5m5+ztR26wH0s0szd3rY1hfGWHMjMLcDApzM/jUKZPayg8caWRjRfuVy4aKGv7y5200tQT9hZmpEU6JvnIpzOXEgizSkofPsE2ReIplkEwByqKWy4GPd7WNuzeZ2QFgPITDcwKfB97pECKPmlkz8AzwQ+9kxICZLQYWA0ybNm2ApyIjSW5GCmcfP56zj28fbVPf1Mzm3YfCcDnAhooanllVzuNvNgOQnGTMnJjVdtXS2kSWm6E+BJFYBklnbQMdf+B3u42ZzSFo7rosav2X3P0jM8smCJIvE/SzHH0Q94eAhyAYtdW3qstok5YcYe6UXOZOyQWmAtDS4uyorg2vWg6wfmcNr2+u4nerP2rbr2hsRttVS2vzWGFuuuYNk1EllkFSTuv/yEARsLOLbcrNLBnIBaoBzKwIeBa4wd23tu7g7h+F7wfN7AmCJrRjgkRkoJKSjOkTMpk+IZNFpxa2lVcerGdDRU3bqLENO2t4YcNuWq+Lx45JabtyCUImh+Pzs4io30VGqFgGyUpglpnNAD4CrgW+2GGbpcCNwJvAVcDL7u5mlgc8B3zH3f/SunEYNnnuXmVmKcCngZdieA4ix8jPTuOC7HwuODG/rexwfRPv7ao5atTYL97YTkNTMCQ5PSWJkwpy2oJlzuQcTi7IISNV/S4y/MX0hkQzWwjcSzD89xF3v8vM7gRK3X2pmaUDvwROI7gSudbdt5nZd4HvAJujDncZcBj4M5ASHvMl4HZ3b+6uHrohURKhsbmFrZWHjho1tn7nAWrqgrvwk8IhyVPHjWFa+Jo6bgxTx2UwbdwYstPV/yKJpTvboyhIZKhwdz7af6QtWN7ffZCyfbWUVR/hwJHGo7YdOyaFaePGUBQVNNPGjWHq2DEU5qWTEhldd1lL/OnOdpEhyMwoGjuGorFjuHxOwVHrDtQ2Uravlh3VtZRVB+87qmtZ/9EBXli/i8bm9l/6IknG5Lz0tmCZ2iFs8sakqMNf4kZBIjJE5I5JIXdM68ixozW3BDdX7tgbhExr4OyoruWljbupOtRw1PZZaclhuGRENZkFITMlL4P0FPXNyOBRkIgMA5EkY0peBlPyMjjnhGNnmz1c30T5viNt4VIWvrZVHuaVTZXUNx09m29BTvoxfTKtr/zsNF3NSJ8oSERGgMy0ZE4qyOakguxj1rk7lYfq25vL9h5pu6J5Y2sVu96pI7qrNC056aimsqKxYdCMD5rRMjX5pXSgfxEiI5yZMTE7nYnZ6Zxx3Lhj1tc1NvPR/iNtVzHtVzVHWPFBNYfqj37Wy/jM1A4jzTKYGjaZTcpJV7PZKKQgERnl0lMinJCfxQn5Wcesc3f2Rw0CaG82O8KasmACzOaWo0d+5makMCknjUk5QXhNykmjILf986ScdPKz0zTqbARRkIhIl8yMsZmpjM1M5dSivGPWNzW3UHGgjh3VtVQcqGN3TfSrnq17qthzsL5tQsz24wZXNhOz0ynIDQImCJr2sJmUk874zFTNxDwMKEhEpN+SI0ltI8K60tLiVNc2sOtAHXsOBgHTGjR7aurYVVPH2vID7D1cT8fb2pKTjPzsNCbmpDMpO+2YoGldzs3QcOdEUpCISEwlJRkTstKYkJVGMJ1e5xqbW6g6VB8VNO1XNrtr6vhwby0rtlezv7bxmH1Tk5OCJrSc9DB02gNnYvhekJOugQIxoj9VERkSUiJJbc+P6U5dYzN7aurZfbDumCub3TV1bNxZw59q9lDbcOzMSVlpyUGwHBU0QchE999owEDfKEhEZFhJT4kwbXwwHLk7h+qbgqA5UBeGTnBlsyd8L/1wH3tq6mlobjlm3zGpEXLSU8jJSA7fU8hJTw7fO5YfvZydnjzqBhIoSERkRMpKSyari9ForVpHpR0dNHXsr22kpq6RmiNN1NQ1sudgHVv2NIVljbT0MEVhd0GUnd51CLVuM9yCSEEiIqNW9Ki0kwt63h6C8KltaD4qaGqORAXPkaNDqKaukcqD9WytPBSuazpmyHRHGSmRfl0NJSqIFCQiIn1gZmSmJZOZlkxh12MHutSfIKo61MC2qsP9CqKHbihhxoTMfp5t7yhIRETiKN5BlJkW+4EDChIRkWFkoEEUC8OrR0dERIacmAaJmc03s01mtsXM7uhkfZqZPRmuf9vMpket+05YvsnMLu/tMUVEJL5iFiRmFgEeABYAs4HrzGx2h81uAfa5+0zgR8A94b6zgWuBOcB84KdmFunlMUVEJI5ieUVyFrDF3be5ewOwBLiiwzZXAI+Fn58GPmXBhDlXAEvcvd7dPwC2hMfrzTFFRCSOYhkkU4CyqOXysKzTbdy9CTgAjO9m394cEwAzW2xmpWZWWllZOYDTEBGR7sQySDqbirPj4Oeutulr+bGF7g+5e4m7l+Tn53dbURER6b9YBkk5MDVquQjY2dU2ZpZMMDVodTf79uaYIiISR7EMkpXALDObYWapBJ3nSztssxS4Mfx8FfCyu3tYfm04qmsGMAtY0ctjiohIHMXshkR3bzKzrwLLgQjwiLuvN7M7gVJ3Xwo8DPzSzLYQXIlcG+673syeAjYATcDfu3szQGfH7Kkuq1atqjKzD/t5KhOAqn7uO9SMlHMZKecBOpehaqScy0DP47jebGTe8ZFkchQzK3X3kkTXYzCMlHMZKecBOpehaqScS7zOQ3e2i4jIgChIRERkQBQkPXso0RUYRCPlXEbKeYDOZagaKecSl/NQH4mIiAyIrkhERGRAFCQiIjIgCpJujJQp683sETPbY2brEl2XgTCzqWb2JzPbaGbrzewbia5Tf5lZupmtMLO/hufy/UTXaSDC2bnfMbP/TnRdBsLMtpvZu2a2xsxKE12fgTCzPDN72szeC//PnBOz71IfSefCKevfBy4lmJplJXCdu29IaMX6wczOBw4Bj7v73ETXp7/MrBAodPfVZpYNrAKuHKZ/JwZkuvshM0sBXge+4e5vJbhq/WJmtwMlQI67fzrR9ekvM9sOlLj7sL8Z0cweA15z95+HM4GMcff9sfguXZF0bcRMWe/ufyaYOWBYc/cKd18dfj4IbKSL2Z+HOg8cChdTwtew/K3OzIqARcDPE10XCZhZDnA+wewhuHtDrEIEFCTd6fWU9RJ/4dM0TwPeTmxN+i9sDloD7AFedPfhei73Av8LaEl0RQaBAy+Y2SozW5zoygzA8UAl8GjY5PhzM8uM1ZcpSLrW6ynrJb7MLAt4Bvif7l6T6Pr0l7s3u/s8glmszzKzYdfsaGafBva4+6pE12WQnOvupxM8hfXvw2bh4SgZOB34mbufBhwGYtbPqyDpmqasH4LC/oRngF+7++8SXZ/BEDY5vELwWOnh5lzgs2HfwhLgYjP7VWKr1H/uvjN83wM8S9DEPRyVA+VRV7lPEwRLTChIuqYp64eYsIP6YWCju/9HouszEGaWb2Z54ecM4BLgvcTWqu/c/TvuXuTu0wn+j7zs7tcnuFr9YmaZ4SAOwmagy4BhOdLR3XcBZWZ2Ulj0KYLZ1GMiZtPID3ddTYOf4Gr1i5n9BrgQmGBm5cD33P3hxNaqX84Fvgy8G/YtAPxvd38+gXXqr0LgsXB0YBLwlLsP66GzI8Ak4Nng9xWSgSfc/Q+JrdKAfA34dfiL8Dbg5lh9kYb/iojIgKhpS0REBkRBIiIiA6IgERGRAVGQiIjIgChIRERkQBQkIoPAzJrDGWNbX4N2F7GZTR/uMzfLyKb7SEQGx5FwuhORUUdXJCIxFD7f4p7w2SMrzGxmWH6cmf3RzNaG79PC8klm9mz4nJK/mtknwkNFzOy/wmeXvBDeDS8yJChIRAZHRoemrWui1tW4+1nA/QQz5RJ+ftzdTwV+DdwXlt8HvOruHyOYG6l1NoVZwAPuPgfYD3w+xucj0mu6s11kEJjZIXfP6qR8O3Cxu28LJ5zc5e7jzayK4CFdjWF5hbtPMLNKoMjd66OOMZ1gmvlZ4fK3gRR3/2Hsz0ykZ7oiEYk97+JzV9t0pj7qczPq35QhREEiEnvXRL2/GX5+g2C2XIAvETxqF+CPwG3Q9uCrnHhVUqS/9FuNyODIiJqRGOAP7t46BDjNzN4m+MXturDs68AjZvYtgifZtc7M+g3gITO7heDK4zagIua1FxkA9ZGIxFDYR1Li7lWJrotIrKhpS0REBkRXJCIiMiC6IhERkQFRkIiIyIAoSEREZEAUJCIiMiAKEhERGZD/H1wdvhmETcTWAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -939,7 +930,7 @@
     "#### Questions\n",
     "\n",
     "1. Look at the *Accuracy plot* above. Which information can you gather from it? What is happening to the *training* and *test* accuracy over epochs?\n",
-    "2. Is the behaviour of *training* and *test* accuracy that you are observing desirable? \n",
+    "2. Is the behaviour of *training* and *test* accuracy that you are observing desirable? Is *overfitting* occuring?\n",
     "3. Should you look at the *training* or at the *test* accuracy to have an estimate of the generalization ability of the model?\n",
     "4. Compare the *Accuracy plot* with the *Model loss plot* below. Do they show the same qualititive behaviour?\n"
    ]
@@ -951,6 +942,29 @@
     "### 4.3 Adding regularization (using dropout layers)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To have a valuable machine learning model, it is of paramount important to make sure that the model generalizes well to unseen samples.\n",
+    "\n",
+    "Bengio in Ref. [1] defines regularization as *any modification we make to a learning algorithm that is intended to reduce its generalization error but not its training error.* In practice, additional terms are added to the training optimization objective to prevent overfitting or help the optimization.\n",
+    "\n",
+    "There are numerous ways to regularize a neural network; we refer the interested reader to Chap 7 of Ref. 1: https://www.deeplearningbook.org/contents/regularization.html\n",
+    "\n",
+    "Here, we use **dropout layers** to regularize our neural network. To explain in a few word what dropout is, we report below the abstract from the article that introduced dropout (Srivastava et al., J. Mach. Learn. Res. 15 1929 (2014))\n",
+    "\n",
+    "*Deep neural nets with a large number of parameters are very powerful machine learning systems. However, overfitting is a serious problem in such networks. Large networks are also slow to use, making it difficult to deal with overfitting by combining the predictions of many different large neural nets at test time. Dropout is a technique for addressing this problem.*\n",
+    "\n",
+    "*The key idea is to randomly drop units (along with their connections) from the neural network during training. This prevents units from co-adapting too much. During training, dropout samples from an exponential number of different “thinned” networks. At test time, it is easy to approximate the effect of averaging the predictions of all these thinned networks by simply using a single unthinned network that has smaller weights.*\n",
+    "\n",
+    "*This significantly reduces overfitting and gives major improvements over other regularization methods. We show that dropout improves the performance of neural networks on supervised learning tasks in vision, speech recognition, document classification and computational biology, obtaining state-of-the-art results on many benchmark data sets.*\n",
+    "\n",
+    "For the full article \"Srivastava et al., Dropout: A Simple Way to Prevent Neural Networks from Overfitting\", please visit http://jmlr.org/papers/volume15/srivastava14a.old/srivastava14a.pdf.\n",
+    "\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -1012,7 +1026,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " <span style=\"color:red\"> **Run the cell below to start training your *regularized* convolutional neural network. The full optimization should take approximately 7 minutes; please take this time to read carefully the materials above, and maybe check out some external references.**</span>."
