diff --git a/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb b/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb
index 912b0ec3aba18b42bcb4987f4110c38d4a1ac0c4..2f99b8058b9345915cb0f53b907d15177920e9a9 100644
--- a/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb	
+++ b/notebooks/Block_4/Jupyter Notebook Block 4 - Convolutional Neural Networks.ipynb	
@@ -2052,11 +2052,11 @@
     "\n",
     "\n",
     "In this case, let’s consider a large convnet trained on the ImageNet dataset (1.4\n",
-    "million labeled images and 1,000 different classes). ImageNet contains many animal\n",
+    "million labeled images and 1'000 different classes). ImageNet contains many animal\n",
     "classes, including different species of cats and dogs, and you can thus expect it to perform well on the dogs-versus-cats classification problem.\n",
     "\n",
     "We’ll use the VGG16 architecture, developed by Karen Simonyan and Andrew\n",
-    "Zisserman in 2014.1 Although it’s an older model, far from the current state of the art\n",
+    "Zisserman in 2014. Although it’s an older model, far from the current state of the art\n",
     "and somewhat heavier than many other recent models, we chose it because its architecture\n",
     "is similar to what you’re already familiar with, and it’s easy to understand without\n",
     "introducing any new concepts. This may be your first encounter with one of these\n",
@@ -2068,21 +2068,131 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.7.1\n"
+     ]
+    }
+   ],
    "source": [
+    "# Import TensorFlow and TensorFlow Datasets\n",
+    "import tensorflow as tf\n",
+    "print(tf.__version__)\n",
+    "\n",
+    "# Helper libraries\n",
+    "import math\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tensorflow.keras.utils import to_categorical\n",
+    "\n",
     "# Defining the loss tensor for filter visualization\n",
     "\n",
     "model_vgg16 = tf.keras.applications.VGG16(weights='imagenet',\n",
     "                                   include_top=True)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1 Instantiating the VGG16 Architecture"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "model_vgg16 = tf.keras.applications.VGG16(weights='imagenet',\n",
+    "                                   include_top=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We pass two arguments to the constructor:\n",
+    "\n",
+    "- `weights` specifies the weight checkpoint from which to initialize the model.\n",
+    "\n",
+    "- `include_top` refers to including (or not) the densely connected classifier on\n",
+    "top of the network. By default, this densely connected classifier corresponds to\n",
+    "the 1'000 classes from ImageNet. Because we intend to use the entire pretrained VGG16 network, we will include it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"vgg16\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " input_4 (InputLayer)        [(None, 224, 224, 3)]     0         \n",
+      "                                                                 \n",
+      " block1_conv1 (Conv2D)       (None, 224, 224, 64)      1792      \n",
+      "                                                                 \n",
+      " block1_conv2 (Conv2D)       (None, 224, 224, 64)      36928     \n",
+      "                                                                 \n",
+      " block1_pool (MaxPooling2D)  (None, 112, 112, 64)      0         \n",
+      "                                                                 \n",
+      " block2_conv1 (Conv2D)       (None, 112, 112, 128)     73856     \n",
+      "                                                                 \n",
+      " block2_conv2 (Conv2D)       (None, 112, 112, 128)     147584    \n",
+      "                                                                 \n",
+      " block2_pool (MaxPooling2D)  (None, 56, 56, 128)       0         \n",
+      "                                                                 \n",
+      " block3_conv1 (Conv2D)       (None, 56, 56, 256)       295168    \n",
+      "                                                                 \n",
+      " block3_conv2 (Conv2D)       (None, 56, 56, 256)       590080    \n",
+      "                                                                 \n",
+      " block3_conv3 (Conv2D)       (None, 56, 56, 256)       590080    \n",
+      "                                                                 \n",
+      " block3_pool (MaxPooling2D)  (None, 28, 28, 256)       0         \n",
+      "                                                                 \n",
+      " block4_conv1 (Conv2D)       (None, 28, 28, 512)       1180160   \n",
+      "                                                                 \n",
+      " block4_conv2 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
+      "                                                                 \n",
+      " block4_conv3 (Conv2D)       (None, 28, 28, 512)       2359808   \n",
+      "                                                                 \n",
+      " block4_pool (MaxPooling2D)  (None, 14, 14, 512)       0         \n",
+      "                                                                 \n",
+      " block5_conv1 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
+      "                                                                 \n",
+      " block5_conv2 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
+      "                                                                 \n",
+      " block5_conv3 (Conv2D)       (None, 14, 14, 512)       2359808   \n",
+      "                                                                 \n",
+      " block5_pool (MaxPooling2D)  (None, 7, 7, 512)         0         \n",
+      "                                                                 \n",
+      " flatten (Flatten)           (None, 25088)             0         \n",
+      "                                                                 \n",
+      " fc1 (Dense)                 (None, 4096)              102764544 \n",
+      "                                                                 \n",
+      " fc2 (Dense)                 (None, 4096)              16781312  \n",
+      "                                                                 \n",
+      " predictions (Dense)         (None, 1000)              4097000   \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 138,357,544\n",
+      "Trainable params: 138,357,544\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
    "source": [
     "model_vgg16.summary()"
    ]
@@ -2091,7 +2201,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 1 Run VGG16 on a single image\n",
+    "### 2.2 Run VGG16 on a single image\n",
     "\n",
     "Remember our `model_vgg16` object is still the full VGG16 model trained on ImageNet, so it has 1000 possible output classes.\n",
     "ImageNet has a lot of dogs and cats in it, so let's see if it can predict the images in our Dogs vs. Cats dataset.\n",
@@ -2100,11 +2210,26 @@
     "the network was trained on. "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.2.1 Preprocessing a single image"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 224, 224, 3)\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "import PIL.Image as Image\n",
@@ -2114,9 +2239,20 @@
     "from tensorflow.keras.preprocessing import image\n",
     "import numpy as np\n",
     "\n",
-    "img = image.load_img(img_path, target_size=(224, 224))\n",
-    "img_tensor_orig = image.img_to_array(img)\n",
-    "img_tensor_orig = np.expand_dims(img_tensor_orig, axis=0)\n",
+    "def get_img_array(img_path, target_size):\n",
+    "    # Open the image file and resize it\n",
+    "    img = tf.keras.utils.load_img(img_path, target_size=target_size)\n",
+    "    # Turn the image into a float32 NumPy array of shape (224, 224, 3)\n",
+    "    array = tf.keras.utils.img_to_array(img)\n",
+    "    # Add a dimension to transform the array into \n",
+    "    # a “batch” of a single sample. \n",
+    "    # Its shape is now (1, 224, 224, 3).\n",
+    "    array = np.expand_dims(array, axis=0)\n",
+    "    return array\n",
+    "\n",
+    "\n",
+    "img_tensor_orig = get_img_array(img_path, target_size=(224, 224))\n",
+    "\n",
     "img_tensor =img_tensor_orig/255\n",
     "\n",
     "print(img_tensor.shape)\n"
@@ -2126,14 +2262,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Displaying the test picture"
+    "#### 2.2.2 Displaying the test picture"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
@@ -2143,9 +2292,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 29,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 1000)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "result = model_vgg16.predict(img_tensor_orig)\n",
     "result.shape"
@@ -2162,9 +2322,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 30,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "552"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predicted_class = np.argmax(result[0], axis=-1)\n",
     "predicted_class"
@@ -2174,16 +2345,36 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Decode the predictions\n",
+    "#### 2.2.3 Decode the predictions\n",
     "\n",
     "To see what our `predicted_class` is in the ImageNet dataset, download the ImageNet labels and fetch the row that the model predicted."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 31,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[('n03325584', 'feather_boa', 0.35417125), ('n02123045', 'tabby', 0.29227364), ('n02127052', 'lynx', 0.109391265)]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "print(tf.keras.applications.vgg16.decode_predictions(result, top=3))\n",
     "\n",
@@ -2201,11 +2392,69 @@
     "before."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.3 Running single image with model trained entirely on dog and cat image dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.1 Preprocessing a single image"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 35,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv2d_4 (Conv2D)           (None, 148, 148, 32)      896       \n",
+      "                                                                 \n",
+      " max_pooling2d_2 (MaxPooling  (None, 74, 74, 32)       0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_5 (Conv2D)           (None, 72, 72, 64)        18496     \n",
+      "                                                                 \n",
+      " max_pooling2d_3 (MaxPooling  (None, 36, 36, 64)       0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_6 (Conv2D)           (None, 34, 34, 128)       73856     \n",
+      "                                                                 \n",
+      " max_pooling2d_4 (MaxPooling  (None, 17, 17, 128)      0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_7 (Conv2D)           (None, 15, 15, 128)       147584    \n",
+      "                                                                 \n",
+      " max_pooling2d_5 (MaxPooling  (None, 7, 7, 128)        0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " dropout_3 (Dropout)         (None, 7, 7, 128)         0         \n",
+      "                                                                 \n",
+      " flatten_1 (Flatten)         (None, 6272)              0         \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 512)               3211776   \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             (None, 2)                 1026      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 3,453,634\n",
+      "Trainable params: 3,453,634\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
    "source": [
     "model = tf.keras.models.load_model('./Daten/cats_and_dogs_small_1.h5', compile = False)\n",
     "model.summary()"
@@ -2220,28 +2469,57 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 34,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 150, 150, 3)\n"
+     ]
+    }
+   ],
    "source": [
     "img_path = './Daten/cat_1700.jpg'\n",
     "\n",
-    "from tensorflow.keras.preprocessing import image\n",
-    "import numpy as np\n",
+    "img_tensor_orig = get_img_array(img_path, target_size=(150, 150))\n",
     "\n",
-    "img = image.load_img(img_path, target_size=(150, 150))\n",
-    "img_tensor = image.img_to_array(img)\n",
-    "img_tensor = np.expand_dims(img_tensor, axis=0)\n",
-    "img_tensor /=255\n",
+    "img_tensor =img_tensor_orig/255\n",
     "\n",
     "print(img_tensor.shape)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### 2.3.2 Decode the predictions"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[0.9159515  0.08404852]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "result = model.predict(img_tensor)\n",
     "print(result)\n",
@@ -2263,8 +2541,14 @@
     "# Part 3 - Visualizing what ConvNets Learn\n",
     "\n",
     "\n",
-    "It is often said that deep-learning models are \"black-boxes\" : learning representations that are difficult to extract and present in a human-readable form. Although this is partially true \n",
-    "for certain types of deep-learning models, it is definitely not true for ConvNets. The representations learned by ConvNets are highly amenable to visualization, in large part because they are _representations of visual concepts_. Since 2013, a wide array of techniques have been developed for visualizing and interpreting these representations. We won't survey all of them, but we will cover three of the most accessible and useful ones:\n",
+    "A fundamental problem when building a computer vision application is that of _interpretability_: why did your classifier think a particular image contained a fridge, when all you can see is a truck? This is especially relevant to use cases where deep learning is used to complement human expertise, such as in medical imaging use cases. We will end this chapter by getting you familiar with a range of different techniques for visualizing\n",
+    "what convnets learn and understanding the decisions they make.\n",
+    "\n",
+    "\n",
+    "It’s often said that deep learning models are “black boxes”: they learn representations\n",
+    "that are difficult to extract and present in a human-readable form. Although this\n",
+    "is partially true for certain types of deep learning models, it’s definitely not true for convnets. The representations learned by convnets are highly amenable to visualization, in large part because they’re _representations of visual concepts_. Since 2013, a wide array of techniques has been developed for visualizing and interpreting these representations. We won’t survey all of them, but we’ll cover three of the most accessible\n",
+    "and useful ones:\n",
     "\n",
     "- _Visualizing intermediate ConvNet outputs (intermediate activations)_ -- Useful for understanding how successive convnet layers transform their input, and for getting a first idea of the meaning of individual ConvNet filters.\n",
     "\n",
@@ -2272,7 +2556,7 @@
     "\n",
     "- _Visualizing heatmaps of class activation in an image_ -- Useful for understanding which parts of an image were identified as belonging to a given class, thus allowing you to localize objects in images.\n",
     "\n",
-    "For the first method - activation visualization - we will use the small ConvNet that we have trained from scratch on the dogs-versus-cats classification problem before. "
+    "For the first method - activation visualization - we will use the small ConvNet that we have trained from scratch on the dogs-versus-cats classification problem before. For the next two methods, we’ll use a pretrained Xception model."
