0.0.1 Introduction - Keras Functional API.ipynb

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    "------"
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   "source": [
    "<div> \n",
    "    <center><strong><h5>Higher Order Tutorial on Deep Learning!</h5></strong></center>\n",
    "    <center><strong><h2>0.0.1 Keras Functional API</h2></strong></center> \n",
    "<div>"
   ]
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   "metadata": {},
   "source": [
    "------"
   ]
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   "source": [
    "### Keras Functional API:\n",
    "##  `tl;dr:  next_layer(**params)(previous_layer)`\n",
    "\n",
    "Example (LSTM):\n",
    "```python\n",
    "x = LSTM(64)(x)\n",
    "x = Dense(2, activation='relu')(x)\n",
    "```\n",
    "\n",
    "Example (CNN):\n",
    "```python\n",
    "x = Conv2D(64, 3, 3)(x)\n",
    "x = Flatten()(x)\n",
    "x = Dense(2, activation='relu')(x)\n",
    "```\n",
    "\n",
    "Example (Concatenate): \n",
    "```python\n",
    "lay1 = Dense(50, activation = 'relu')(x)\n",
    "lay2 = Dense(50, activation = 'relu')(x)\n",
    "x = concatenate([lay1, lay2])\n",
    "```"
   ]
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   "cell_type": "markdown",
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   "source": [
    "------"
   ]
  },
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   "metadata": {
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   "source": [
    "<img style =\"width:50%;\" src=\"images/intro.jpg\"/>"
   ]
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   },
   "source": [
    "# About Neural Networks"
   ]
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    "- Supervised Learning models.\n",
    "- Represented by a weights tensor.\n",
    "- Trained by Gradient Descent.\n",
    "<img src=\"imgs/single_layer.png\" width=\"65%\" />\n",
    "(Source: Python Machine Learning, S. Raschka)"
   ]
  },
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   "source": [
    "### Weights Update Rule"
   ]
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    "- We use a **gradient descent** optimization algorithm to learn the _Weights Coefficients_ of the model.\n",
    "<br><br>\n",
    "- In every **epoch** (pass over the training set), we update the weight vector $w$ using the following update rule:\n",
    "\n",
    "$$\n",
    "w = w + \\Delta w, \\text{where } \\Delta w = - \\eta \\nabla J(w)\n",
    "$$\n",
    "\n",
    "<br><br>\n",
    "\n",
    "In other words, we computed the gradient based on the whole training set and updated the weights of the model by taking a step into the **opposite direction** of the gradient $ \\nabla J(w)$. \n",
    "\n",
    "In order to fin the **optimal weights of the model**, we optimized an objective function (e.g. the Sum of Squared Errors (SSE)) cost function $J(w)$. \n",
    "\n",
    "Furthermore, we multiply the gradient by a factor, the learning rate $\\eta$ , which we choose carefully to balance the **speed of learning** against the risk of overshooting the global minimum of the cost function."
   ]
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   "source": [
    "### Gradient Descent"
   ]
  },
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   "source": [
    "In **gradient descent optimization**, we update all the **weights simultaneously** after each epoch, and we define the _partial derivative_ for each weight $w_j$ in the weight vector $w$ as follows:\n",
    "\n",
    "$$\n",
    "\\frac{\\partial}{\\partial w_j} J(w) = \\sum_{i} ( y^{(i)} - a^{(i)} )  x^{(i)}_j\n",
    "$$\n",
    "\n",
    "**Note**: _The superscript $(i)$ refers to the i-th sample. The subscript $j$ refers to the j-th dimension/feature_\n",
    "\n",
    "\n",
    "Here $y^{(i)}$ is the target class label of a particular sample $x^{(i)}$ , and $a^{(i)}$ is the **activation** of the neuron \n",
    "\n",
    "(which is a linear function in the special case of _Perceptron_)."