+    " <span style=\"color:red\"> **Run the cell below to start training your *regularized* convolutional neural network. The full optimization should take approximately 7 minutes (~1 min per epoch); please take this time to read carefully the materials above, and maybe check out some external references.**</span>."
    ]
   },
   {
@@ -1026,21 +1040,21 @@
      "text": [
       "Train on 60000 samples, validate on 10000 samples\n",
       "Epoch 1/7\n",
-      "60000/60000 [==============================] - 73s 1ms/step - loss: 0.2825 - acc: 0.9128 - val_loss: 0.0613 - val_acc: 0.9796\n",
+      "60000/60000 [==============================] - 74s 1ms/step - loss: 0.2644 - acc: 0.9187 - val_loss: 0.0577 - val_acc: 0.9808\n",
       "Epoch 2/7\n",
-      "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0900 - acc: 0.9734 - val_loss: 0.0453 - val_acc: 0.9850\n",
+      "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0911 - acc: 0.9733 - val_loss: 0.0403 - val_acc: 0.9866\n",
       "Epoch 3/7\n",
-      "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0664 - acc: 0.9797 - val_loss: 0.0381 - val_acc: 0.9862\n",
+      "60000/60000 [==============================] - 74s 1ms/step - loss: 0.0678 - acc: 0.9794 - val_loss: 0.0347 - val_acc: 0.9887\n",
       "Epoch 4/7\n",
-      "60000/60000 [==============================] - 70s 1ms/step - loss: 0.0554 - acc: 0.9829 - val_loss: 0.0329 - val_acc: 0.9887\n",
+      "60000/60000 [==============================] - 74s 1ms/step - loss: 0.0551 - acc: 0.9832 - val_loss: 0.0294 - val_acc: 0.9894\n",
       "Epoch 5/7\n",
-      "60000/60000 [==============================] - 68s 1ms/step - loss: 0.0475 - acc: 0.9857 - val_loss: 0.0290 - val_acc: 0.9902\n",
+      "60000/60000 [==============================] - 73s 1ms/step - loss: 0.0469 - acc: 0.9859 - val_loss: 0.0302 - val_acc: 0.9894\n",
       "Epoch 6/7\n",
-      "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0417 - acc: 0.9876 - val_loss: 0.0292 - val_acc: 0.9900\n",
+      "60000/60000 [==============================] - 73s 1ms/step - loss: 0.0404 - acc: 0.9877 - val_loss: 0.0300 - val_acc: 0.9893\n",
       "Epoch 7/7\n",
-      "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0376 - acc: 0.9886 - val_loss: 0.0309 - val_acc: 0.9899\n",
-      "Test loss: 0.0309031607253899\n",
-      "Test accuracy: 0.9899\n"
+      "60000/60000 [==============================] - 71s 1ms/step - loss: 0.0349 - acc: 0.9892 - val_loss: 0.0298 - val_acc: 0.9906\n",
+      "Test loss: 0.029760151506797776\n",
+      "Test accuracy: 0.9906\n"
      ]
     }
    ],
@@ -1068,7 +1082,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1097,7 +1111,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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gO8SC4ljcpueAG8xsQTCwfUOwbt5r3BhhcCTKc2+1pboUEZGESVpYuPsw8EViP/J7gS3u/paZPWRmjcFufwWUAP9oZjvMbGtw7HHgq8QCZxvwULBu3quvLWd5ZRFP6QI9EckgOcl8c3d/BnjmjHV/Grd83XmO3QxsTl51yWFmNNZHeOTlA7T3DlBdmp/qkkREZk1XcCdBY32EqMMzu3V/bhHJDAqLJFhTU8pFi0v5nzs+SHUpIiIJobBIksaNEd443EXz8b7JdxYRmecUFkly24YIAE/t0kC3iKQ/hUWS1C0sYtOyCl2gJyIZQWGRRI31Ed5u62X/0d5UlyIiMisKiyT61IYIIUM3RRKRtKewSKLq0nyuXFXF1p1HcNf9uUUkfSkskqyxPsKhzj52tXSnuhQRkRlTWCTZjesWkxcOqStKRNKawiLJygtzuebCap7edYSRqLqiRCQ9KSzmQGN9hKM9A7z+XlrMhSgichaFxRy47uIaivLC6ooSkbSlsJgDhXlhrl9bw7NvtjI4HE11OSIi06awmCON9RG6+ob4xYHU3ytcRGS6FBZz5NfWVFNRlKvpP0QkLSks5kheToib1y3h+T1H6R8cSXU5IiLTorCYQ431EfoGR3hx79FUlyIiMi0Kizl0xcqF1JTl66woEUk7Cos5FA4Zt26I8NN97XT3D6W6HBGRKVNYzLHG+giDI1Gee7Mt1aWIiEyZwmKObagtZ3llkbqiRCStKCzmmJnRWB/h1Xc7ONZ7KtXliIhMicIiBRrrI0QdntnVmupSRESmRGGRAmtqSrlocam6okQkbSgsUqRxY4Q3DnfRfLwv1aWIiExKYZEit22IAPDULrUuRGT+U1ikSN3CIjYtq9BcUSKSFhQWKdRYH+Httl72H+1NdSkiIuelsEihT22IEDI00C0i857CIoWqS/O5clUVW3cewV335xaR+WtKYWFmq8wsP1j+hJn9oZlVJLe07NBYH+FQZx+7WrpTXYqIyDlNtWXxBDBiZquB7wErgX9IWlVZ5MZ1i8kLh9QVJSLz2lTDIuruw8BvAl939/8dWDLZQWZ2k5ntM7MDZvbABNs/bmZvmNmwmd1+xrYRM9sRPLZOsc60U16YyycurOapnUcYiaorSkTmp6mGxZCZ3QN8Fng6WJd7vgPMLAw8AtwMrAXuMbO1Z+x2GPhdJm6l9Lv7xuDROMU601LjxgjHegf45XudqS5FRGRCUw2L+4CPAX/u7u+Z2Urg7yc55grggLsfdPdB4HHg0/E7uPv77r4LiE6z7oxy7UU1FOeFeUpdUSIyT00pLNx9j7v/obv/yMwWAKXu/vAkhy0FmuNetwTrpqrAzJrM7DUz+42JdjCz+4N9mtrb26fx1vNLYV6Y69fW8MzuNgaHszo3RWSemurZUK+YWZmZLQR2Ao+Z2V9PdtgE66bTKb/M3RuA3wa+bmarznoz90fdvcHdG6qrq6fx1vNP48YI3f1D/Hx/+oaeiGSuqXZDlbt7D/BbwGPufhlw3STHtAB1ca9rgSn3s7j7keD5IPAKcOlUj01HV6+upqIoV2dFici8NNWwyDGzJcCdjA9wT2YbsMbMVppZHnA3MKWzmsxsQdx1HVXAVcCeKX5uWsrLCXHzuiW8sOco/YMjqS5HROQ0Uw2Lh4DngHfdfZuZXQDsP98Bwam2XwyO2wtscfe3zOwhM2sEMLPLzawFuAP4jpm9FRx+MdBkZjuBl4GH3T2jwwJiF+j1DY7w4t6jqS5FROQ0linTTDQ0NHhTU1Oqy5iVkahz5cMvsaG2gr/9nYZUlyMiWcDMtgfjw+c11QHuWjN70syOmdlRM3vCzGpnX6bEC4eMWzdE+Om+drr7h1JdjojImKl2Qz1GbLwhQuz016eCdZJgjfURBkeiPPdmW6pLEREZM9WwqHb3x9x9OHj8dyC9z1WdpzbUlrO8skhnRYnIvDLVsOgws3vNLBw87gU0N0USmBmN9RFefbeDY72nUl2OiAgw9bD4HLHTZtuAVuB2YlOASBI01keIOjyzqzXVpYiIAFOf7uOwuze6e7W7L3L33yB2gZ4kwZqaUi5aXKquKBGZN2Zzp7wvJ6wKOUvjxghvHO6i+XhfqksREZlVWEw095MkyG0bIoDuzy0i88NswiIzruabp+oWFnHZ8gWatlxE5oXzhoWZ9ZpZzwSPXmLXXEgSNdZHeLutl3eO9qa6FBHJcucNC3cvdfeyCR6l7p4zV0Vmq1vWLyFksHWHWhciklqz6YaSJKsuzeeq1VVs3XmETJnDS0TSk8JinrutPsLh433sbOlOdSkiksUUFvPcjZcsJi8cUleUiKSUwmKeKy/M5RMXVvP0riOMRNUVJSKpobBIA40bIxzrHeCX72k6LhFJDYVFGrj2ohqK88K65kJEUkZhkQYK88Jcv7aGZ3a3MTgcTXU5IpKFFBZponFjhO7+IX6+vz3VpYhIFlJYpImrV1dTUZSruaJEJCUUFmkiLyfEzeuW8MKeo/QPjqS6HBHJMgqLNNJYH6FvcIQX9x5NdSkikmUUFmnkipULqSnLV1eUiMw5hUUaCYeM2zZE+Om+drr7hlJdjohkEYVFmmncGGFwJMo/v6X7c4vI3FFYpJn1S8tZUVmkrigRmVMKizRjZjTWR/i3dzs51nsq1eWISJZQWKShxo0Rog7/a5e6okRkbigs0tDqRaVcvKRMXVEiMmcUFmmqsT7Crw530Xy8L9WliEgWUFikqdvqlwCodSEic0JhkaZqFxRx2fIFmrZcROaEwiKNNdZHeLutl3eO9qa6FBHJcEkNCzO7ycz2mdkBM3tggu0fN7M3zGzYzG4/Y9tnzWx/8PhsMutMV7esX0LI0P25RSTpkhYWZhYGHgFuBtYC95jZ2jN2Owz8LvAPZxy7EHgQ+ChwBfCgmS1IVq3pqro0n6tWV7F15xHcdX9uEUmeZLYsrgAOuPtBdx8EHgc+Hb+Du7/v7ruAM2//diPwgrsfd/cPgReAm5JYa9q6rT7C4eN97GzpTnUpIpLBkhkWS4HmuNctwbpkH5tVbrxkMXnhkLqiRCSpkhkWNsG6qfaVTOlYM7vfzJrMrKm9PTtvN1pemMsnLqzm6V1HGImqK0pEkiOZYdEC1MW9rgWm+ufvlI5190fdvcHdG6qrq2dcaLpr3BjhWO8Av3yvM9WliEiGSmZYbAPWmNlKM8sD7ga2TvHY54AbzGxBMLB9Q7BOJnDtRTUU54V1zYWIJE3SwsLdh4EvEvuR3wtscfe3zOwhM2sEMLPLzawFuAP4jpm9FRx7HPgqscDZBjwUrJMJFOaFueGSxTyzu43B4TPPFRARmb2cZL65uz8DPHPGuj+NW95GrItpomM3A5uTWV8maayP8OSvPuBn77Rz3dqaVJcjIhlGV3BniKvXVLGgKFdzRYlIUigsMkRuOMTN65fwwp6jPLu7Vd1RIpJQSe2Gkrn1uatW8Mrbx/iDH77BwuI8fvPSpdx1eR0fqSlNdWkikuYsU6aJaGho8KamplSXkXIjUedn+9v5x6ZmXthzlKERZ2NdBXddXsetG5ZQWpCb6hJFZB4xs+3u3jDpfgqLzNV5YoAnf/UBW5qaeefoCQpzw9yyfgl3XV7H5SsWYDbRtY8ikk0UFjLG3dnR3MWWphae2nmEEwPDrKwq5o6GWm7fVMuisoJUlygiKaKwkAn1DQ7zzO42tjQ18/p7xwmHjE9eWM2dDXV88qJF5IZ1zoNINlFYyKTe6zjJlqZmntjewrHeAapK8vnMpqXc0VDH6kUlqS5PROaAwkKmbHgkyiv72tnS1My/vH2M4ajTsHwBdzbU8akNSyjO10lzIplKYSEz0t47wD+90cKPm5o52H6S4rwwt26IcOfldWxaVqFBcZEMo7CQWXF33jj8IT/e1szTu1rpGxxh9aIS7myo5bc21VJVkp/qEkUkARQWkjAnBob5X7uOsKWphe2HPiQnZFx78SLubKjjmo9Uk6NBcZG0pbCQpDhwrJctTS380xstdJwYpKYsn89squXOhjpWVBWnujwRmSaFhSTV0EiUf3n7GFu2NfPyvmNEHT66ciF3NtRxy/olFOaFU12iiEyBwkLmzNGeU/xkewv/2NTM+519lObncNvGCHc21FFfW65BcZF5TGEhc87def294/y4qZlndrdyaijKhTWl3Hl5Hb956VIWFuelukQROYPCQlKq99QQT+1s5cdNzexs7iI3bNywdjF3NNTya2uqCYfU2hCZDxQWMm/sa+vlx9uaefJXLXzYN0SkvIDbL6vljoY66hYWpbo8kaymsJB5Z3A4yot7j/Ljbc38bH877nDlqkruuryOGy9ZTEGuBsVF5prCQua1I139PLG9hS3bm2k+3k9ZQQ6f3hi7WdO6peWpLk8kaygsJC1Eo85rBzvZ0tTMs2+2MTAcZe2SMu66vI7b6iMaFBdJMoWFpJ3uviG27vyAHzc18+YHPQCsqCyivq6C+toK6usquCRSpu4qkQRSWEhae+tINz97p4OdzV3sbOmitfsUADkh46IlpWPhsbGuglXVJTq7SmSGphoWmnta5qVLIuVcEhkfuzjac2osOHY2d7N15xF++MvDABTnhVlfWx4LjyBElpQX6GJAkQRSy0LSUjTqvNd5MhYgzV3saOlm75EeBkeiAFSX5lNfW8HGuliIbFhaQXlRboqrFpl/1LKQjBYKGauqS1hVXcJvbaoFYGB4hLdbe9nZ0sWOIERe3Ht07JgLqoqD8Y9YgFy8ROMfIlOlloVktJ5TQ+xu6R4Lj50tXRztGQAgN2xcvKQsbvyjnAuqSghp/EOyiAa4Rc6hrftUMPYRC49dzd30DgwDUJKfw4ag5RHrxqpgcXlBiisWSR51Q4mcw+LyAhaXL+bGSxYDsfGPgx0n4wbQu/juzw8yNBL7Q2pRaf7YmVf1tRWsry2nvFDjH5JdFBYjw/Cju2HFVbDmBli0FnQWTVYJhYzVi0pYvaiEz1w2Pv6xt7U3bgC9ixf2xI1/VBePnXkVG/8oJT9H4x+SudQN1dUMj98Dbbtjr8uWwprrY8Gx8hrIL0lsoZK2uvtj4x+jA+g7mrto7x0f/1i7pOy0CwgvqCrW+IfMexqzmK6eI3DgRdj/PLz7Cgz2QjgPll8Ja26MhUflKrU6ZIy70xZc/7GjuZudzV3s/qCbE8H4R2l+DhvqyqmvreAjNaUsqyxi+cIiFhbn6RoQmTcUFrMxPAjNr8E7z8H+F6BjX2z9gpWx0FhzQ6zbKrcwMZ8nGWMk6hxsPxE7+yq4gHBvaw/D0fF/ZyX5OdQtjAXHssoili0sYnllEcsXFhOpKCAnHErhN5BsMy/CwsxuAr4BhIHvuvvDZ2zPB34AXAZ0Ane5+/tmtgLYCwS/0rzm7l8432cl9WyoD9+Phcb+F+C9n8FwP+QUwsqPj3dZLVienM+WtDcwPELz8T4OdfZxOO559DE4HB3bNxwyllYUsryyaCxQxpYriynJ1zCjJFbKw8LMwsA7wPVAC7ANuMfd98Tt8++BDe7+BTO7G/hNd78rCIun3X3dVD9vzk6dHeqH9/811l21/7lYkABUXTgeHMs+BjmaLVUmF406R3tPcbizj0PH+zg8GijH+zjceZIP+4ZO27+yOC8IjliQjIbI8soiqkvyNUYi0zYfwuJjwJ+5+43B6z8BcPe/iNvnuWCffzOzHKANqAaWM1/DIp47dL4bBMfzcOhfYWQQ8krggk8EXVbXQ1lkbuuSjNFzamg8QMZaJCc51NnHka5+4nq3yM8JjXVpjbdKiqlbWETdwkKdrSUTmg/XWSwFmuNetwAfPdc+7j5sZt1AZbBtpZn9CugB/ou7/zyJtc6MGVStjj0+9u9h4ESsm2o0PN5+OrZfzfrxVkft5RBWV4JMTVlBLuuWlk94Q6ihkSgffNgfa4UELZHRQHn13U76BkfG9jWDJWUF462SymKWLRwfL6koUktYzi+Zv1oTtYfPbMaca59WYJm7d5rZZcD/MLNL3L3ntIPN7gfuB1i2bFkCSp6l/BK46JbYwx2O7Q2C4wX412/AL/4aCipg9bWx4Fh1LZRUp7pqSVO54RArqopZUVV81jZ3p+PEIIePnxxvlQRB8vK+dtp7W06uUuOeAAALsElEQVTbv6wgJzhbq3h80D0YgF9SXqgp4CWpYdEC1MW9rgWOnGOflqAbqhw47rG+sQEAd99uZu8CHwFO62dy90eBRyHWDZWMLzFjZlCzNva4+o+gvwsOvjw+UP7mE4DB0k3j3VVLLoWQzoSR2TMzqkvzqS7N57LlC8/a3jc4TPPxfg51nhwbaD/U2cee1h6e39M2dvU6xK4hqV1QNNYSWVJRwJLyAhaXFcauhi8roDBPXVyZLpljFjnEBrivBT4gNsD92+7+Vtw+/wFYHzfA/VvufqeZVRMLjREzuwD4ebDf8XN9XlrNDRWNQtvOIDieh5YmwKGoKuiuuh5W/ToULkh1pZKFRqJOa3f/+KB7MPB+6PhJDnf20XNq+KxjKopyWVxWwOLyWJDUlMU/x0KlrCBH15fMQykf4A6KuAX4OrFTZze7+5+b2UNAk7tvNbMC4O+AS4HjwN3uftDMPgM8BAwDI8CD7v7U+T4rrcLiTCc74d2XYsFx4EXo/xAsDHVXjI911KzTBYEyL/QNDtPWfYq27lO0dp+irWd8+WhP7LnjxMBZxxXmhmMtkqA1cnqwxAKlsjhPZ3TNsXkRFnMprcMiXnQEPtg+PkjeujO2vjQyHhwXXAP5pamtU+Q8BoejHOs9O0Ta4sLlaM+p0y5WhFiX16LSIETKC1gShEp8sNSUFZCrCxcTRmGRKXrb4qYheRkGeiCUG0xDElxNXrVGrQ5JO9Go03FyYOJACUKltbufU0PR044zg6qS/Am7veJbLUV5OutwKhQWmWhkCJp/OT4NSfve2PqK5XHTkFwNeUWprVMkQdydnv5hWnv6Y4HSfXYLpbW7f8JxlPLC8XGU04KlfHSAvoDywtysH0dRWGSDrsNx05D8FIb6YmMdxVVQsghKamKP4upgeVHc86LYabxZ/g9FMkP8OErbBF1e5xpHyQ0blcWxs8aqSvKoKsmnqjSfqpLxddUlsdcVRZkZLAqLbDN0KnYF+eHX4EQbnDgGJ44Gz8cgOnT2MeG884fJ6HLxIk3VLmkvfhxlNEQ6TgzS3jtAx4nxR+eJwbPGUmA8WKpKg1AZC5TxUBl9XV6YmzYD9QoLGeceO8NqNEBOtgdBEhcmo9v6OsCjZ79HbnFcgFSPt1pGwyQ+ZHLy5/47iiRINOp09Q/FwqN3gPYTA0GgDJ4WKu295w6WnJBRGbRUxgMlCJXS/FhrJVhfkeJgmQ/Tfch8YQZFC2OPRRedf9/oCPR1xoVJe1yoHIWTx6BjP7z/i1gATaSgfIIwmaDVUlSlqU9k3gmFjIXFeSwszuMjNec/6zAadbqDYDkrVIKg6TgxwNutvXScGJg0WMYepae3VEZDJ5XBon+pcrpQePwHnfXn33d4MK6VEhcmY11g7XBkR+z1YO8Eb2BQVHlGF1hcq6W4Ogib6th+IV0lLPNLKGQsKM5jQXEeayYJFvdYsLSPhcggHb3jrZSOYN07R2PBEn8V/ahwyKgsHh9bqQ6CZXV1CXc01E3wqYmjsJCZy8mD8qWxx2QG+84IkglaLcffjS0Pn5rgDUaDZVFsAL84CJGS6tjzma91YyqZZ8yMiqI8KoqmHiyxIBk8I1DGWy/7g2Cpr61QWEiGyCuCvBWwYMX593OPXUsyGiQn28cfJ46NLx95I7bPhC0WIK90/Kyw4urxx1jYjAZMVWxalQw8y0XSV3ywrF50/n3d/axrUZJBYSHzi1lszKOgPDb1+2SG+uPCZDRYjsHJjiBcjsHxg7GzxPo6OXviY2IXOY4FSPUkrZcqCOcm/GuLzJSZzclEjgoLSW+5hVCxLPaYzOjg/VgrpSMIljOCpuOd2PaRs8/LB2ItkfjwOGfrZRHkFc+s1eIeOystOgI+AtHhYDlYFx0O1o+MP8cvT7Ru7JjoGe8ZrJvqe44yY+wuAzNaJvZ6wuWp7HPG8mzqGF030bOFzr0NC97qfNun+jzZ+4TOvS23AMprSSaFhWSP+MH7mkvOv687DPRO3AUWHzZH34SD7XCqe+L3ySmMtUzCeef5EZ/gB9lHJn4/kYksbYDffympH6GwEJmIGRSUxR6Vqybff3ggrqXScXq4jF4UaWEI5cRCy0Kx51BOsH503ej2YN1py9PYd+yzQrM8/sxag7+y8VigwsyWIe716LLPYnmqnz3R/lN5jsYtT/fYcz3P4H08evr3Gd1WePY9SxJNYSGSCDn5Uz8zTCQNaZ5fERGZlMJCREQmpbAQEZFJKSxERGRSCgsREZmUwkJERCalsBARkUkpLEREZFIZc6c8M2sHDs3iLaqAjgSVk0qZ8j1A32W+ypTvkinfA2b3XZa7e/VkO2VMWMyWmTVN5daC812mfA/Qd5mvMuW7ZMr3gLn5LuqGEhGRSSksRERkUgqLcY+muoAEyZTvAfou81WmfJdM+R4wB99FYxYiIjIptSxERGRSCgsREZlU1oeFmd1kZvvM7ICZPZDqembKzDab2TEzezPVtcyWmdWZ2ctmttfM3jKzL6W6ppkwswIze93Mdgbf4yuprmm2zCxsZr8ys6dTXctsmNn7ZrbbzHaYWVOq65kNM6sws5+Y2dvBv5mPJeVzsnnMwszCwDvA9UALsA24x933pLSwGTCzjwMngB+4+7pU1zMbZrYEWOLub5hZKbAd+I10+/9iZgYUu/sJM8sFfgF8yd1fS3FpM2ZmXwYagDJ3vzXV9cyUmb0PNLh72l+UZ2bfB37u7t81szygyN27Ev052d6yuAI44O4H3X0QeBz4dIprmhF3/xlwPNV1JIK7t7r7G8FyL7AXSLv7lXrMieBlbvBI27/OzKwW+BTw3VTXIjFmVgZ8HPgegLsPJiMoQGGxFGiOe91CGv4oZTIzWwFcCvwytZXMTNBtswM4Brzg7mn5PQJfB/5PIJrqQhLAgefNbLuZ3Z/qYmbhAqAdeCzoHvyumRUn44OyPSxsgnVp+5dfpjGzEuAJ4I/cvSfV9cyEu4+4+0agFrjCzNKyi9DMbgWOufv2VNeSIFe5+ybgZuA/BN246SgH2AT8f+5+KXASSMrYa7aHRQtQF/e6FjiSolokTtDH/wTwQ3f/p1TXM1tB18ArwE0pLmWmrgIag77+x4FfN7O/T21JM+fuR4LnY8CTxLqk01EL0BLXYv0JsfBIuGwPi23AGjNbGQwM3Q1sTXFNWS8YGP4esNfd/zrV9cyUmVWbWUWwXAhcB7yd2qpmxt3/xN1r3X0FsX8n/+Lu96a4rBkxs+LgxAmCLpsbgLQ8i9Dd24BmM7swWHUtkJQTQXKS8abpwt2HzeyLwHNAGNjs7m+luKwZMbMfAZ8AqsysBXjQ3b+X2qpm7Crg3wG7g/5+gP/L3Z9JYU0zsQT4fnDWXQjY4u5pfcpphqgBnoz9TUIO8A/u/s+pLWlW/iPww+AP3oPAfcn4kKw+dVZERKYm27uhRERkChQWIiIyKYWFiIhMSmEhIiKTUliIiMikFBYi02BmI8FMpaOPhF0ta2YrMmHWYMlMWX2dhcgM9AfTd4hkFbUsRBIguD/C14L7V7xuZquD9cvN7CUz2xU8LwvW15jZk8G9Lnaa2ZXBW4XN7G+D+188H1z5LZJyCguR6Sk8oxvqrrhtPe5+BfAtYjO0Eiz/wN03AD8Evhms/ybwU3evJzaXz+jMAWuAR9z9EqAL+EySv4/IlOgKbpFpMLMT7l4ywfr3gV9394PBJIht7l5pZh3EbuQ0FKxvdfcqM2sHat19IO49VhCbxnxN8Po/Abnu/l+T/81Ezk8tC5HE8XMsn2ufiQzELY+gcUWZJxQWIolzV9zzvwXLrxKbpRXgfyN2a1WAl4A/gLEbJJXNVZEiM6G/WkSmpzBuJlyAf3b30dNn883sl8T+CLsnWPeHwGYz+z+I3dFsdEbQLwGPmtnnibUg/gBoTXr1IjOkMQuRBAjGLBrcvSPVtYgkg7qhRERkUmpZiIjIpNSyEBGRSSksRERkUgoLERGZlMJCREQmpbAQEZFJ/f/Nctzz5QsKlQAAAABJRU5ErkJggg==\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1134,11 +1148,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## 5. Opening the black box with saliency maps\n",
+    "## 5. Opening the black box with attentive response maps\n",
     "\n",
-    "In the previous section, we built a model which classifies the handwritten digits of the MNIST dataset with a satisfactory accuracy. But how can we assess which parts of a given image the network utilizes to arrive at its classification decision?\n",
+    "In the previous section, we built a model which classifies the handwritten digits of the MNIST dataset with satisfactory accuracy. But how can we assess which parts of a given image the network utilizes to arrive at its classification decision?\n",
     "\n",
-    "To answer this question, in this tutorial, we will compute **attentive response maps**.\n",
+    "To answer this question, in this tutorial we will compute **attentive response maps**.\n",
     "\n",
     "The main idea is to invert the data flow of a convolutional neural network, going from the last layers activations until image space. Then, an heatmap is constructed to shows which parts of the input image are most strongly activating when a classification decision is made - and thus are the most discriminative. \n",
     "\n",
@@ -1146,23 +1160,21 @@
     "\n",
     "This is not the only technique to explain the classification decisions made by convolutional neural networks; some useful references are listed below:\n",
     "\n",
-    "1. M.D. Zeiler, D. Krishnan, G.W. Taylor, and R. Fergus, *ImageNetClassification with deep convolutional neural networks*, in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2528–2535 (IEEE, San Fransisco, CA, 2010).\n",
-    "\n",
-    "2. M.D. Zeiler, and R. Fergus, *Visualizing and Understanding Convolutional Networks* 818-833,  https://doi.org/10.1007/978-3-319-10590-1_53 (2014).\n",
+    "1. M.D. Zeiler, and R. Fergus, *Visualizing and Understanding Convolutional Networks* 818-833,  https://doi.org/10.1007/978-3-319-10590-1_53 (2014).\n",
     "\n",
-    "3. K. Simonyan, A. Vedaldi, and A. Zisserman, *Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps* (2014) (https://arxiv.org/pdf/1312.6034v2.pdf)\n",
+    "2. K. Simonyan, A. Vedaldi, and A. Zisserman, *Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps* (2014) (https://arxiv.org/pdf/1312.6034v2.pdf)\n",
     "\n",
-    "4. S. Bach, et al. *On pixel-wise explanations for nonlinear classifier decisions by layer-wise relevance propagation*, PLoS ONE 10, e0130140 (2015).\n",
+    "3. S. Bach, et al. *On pixel-wise explanations for nonlinear classifier decisions by layer-wise relevance propagation*, PLoS ONE 10, e0130140 (2015).\n",
     "\n",
-    "5. G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.R. Müller, *Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit.* 65, 211–222 (2017).\n",
+    "4. G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.R. Müller, *Explaining nonlinear classification decisions with deep Taylor decomposition. Pattern Recognit.* 65, 211–222 (2017).\n",
     "\n",