    ]
   },
   {
@@ -2281,17 +2565,65 @@
    "source": [
     "## 3.1 Visualizing Intermediate Layers\n",
     "\n",
-    "Visualizing intermediate layers consists of displaying the activation maps that are output by various convolution and pooling layers in a network, given a certain input (the output of a layer is often called its __activation maps__, the output of the activation function). \n",
     "\n",
-    "This gives a view into how an input is decomposed into the different filters learned by the network. You want to \n",
-    "visualize activation maps with three dimensions: width, height, and depth (number of filters = number of activation maps). Eeach activation map encodes relatively independent features, so the proper way to visualize these activation maps is by independently plotting the contents of every activation map as a 2D image. Lat's start by loading the model that we saved before. "
+    "Visualizing intermediate activations consists of displaying the values returned by various convolution and pooling layers in a model, given a certain input (the output of a layer is often called its __activation__, the output of the activation function). \n",
+    "\n",
+    "This gives a view into how an input is decomposed into the different filters learned by the network. We want\n",
+    "to visualize feature maps with three dimensions: width, height, and depth (number of filters = number of activation maps or simply channels).\n",
+    "Each channel encodes relatively independent features, so the proper way to visualize\n",
+    "these feature maps is by independently plotting the contents of every channel as a 2D\n",
+    "image. Let’s start by loading the model that we saved before. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 36,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Model: \"sequential_1\"\n",
+      "_________________________________________________________________\n",
+      " Layer (type)                Output Shape              Param #   \n",
+      "=================================================================\n",
+      " conv2d_4 (Conv2D)           (None, 148, 148, 32)      896       \n",
+      "                                                                 \n",
+      " max_pooling2d_2 (MaxPooling  (None, 74, 74, 32)       0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_5 (Conv2D)           (None, 72, 72, 64)        18496     \n",
+      "                                                                 \n",
+      " max_pooling2d_3 (MaxPooling  (None, 36, 36, 64)       0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_6 (Conv2D)           (None, 34, 34, 128)       73856     \n",
+      "                                                                 \n",
+      " max_pooling2d_4 (MaxPooling  (None, 17, 17, 128)      0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " conv2d_7 (Conv2D)           (None, 15, 15, 128)       147584    \n",
+      "                                                                 \n",
+      " max_pooling2d_5 (MaxPooling  (None, 7, 7, 128)        0         \n",
+      " 2D)                                                             \n",
+      "                                                                 \n",
+      " dropout_3 (Dropout)         (None, 7, 7, 128)         0         \n",
+      "                                                                 \n",
+      " flatten_1 (Flatten)         (None, 6272)              0         \n",
+      "                                                                 \n",
+      " dense_2 (Dense)             (None, 512)               3211776   \n",
+      "                                                                 \n",
+      " dense_3 (Dense)             (None, 2)                 1026      \n",
+      "                                                                 \n",
+      "=================================================================\n",
+      "Total params: 3,453,634\n",
+      "Trainable params: 3,453,634\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
    "source": [
     "model = tf.keras.models.load_model('./Daten/cats_and_dogs_small_1.h5')\n",
     "model.summary()"
@@ -2307,19 +2639,40 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 48,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 150, 150, 3)\n"
+     ]
+    }
+   ],
    "source": [
-    "img_path = './Daten/cat_1700.jpg'\n",
-    "\n",
-    "from tensorflow.keras.preprocessing import image\n",
+    "from tensorflow import keras\n",
     "import numpy as np\n",
     "\n",
-    "img = image.load_img(img_path, target_size=(150, 150))\n",
-    "img_tensor = image.img_to_array(img)\n",
-    "img_tensor = np.expand_dims(img_tensor, axis=0)\n",
-    "img_tensor /=255\n",
+    "# Download a test image\n",
+    "img_path = keras.utils.get_file( fname=\"cat.jpg\", origin=\"https://img-datasets.s3.amazonaws.com/cat.jpg\")\n",
+    "# Alternatively, use the following one\n",
+    "#img_path = './Daten/cat_1700.jpg'\n",
+    "\n",
+    "def get_img_array(img_path, target_size):\n",
+    "    # Open the image file and resize it.\n",
+    "    img = keras.utils.load_img(img_path, target_size=target_size)\n",
+    "    # Turn the image into a float32 NumPy array of shape (150, 150, 3)\n",
+    "    array = keras.utils.img_to_array(img)\n",
+    "    # Add a dimension to transform the array into\n",
+    "    # a “batch” of a single sample. Its shape is now\n",
+    "    # (1, 150, 150, 3)\n",
+    "    array = np.expand_dims(array, axis=0)\n",
+    "    return array\n",
+    "\n",
+    "\n",
+    "img_tensor = get_img_array(img_path, target_size=(150, 150))\n",
+    "\n",
     "\n",
     "print(img_tensor.shape)"
    ]
@@ -2333,13 +2686,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.imshow(img_tensor[0])\n",
+    "plt.axis(\"off\")\n",
+    "plt.imshow(img_tensor[0].astype(\"uint8\"))\n",
     "plt.show()"
    ]
   },
@@ -2347,68 +2713,207 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In order to extract the activation maps we want to look at, we will create a Keras model \n",
-    "that takes batches of images as input, and outputs the activation maps of all convolutional \n",
-    "and pooling layers. To do this, we will use the Keras class `Model`. \n",
-    "\n",
-    "A model is instantiated using two arguments: an input tensor (or list of input tensors) \n",
-    "and an output tensor (or list of output tensors). The resulting class is a Keras model, just \n",
-    "like the `Sequential` models we are familiar with, mapping the specified inputs to the specified outputs. \n",
-    "\n",
-    "What sets the `Model` class apart is that it allows for models with multiple outputs, unlike `Sequential`. "
+    "In order to extract the feature maps we want to look at, we’ll create a Keras model that takes batches of images as input, and that outputs the activations of all convolution and pooling layers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Instantiating a model that returns layer activations"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
-    "layer_outputs = [layer.output for layer in model.layers[:8]]\n",
-    "activation_model = tf.keras.models.Model(inputs=model.input, outputs=layer_outputs)"
+    "from tensorflow.keras import layers\n",
+    "layer_outputs = []\n",
+    "layer_names = []\n",
+    "for layer in model.layers:\n",
+    "    # Extract the outputs of all Conv2D and MaxPooling2D layers and put them in a list.\n",
+    "    if isinstance(layer, (layers.Conv2D, layers.MaxPooling2D)):\n",
+    "        layer_outputs.append(layer.output)\n",
+    "        # Save the layer names for later\n",
+    "        layer_names.append(layer.name)\n",
+    "# Create a model that will return these outputs, given the model input.        \n",
+    "activation_model = keras.Model(inputs=model.input, outputs=layer_outputs)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When fed an image input, this model returns the values of the activation maps in the original \n",
-    "model. Now we will encounter a multi-output model : until now, \n",
-    "the models you have seen have had exactly one input and one output. In the general case, a model \n",
-    "can have any number of inputs and outputs. This one has one input and eight outputs : one output per intermediate layer."