   ]
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    "We define the **activation function** $\\phi(\\cdot)$ as follows:\n",
    "\n",
    "$$\n",
    "\\phi(z) = z = a = \\sum_{j} w_j x_j = \\mathbf{w}^T \\mathbf{x}\n",
    "$$"
   ]
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   "source": [
    "### Binary Classification"
   ]
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   "source": [
    "While we used the **activation** $\\phi(z)$ to compute the gradient update, we may use a **threshold function** _(Heaviside function)_ to squash the continuous-valued output into binary class labels for prediction:\n",
    "\n",
    "$$\n",
    "\\hat{y} = \n",
    "\\begin{cases}\n",
    "    1 & \\text{if } \\phi(z) \\geq 0 \\\\\n",
    "    0 & \\text{otherwise}\n",
    "\\end{cases}\n",
    "$$"
   ]
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   "source": [
    "## Multi-layer Neural Network Architecture"
   ]
  },
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   "source": [
    "<img src=\"imgs/multi-layers-1.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
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    "Now we will see how to connect **multiple single neurons** to a **multi-layer feedforward neural network**; this special type of network is also called a **multi-layer perceptron** (MLP). \n",
    "\n",
    "The figure shows the concept of an **MLP** consisting of three layers: one _input_ layer, one _hidden_ layer, and one _output_ layer. \n",
    "\n",
    "The units in the hidden layer are fully connected to the input layer, and the output layer is fully connected to the hidden layer, respectively. \n",
    "\n",
    "If such a network has **more than one hidden layer**, we also call it a **deep artificial neural network**.\n"
   ]
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   "source": [
    "### Notation"
   ]
  },
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   "source": [
    "we denote the `ith` activation unit in the `lth` layer as $a_i^{(l)}$ , and the activation units $a_0^{(1)}$ and \n",
    "$a_0^{(2)}$ are the **bias units**, respectively, which we set equal to $1$. \n",
    "<br><br>\n",
    "The _activation_ of the units in the **input layer** is just its input plus the bias unit:\n",
    "\n",
    "$$\n",
    "\\mathbf{a}^{(1)} = [a_0^{(1)}, a_1^{(1)}, \\ldots, a_m^{(1)}]^T = [1, x_1^{(i)}, \\ldots, x_m^{(i)}]^T\n",
    "$$\n",
    "<br><br>\n",
    "**Note**: $x_j^{(i)}$ refers to the jth feature/dimension of the ith sample"
   ]
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   "source": [
    "### Notes on Notation (usually) Adopted\n",
    "\n",
    "The terminology around the indices (subscripts and superscripts) may look a little bit confusing at first. \n",
    "<br><br>\n",
    "\n",
    "You may wonder why we wrote $w_{j,k}^{(l)}$ and not $w_{k,j}^{(l)}$ to refer to \n",
    "the **weight coefficient** that connects the *kth* unit in layer $l$ to the jth unit in layer $l+1$. \n",
    "<br><br>\n",
    "\n",
    "What may seem a little bit quirky at first will make much more sense later when we **vectorize** the neural network representation. \n",
    "<br><br>\n",
    "\n",
    "For example, we will summarize the weights that connect the input and hidden layer by a matrix \n",
    "$$ W^{(1)} \\in \\mathbb{R}^{h×[m+1]}$$\n",
    "\n",
    "where $h$ is the number of hidden units and $m + 1$ is the number of hidden units plus bias unit. "
   ]
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   "source": [
    "<img src=\"imgs/multi-layers-2.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
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   "source": [
    "## Forward Propagation"
   ]
  },
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   "source": [
    "* Starting at the input layer, we forward propagate the patterns of the training data through the network to generate an output.\n",
    "\n",
    "* Based on the network's output, we calculate the error that we want to minimize using a cost function that we will describe later.\n",
    "\n",
    "* We backpropagate the error, find its derivative with respect to each weight in the network, and update the model."
   ]
  },
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   "source": [
    "### Sigmoid Activation"
   ]
  },
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   "metadata": {},
   "source": [
    "<img src=\"imgs/logistic_function.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"imgs/fwd_step.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"imgs/fwd_step_net.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
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   "source": [
    "## Backward Propagation"
   ]
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   "source": [
    "The weights of each neuron are learned by **gradient descent**, where each neuron's error is derived with respect to it's weight."
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<img src=\"imgs/bkwd_step_net.png\" width=\"50%\" />\n",
    "\n",
    "_(Source: Python Machine Learning, S. Raschka)_"
   ]
  },
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   "metadata": {
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     "slide_type": "subslide"
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   "source": [
    "Optimization is done for each layer with respect to the previous layer in a technique known as **BackPropagation**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"imgs/backprop.png\" width=\"50%\">"
   ]
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   "source": [
    "------"
   ]
  },
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   "source": [
    "# Keras Functional API\n",
    "\n",
    "\n",
    "- Creating models with keras's by the Sequential API is easy and simple, but it is impossible to create complex model structures, which we would need.\n",