-    "6. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, *Visual Explanations from Deep Networks via Gradient-based Localization*, https://arxiv.org/pdf/1610.02391.pdf (2017)\n",
+    "5. R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, *Visual Explanations from Deep Networks via Gradient-based Localization*, https://arxiv.org/pdf/1610.02391.pdf (2017)\n",
     "\n",
-    "7. Kumar, D., Wong, A. & Taylor, G. W. Explaining the unexplained: a class-enhanced attentive response (CLEAR) approach to understanding deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1686–1694 (IEEE, Honolulu, HI, 2017).\n",
+    "6. Kumar, D., Wong, A. & Taylor, G. W. Explaining the unexplained: a class-enhanced attentive response (CLEAR) approach to understanding deep neural networks. In 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 1686–1694 (IEEE, Honolulu, HI, 2017).\n",
     "\n",
     "For an application of convolutional neural network interpretation to a materials science problem:\n",
     "\n",
-    "8. A. Ziletti, D. Kumar, M. Scheffler, and L. M. Ghiringhelli, *Insightful classification of crystal structures using deep learning*, Nature Communications 9, 2775 (2018)"
+    "7. A. Ziletti, D. Kumar, M. Scheffler, and L. M. Ghiringhelli, *Insightful classification of crystal structures using deep learning*, Nature Communications 9, 2775 (2018)"
    ]
   },
   {
@@ -1173,7 +1185,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.image.AxesImage at 0x7fbffc21b550>"
+       "<matplotlib.image.AxesImage at 0x7ff084c7db38>"
       ]
      },
      "execution_count": 19,
@@ -1229,12 +1241,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 1296x432 with 1 Axes>"
       ]
@@ -1246,7 +1258,6 @@
     }
    ],
    "source": [
-    "#for modifier in [None,'guided', 'relu']:\n",
     "for modifier in ['guided']:\n",
     "    grads = visualize_saliency(model, layer_idx, filter_indices=class_idx,\n",
     "                               seed_input=x_test[idx], backprop_modifier=modifier)\n",
@@ -1257,12 +1268,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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VhWzt3HXlt1eX8vHztfhJHyuE31+b/bcvf34p/8SPXzE4u+W4Q2rLbLy+lo/pQvbA4uw+jzwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQNTXuBdi7vebmNYOzd770kNLsbZuura6zJKy+4LZS/ndOP6GUf/ORXyrlAdjZ1lq8eBI7OO4YHl5Xmx3Fu92fXv7JwdkNT3hUafZvveittWXWTw/P3lQbHZtHeFzeMrrROxxRyK4oTZ65+KBS/uIznlTKn/CmK4aH9y+Njn3inlL+mQ/5wuDsp195Wm2Zc55Yy5c+f1tt9gAeeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBLeQQAAKBratwLMFrTuWyk87/85FZIXzuyPZaUzFJ86vu2l/Kj/Jy54Xdr+SNPH80eAKNTPFptrcXvjX2Gh7fUZsfFxXzBS895dyl/6fc/tZS/4NQXDQ+vL42OWF+7342onI2qs1cU84ePbva6WvzdP/HSUn7ra/5icPaWOKI0+x/jOaX8HXHw4Ozyl95emr3lgJWlfLy9kL2mNnoIjzwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQNTXuBaj76i/uNzg707aNcBPGYeNPH1LK/91hl5byM21ZIVv7/HrY60vx2F6LA+x9ltfiB8cdw8M31GbHt4v5m4dHV67bUhr9Y8f9Qyn/+bOeOTh768UPL82O9bV4xO2F7EHF2dPFfHV+wTW1+Ie3nVHK37Zs+HnnujimNPuq7zyulD/wIZsHZ495yHWl2dc+fWUpHx8oZIu30RAeeQQAAKBLeQQAAKCrWx4z87zMvCUz1+902Rsy8/rMXDf733NHuyYAsJQ5jwCM35BHHs+PiFN3cfnbWmvHzf73ifldCwDgPzk/nEcAxqpbHltrn4/ad/4CAMwr5xGA8ZvL9zy+MjO/PPs0kofuLpSZZ2fm2sxcOxP3zOHdAQA8QPk8EnHrQu4HMDH2tDy+IyIeFRHHRcSNEfGW3QVba+e21ta01tZMx757+O4AAB5gj84jEYct1H4AE2WPymNr7ebW2rbW2vaIeFdEHD+/awEAPDjnEYCFtUflMTNX7fTXM2IPfn0qAMBcOI8ALKypXiAz3x8RJ0XEoZm5KSJeHxEnZeZxEdEiYmNEvHyEOwIAS5zzCMD4dctja+3MXVz8nhHsAgCwS84jAOPXLY8sPq99xsfGvQIdU8ccPTh711MeVpr95z//f6vrjMyl9ywv5fPerSPaBGCUKl+7il/nht9dRETE0+KLw8P/XJtddsLw6JZja6MfFV8v5X8qPjo4+55TX1lb5gO1eNxR+Y0y08XhBxbzBxXzBRtq8ds/dVQp/8WfOLH2Dgru3VL7IZ53bB1emW7dcnhtmbW1eNxWzM+zufyqDgAAAJYI5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAICuqXEvAJPoqt89cnD2K6f86Qg3qbtg86GDs+/49eeXZi+/+tLqOgCLwMyIshEx/O4iIiKO+876wdlvXFKbvXplLZ+PGJ79+v6PLM3+Xqwo5Q+OO4aHh9/N7aHK58DdxdkHFfOVj2OxFhQ+5BERsaEWv/X6wwdnv/+ojbXhRxR3ueLhw8P/WJsdnyrmix/H+eaRRwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqmxr0A7A2mP7eqlP+DVReMaJPRO//6pw3OLv/YpSPcBGAJ2FKLbyuc3G6pjY7Lb6/lf+b9w7Orn72xNPvv9n1eKf/ue146PPyB0uiIzcV8HFLIrqgOLzqwkM3a6ANq8ernety0fHD0huUPK42euaPycYmIiwvZj9dGx0XF/NZWfIP55ZFHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAupRHAAAAuqbGvQB1y3L74Ox0LhvhJhF3/uwJI5v9u7/3nlL+R1ZsGdEm9Y/jTNtWSI/2NqpqP3r9uFcAWGSmC9mttdEba/HP7f+swdlTH/HPpdmf/EZtlyP+anj26T83/OwSEfGJU55byn/nOUcOD1/0vdLs2u0fEXFEMV/RivkcHj24OPrQYr74TyM2DI/ObDhoZLMjIuLiQnZdcfbW6m1a+UDOf9XzyCMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdU+NegLo3f/B5g7MveMnbR7hJxOf/+M8GZ2fathFuEjHTRjq+ZNTXteIJF76ilD82Lh/RJgB7q5WF7N210etq8TfF/x6cPfVD/1ya/ZtPr+1y5Xdr+YpLv/7M2htcdEkhXL2fe0EtvvyQ4dkttdEjtbyYr+6+qZi/bUTZiIhrivkNhewdxdmxtfoGY+WRRwAAALq65TEzj8nMz2bm1Zn5lcz8ldnLV2bmZzLz2tk/Hzr6dQGApch5BGD8hjzyuDUifq219oMRcUJE/HJmPi4iXh0RF7bWjo2IC2f/DgAwCs4jAGPWLY+ttRtba5fPvnxXRFwdEUdFxGkR8d7Z2Hsj4vRRLQkALG3OIwDjV/qex8xcHRE/FBGXRMQRrbUbI3Z8QY+Iw3fzNmdn5trMXDsT98xtWwBgyZvreSTi1oVaFWCiDC6PmXlARFwQEa9qrd059O1aa+e21ta01tZMx757siMAQETMz3kk4rDRLQgwwQaVx8ycjh1fqN/XWvvQ7MU3Z+aq2deviohbRrMiAIDzCMC4DflpqxkR74mIq1trb93pVR+NiLNmXz4rIj4y/+sBADiPACwGUwMyJ0bEz0XElZl536+yfU1EvDki/iYzXxIR34qI549mRQAA5xGAceuWx9baRRGRu3n1s+d3HQCAB3IeARi/IY88ssg88oO3Dc5e+qLlpdnH77ulug67cOk9wz/u5970rNLsf/+lI0v5H/jGhlJ+WykNsBSsLGS31kZfXItf9BsnD84+9Y8/V5r98c0/Wco/9Z2bB2e/csojS7Pjgt39f4LdGfyzk6J+/J2uxUd6ui5+flV2H3683KF2vKjPr7ijmK/uUjoet+LwqvHWt9Kv6gAAAGBpUh4BAADoUh4BAADoUh4BAADoUh4BAADoUh4BAADoUh4BAADoUh4BAADoUh4BAADoUh4BAADoUh4BAADomhr3AtRtu+prg7Ov+18vLc2+7r9tL+W/9uPvLOWXil867xWDs8e86YvF6f9ezAMwN/uNbvTm79Xy5