+    "When fed an image input, this model returns the values of the layer activations in the\n",
+    "original model, as a list. This is the first time you’ve encountered a multi-output\n",
+    "model in this book in practice since you learned about them in chapter 7; until now,\n",
+    "the models you’ve seen have had exactly one input and one output. This one has one\n",
+    "input and nine outputs: one output per layer activation."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Running the model in predict mode"
+    "#### Using the model to compute layer activations"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
-    "layer_activation_maps = activation_model.predict(img_tensor)"
+    "# Return a list of nine NumPy arrays: one array per layer activation.\n",
+    "activations = activation_model.predict(img_tensor)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For instance, this is the output volume of the first convolution layer for the cat image input:"
+    "For instance, this is the activation of the first convolution layer for the cat image input:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 148, 148, 32)\n"
+     ]
+    }
+   ],
+   "source": [
+    "first_layer_activation = activations[0]\n",
+    "print(first_layer_activation.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It’s a $148\\times 148$ feature map with 32 channels. Let’s try plotting the fifth channel of the activation of the first layer of the original model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualizing the fifth channel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff4ec231410>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAQcAAAECCAYAAADzZhIUAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAABPL0lEQVR4nO29eZgd51ng+3ur6ix9et/U2jdLsi07trxLdkhCEsgKhkvIhOGSsAxwmTsDDHAhgQtz7zDMwwwzA+GZewO5bIEHkpgkTEIIJMTZDN7i3ZZl2bK27pbUUqv35SxV9d0/vq++qtM6LfUqtaTv9zy2TtepU8s5VW+9+ytKKRwOh2Mu3pU+AIfDsTZxwsHhcDTECQeHw9EQJxwcDkdDnHBwOBwNccLB4XA05IoLBxF5p4gcFpEjIvLhVd7XFhH5uoi8LCIHReTnzPIuEflHEXnN/Nu5Cvv2ReRZEfmi+XuHiDxhzvvTIpJf6X2a/XSIyGdE5BUROSQiBy7T+f478x2/JCKfFJHiapyziPyJiJwVkZcyyxqen2h+3+z/BRG5cxX2/Tvmu35BRP5GRDoy733E7PuwiLxjJfebee8XRUSJSI/5e+nnrJS6Yv8BPvA6sBPIA88De1dxfxuAO83rVuBVYC/wX4APm+UfBv7zKuz7F4C/Ar5o/n4I+IB5/QfAz6zSOX8C+FfmdR7oWO3zBTYBx4CmzLn+6GqcM/Am4E7gpcyyhucHvBv4e0CA/cATq7Dv7wYC8/o/Z/a911zfBWCHue79ldqvWb4F+DJwAuhZ7jmv+MW4yJM8AHw58/dHgI9cxv1/Hvgu4DCwwSzbABxe4f1sBh4G3gp80fxQw5mLqO57WMH9tpubVOYsX+3z3QT0A11AYM75Hat1zsD2OTdow/MD/hD4oUbrrdS+57z3/cBfmtd117a5iQ+s5H6BzwC3A8czwmHJ53ylzYrkIkoYMMtWHRHZDtwBPAH0KaVOm7fOAH0rvLvfA34ZiM3f3cCYUio0f6/Wee8AzgF/akyaPxKRZlb5fJVSg8B/BU4Cp4Fx4GkuzznD/Od3ua+3H0c/tVd93yLyIDColHp+zltL3u+VFg5XBBFpAT4L/LxSaiL7ntLidcVyykXkvcBZpdTTK7XNRRCg1c+PKaXuAKbRarZlpc8XwNj4D6KF00agGXjnSu5joazG+S0EEfk1IAT+8jLsqwT8KvAbK7ndKy0cBtF2UsJms2zVEJEcWjD8pVLqc2bxkIhsMO9vAM6u4C4fAL5XRI4Dn0KbFh8FOkQkMOus1nkPAANKqSfM359BC4vVPF+AtwPHlFLnlFI14HPo7+FynDPMf36X5XoTkR8F3gv8sBFOq73vG9CC+HlznW0GnhGR9cvZ75UWDt8Gdhsvdh74APCF1dqZiAjwx8AhpdR/z7z1BeBD5vWH0L6IFUEp9RGl1Gal1Hb0+X1NKfXDwNeB963GPjP7PgP0i8iNZtHbgJdZxfM1nAT2i0jJfOfJflf9nA3znd8XgA8aD/5+YDxjfqwIIvJOtAn5vUqpmTnH9AERKYjIDmA38ORK7FMp9aJSap1Saru5zgbQjvczLOecV8IhtEyHzrvRUYPXgV9b5X29Ea1ivgA8Z/57N9oH8DDwGvBVoGuV9v8W0mjFTvTFcQT4a6CwSvvcBzxlzvl/Ap2X43yB/xt4BXgJ+Au0l37Fzxn4JNqvUTM3xU/Md35oR/D/Y661F4G7V2HfR9A2fnJ9/UFm/V8z+z4MvGsl9zvn/eOkDskln7OYDTgcDkcdV9qscDgcaxQnHBwOR0OccHA4HA1xwsHhcDTECQeHw9GQVRMOsshqSxH5qdU6FrffK7/fK7lvt9+lsSrCQUR8dGz1XehqtB8Skb2X+NiVumjdfq/9fbv9LoHV0hzuBY4opY4qparotOEHV2lfDodjFQguvcqSaFQJdt98K+eloIqUaJOuFc/IkqYihBEAqpiDyZm694uUaM/3KWJdMKniGEximIgkGWeI5+n3zGtEzDbzKF+/9ioRqlLR6/g+BL7eSWgKEWObwUZRmvV+gbiUI8rrbfgVRbVLr57Ph1Rncnp5GSSp6fTmVBKJ+VeBmDeUV79cmcdArrWTpr4tNnVOzdlEsn7d67o3zfrxhcvqyGxDFCiBfKmT5p4tKnkvWd7gNOx5zN1mch6iQOIFXC4KCsUOWjs2q+wy+9ITJEkEnHve9vgbvH8xzIkUCu20tW5SF247+6XOv1H7HcxNVDTXHkql72VeF70W2vPrMufb8FcGgdlwkmo82+AXXj3hcEmMXfRToG/QN/rfAyq9MRFB8rpRkKpU6r+Q+bYZ6NNRyc0IBB19RFvWAeCfHkHltHCIRkfTddZvoratF4DcwHnw9U0dnzsPkRYs8ewsktc3quQCpLlZ72tDN2F7k97+bA3v1ZN6o/kcrOvWr08N6W1Mz6JqVQC85ma8Xv3+zE19KCNHml8+ixodA+D0/3oL0+aabjmhBQRg1wUImwS/aq4/IRVUNUXYpF/HhXT92E9vbL+a3px+FSQy2/Ekfe1L/TVlvn6/rPBr6TYBvCi9YZXMuemSn88Tu47E6fYkhrBojj1UeBH2PJLPxoHghek2PXOMEmW2oxRxLhHWyn4fohTKM69jZYvnvUjZ70AUeLXYriPVegkoSqGS69CT9CGSEVJKMt+XCCTnGs3ZVvK3pNvB89JthjFSM19CGOn9mfVVoCWk1CKo6WtdylVUrUYjxDMStcG98+jwQw0/A6snHC5ZCaaU+jjwcYA2r1tJLkDVQsQ8bVWtmj6FgyC94bNfJhmBEEUocyNnCc8MEZh1woFBvNtv1m9khEM4MIgM6MOLCgW8nVv1tjesg7PDAARtrVAwwmpqWgsywDs/AZ1aOETNOTwjWKLh8/jmOFVV/2jie6jQXKC+j0rO1Ydqi35d7GrBm5kFYONnj1LbuR6AkZub0hvMhzi56KPMa5W5kfKSCoXME9ev1GsXiaCIc4CfXPjYm0AiUuMzxgoigChnVk8UIx+UuTGDiqpTS+IguTGxWpIXYm/2sEnq1k+EE1K/vr3BI6WPLTnFRGDGogUKEBVTqzkOxJ63xII/m96sdnmYChD9ZWWECaCQ9CadT2vJqGMSx3Z7yk+PRZSC2Cz3hORHkzh9+iuRVCZnjkl5XtoVJHNskBEC0Pgh6vv2YadXyfxGDVgtn8NlrbZ0OBwrz6poDkqpUET+DboVlg/8iVLq4HzrC9q+x/dR4YWqkQrDhmZFnUZxEcJTZwDwikXCtqJ+PUcDSfBaW4ha9Tr+dBXW9ejdxrE2FQBaSlDR5gFRTP7kCABxa5PVKAAwGouUtGahZmbr92k0EQkV0xu1nI7ybbR06P2HJZ/ZbqNdeOkTlPRhU/cUUZI+feNcZrmHfcpGBazKXvfk9eZoFJn3JEzXz/o9ctPmqV9In2zJtuM55kjyOSWZ115qSkiUajcqljoTI7t+8lpUVqPIqPWZdfR6+j2/lmovqNT8ij2spqE87FM6DgTPS7ZvNhipOo0m+a6V76WmSZQ5Ft+zpoEKPGuyKH3Bm/VjbU4kJOcXx6n2BnYdqdYg0UJqYZ0mMJ/JnfjKiGOrXdhlF2HVfA5KqS8BX1rQyr6HtDQTj4whgb6qVVirP9nsa8/cMHMEg+SM2m/sektsvkC/iD+j35v+vnsp/c0TdhX/xl16G9WaFgpApa+F3Lg29CWMra3nzVSRstlHGKLaWvR+fR/P+CK8KE7tzYLW79XMrDWD4koFzzhK47xnb8BKhxA2mfPwUjUaj3pBkDjmsuefuUn8CCQyF3o+Xd+70PIyG8ysk/n6RFFnzyevvVoqFJJtRvn0GLN+AEh9G9kbN86l68wVTvbEMj+x/j7MZ/3U1xIHYrfr1TI+h1BZcyMO5pgtGfXdCtI44w/JOEutKUPmu6hFxHnf7CdOH15ZsyKKUbnkADL+jzDzJWQEQ51zNFKI8Sfge/baw88hs1W7farpwzRbYS2Jw3yuwMg42MmYoY1wGZIOh6MhVyxaUUcuh9rch1epIkZlUlGBeHKy8fpx48ef1Rg8v/E6u7ehntbWTSl3W/17Z8/rbazrhrPaTCiEESpnTIMowjtrjsf3Uc3aVJCJKSTRAEo5/EI6ikESB6YxQVS1aiMh4qchB+XXP9mt+kvqkY8yDjXlk3ki6kgDUO9My5geKvMIUJI6EPV5petL5slqzRM/u1zsfpWn7HYis8yvppGLLBJnHIaS0XrmmDV2fYXVQJSXmjVeWB8ZsU7WWupIVEFqJsQ5IcqlXv5spKMucpGcX8YRS6SQZJsZU0rMH3Hex6+Y3z3n1UVp7PeeiVbYv0GbLt48j+w443xMrr04hsRc8UhD5NV67dqaDGFU/31K+h1kQ5+XckiuCeGgPCEq5QiKRVTZxOtqNbyitr3jZNlCiSNremSFRPzcy/Z1MDSe1VhtaNOrVm2IUcVxqludG7c+BALf2n2qucmqlF41QjWlccNEKNgITKwgNqGnjOc4mIrwIhN18TLuBB+UERR+RdVFKOwNFmdumEzIMPZJj13mrG9Ow6+kryWec4NmhEZdLoKNbqTSxEtCmkH6WvnUmQxW2ATptrXan4ZekxvZi1M/Q+wLyk9TBbLRktQXkTEZFNasQJExPdLz8LJ+AU+sMNb+jUSwpF+GZ3bkVxQql9j+sV0na3LaA7UHiV2njkRoePUCJH0/NX30ucbp8sSk9Tz7sMn6HsRP83CywkNlfA54HhJFzqxwOByLZ01oDlKLyJ0eI55Jsxfjcjl9+s8lWZ5NmoK6nIe6qEHyfqFgcyeo1vA7O4H6hCgVhjaxJFrfYb3N0a6N+IlzMk4dQRLFJNmVhDEya7YvkmoMVaNBZFXJOE6TrXJe6uWO67386RM3VYujQvZRTn2uQkZFrlPxM2ZCYoaETenrOHslZB48SKrWZ9eTmDrHH+gns30/8zCsy4TMOil9qT+/KH2dPPGzTkVtVqT7TM7Pr2VyHub63zLfgZdN7MqsmHyfyktNLq+Wai/Jb5NEG5LvxX4nhSBNapqjNVhzw/fsOtnvIxvd0AfTQItQKv1BohiVRM2iyF5jc/N/GkXxJJNkRZxxos7DmhAOqBhmy6AUcdkkPhUK9qbSC8xFUSgQJzf4HE9soySouvdroTVVwlOn8dvbLlynWiUcPAWAXypS3dwBQDBZRcyxqdNnEWMPqlweKZnwaDW0yS7R5CRijtNrKtrjs74G37dCxYsUgZGLtZb0gvJqqY0vsUrV34xHPuvBlzh70emIBegogpex4a1ZUc2EBmNSkyQ/Z5tmfaXSmy0b1qw1G9OnrAjK6eeS/Sj7vzn7ydy4yqvfdnKu9Zmb6c2r/FRYqky2Yl1GZU7ScKDKJIvF9X6d/GRkt2NNK0Xq2bfC17PmQdY0kTD93UWl4U7lS7p8boZkI1tfqTQRLQISYRSrVMf3xJYD6PXSTMskQ1JE5g9ZJlGMOE59EfPgzAqHw9GQNaE5qGKeys2byL/Ub9OPVbliayuyzpY4E9fF81PzIVuEMh9xhOR1HoLf0gwdRnMYG88cjLLahcxWyA+M6dfVGmpiSq9SrSGlkl7e2my9ynFTDi+JTStlNR/79PJ9q91I5py8Wmyfgn4lddjFeYVk6hf8cvJEAo+sipxSl8hkXgfljNMyTJ/QSkjzEjKOa79c/xSP8uk6Vu2u1icfQb224IWk2oJKz0n51OU82LyFjEkU57NaAaSqQH3eS5qSndY8XBCxMUS5+miPNRUiRZRPk0bsUz8Q/fQGMMcikaqLSiTJUXX1Jwqb/6A/k336p5GQhteqUtaMrXNgRpGNYkgYpdpILbTbUZWq3b5SKi3vyJgSdbUXvn91RCuigjC+o0DvUCecSEswbCIHGZNBqTTZKYrSLzkTvpRcPs20nPMjSFeHflELiTp18pJXLNZFRLyOdv3RqSkwX2g8NY3X1qrf98RWWipPrHC4IHszuZCSdaMoTfJSCs94nf3ZEFEmWzJOk4q8WZ3RCBDManU/wSbURJmCoqje+19n9ycmqEBsLp2wOd2XnwkIeVVFtV3sfn1jxVU605u8UKXOzAF90ycmCJLZZiasqr8z8y/1JKaSX0nDpCp734mgvOzNqS5YJw4ypkdGL/Yi1VBwRLk0gUrvPP0u0wxIvSzOezaxTCJFnK2zMNdqHAjZ7EcbvvQlk3gFtnIi608QMSEnQEX15kPyHQR+mhwlUu9bSO4R8dJzz5gzEgRplbGY43TRCofDsVjWhObgVRUtp0III+vow/OIp7QaXydds59rKtqKx2zKdF369NyEKCONw8FTBCZJKfbroyKRKZnWuQhGdcxIaMnliCf1scnoOGKiJ9LRaqW6196WPk3GTfKUClOzAh9VNBqQ75Gf0PvJzcB0n6nQHImpNXvmGElj5rF+ukLy5DOx+bykCUMR1mSIimlUIGyB/Lj+Y2pLGq3II1Tb9PLCiDDbZxxv68twUid85XZNIkYdif653T5xpzfrfwujYk2G4oiyWk824hEV0lwIJGO+xBCU07yJbC1G+sWn5pF2bKaRi2qL/p78qiJMqkVrKvMUV3U1FFbriTLp1rW0fFtlkr/i5NoLBK+a9PSQNHoRSJranklzV75Xp02SPtitVpm9riXKlGlHmWhC1mSWVDMh41RUHiRjSJVSaep+4Nd9VjLbVGG09s0KiRXBdIhUa0TTulz5gvqIbJjGvHfBOo2YE9JURuB4zc1pOLKlGaanAfA72iGphTACAMArldKQZC6X+hzyOZv4FPa0Epw3QqOpKU3oanTO+ZwNe1bbc/hlEzIt+ragqNLm2Zskyqc2c7kbSkP6j6CsbJESKo10SAjl7tR8CJuMANlUpmySiqJywP03vwrA0fEexme1r2VX9zmefXUbAHdsGaB7l/5uvvrqTdy+dQCAU2+vMHSqA4Bci/5ebrh/iNf+4QZ9jF1ihYBXg2DG7LNJ6nwV2QSkhKyZ4EVp8pdfVXXCJLkmsqHPbGFXlM8kOEWpQPXCNDlK+fX7ziZWqbl6tcqYcyo14bSQS/0fyV0ltfQG9yphxuch1JV+J2ZhJVs8o9JMyLkPxwYPymxjIiANcc5TW6GUMslSF2zK4swKh8PRkDWhOShfqLbnkLgL7/TQim5bfL/eJOjs0P/GaamsZBpxxNOzeEZzYOfWNG21WkO1aPU6bM5b55ZXCeuq85I6CzUxSTQxceEBGRPHa2tFTevkhuLpGYYe0E7QtuOhdTyev7NK6VV9LNV2RfvrenlUgNkevf+ms8qq4+UesQ6zsAQ8MAbADZ2j1EwBxJnJVjzzCBuvlXji5HYAOltnmBrWkZznTrZRPK+/k2NP7uaYebq0C7y8Ybfe/q5ZCE0s/aT+3CHp4w3v1prI0y/tREr6ew9OFchNGvXXT8u0c9OZp3whbdLiZ/1wSic5JdjU64yD0atltCehLqci+WwcpDkjsaTawgVp45lqzXSfmXyK5LCE9Okv1FVwJhqCl2neovMTjEmSbSrjSWpKZDWCwE9zEgr5NLqVTbrL1m3MzflJ1okibcckJFrKJXIcYI0IB2LwyxHKE7wWfaFFSaLTMpmbKRYe123c/J4eYhPC9Ls70/VrVaSob8haR5GpLVrVzmU6BwVTEU1HTaHW+ASeucnxPCLjc1C1an3GJtSreJ5HfMMmAKKmHFUTVe3/wZCmV/TnetdNsG6HNlP6ipM8c7M27qWS4303PgPAJ7/0JpqGzA99YIx/deMjALwwtZmvPH8rAEde6KDlpN53x9mIptP6eDfMTCHjqenUHejvPDyRbf/ZmGwvjSTTtHbbdp5/lxYeN957koqpF/muO17h5akN+jwKEzxyRpse5862UTyhv2t/FgIytr9NRpI6v0MiCLOhyTgnqQBRadRDFLaMXaJMmFfmmDPJzzJnue2ylNzf2ZtaMje7SoWcV4vrwpA2lOlJnUBIBVtcH77MJkslpkomZAnYDmKS8UvMvdVt2DIIbBQjuR4XijMrHA5HQ+SCZhBXgLbWTeqeO/41cd6n+Kru2hSdG07rIERS1Wiecu2FInfdov+NFGGrfmrlhqeIDr0GgL9rB6qkl0slRJJy69ky0dlzeiPzfGcL7UyVMPbBAwAUxtNzGrrbJz+hnwOdr4WceqOJVhQV7Vu0pjM+VqKlXTtu79twkidPbwVg4nwzMqufKr1PenT/3WEAovMjCz6m5WIb29y+h7P36ryQ6c2KqGjU+6aIN9ykNZMf3/hP/PpL36vX6W+l9WjiREudh341TcLyK2kOg5+JLORm4jQXwc+kRmeckxJlIhdxvbaQ7TRlIxp+2mQ320im7reXzNPfNrtVaQ1FrFItQgSp6Kf56G0dFEf0b14YLuNVTKXu7JxygWRfgZ/mPIikeQ5xbOuAiKLUtIiVbYasqrW0DYJSdeaEUorHhv+a8drZhjbGmhEO9+7710xuLVIc0SdbOnw2DSMW86n9lc/hjZnQYOCjkv4JSukWWoAan7Q3qRSLqI261Zs3NGIbts72FclN6y88P1JGPfXSqp1fkrSFitPjyuXxdumIwNCbemyor62/RuGUVvXjl16x2/C7u8CYP1FHiYkbtPk10+fReVifd9PXX1x8eftlxu/tZer+HQAMvyFg3Zt0HUtP0xRPH9ypV4qh1K9V4OJIGmoMZlL/ylwzIVuUltzgSKazU5S2lZNMSXjWXyAxdctT30UqJKyZkuns5FVj/FrqT8h2nLal3Aqr+09ua7JmUPPALN6U6Ug+k/ntoij1KQT1oXYrKOKM/0GpugSnRiMVsiRC4tHhhxivNhYOzqxwOBwNWROaQ3tunTrQ84NMvHEHuSktFZv6J1JnWS5IU5QzPfdkasY2fVW+ZyMFhJHNZyCXt/kGcztLNZpzsZLM7Wnpt7XBBj1DI+os4Y/o/AHVlEeOmdb4jSIc1zAjP6ZNq+H9IT954FsAnCh3MTSrPbSHHtmJZ9r55yagMGrMjZpK8ygilaniTJ2ZEmWck3Fa1h3lpb7hS6I5qDlJV4ZEmwjK6RPYq6m0Z6dg81SkFtXnM2SausRGi8i9cJTabVpL8qdrabQC8KaMc7tcSZ/+QZA2N1YqvQdqYV2zWZV14ksmEpeNTGTzHEQuqjmsDeFQXK8ObPsQcUuT1WXKfSV882PEOaH0ihkMc+68tacoFKwfwCuVbGKSKpfrBEHdTZrpBWFLtnN5onPnFnXMixEstunsuh7UhD4uaWslHta+gHnb4V1HyF23cP42/XuM74buO84C0JKvcvSUNguLLzdRGEtDjcGsfp2bTcuks0VeFwx4yhRN2T4PKjVPdBPaCxOiPNvPQWWiH2ntildNIxQSxQ3LsZUvBKdH9euWUmoazMl+lGzh3qzpH5LLpV3FigU9NwWQ9tZ0nSCwJoaq1TKRsjjTiSyuv25jxWMjn5nX57Bks0JEtojI10XkZRE5KCI/Z5Z3icg/ishr5t/OS23L4XCsPZasOYjIBmCDUuoZEWkFnga+D/hRYEQp9dsi8mGgUyn1KxfbVpvXrfbn3om/eQNxm376eyOTxMbL7vV2W8ejOn02jdeu60YV9Gvv7CiqXXvH8QRGtGdfcjnica2qL+cJ7be1LVvl90olJt57GwAdj/YTDgxe4hPXL7MP3gvAwFs9pEury3HVh6p+nhXPBBSNstc0EqeORJXp4BRTX56eOLUzDsxsafvchCg7+q+aagtJbYUXKZ0eDXjVzBQp37OJVHVdnupqLJRNapJI2TR6FfiE3bpSOHfyHKrdjDw4PsDsm3WUrfnwubT6MoxSzbWrPe1CVs6Mj/Tqn//J+vGWdYzvbuGlf/g9ps/3N9QclpwEpZQ6DZw2rydF5BB6gO6DwFvMap8AvgFcVDigFKpWJTx2Ar9Xz6wMM2p+fGKm8ecy7d1igDOLy67027QaG8+W8Tf2AVDb3E0wpsOEDJ4hMolSixUMfncX0qQzKuMuLbSi1iItDz2uX+fy837WAU2ffxKAmx5fx7n36KSp/PuH6Czq3+agtxm/os3L/KRYf4DKlkZnekdkZ1vMHYhj/QxzbhEvrL/Jg9koTYyKM9ES30sH32TmaQqpyaAyHadVXvCSKH2lks67jCLksecBOPRnd/GL934FgGavwn948iYAfvqOg9zRdByAn//ET1I6rbff93fHwHQcI5MVrNZ1Uu3RD9zZ3hxTm7SwCJug6Vxaq9KIFYlWiMh24A7gCaDPCA6AM0DfSuzD4XBcXpbtkBSRFuCbwG8ppT4nImNKqY7M+6NKqQv8DnOmbN/1Rnk3wZbNqIJ+GsQdzXhmOhVDw6mzsa0ZTmlnVTQ+seikqEQzwRPCnTqlNzh4bMWiBNbk8X3CA1oVjIpaPOf/4dsrso/rDuNEDr9zH0P36CSrym0zlEr68Tv9Wgetx/VTuTAe2zL0rIkR56Q+byFTupx2z8rmSIBfyeQuoB2SiQmhJ4EZzSKM6yMU2UMvpw7G7NTsJCfnyL/axJaH9XkM/WyZh+74IwA+cuL7GZ7Vjuzxr65H3a812MqrbWz6pt5m24f7OfS4zhnJTQrbP6u17ai1SHmd1lqnN/hp9CaEltP6s00nJ+FYP4/PfJHxaHjloxUikgO+CHxZKfXfzbLDwFuUUqeNX+IbSqkbL7ad9uJ6dWDrB5l8wzpG9+gLYf2T5brBI54ZIBKWUktIFOTG02azsfE/+NNVouYk8QiCJGQ4cDoVMjMzxKZMe7kkbeVUFNeVkfumo5S0aNvR+RiWT7BpIwCnv3cbU2/Wv19X2wxDJ7sAaD8YUBzRN3V20ne29FtHNNJMyIS6zEnSqIdfTYWEF6WvbU1EpsDKO3kWjK9g9J4+xnbrO7O8McRv09fG5p4xfn7HVwH4P198kJf2/yUAdz71L6h9S89M2fhfH7XH4e/dQ/SyLmgLtm1Jo1zT04z9iA4Fj+82xXCADDSRGzdZtq9GtPSb5c8erg93Ak+oh5lQIyserRDgj4FDiWAwfAH4kHn9IeDzS92Hw+G4ciwnWvFG4BHgRdI+OL+K9js8BGwFTgDvV0pdNLm/sHWL2vhLP0+cjwmmtObQ9RK0HdUSzwtjghNnzcp5qlu0dM2dn7bTf1RTgThvIheVWppA5XnEI9pxuVKagt6uKdNuKtrtzu1F6Vg9vFKJ8e/RkZ/T3xnznru0I+9LT+yj9wn9zMtPx2mb/2r9yLzs/Its6XfabStNmkrG3ulaiWR7kU1q8sIY9e0XARj58QPE368rdkdHW8j1m/qdiTS3IjcJXYf1dXv2jjzb/kpXCof9A/b8/I52Zu/TFa6lFwao7dCuO3n0eYIdOu1ezcwizVoTplqjskuvU+7OWXMqNx3ZjlVeNU618TAGgSef+xgTU4MrHq34Jy7w71retpht+RVoO+LR/VKV/GktR6LOEv646QpVyNWtb4tZwkjPuwAYHkEZv4EqFolW8yYVsb6OrMBxguHyEc/M0PppHfkpjN3NV8/eCcBPf/9X+YP8WwDo+5ZP6az+nSa25CiOm5skVHVXbmI2AKkZUokv7K5djdMRdXFMcF5H0aJXX6f/M7o8PnhM2Pi+EwD0LKDtwKav1A0Sp/rOewAY3x6w7tv6eo5GRvFN8tzMe+9l5GZ92275/BDhq6+n52HM1uaL7O+CG1bNzruuq61wOBwNWRPp023Spe6TemXDKxaRHVsAiJsLeGP6CR29ftzOs/A3bbDrR4OnL3C2OK4f1IHbATjyw0Wkpp+PLcc92vpNb85cqtbnJ+P6Sd8ZU8KrpNqFTWbKahDGTAmOnLJT1Gf+NCD/XSeWfQ514xoBuecNAPhnRhk7sNkubx7QT/vpTUU6HjkOQLjIHJ+Eizkk10QnKCkW8G/YQ9RWJBg4bxYK8YDu7eAV8rZPAErZLzA8dmLe3gqO64skeWgXt3P0fTqMN3lDROmcvu7bD09S6TFJaXkvrb+A+l4M2ctpbns4pfCfMWX0bW28+m91H42d3/XY4g94v/aX8MSLaaRj51bbV2TiX+6n7Yh+IJ59+xZ6H9VhysqmdruJ5oEyEwe2A9A0tB559PnFH8dFcGaFw+FoyJrQHFS5YuO4WeeM16rTjuOx8cbVj05rmBevuXllozNXCfLY8xTffD8AW99xnDOHjGc/5xMk7f9VGq3Ay9RASNrhRcLY9nDMDRvH48HDDHxYb3v6hhp7fnIJGgPa6ZhEUZrW9zF7i+4l6g9OMPmB/QB0fukQ8W6tmfQ+Psz47boytfXYtE33ntjVTOfDR/X5zcwSs7KsCeEwH66UeemsVo+Kq4Gtf6tN09dbt1Hdp7+H/GSJlkFT9hwq/MRkkLQkOxuqVALBmI4+RQd1u73+z9xK6e/1+5t+e3HZrl5rK+G+XQAUHn4eb7v2IUQjo+S+qs1nddtNdHzpZf2BTX1pqLQW0v6sDuVP3tpLcVifR8fnnqN2t6658B59cVHHs6BjXvEtOhyOa4I1rTk4ls71HLlJnvS9z9zHuTv1JV4rpaaEX4nTasmcZ6sovUxHpuCFo1ZzHf7bPQBs+Z7F9xkNdm4HIDx6HO+RZ/X+t22h2qcrgiu39FAYNS2tvvmsTQ+f2tVJbkprPRO3r6Mwql+3HJu0pQGzb30DTV/TGkP4wG12+yvFmhMOwRatbsXdbemPNjkDk9p+jsfG6/vw50ybuNlZO/NCmpsJk+E4y+xW7bh6aTk5w8Q27beaXQdtJ03rtNkokymo8ExXJn+mSvz8IQCO/8b9dN2v1f2ed766pP1LLk949PgFy9X4BLlmM3/04Aki03rA37uHmS1aaLQ8M0C0QdeLlMoRs+tN/U5QotqqY7Jtr4yhbtSFV94jz+Ldpk2M+IW0MfFycGaFw+FoyJrTHJLJ07XOImHJTPYJmyic11qB31yyfRjj8UlUOXVaJo1ZSP51XN88e4jyD+h0ZKlBWNLPwiAd8oVEsW3UEj9/iKP/RVc5bvjnkKb/cHRZu1e1qm0REJ07Z6Nv0dh43TWaJDtx+AR5E7Vj00a847otitfdSYtpX1De0ELbt/Rxhbs3Io+9YLfh9Wun5UpFLdaccIhe0yfuvwbZJjVJ0PL69cE7FosKQzoP6tfVtsxYPV9sCwAJYzuz5PX/tp8bf8fceEvMOMzilUqoTMQtG33zTOsAABk3odK92/GHzYOvuUilV69TODONVM08l0NnqNyqM4f9bz6P39Ght3F2jLhPmyGL7Yg27/GvyFYcDsc1x5rTHByOlaTna7rm4fWf3kZhPGkwK4jRHNSzB5n9snbq7fm351ZEY/C79RM8Oj+CemCf3k8geN96Tr/f2kpsqon9dT1pZeX+2xjZr8uuOw5NUjinNQpV8JEZfbzTe9fTdNo45+/eC9OmudD4lHWmrhROODiuaZRpx+ZVhPFdSSgzR8s39JTyV//0Lva842kAViKuFWzZbPsyBBvWEz2hTRa5dQ+YjmDZloTh4Cm8fXv1MQ6N0zWqBUJlUzv5pCS8KWdNjKbBSaJmXWdU7crTfNK0TDw/agWR/PNzK3AmzqxwOBzz4DQHxzVNMsms+9BOBt6h/fhtfzPF4Y/qGoY9P/b4yuzI5OSE/QMEG/Sw5roW8c+9nEYlvv0iwWZdTzF2/xY7AtLraaJ43MxqqUaU1+sIXWF4lrig3fPlja22M1XpyCjRZh0N8cpl/ONai1gpp70TDo7rgtZXRtmMboI+9ttVbvo506l5ORs1rQLHf+geOr9gwiJxTGzG1c2tDZrcrk2Dyr4DtkNV+8FRZjfrEGfxxX5o06aHX4ls4Velp4niGdMk2fPwps0IvDDCM5GOeLaMGl/ZOavOrHA4HA1xmoPjuiB6+VVa8trxF7/r9RVxPiap+e1/+XjDxCO/pxu6OgD9lG/7W92MxevuYuZWXUPB0DB5UwtCRzt4On3aPz0CffqzYVMpHdQbh+lYP9+DUZ1M5e3abmtKVgqnOTgcjoasxMQrH3gKGFRKvVdEdgCfArrRw3V/RClVvdg2GvWQdDjWGrMP3mtneF6M6C26E3bh+DDhiX69MHOfZUcYqAf2UWvVxYNN/RP26R9sWA/J8OjJKWbu0/NCmw+dRU2a/G/fhw6