wyPXnrRs0qjj/z7m0r5X3352wZnvxqPLc2O9bV4xBML2ZXF2QfW4luL40uqR/eZ4dGtxdmbspivxUerjXuBnexddcwjjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHRNjXsBRmvFRy4t5R/zkdr8Z575y4Oz0y++uTT7U4//YCl/yvoXDs5uP//w0uyWpXisXnfr4Oy22mgAJsrdxfyG4dGLjypN3v7Co0v5t7z4tcPDW0ujIy4q5qNyv76iOLt4CNhSHL9oVG+kmRHPr6hWmukRz59cHnkEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgK1trC/bODsqV7an57AV7fwCL3SXtwriz3Z7j3gOWksw1LeJLlbcY2S4RM8X8nSPZYodDavEjC9mp2ui4rZjfUglXz76L6Uv0wp3b+7aOe4E5qH5CLgU/HK2t7X6ye+QRAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACArqlxLwAAsLi1Ec6eLuYPGckWO8zU4jfdOZo1IiJixYjzFaO8/fdm1c/dxcRtuqc88ggAAEBXtzxm5jGZ+dnMvDozv5KZvzJ7+Rsy8/rMXDf733NHvy4AsBQ5jwCM35CnrW6NiF9rrV2emQdGxGWZ+ZnZ172ttXbO6NYDAIgI5xGAseuWx9bajRFx4+zLd2Xm1RFx1KgXAwC4j/MIwPiVvucxM1dHxA9FxCWzF70yM7+cmedl5kPneTcAgAdwHgEYj8HlMTMPiIgLIuJVrbU7I+IdEfGoiDgudvyfwLfs5u3Ozsy1mbl2Ju6Zh5UBgKVqPs4jEbcu2L4Ak2RQeczM6djxhfp9rbUPRUS01m5urW1rrW2PiHdFxPG7etvW2rmttTWttTXTse987Q0ALDHzdR6JOGzhlgaYIEN+2mpGxHsi4urW2lt3unzVTrEzImL9/K8HAOA8ArAYDPlpqydGxM9FxJWZuW72stdExJmZeVzs+C2bGyPi5SPZEADAeQRg7Ib8tNWLIiJ38apPzP86AAAP5DwCMH6ln7YKAADA0jTkaasAAHA/03vpbGBPeeQRAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACArqlxLwAAsHS1cS+wk+qx8MCRbMHeYjF97rJQPPIIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAV7bWFu6dZd4aEd/cxasOjYjbFmyR8XE9J89Sua6u5+h8f2vtsAV+n7CkOY+4nhNmqVzPiKVzXRfteWRBy+Nul8hc21pbM+49Rs31nDxL5bq6nsBSsFS+Briek2WpXM+IpXNdF/P19LRVAAAAupRHAAAAuhZLeTx33AssENdz8iyV6+p6AkvBUvka4HpOlqVyPSOWznVdtNdzUXzPIwAAAIvbYnnkEQAAgEVsrOUxM0/NzK9m5obMfPU4dxm1zNyYmVdm5rrMXDvufeZLZp6Xmbdk5vqdLluZmZ/JzGtn/3zoOHecD7u5nm/IzOtnb9N1mfncce44HzLzmMz8bGZenZlfycxfmb18om7TB7meE3ebAn1L5TwyqWeRCOeRSbvvch5ZvLfp2J62mpnLIuJrEXFyRGyKiC9FxJmttavGstCIZebGiFjTWpuo302Tmc+MiM0R8ZettSfMXvZHEXF7a+3Ns3fCD22t/dY495yr3VzPN0TE5tbaOePcbT5l5qqIWNVauzwzD4yIyyLi9Ih4cUzQbfog1/MFMWG3KfDgltJ5ZFLPIhHOIzFh913OI4v3PDLORx6Pj4gNrbV/a63dGxEfiIjTxrgPe6C19vmIuP1+F58WEe+dffm9seMfwV5tN9dz4rTWbmytXT778l0RcXVEHBUTdps+yPUElh7nkQngPDJZnEcWr3GWx6Mi4rqd/r4pFvkHa45aRHw6My/LzLPHvcyIHdFauzFixz+KiDh8zPuM0isz88uzTyPZq586cX+ZuToifigiLokJvk3vdz0jJvg2BXZpKZ1HltJZJGKC77t2YWLvu5xHFtdtOs7ymLu4bJJ/9OuJrbUnR8SPR8Qvzz7tgL3bOyLiURFxXETcGBFvGe868yczD4iICyLiVa21O8e9z6js4npO7G0K7NZSOo84i0ymib3vch5ZfLfpOMvjpog4Zqe/Hx0RN4xpl5Frrd0w++ctEfHh2PE0mUl18+xzuO97LvctY95nJFprN7fWtrXWtkfEu2JCbtPMnI4dX8De11r70OzFE3eb7up6TuptCjyoJXMeWWJnkYgJvO/alUm973IeWZy36TjL45ci4tjMfERm7hMRL4yIj45xn5HJzP1nvwk2MnP/iDglItY/+Fvt1T4aEWfNvnxWRHxkjLuMzH1fvGadERNwm2ZmRsR7IuLq1tpbd3rVRN2mu7uek3ibAl1L4jyyBM8iERN237U7k3jf5Twy8BdaAAAA10lEQVSyeG/Tsf201YiI2R87+/aIWBYR57XW3jS2ZUYoMx8ZO/4PX0TEVET89aRc18x8f0ScFBGHRsTNEfH6iPj7iPibiHh4RHwrIp7fWturv7l7N9fzpNjxdIIWERsj4uX3PQ9/b5WZT4+IL0TElRGxffbi18SO599PzG36INfzzJiw2xToWwrnkUk+i0Q4j8SE3Xc5jyze88hYyyMAAAB7h3E+bRUAAIC9hPIIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABA1/8HuxXzMNXblFgAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1274,7 +1285,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1286,7 +1297,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1298,7 +1309,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1310,7 +1321,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1322,7 +1333,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1334,7 +1345,7 @@
     },
     {
      "data": {
-      "image/png": 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h77ytlN/r+z8o5d8dDx2c/fMLV5Zmf/LhHy/l/+zB9xuc3fM7pdEABZtGlI2IB9Tiq2PN8HBtCxDtp7X8xPDtSMQLa7O/e/xhpfzaOHp4eEltLfHw+9fyhxfyr6qNjkOK+TML2ev2KQ7fUMzvXcxXbC7mD6/FTx8ePeNXfqs0emlsLOVPOe2vh4dvLwz+8rCYM48AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0KY8AAAB0LR73AqjLx2wY2eyJH+w9stlV3zv9gFL+u09/9+DstuJaXnrtc0r5Df91xeDsqq9dUpq9tZQerWuuPaR2hYePZh0Ao7W5kN1UG72kFj84bh0eXl+bnQ+s5eOFw6PXv+qg0ujz4vml/MZYOjj7kFdfVZr9i1fX9kYHxu2Ds3vG3aXZV6x7dCm/bcm+pXxN8b5ezj9ieHTxU2qjX1uLn/TSDw3O/kZ8pDT7hlhZyu93+E8GZ3/+oML33Z7DYs48AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0LV43Aug7uEHrxv3EqYkn/DIUv68p7yn+BkmBicf+YVTSpNXvXJtKZ93fruUXyjeeOsTS/klX7hicHZbdTEAc8Eod2L3K+ZX1+Lt5cOzfxGvKc2+IJ5byi+NjYOzp8WbS7N/Pf6mlL81lg/OvjVOLc3+9mVPKuXjykp4c2122aZa/ID7D8+eUxt9yknvLOVPjbcOzq64dX1p9ucOfmYp//PrDiqEC4O3Dos58wgAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAECX8ggAAEDX4nEvgLoH7XP74Owe1f8/kK24muG+/9t7lfKPmJgo5Z/wjZcNzh750m+VZm8rpReOif3uLuXv2FK7D2y7885SHoAd3K+YP7QW//yyJw/OfjD+XWn2jR9YVcrv8cw7BmffuPyPS7OX/a/aY9Gy468fnF33qOWl2fHhWjzWVMKbi8Nr+7Ty/EOGRx9y0lWl0W+IPy3lDzvjJ8PD+5VGx/JXr6td4ceF7HcL2YF3c2ceAQAA6FIeAQAA6OqWx8w8OzNvzcwrd7js9My8KTMvm/zvuaNdJgCwkNmPAIzfkDOP50TEiTu5/O2ttWMm/7tgZpcFAPDPnBP2IwBj1S2PrbUvRcT6WVgLAMBO2Y8AjN90fufxtZl5+eTTSA7cVSgzT8nMNZm5ZnPcNY1PBwBwL+X9iA4KMDVTLY/viYgjI+KYiLglIs7YVbC1dlZrbXVrbfVE1F6mHwDgPkxpPxKxbLbWBzCvTKk8ttbWtda2tta2RcT7IuLYmV0WAMB9sx8BmF1TKo+ZueOfkX1+RFy5qywAwCjYjwDMrsW9QGZ+KCKeFhEPyMwbI+JNEfG0zDwmIlpEXBcRrx7hGgGABc5+BGD8uuWxtfaSnVz8/hGsBQBgp+xHAMavWx6Ze7a14c823hbbasNbFlcz3KHLby/lq2s/+qB1g7P/WJq8sCw66ojB2atOOLs0+4TLX1TK7x8/KOUB2EH1dQrvV4v/II4cnL3xwlW14WfW4tu+sO/g7Lvf95rS7Ee/+opS/ltxzODsJ7/9wtLsOKcWj7i8kK3WgqXF/KZa/Lbh0Rt+urI2+/61eJw3PPrDr9RGn3jEF2tXWF7IrqmNHmI6f6oDAACABUJ5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoGvxuBfAwnHAK+8s5S/58kQpf+aD/25w9slvObU0+6Hvur6U33LTzaX8XPKIjww/1nVbN5VmL3nnsuJqflDMA4xbcWtVe2iM2+OA4eGDa7PjgbX4ulg+PPzd2uy4ePPI8p886oWl0b/6BytL+evuOnx4+LTS6Ii4qJjfWMgeVpxd26fV1hIRtw2Pbr54/9Lotb96dCl/2M++ODh7bmlyxOkXFK/wnyvhqwvZuwalnHkEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACga/G4F0DEoqOOKOVPuN8/jGglo7XlpptL+bc883ml/GM/du3g7JUve1dp9m899eml/C2/umxwdutP15dm3/7yJ5fyT3n9JaX8G5d/ZXD2CR8+tTT7yE9dXMoDzA17j2707bX4ZXHM8PALarPvOLh2TuGGWDk8fGdtLRGbivnCY90fPas0+dvXPKm2lMpt+qna6IiNxfwI77uxYcT5ggfU4otia+0KzxgePX3f2ugNZ0zUrnBmJVzZY24ZlHLmEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgC7lEQAAgK7F414AEVuv+WEp/+EfHzs4+/wjP1WafdhTflTKL9p//8HZrRs2lGZvufa6Uv7Sxw3/fyEnvPy3S7OXXX57KZ8P2Dw4+8MzV5ZmX3XCmaX8uq2bSvknfPjUwdkjT724NBtg97TP6EbfWIufH782OPu+lZeUZm+JRaX8ZXFMZXjR8P3FdhPDo1sur43+y0fX8iXXFfPLRpyvqO3ryvn9hkeXrb6pNHrv+EUpf8dbhu8xz9/r35Rmn3LH+0r5eG8lvHchO+wYnXkEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACgS3kEAACga/G4F0Ddna/af3D2bR97eGn2Jx/+iVL+dRcePzj79fc+uTR7v5u3lPIVP3nitlL+ib99bSl/xgMvGpzdo/j/cM762eGl/Dlv/del/JFnf62UB5j/lhaye9dGX1eL/+SMBw/O/v5r/2dp9p573V1byxeHryWuKY2OWFLM37msEF5fHP6VYr5yHyjeX+LgYn6fQnZDcfZEMV+5jSJiv+HR2287oDT675b/Win/1r1OHZz9xLkvKc2O4aO3u+3GQrhy/xq2H3XmEQAAgK5ueczMlZn5+cxcm5lXZebrJi9flpmfzcyrJ/89cPTLBQAWIvsRgPEbcuZxS0T8XmvtERHxpIh4TWYeHRGnRcSFrbVVEXHh5PsAAKNgPwIwZt3y2Fq7pbX2zcm3N0bE2ohYEREnRcS5k7FzI+J5o1okALCw2Y8AjF/pdx4z8/CIeFxEXBIRy1trt0Rs/4Eeu/gN3sw8JTPXZOaazXHX9FYLACx4092P1F8wBYCIQnnMzP0i4mMR8frW2uCXY2qtndVaW91aWz0Re01ljQAAETEz+5Hyqz4CEBEDy2NmTsT2H9QfaK19fPLidZl56OTHD42IW0ezRAAA+xGAcRvyaqsZEe+PiLWttbft8KHzI+LkybdPjojaHwgEABjIfgRg/BYPyBwfES+PiCsy87LJy94QEW+OiI9m5isj4kcR8cLRLBEAwH4EYNy65bG1dlFE5C4+/IyZXQ4AwL3ZjwCMX7bWZu2T7Z/L2nHp5/tsWvyQw0v5k/7+66X8yftfX8qP0h6FFw/eFttGuJKax1z0ylL+qN+9rZTfctPNpTyz65J2YWxo63e1IQZGIPNRLeKjhWtsGtlaIlbV4ofvPzz7qNroWFLM/7iQvaY6e3PxCtcVstVX262uZaKQXVqcvXcxX1G9n28p5ovHesARw7Mvq42O1cX8Zf3IP3lvcfadlxevULk/Fn5exAuitSu6+5HSn+oAAABgYVIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6MrW2qx9sv1zWTsunzFrn4+6RcsPLuV/9O+PGpy944jNpdmfPvEdpfyvfPr1w8Mjvts/7C/vHJxt37hihCthrrukXRgb2voc9zpgIcl8VIv46LiXMUUThezika2ibsuI59f2GAtH5etevb/cv5hfXsxXVDd2txbzGwrZ3fW++KJo7crufsSZRwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALoWj3sBzC1b191ayq94cy1f8V/i+FL+ofGNEa2kro17AQDMoIkRzt5UzFced6uzNxfzla/L0uLsvUeYH+XtOWqjvk0rqjWiujvaWMheXpy9tpiv3L9WFWcfXMxvKWRn/vZ35hEAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAICuxeNeAADA3LZ5hLOrW7GlhezexdmjVD3OiZGsYrvq7TmX1jLKrXt19oZiflMxX/naVO/rq4r5ytemupYtxfx4OfMIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABAl/IIAABA1+JxLwAAgKH2H+Hs6rZwy0hWMTWbd9PZozbKrf7GEc6uqn5fLBvJKrarfl/sXvcvZx4BAADo6pbHzFyZmZ/PzLWZeVVmvm7y8tMz86bMvGzyv+eOfrkAwEJkPwIwfkPOZW+JiN9rrX0zM5dGxKWZ+dnJj729tfbW0S0PACAi7EcAxq5bHltrt0TELZNvb8zMtRGxYtQLAwC4h/0IwPiVfucxMw+PiMdFxCWTF702My/PzLMz88AZXhsAwL3YjwCMx+DymJn7RcTHIuL1rbUNEfGeiDgyIo6J7f8n8IxdXO+UzFyTmWs2x10zsGQAYKGaif1IxPpZWy/AfDKoPGbmRGz/Qf2B1trHIyJaa+taa1tba9si4n0RcezOrttaO6u1trq1tnoi9pqpdQMAC8xM7UdG+zL9APPXkFdbzYh4f0Ssba29bYfLD90h9vyIuHLmlwcAYD8CMBcMebXV4yPi5RFxRWZeNnnZGyLiJZl5TES0iLguIl49khUCANiPAIzdkFdbvSgicicfumDmlwMAcG/2IwDjV3q1VQAAABamIU9bBQBg3tsy7gXsYPO4F0BXtUZU719qylzkzCMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdyiMAAABdi8e9AAAAhto87gXAFI2ydlS/L3wfTZUzjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHQpjwAAAHRla232PlnmTyLi+p186AERcdusLWR8HOf8s1CO1XGOzmGttYNm+XPCgmY/4jjnmYVynBEL51jn7H5kVsvjLheRuaa1tnrc6xg1xzn/LJRjdZzAQrBQfgY4zvlloRxnxMI51rl8nJ62CgAAQJfyCAAAQNdcKY9njXsBs8Rxzj8L5VgdJ7AQLJSfAY5zflkoxxmxcI51zh7nnPidRwAAAOa2uXLmEQAAgDlsrOUxM0/MzO9l5jWZedo41zJqmXldZl6RmZdl5ppxr2emZObZmXlrZl65w2XLMvOzmXn15L8HjnONM2EXx3l6Zt40eZtelpnPHecaZ0JmrszMz2fm2sy8KjNfN3n5vLpN7+M4591tCvQtlP3IfN2LRNiPzLfHLvuRuXubju1pq5m5KCK+HxHPiogbI+IbEfGS1tp3xrKgEcvM6yJidWttXv1tmsw8ISJ+HhH/u7X2qMnL/kdErG+tvXnyQfjA1tofjHOd07WL4zw9In7eWnvrONc2kzLz0Ig4tLX2zcxcGhGXRsTzIuIVMY9u0/s4zhfFPLtNgfu2kPYj83UvEmE/EvPssct+ZO7uR8Z55vHYiLimtXZta+3uiPhwRJw0xvUwBa21L0XE+l+6+KSIOHfy7XNj+zfBbm0XxznvtNZuaa19c/LtjRGxNiJWxDy7Te/jOIGFx35kHrAfmV/sR+aucZbHFRFxww7v3xhz/Is1TS0iPpOZl2bmKeNezIgtb63dErH9myIiDh7zekbptZl5+eTTSHbrp078sswApcXrAAACMUlEQVQ8PCIeFxGXxDy+TX/pOCPm8W0K7NRC2o8spL1IxDx+7NqJefvYZT8yt27TcZbH3Mll8/mlX49vrT0+Ip4TEa+ZfNoBu7f3RMSREXFMRNwSEWeMdzkzJzP3i4iPRcTrW2sbxr2eUdnJcc7b2xTYpYW0H7EXmZ/m7WOX/cjcu03HWR5vjIiVO7z/oIi4eUxrGbnW2s2T/94aEefF9qfJzFfrJp/Dfc9zuW8d83pGorW2rrW2tbW2LSLeF/PkNs3Midj+A+wDrbWPT148727TnR3nfL1Ngfu0YPYjC2wvEjEPH7t2Zr4+dtmPzM3bdJzl8RsRsSozj8jMPSPixRFx/hjXMzKZue/kL8FGZu4bEc+OiCvv+1q7tfMj4uTJt0+OiE+McS0jc88Pr0nPj3lwm2ZmRsT7I2Jta+1tO3xoXt2muzrO+XibAl0LYj+yAPciEfPssWtX5uNjl/3I3L1Nx/ZqqxERky87+46IWBQRZ7fW/nRsixmhzHxIbP8/fBERiyPig/PlWDPzQxHxtIh4QESsi4g3RcTfRsRHI+LBEfGjiHhha223/uXuXRzn02L70wlaRFwXEa++53n4u6vMfEpEfDkiroiIbZMXvyG2P/9+3tym93GcL4l5dpsCfQthPzKf9yIR9iMxzx677Efm7n5krOURAACA3cM4n7YKAADAbkJ5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoOv/A1V3Mhae861WAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1346,7 +1357,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1358,7 +1369,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]
@@ -1370,7 +1381,7 @@
     },
     {
      "data": {
-      "image/png": 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Tlu06OHvRc/5nae03HvfKUj6u+motD8DCma5e4YeF7LLSyofFTbVRPlfI3ltb+rHP/X7tCiUra/G9istfV8i+rbb0FS87sZR/8slfG5x9VnyptPaxMXwPGBFx4SEvK+VLNo5u6YioNaby53RRZf0RND1nHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOhSHgEAAOiaGvcAjNYBF/9jKf+t//RAKX/WY68fnH3jJ9aW1t4SrZSv+JWbnlfK//C39i3lT/nIFwZnX7HnLaW1b/qt2v/5HHpVKQ7AQirvxFaOYoqIiLg+jq5d4UWF7DG1pW+KQ2tXWF7J7l5be69aPG4uZL9RXHtFMX/y8OjT7qzt0+7cb7/aLBtr8bi2mK94XDG/qpCdLq69k3HmEQAAgC7lEQAAgK5ueczM92XmnZl53TaXvSUzb8vMa2f+PHe0YwIAS5n9CMD4DTnz+IGIOGk7l7+ztXbMzJ9Pze9YAAD/ygfCfgRgrLrlsbV2eUTcvQCzAABsl/0IwPjN5WceX5OZX515GsneOwpl5hmZuSYz12yOTXO4OQCARyjvR2LzXQs5H8DEmG15fHdEHBpbX3h5XUScu6Nga+281trq1trqZbHbLG8OAOARZrUfiWW1X78EwFazKo+ttTtaaw+21rZExHsi4tj5HQsA4NHZjwAsrFmVx8w8cJt3T4mI63aUBQAYBfsRgIU11Qtk5kci4viIWJWZt0bEmyPi+Mw8JiJaRNwcEa8a4YwAwBJnPwIwft3y2Fp76XYufu8IZgEA2C77EYDx65ZHdm7T675Xyp9x1pml/PvPecfg7BHL9iitHW1LKX7YZ185OHvUa75RWnvL/deX8m/73PMHZ09/wbtLa7999cdL+b94yvNK+S3/sLaUB5h4GwvZ6s5qr2J+Yw7P3ldb+o9ue2Mpv+G0lYOzd8Z+pbXXfvJppXzpflxRW7qcL20x7q2tPbVnKf7k+Orw8EW1UQ591U21K9xci0dcUcjuU1t649G1/PLK2rWldzZz+VUdAAAALBHKIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF1T4x6AxWXFx64u5V8RvzM4e/eLf1Bae+M/71bKP/GsmwZnH7z//tLaVUe+6frB2RMOf2Fp7UuedGEp/+Y31/6P6KDaOACTb3pE2YiIvUaY31hc+8zlpfi7VwzfA5Tvl/XF/IpCtnqfV2cv5VfW1l5Vi+8WD1TCJbvGptoVjqrFI55RyG6uLV37pz7arwE7GWceAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6FIeAQAA6Joa9wDs3FZ87OpCdoSDRMSDo12+ZMuGDYOz9170E7XFn1SLv/0nLyzl/+zA4wdnp9d9rzYMwKSbLuaXF/OVndt9xbWvKuZvLeYrDivmDylkq7vfjcX8qkJ2edbWvqcWPyfeMDj7I6f9oLT2/nFnKf97zzy7lP9g+7XB2e+cf1Rp7fhMLT7Sf+s7WRtz5hEAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAIAu5REAAICubK0t2I3tmfu0Z+QJC3Z7sFN4zC6l+F2fOKyUv/rpHy7ln/hXrx6cPfT1V5XW5pGubpfFve3uHPccsJTkytUtVq8Z9xizM1XIThfXXl/M31PIVuaOiFhVzK8oZKuzVO/HyvrVWTYW8xUvq8XffvprS/nf/R9/UruBTcOj/+13zywtffZr31mb5c8L2UNqS8dRxXzl32Pl38ua1dE2rOnuR5x5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoEt5BAAAoGtq3APAkrflwVL8sefuXsqv/+APS/m1L/nTwdnnf/jXSmu3a75eygPwMNMjXPuAYv6QUQwxo3qc940oGxGxvJiv3I8rimtfWszffMnw7BeeWVr6ltMPLuWvP7MUjwsK2Vf87odKa5993Ttrw0wX7sdbT6ytvboWL31ubCyuPYAzjwAAAHR1y2NmHpyZn8/MtZn59cx83czl+2TmJZl5w8zfe49+XABgKbIfARi/IWcepyPi9a21J0bEcRHx6sw8OiLeFBGXtdYOj4jLZt4HABgF+xGAMeuWx9bautbal2fe3hARayPioIg4OSLOn4mdHxEvGNWQAMDSZj8CMH6ln3nMzEMi4qkRcXVE7N9aWxex9Qt6ROy3g+uckZlrMnPN5tg0t2kBgCVvrvuR2HzXQo0KMFEGl8fMXBERF0bEma21e4der7V2XmttdWtt9bLYbTYzAgBExPzsR2LZvqMbEGCCDSqPmbkstn6h/lBr7eMzF9+RmQfOfPzAiLhzNCMCANiPAIzbkFdbzYh4b0Ssba29Y5sPXRwRp868fWpEfHL+xwMAsB8BWAymBmSeGREvj4ivZea1M5edHRFvi4gLMvP0iPhuRLxoNCMCANiPAIxbtzy21q6IiNzBh0+Y33EAAB7JfgRg/IaceQQWkcd88Sul/PHnn1XKX//rfzo4u+GtPyytveeLVpbyWzZsKOUBJt50IVvd5Y06X1E5zlFbTLOUDX5NqYiofc99cMQ1Ys9C9ltxZG3xL9TiEf9veHTjibWll9fisbGYn2elX9UBAADA0qQ8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0KU8AgAA0DU17gGA0TrsvFtK+Q++6IDB2cuf/NeltU96yq+X8o+54tpSHmDijXLntnHE+VGq3C/V+7CaH+X9sqJ6hROHRw/Ys7TyzXFIKX/0W0vxOLqwBXhHPLm2+PG1eHzh1OHZVcW17yvmp4v5eebMIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF3KIwAAAF1T4x4AGK3pW24t5S845dmDsy+/9KOltdeftbGU3++KUhyAuZge9wDbGOUOdfkI146IuK+QrX1bjFhRzB+25/Ds42pLf/qLLyzlf/Psc0v5/xAfGpz96fjb0tp/8Pk3lPLvfPC3B2fvri0dcWkxX/nc2Ku49gDOPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANClPAIAANCVrbUFu7E9c5/2jDxhwW4PGK2VX1pVyr/rCZ8o5U9/8W8OD1/11dLai8XV7bK4t92d454DlpJcubrF6jXjHmPnNz3CtadGuPaojfJ+WT7ifMWtxfwBtfi+n/7u4Oydtzyhtvira/HNHxye3fXGYrf6mVq89JgeU8iuWR1tw5rufsSZRwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqURwAAALqmxj0AsPP6wSmtlL/6b3+0lP+nI/cYnN37qtLSAMyVXeTcTY94/RWF7D3Fta8t5uOGUvqusw4fnP3Mf392ae2TjvhiKf/AcufbHuKeAAAAoKtbHjPz4Mz8fGauzcyvZ+brZi5/S2belpnXzvx57ujHBQCWIvsRgPEb8oSD6Yh4fWvty5m5MiKuycxLZj72ztbaOaMbDwAgIuxHAMauWx5ba+siYt3M2xsyc21EHDTqwQAAHmI/AjB+pZ95zMxDIuKpEXH1zEWvycyvZub7MnPveZ4NAOAR7EcAxmNweczMFRFxYUSc2Vq7NyLeHRGHRsQxsfV/As/dwfXOyMw1mblmc2yah5EBgKVqPvYjsfmuBZsXYJIMKo+ZuSy2fqH+UGvt4xERrbU7WmsPtta2RMR7IuLY7V23tXZea211a231sthtvuYGAJaY+dqPxLJ9F25ogAky5NVWMyLeGxFrW2vv2ObyA7eJnRIR183/eAAA9iMAi8GQV1t9ZkS8PCK+lpkP/TrQsyPipZl5TES0iLg5Il41kgkBAOxHAMZuyKutXhERuZ0PfWr+xwEAeCT7EYDxK73aKgAAAEvTkKetAmzXg+u/X8qfd8SPl/J7x5WlPADsVKo78eli/r4Rrv24Yv57h9fy1/YjD3nOP3yhtPS/Pedzpfz1m44eHv7r0tIRhxTzY25vzjwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQpTwCAADQNTXuAQAAYGIspt31fSNc+7AR56cL2TNrS18Z/752hVE6YNwD1DjzCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQJfyCAAAQFe21hbuxjLviojvbOdDqyJi/YINMj6Oc/IslWN1nKPzhNbavgt8m7Ck2Y84zgmzVI4zYukc66LdjyxoedzhEJlrWmurxz3HqDnOybNUjtVxAkvBUvka4Dgny1I5zoilc6yL+Tg9bRUAAIAu5REAAICuxVIezxv3AAvEcU6epXKsjhNYCpbK1wDHOVmWynFGLJ1jXbTHuSh+5hEAAIDFbbGceQQAAGARG2t5zMyTMvObmXljZr5pnLOMWmbenJlfy8xrM3PNuOeZL5n5vsy8MzOv2+ayfTLzksy8Yebvvcc543zYwXG+JTNvm3lMr83M545zxvmQmQdn5uczc21mfj0zXzdz+UQ9po9ynBP3mAJ9S2U/Mql7kQj7kUn73mU/sngf07E9bTUzd4mIb0XEiRFxa0T8fUS8tLV2/VgGGrHMvDkiVrfWJup302Tmz0bEfRHxl621n5i57I8i4u7W2ttmvgnv3Vp74zjnnKsdHOdbIuK+1to545xtPmXmgRFxYGvty5m5MiKuiYgXRMRpMUGP6aMc54tjwh5T4NEtpf3IpO5FIuxHYsK+d9mPLN79yDjPPB4bETe21r7dWnsgIv4qIk4e4zzMQmvt8oi4+2EXnxwR58+8fX5s/STYqe3gOCdOa21da+3LM29viIi1EXFQTNhj+ijHCSw99iMTwH5kstiPLF7jLI8HRcQt27x/ayzyO2uOWkR8NjOvycwzxj3MiO3fWlsXsfWTIiL2G/M8o/SazPzqzNNIduqnTjxcZh4SEU+NiKtjgh/Thx1nxAQ/psB2LaX9yFLai0RM8Peu7ZjY7132I4vrMR1necztXDbJL/36zNba0yLiORHx6pmnHbBze3dEHBoRx0TEuog4d7zjzJ/MXBERF0bEma21e8c9z6hs5zgn9jEFdmgp7UfsRSbTxH7vsh9ZfI/pOMvjrRFx8DbvPy4ibh/TLCPXWrt95u87I+Ki2Po0mUl1x8xzuB96LvedY55nJFprd7TWHmytbYmI98SEPKaZuSy2fgH7UGvt4zMXT9xjur3jnNTHFHhUS2Y/ssT2IhET+L1reyb1e5f9yOJ8TMdZHv8+Ig7PzB/LzF0j4iURcfEY5xmZzNxj5odgIzP3iIifj4jrHv1aO7WLI+LUmbdPjYhPjnGWkXnoi9eMU2ICHtPMzIh4b0Ssba29Y5sPTdRjuqPjnMTHFOhaEvuRJbgXiZiw7107Monfu+xHFu9jOrZXW42ImHnZ2XdFxC4R8b7W2lvHNswIZeaPx9b/4YuImIqID0/KsWbmRyLi+IhYFRF3RMSbI+ITEXFBRDw+Ir4bES9qre3UP9y9g+M8PrY+naBFxM0R8aqHnoe/s8rMn4mIL0XE1yJiy8zFZ8fW599PzGP6KMf50piwxxToWwr7kUnei0TYj8SEfe+yH1m8+5GxlkcAAAB2DuN82ioAAAA7CeURAACALuURAACALuURAACALuURAACALuURAACALuURAACALuURAACArv8PRZu+pfOQ9swAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 1296x432 with 2 Axes>"
       ]