taTAyTmwKuBbTy+NiPSRXQjj8AnA30GaEw0PA55RSnxKRPwCeV0p97GLbcMLBsZbxO7UjMxodRcwAZFWr2hRo2bbJ9n5cKF6zjkTE09N2oDPrexnfp6MPLQ89brevbt6BekY3gfFuuRGO9dvtiKlEVuMTeD16nstiJqutysQrABHZDLwH+CPztwBvBT5jVvkE8H3L2YfD4bgyLNch+XvALwOt5u9uYEwpleg1A8CmZe7DsUbw29pWbODw1UTSbwGws1DV/bcTJ92e59Ea/I72tFIYrNbh7dxKdPiIXSeeNoNljvXTYlKp4zffgf+yzrSMnz5om8CogdOwWY9kkIkpwtOmQ/u+vYTPvbzsc82yZOEgIu8FziqlnhaRtyzh89kp20s9DMdlINimqwCt/XyNMnduRCPK770XgOIXn7Rpz/FzL+Pv3gnA2Tf3EZS1qT693qO13wz1LcepvyKKbKKUam5CjR2z20+GL/uvDCJ57Yvwe7rtcUlTE+q88S1Ua2mnqedexrv9Zn08K1RjsRzN4QHge0Xk3UARaAM+CnSISGC0h81AQwNIKfVx4OOgfQ7LOA6Hw7EKrMjEK6M5/JJxSP418NmMQ/IFpdT/e7HPO4ek44piMh0X01Iw2Lwpdfzd+waqXbqNW9O3X7fFXgDSp1vKy/Qsgz+otYsN3xhBHTIt5WtVq4HI0QFYrx2SnBuxE7Xink68YWPaiBCPjunD7mi3VaRZk8+7/eYFaw+Xe+LVrwCfEpH/CDwL/PEq7MOxBsiWJl+1eH6dUEgiB/P5VpIep5Gx9QH8E0PknzRT4Hu68dr1NsKBQeRGPTfDr4X0/f6jANS+8078O24E4NxdLfR+TM+/qLzrHpr+Sfd/FN9DVUwr/XMjYEZAqmoVMR2lsuXl0cQEwXY95yJcA2aFRSn1DeAb5vVR4N6V2K7D4bhyrNlBuo61x/UwRav29rsAyH316Yuu5/f22opPr1hETAQhbi+hnj4472dAa1r+jdrE4Ox5Rt+hW9+3/dXjdoamPzxpk6Dippyd2+mPT6OmTbHV2Dj+Bt0cJho4lSY/iSx4GtyqJkGtBE44ONYiSegR08ZeVSr4e3SmYvTq6/i3aNPgYjUNSbKT19M1b7THJkFt6kMmTYOX9Z3pDS5i6ym86QoyoQV0bUuPHSDs3XoTcjpp/LJwM2/VkqAcDse1i6vKdFz3JDkHYcbJOPX+/bQcNybUk+kcyijp90hjjSH+jjvIvaKTl6SQtxENr6PdDslt/VR9gxnr/JyYsJETv72FuGhuT6WIC/p1nGvCK+j8h9zxIUKzvgydJxoZZSVxwsGxZOb2LPRvuZH4yHH93hofx+fv1XY+tZDwtQvnU7Q8tPAOUd7tNzOxR5sG7c+dI9yjk4KnNxVpeUgLh3DwFK2fOgXA2X9zP+v/aQwA6T9TZwb4vbo+gjACz/gcch5e2fTCnCwTt+lpWaqzDb+ml0fnzukScSAaPr/gY7/oea3IVhwOxzWH0xwcSybRGBKnG0PDdo7pWicyk6XK772XphbzJH62cZRhPhLNqVr0aT9oVPp8jtE9enutgzXrwIxPDBDfrdOb1/2PRym/424Acs1bCcZ0FEPGJsHTz2vlCWIG+4pgzQoJ83gTuhYj7G0lMHM+GT6/YhpDghMO1wvG273QENdisOHNqyjMmdy0xS8+Sd03Ymz48rvvovjF+t4N3r69xKa4ybv1JmIjHPPNzZDTt9L4d99M15/qpKZg+1amb9ahxuodvbR+OjVV8l9+Sq+zvo+pu3WiVPMrVSRKe0olg6QlilGBFhrVnhK5MaPwxwo1mPpJVhpnVjgcjoa4PAcHABIEi+ogdLWTDLXNdogO1vfVpSQn0YWO57W6Hh16jfjNd+jPf/NZm0qtpqbTpivVKuNv0hO02l8eY3JPBwBtTw1y6nt0enPH0RqFYW0aJHM6wVRfbtaahsqlk2KjYmA1PonT+zXXf97mYBBGhIOnFv09XO7aCsdVyPUmGBKhMPqhA4jR5Ntfn0EywiEJOWa9KN43nwVA7rqF6HkdylRhSNBsukLl87T89RMAhPffTulv9OvZt9/F+r940ayTsxEK9cA+gnNGQAU+3rkxAOLeDoj1gflxzPgeLcxKQzUKQ7pNnJqatr0m/Bt3Le9LaYAzKxwOR0OcWeG4bsj2ggzfZmooxsrMbNYmQdPnn7TLg4cvrK2oK9PObnfXDuJ+rdKrSgWvqMu3kyaylyJY32c25BN36nwJb2wS1aRnYpILrENSIoWcH9PbHxsnTsrD4wgpFOwxLBRnVjgc1Ld7S25+BbQc1UKj9sZ96fIH9qXJXUaohAOD9sYP776J/KDeXnjkmA3nqmrVCoW5beLmQ5lEJsIQGTitX7e2IiZMqfI5OHLSHHgAxoSJy2WqJiSa//JTK5545swKh8PREGdWOK475vaKbFRbsRjmRjnm67mZNImlkCc6qesv/E0bwDR1odRky7Gl1IQqaS2FsyOoGb1cVWu2yW2wczvh0eNLOuYEZ1Y4rnsm/8V+Wk+YG+zxFwh2bLPvhcdOAPU3eTaikSBBYLs8ZeshsoIBz68XCiapKljXQ3hKmwySz+Pt1uHOuJDDGzdDcqdnkMRkKBVRh3XNh9dUtKHMRDAAdYJhroBaCZxZ4XA4GuI0B8c1Sza60Prpx635cPQ37mfbl3SZtHrqJbj3DQCET76YTqKanLTrV3fpLk+54SnbaGXsgwfo+PPHLtxptkmtCL5Jjso+1b22NrsdNTqGajLRjYkpvFbj2Dx8FJTOc4imptNqzZmZ1Gw5OWA1ipXWGsD5HBzXCVM/eJ9NToI0AnHmAzex4W91JCAcGETu0YJCZmuEHfqmrXTr0unWpwdRrVrtz46/q7z7Hgpf+vYlj8F2fPIEMVEPFYa2YEoKBVRNJ6OJl3ED+L71kXi33UT8wiuLOPOL4zpBORyORbMszUFEOtBzMm9Fh4x/HDgMfBrYDhwH3q+UumiLGqc5OFaDuXkGyTQp6exgZo8uk85/+Slm/pf7AGg9NKLLpoGpu7fS/MowoEujAYLD/dYROVcTSZh+333kJiO77YaI4LW0AFzg9LR4aW2FVyzY6VfZ84nfuI/cYW02xVvXzdvY9mKsWoNZEfkE8IhS6o9EJA+UgF8FRpRSvy0iHwY6lVK/crHtOOHgWC2SJrFSLNheE7EJC4KeeRm8pMfRzR7YQ9MT2lxQ1RoSaJdc0sYtfuM+JnbqXg09D58gNoIiLpcZ+5EDAHT8Rb0f4lJzMLLH6DUVbSGVdLSjZnUyVTwyZqMUXqlUd/zLZVXMChFpB96EGVqjlKoqpcaAB9HTtcFN2XY4rlqWE63YAZwD/lREbgeeBn4O6FNKmRxQzgB9yztEh2PpeF0d+kV7q20O6/f2ojaZ7kuH+2GTvkTzX34KlZRyZ57OiTniTVbo+PPnAAjB9mz08/kLNIaEhUwlT7SCqFa1DtE45xGMmryMobN23XhmJp1sdfzkJbe9HJYjHALgTuDfKqWeEJGPAh/OrqCUUiLS0G5xU7Ydq0ViDqgwJEpurKGzyB236NfHBvCGte0enh9BJnQJdLBhfV2W5NgHjamQhCwz9r7k8tCtIx5Db+6l5wUdmhx4awvb/lBHE5YyJlB9W5d1e7k8UVhruE4iFCY/sJ+OZ7VfJDp8ZNH7uhTLiVYMAANKqcQr8xm0sBgSkQ0A5t+zjT6slPq4UupupdTdOQrLOAyHw7EaLFlzUEqdEZF+EblRKXUYeBvwsvnvQ8Bvm38/vyJH6nDMIashJAQ7txOf0k9/r7W1rhIzaSAbgdUCshGN8PQZq9Z7M1V6HtZp1dk2OBM/ZGZPHJ9l4E06YWnLP4wyclsHANs+frhOY2h0jAtB1appQlamN6e/a0e67UihTgzY/STdrbLnvByWG63Yhw5l5oGjwI+htZGHgK3ACXQo86L6lYtWOFaSRtO/syPq55LcVOzcnCYYZe4Lf7eeaxl1NuMf04Kntnez7Qrl77kBzusbMh6fsIIgO09zKSTCQTb2Ud6hzyn3lTQ86vetQ82YdnPlSl3dxUJZtcIrpdRzwN0N3nJ3usNxleNqKxzXHHVqfdIdKTNPw795N2pAawDx5KRNRAomZvAykYCgb53e3knT/em1iu0n6X3zXDrxa+A8odlnsHM7mKf5cusdEnPi/Jv76P6jC6Mh0VBDd96K4WorHNcFUiggZg5EXC7b1mzz3sCZMfbZ9nLzMfCr9wOw+T89ulKHvKBZI5V33QNA4e8vXdsxF7+nm8dGP8t47ZyrrXA4HAvHmRWO6wJVqdRNtko0Br+zE3qMZpAZqCv5vNU0LuX9r77zHrb8Fz0da+DD97Ppt1dIe7iUVu/5VmO4mMO17iO33gTAqe/qYt0zs6in8vOu64SD45qjrvuzKWDyigWb9egVi7YJrNq2wY64yw72yQqTpGtU0jEKgHvfYCdf5//h25z6BW1WrJhgyBBs39o4GzLTO+KitRt33wrA8e9tZfvn9Xrrf9ccp5qd93POrHA4HA1xDsnrAL+zc+USY5aY1GMrDzvalxX7Xwx1qrYIwVY9vi480V83Di+pkYgnp+z5oZTVNHwTtaC7wzoJ5fwYqk2XXctM2Xac8m/chZiGLctt/rocklF+Q/fBln/UGkajhjSuwex1zkoJBlji2DwR/B6dxLPUDs9LoU7VVso2fvU7O+2Nn1XZ/b51aURjIu2zkIQMvalpZLNuGReeGQLjt0gEH0B5W4dNVAp2bKs3RVaCTBRlLsnk8P4H+9j8Ff2bt37q0JJ35cwKh8PREKc5XKP4Pd22N+GSPr+AJiULRimrMXitrUhJN0y5WBLPUs2XhSCFPMpoU+Hxk/PmPPi33KiP86AemItSDasfVa3KuZ/RFZy9H3vMPsFDUyK+FLzm5rqaimzOQ5LOrU4N2XXUgdthRL/e+DuPEi95z5ldOp+D43Lid7TbJqrs3mYjBVfseDJCNNi00ZoTsnm9bSJrayteO4pXMtO0S00gWvGe/I6dlD6ni5O9228mfn7pqnyWJOoC88/dbFRHshhcg1mHw7FonFnhuAC/t5fYmBMrPZy1brDscy83LEu+nETD5+3TNx4bR3ydFxEdes0+uZOBtnVTtmdm8G/cBWC1BoBaZxMFM1ciOnN2Wd/fQqZ0L1VjWAjOrHBYkhoCKabNdy5ndGEtsazxcguoiVgq0z9wH82f1cLI71u37OIrZ1Y4HI5F48yKawnPx7tNe9iX4ujL5kMk6v71SnhmaOkRk1XUxps/+4Sds5E1Z1YDpzk4HI6GOM3hGiAbapu8QacFj7znfrb81uKKgLIpxVfKQbiWWIkcC79Xt8BfqZRxyeVXXWNIcMLhKscrlYiP6fTfcz9zgN6P6Y5BwXvuof/XdaXglt9cmJBIOiJlqxZXgyRXYCUnN61ZOs3w3BUSDkvpE7lUnFnhcDga4kKZ1wL7bwPAK4dUu3Rqcu6RFy/rU2ahzB1uuxCSfgTqqZdW45Cua1xV5jVOrU1XBZa3l2geME1MMoLhtY/up+tF/fs3alR6OVmsYAAnFK4UyzIrROTfichBEXlJRD4pIkUR2SEiT4jIERH5tJm+7XA4rjKWrDmIyCbgZ4G9SqlZEXkI+ADwbuB3lVKfEpE/AH4C+NiKHK2jIbPd+mec2uSB0im/+ffcw8AP6zZmN/6nMbxp0y79yhziirOSDWzWKtno0ZVguWZFADSJSA0oAaeBtwL/0rz/CeD/wgmHVWVmnVYAW/tjhnQDIOIW6P6a9j8M/GaV6OlNAGx+uAN57PnLclxJExR/y0aidp1U5Z06tyLzFqLR0Ws66iG5/BUTCglLNiuUUoPAfwVOooXCOPA0MKaUSh5QA8Cm5R6kw+G4/CzHrOgEHgR2AGPAXwPvXMTnfwr4KYAipaUehgNY/1Gdx/DqH99N60H9tJ68KaL9A7qCcGy2yPRtpk/Bb62Qc+8i7cpA9xmo3aq7NsfVmJyZMRmu4JSma1FjSFgLkablmBVvB44ppc4BiMjngAeADhEJjPawGRhs9GGl1MeBj4MOZS7jOByG7Q9B/9v1V1noKNM/3GHfU7FREpcRus42bPUKBZIweFKW7Pf2ojboZq0yMWMHzSJCuAZC5lcbSUcpqVRt/8sV4xLCHZYXrTgJ7BeRkuiunG8DXga+DrzPrPMh4PPL2IfD4bhCLFlzUEo9ISKfAZ5BO8GfRWsCfwd8SkT+o1n2xytxoI7GRN95J/7XnwEg/+WnWNehPZKzJ1qZ2qyfDH37hmj7Bf1TR403c1EC07wk+/SS9jYk1tuXFmMWKkX0oum3WCjYqkavo33efpbl77lX/9vhs+kndX/GQ1/dTdsxM6eypvCr+nWUF+t83fjpI6s+SPZKEy2jB2WCBAGYBjYAqpoxVzz/oheEy5C8Som/4w4AvEeepfb2uwDIffVp+76/eydxi45WqGcPEr/ZrJ+o+kvA7+5CTIQg7B9I3zDNTfx1vdDVrpedG0Gami5YVwoFa4aEb7uLgbdqH4nsnqL5q3oOxPQmofugbpHa8tDj9rPl997L8G1a4LSejCmO6Cu7dGzM9nt0LA7X7MXhcCwalz59leI9ojWAqffvt09Xdf/tyKM6hyE7FBaWpzEk8yZVuVLXszBpCGNLm2Nln+DBhvX12oVBVSr4u3YAcPwtearrdaJW+z+1MqPnxdB0Djr+WVeahkCwc7veX6gontea7sgtQvFcDgC/3Eax3GCepWNZOOFwldPy0OOMfkjPTOj8xGP4N+8GYPqGTmbW6Zs6P6XSm23w1OJ3Yga2Zns8BBvWE/d2AOBP6uzLaOC0vfHjgdMNN+Xv3mkFV1xYTzKttvfBfvqadLj1zraTfGzrOwDY9edtTOzWpopfiZneqDXgsCViuqBft50MOHu3TqfpermP4hefXPw5Oi7AmRUOh6MhTnO4Buj8hGnwsnkTo7fpPINyp9B2Uqv7pddHl6YxNCAZOks+B55+toS9uqFJdU8vTY+9CszfVl2dGrI9EP0ytPROAfCL277C7Xkd0fjNobdT2jOm1y8GeDWtXsysC6i1aUdl+ys+sbYqmNwMzYN6eX6suqrdn68nXLTCcWnufQMA/mQ59Sls2khsyq8X0lLO79CmQe32neQGxwCIjhxj5It7ABg+28aWTVo4DE82o5S+wcvjBUqv64hG4cB5Ko9p4RQ1KdpMpK9lsMrMei0pOl8Yg2M67+5K1yZcDbhohcPhWDTOrHBcmidfBHS+TGJWhIOnbElxEk0Ijx6fdxNi1p3pK9BxVEcoKu++h8Kf6ueTvEkYntTRj+3dI7x/gx5j/+rsesZu17kVDx/dQ3W3TuLpfixHUDFaryd0Pal7NEavvm6buuI0h2XhhIPjAuabpOS3tdVlOiZq+0LU98QEaR7sIdygJ2s1HzrL6z+2EYD8hil+du/XAegKpnh4bC8Ak7Uijx/RERDxFYUBbT6ETULrSS1kCo8dJjLHIEGwYp2er3ecWeFwOBriNAfHBWS1hmDLZpvMlFRkLgXb9r4SMrtemwnNrw+y7Td0pOX4bx7gd5V2Sm/uHuNNvbrO4q++vA/ZriMf/kCRSp+OwHQe8ikOTtVtGy6cNbHScyOuJ5xwcFyAf8uNRAd1AVXYP1A3CTspwlK5AIZ1tuRimsb6wxOMPaBDn8UvjiAFPbR3+68/xqlf0nM2+nua+euxzQAU9o8Sv9qhP1sWdvz0t4F6oQXzd6h2QmHpOLPC4XA0xOU5XMP4bSY56c5d+N945qLrSi7fsPtQsH0r4fGTyz4Wr6gb36ooRvbqJiaH/12J3T/6dMP1k5Tw7ufH8Ua12TBfw5PRDx2wiWDXQ+PZleRieQ5OOFyjqAf24Y9rWz1+6ZUrfDT12CzLMOT1/0NHJbb/2uLmaQTr+5i6WxdblU5OoA4fA9KuVI6F4ZKgHA7HonGag+OKEezYhprUEYfJN+3i7J36WbX9i9NIRVeCqmcP2iSr/u/fSG5KX689f/jYJec6rPZA4JVgrmP1cuPG4TkWhb/nhhVpUXYpov5BogO6bmOm16ProGlY6wlhl/ZRjP/EAZpGTFeogZiW/ln7+TiT+DQ3hAnzF39dNkQQ06LNa29LC8LEQwK9/EoKhkvhzAqHw9EQpzk4AF3uHQ7oasbo1dcvy2RrFYa2o1XPI3OOx/zbvcDtLJVsT8uFkERd5molyfQtVa3a19LWCjl9JmpmFqo1s7yEGl96QtnlwgkHBwDhwCB+p655iGdm5hUK6sDtAJdtpN5qsyDB4KXdm61Q8Hz8XjOjQ8R2eFbNTUhNCytVKiJTZvBOpYIq631Fo6O2M/da5pJmhYj8iYicFZGXMsu6ROQfReQ182+nWS4i8vtmwvYLInLnah68w+FYPRbic/gzLhxz92HgYaXUbuBh8zfAu4Dd5r+fwg3QvaqIRkftU80rlfBKJZveDIAIuVMj5E6N1D1Nx37kAGM/cmDVjkuCIH3SiqSOvctFHEEcIZ7Y/UsugDDU/4lA4Ov/AMoV/d/wKCqfQ+VzSFcn0lxCmksgggrDZZlDl4NL6jZKqW+JyPY5ix8E3mJefwL4BvArZvmfKx0ffVxEOkRkg1KqcbdRx5oiMSui0VFrW0s+n6reShGe1N718DvvJP9t3RKu4y9MduI8pd7Lpe4mWo3QuxF0fkvzRYvLVBjaOhMANa1NBsnnUTPa3BARKBqBms+BpwWZyudQpXV6nYmpNTEL81IsNVrRl7nhzwB95vUmIJvj6qZsOxxXKcv2iiillIgsWpy7Kdtrj6QmwSsWrYPtggQj8+QuHOy3DVbs56/S8XRes77+oomJ1FyKL5wT53e0E89qDcErFKBJa1fkc1hDx/NQTVpzUDkfb1z311Qzs0ir0TqKhatCc1iqcBhKzAUR2QAkV8UgsCWznpuyfRUSl8t2/gRHjtnQXDwzY8fw8ciz84b11iQXmSptBaDnNxQKiSkRjY0TbNGl5KpcAWNuxbMjyEajPI+MIZERAuMTYBrriucRHjm2UmdzWViqWfEF9ARtqJ+k/QXggyZqsR8Yd/4Gh+Pq5JK1FSLySbTzsQcYAv498D+Bh4CtwAng/UqpERER4H+goxszwI8ppZ661EG42oq1i9/R3rCZS7Bjmx09F2xYrxcqheruALDNYtYiC6m5SCI2kHbACjasR83q9O14tmwdtVIooExug9/SbE0Pf/OGNT+ez5VsOxZEo0Imr1Ri+rt0tmTTF759VQ2KuVhvh2RsoDoxSDwzc8H7UihAXH+uKqzZ8/e7u4hGzLaVqjO95mUNDttxJdsOh2PRrP0cTsdlI9EY/O4uoht0BHq2t8jkFn2ZTP7MATb+jR6CG54+s3aehHMcif4tNwLzmzbh2+7Ce1LnaFQP3Ixf1p+Vf37OnpOI4PXovI/ElIjGqmkuyPkRJKcncamwlmoMc4/FdOOKJiau/Pe0SJxZ4bgoWds7npmxCUnztZVbS/h961LToFK5dPdsz8czCUzS3Ex0XjfQTUKd8eRkmhy2ZSOqX88fjas1nTGJDnEm+1lsUdeVwJkVDodj0Tiz4jqhTr1dAEkEQrWUiF47mr5h1O61qjVkNZpsUlY27Xle4ig1D2Zm0jyOxNzqW0c8MqbfP3OuLuKhks5VIrYeZa1rDZfCCYfrhMUOpAlPn7GvrW1dqy7Obr5MPolsaDIrtLxSyd7sXkd7w2ngXrGIimJznHE6C/TMkN1mMqsj2/1a1aq2GMxrb7Pmy7XU+dqZFQ6HoyFOc7jKee2j+9n9c4+v2vb9nu664bmLYpU1hktN987mHISDpxqvUy5bEyo6N0x4ZijdvkmVTjSGuQ5Gr12bavHU9FVvQjTCRSuuAV77s7sA2POTL65ZX8Cl8NvaljyL0+/tRWVMhosmIl2KJJTp+xf0W/CamxuaJlczLlrhcDgWjTMrrgGSkXJZHdDv7UVadHx+ofn9wWad+KRmZ4lGdT1FsHG9bTy7mixngvfcYbmJozD75A+2bEZVtVYlTUWU6doUza2UNJp0w1b315jWcCmc5uBwOBriNIdriOkfuI/mzz4BmKepeaAGG9bb0KRXKiH5HABSLDL2Jt23Ifah4/Mv6NdZR97AoC1SkkoNIh3Pn2+obZZg00a9bsYZOJ9vIdi2heiMzktQlQq+6YPQqCJ0oQQb1qNiHaZcy8Nj1ipOOFxDNH/2CWa+/z4ASn/zhF2ezVnA94n2bAXg/BtaaD6rb/bmV4aJ5nHkyeiE/WzcrT302RoCmzBVqxHu1h5+f6aKmtEe/Mq776HSrtX4wnhEMKM/FxV9vKrJMfha/bTthmXi6/s4/X07Aej9g/kH7yYmQd15z4Pf1mZLrFWtuqQGNlIoWKG51pvGLgZnVjgcjoY4zeFawvNpe04/LQ//t/3c8Itp/kOS0ut1tDN0VwsAXa9UyJ3T2kJdivQckti/BAEyOgZA9bvvJCzpZ0vbs+YJ3dpM2GyyBsOYWmcTAIWRChLlzYEIXlU/ZfPnpi/ZFMbvaGfyrTfpzb9w1moM/o27iA4fAbRJshAzpxFzTZw6jWFuhqfn2yIsNTtrtQRVC/G7u/Q6Xe32uK52XJ7DNUbS4zE/MML5+zcA0HZ0lqQDqsp55E4ZlX1o2N4cfneXrUJcKn5vL9HwsH7d3tbQNJjrc5C7bgHAmyzDeZ16PN9xBOv7UKZJa/T6Ccrv0fkdxb99Mt3+RYYAJ+bPQs0NZUwFr7XFHNfogvJIvH17AYife/mS615pXJ6Dw+FYNE5zuEbxbruJsF2r9cHThxeUNZj0bfC6u+b17mdbyfm9vQCoPl2sFL/0Cv7ePQBEh14j2Go6NY9PIE36WLJP7WxTGf/1QVivt1fpa6FwRldCRi+/Wrd/W+zU2jp/C7jd2mkZrmvDm9HDa/2zo3VRk2C97hadTZfWG05b0y+5utJsI9i+Zd7U7rXCxTQH53O4RolfeMWqhfFCP2MEyHyCpPrOexBTfVh85hhqYw8A3oRZv7XV3szBls3UNmk7PMjnCDM+jaTyMRo+D8aEiMC+Dg7CxPt01KXtbH1th20209aCmOOce/Mm/hN5LU0Mi7Jj/UiFgr93D5xOYr5BXULVUuslkoYxzF4FLfsvgjMrHA5HQxbSmv5PgPcCZ5VSt5plvwN8D1AFXke3oB8z730E+An0w+BnlVJfvtRBOLNi7eLv3QMD2hTIOhL93TsviHBIoYAkk7Lm0T78nm49fJaFJzglJkD5ls0ED6f5ENk06WDHNuDiqeLJ+n5vz7xOyZVIvrqaWFZrehF5EzCFHpCbCIfvBr6mlApF5D8DKKV+RUT2Ap8E7gU2Al8F9iilLhwjlMEJh6sb71YdaoxfeqXh+36nnjANXFCnEb/ZRFdOjixqxoO/d481YebLugzfdledMEkItm9NQ58Xuf79G3cBXDOhyUYsK1qhlPoWMDJn2VeUUkkq2OPosXegp2x/SilVUUodA46gBYXD4bjKWAmH5I8DnzavN6GFRYKbsn0NIbk8XovpxRhFSLdu0x4ajSFY32cdhtHweduMRU1Mopp1tKIu1fjuvXjffFZvz9RhgNYm/Md1jsB8TsFsFGO+is6s1uDddhPxC/o4w+Mn69arG0hjEp+CrZsJ52gMfm/vBRWg1zLLEg4i8mtACPzlEj7rpmxfZahalWg0kwRkbkpbjzA9AztNbcW6bsLkBt5/Gzyui7q8W29CFUy48PEX7I0ZDp5KC7W++SwqCQdu2jhvF6fFkAiGhu9l/CO+6e6UzbhMfBV1gmGeobvXEksWDiLyo2hH5dtU6rhwU7YdjmuEJQkHEXkn8MvAm5VSWbf0F4C/EpH/jnZI7gaebLAJxxpkIRWJiTc/npq2JkR8my7p9k8MoY6a5KmNfXi336zff/yFNJqQcVr6N++GwSG77zoNwTyVw8FThG/VadLB1y50LjYiesudevvfeGZB6yfHps6PNoxSNKy0vMa1Blj6lO2PAAUgyU55XCn1v5n1fw3thwiBn1dK/f2lDsJFK9YWwaaNtidjPD1r6wmCLZuJerRw8EcnKd+gMxoT217uugV/WJsadWp5pjGr19paN6g32K7Lx1VTAZWEQeeJeiyFBQ24nYe6lvzXKMvKkFRK/VCDxX98kfV/C/ithR+ew+FYi7j0accFzOcADPsH8Me1ZqByAcHD2utvqxCfPkijVifZiENWa/CKReKOFrPSpd1O2WjBQjtBJxqDemCfHpS7CK5ljWEhOOHgWBSNwoaLKk32fFC62sPr7CB+9bhevnMr1XU6TDrfRZmNFiy22av883M6agI2cuK4OK62wuFwNMRpDo7LS8bLH50btq/9qRmCFXRENsRoDMvpHHU94YSD44qhwhD1wD79epH+gOUQnuhf9NTx6xFnVjgcjoY4zcFxxfB7e4mMxhC95U4WmrS0EjiN4dI4zcFxxchGHy6nYHAsDCccHA5HQ5xwcDgcDXHCweFwNMQJB4fD0RAnHBwOR0OccHA4HA1xwsHhcDTECQeHw9EQJxwcDkdDnHBwOBwNccLB4XA0xAkHh8PRECccHA5HQy4pHETkT0TkrIi81OC9XxQRJSI95m8Rkd8XkSMi8oKI3LkaB+1wOFafhWgOfwa8c+5CEdkCfDeQHTz4LvQgm93oUXcfW/4hOhyOK8GSpmwbfhc99SrbU/xB4M+V5nGgQ0Q2rMiROhyOy8qSfA4i8iAwqJR6fs5bm4Bs5043ZdvhuEpZdJs4ESkBv4o2KZaMm7LtcKxtlqI53ADsAJ4XkePoSdrPiMh6FjllWyl1t1Lq7hyFJRyGw+FYTRYtHJRSLyql1imltiultqNNhzuVUmfQU7Y/aKIW+4FxpdTplT1kh8NxOVhIKPOTwGPAjSIyICI/cZHVvwQcBY4A/x/wr1fkKB0Ox2VnqVO2s+9vz7xWwP++/MNyOBxXGpch6XA4GuKEg8PhaIgTDg6HoyFOODgcjoY44eBwOBrihIPD4WiI6OjjFT4IkXPANDB8BXbf4/Z7ze/b7Xd+timlehu9sSaEA4CIPKWUutvt99rc75Xct9vv0nBmhcPhaIgTDg6HoyFrSTh83O33mt7vldy32+8SWDM+B4fDsbZYS5qDw+FYQzjh4HA4GuKEg8PhaIgTDg6HoyFOODgcjob8/xJ8jxPEWJR3AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 288x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "plt.matshow(first_layer_activation[0, :, :, 5], cmap=\"viridis\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
    "source": [
-    "first_layer_activation_maps = layer_activation_maps[0]\n",
-    "print(first_layer_activation_maps.shape)"
+    "This channel appears to encode a diagonal edge detector—but note that your own\n",
+    "channels may vary, because the specific filters learned by convolution layers aren’t\n",
+    "deterministic.\n",
+    "Now, let’s plot a complete visualization of all the activations in the network. We’ll extract and plot every channel in each of the layer activations, and\n",
+    "we’ll stack the results in one big grid, with channels stacked side by side."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Visualizing every channel in every intermediate activation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7ff487650950>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 1306.29x648 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "images_per_row = 16\n",
+    "for layer_name, layer_activation in zip(layer_names, activations):\n",
+    "    n_features = layer_activation.shape[-1]\n",
+    "    size = layer_activation.shape[1]\n",
+    "    n_cols = n_features // images_per_row\n",
+    "    display_grid = np.zeros(((size + 1) * n_cols - 1, images_per_row * (size + 1) - 1))\n",
+    "    for col in range(n_cols):\n",
+    "        for row in range(images_per_row):\n",
+    "            channel_index = col * images_per_row + row\n",
+    "            channel_image = layer_activation[0, :, :, channel_index].copy()\n",
+    "            if channel_image.sum() != 0:\n",
+    "                channel_image -= channel_image.mean()\n",
+    "                channel_image /= channel_image.std()\n",
+    "                channel_image *= 64\n",
+    "                channel_image += 128\n",
+    "            channel_image = np.clip(channel_image, 0, 255).astype(\"uint8\")\n",
+    "            display_grid[col * (size + 1): (col + 1) * size + col, row * (size + 1) : (row + 1) * size + row] = channel_image\n",
+    "\n",
+    "scale = 1. / size\n",
+    "plt.figure(figsize=(scale * display_grid.shape[1],\n",
+    "scale * display_grid.shape[0]))\n",
+    "plt.title(layer_name)\n",
+    "plt.grid(False)\n",
+    "plt.axis(\"off\")\n",
+    "plt.imshow(display_grid, aspect=\"auto\", cmap=\"viridis\")"
    ]
   },
   {