    "- For instance, inception or residual net structure is impossible to implement using Sequential API since they require operations such as layer merging and multiple outputs\n",
    "\n",
    "The Keras functional API is the way to go for defining complex models, such as multi-output models, directed acyclic graphs, or models with shared layers.\n",
    "\n",
    "This guide assumes that you are already familiar with the `Sequential` model.\n",
    "\n",
    "\n",
    "<br>\n",
    "<img src=\"http://www.deeplearningmodel.net/img/googlenet/googlenet_block.png\" style=\"width: 300px\"/>\n",
    "<center> **Inception Module in GoogleNet** </center>\n",
    "\n",
    "<img src=\"http://cv-tricks.com/wp-content/uploads/2017/03/600x299xResNet.png.pagespeed.ic.M1J-VkbWPB.png\" style=\"width: 300px\"/>\n",
    "<center> **Resudiual Structure in ResNet** </center>"
   ]
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   "source": [
    "## Multi-input and multi-output models\n",
    "\n",
    "Here's a good use case for the functional API: models with multiple inputs and outputs. The functional API makes it easy to manipulate a large number of intertwined datastreams.\n",
    "\n",
    "Let's consider the following model. We seek to predict how many retweets and likes a news headline will receive on Twitter. The main input to the model will be the headline itself, as a sequence of words, but to spice things up, our model will also have an auxiliary input, receiving extra data such as the time of day when the headline was posted, etc.\n",
    "The model will also be supervised via two loss functions. Using the main loss function earlier in a model is a good regularization mechanism for deep models.\n",
    "\n",
    "Here's what our model looks like:\n",
    "\n",
    "<img src=\"https://s3.amazonaws.com/keras.io/img/multi-input-multi-output-graph.png\" alt=\"multi-input-multi-output-graph\" style=\"width: 400px;\"/>\n",
    "\n",
    "Let's implement it with the functional API.\n",
    "\n",
    "The main input will receive the headline, as a sequence of integers (each integer encodes a word).\n",
    "The integers will be between 1 and 10,000 (a vocabulary of 10,000 words) and the sequences will be 100 words long.\n",
    "\n",
    "```python\n",
    "from keras.layers import Input, Embedding, LSTM, Dense\n",
    "from keras.models import Model\n",
    "\n",
    "# Headline input: meant to receive sequences of 100 integers, between 1 and 10000.\n",
    "# Note that we can name any layer by passing it a \"name\" argument.\n",
    "main_input = Input(shape=(100,), dtype='int32', name='main_input')\n",
    "\n",
    "# This embedding layer will encode the input sequence\n",
    "# into a sequence of dense 512-dimensional vectors.\n",
    "x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)\n",
    "\n",
    "# A LSTM will transform the vector sequence into a single vector,\n",
    "# containing information about the entire sequence\n",
    "lstm_out = LSTM(32)(x)\n",
    "```\n",
    "\n",
    "Here we insert the auxiliary loss, allowing the LSTM and Embedding layer to be trained smoothly even though the main loss will be much higher in the model.\n",
    "\n",
    "```python\n",
    "auxiliary_output = Dense(1, activation='sigmoid', name='aux_output')(lstm_out)\n",
    "```\n",
    "\n",
    "At this point, we feed into the model our auxiliary input data by concatenating it with the LSTM output:\n",
    "\n",
    "```python\n",
    "auxiliary_input = Input(shape=(5,), name='aux_input')\n",
    "x = keras.layers.concatenate([lstm_out, auxiliary_input])\n",
    "\n",
    "# We stack a deep densely-connected network on top\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "\n",
    "# And finally we add the main logistic regression layer\n",
    "main_output = Dense(1, activation='sigmoid', name='main_output')(x)\n",
    "```\n",
    "\n",
    "This defines a model with two inputs and two outputs:\n",
    "\n",
    "```python\n",
    "model = Model(inputs=[main_input, auxiliary_input], outputs=[main_output, auxiliary_output])\n",
    "```\n",
    "\n",
    "We compile the model and assign a weight of 0.2 to the auxiliary loss.\n",
    "To specify different `loss_weights` or `loss` for each different output, you can use a list or a dictionary.\n",
    "Here we pass a single loss as the `loss` argument, so the same loss will be used on all outputs.\n",
    "\n",
    "```python\n",
    "model.compile(optimizer='rmsprop', loss='binary_crossentropy',\n",
    "              loss_weights=[1., 0.2])\n",
    "```\n",
    "\n",
    "We can train the model by passing it lists of input arrays and target arrays:\n",
    "\n",
    "```python\n",
    "model.fit([headline_data, additional_data], [labels, labels],\n",
    "          epochs=50, batch_size=32)\n",
    "```\n",
    "\n",
    "Since our inputs and outputs are named (we passed them a \"name\" argument),\n",
    "we could also have compiled the model via:\n",
    "\n",
    "```python\n",
    "model.compile(optimizer='rmsprop',\n",
    "              loss={'main_output': 'binary_crossentropy', 'aux_output': 'binary_crossentropy'},\n",
    "              loss_weights={'main_output': 1., 'aux_output': 0.2})\n",
    "\n",
    "# And trained it via:\n",
    "model.fit({'main_input': headline_data, 'aux_input': additional_data},\n",
    "          {'main_output': labels, 'aux_output': labels},\n",
    "          epochs=50, batch_size=32)\n",
    "```\n",
    "\n",
    "-----"
   ]
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   "source": [
    "## The concept of layer \"node\"\n",
    "\n",
    "Whenever you are calling a layer on some input, you are creating a new tensor (the output of the layer), and you are adding a \"node\" to the layer, linking the input tensor to the output tensor. When you are calling the same layer multiple times, that layer owns multiple nodes indexed as 0, 1, 2...\n",
    "\n",
    "In previous versions of Keras, you could obtain the output tensor of a layer instance via `layer.get_output()`, or its output shape via `layer.output_shape`. You still can (except `get_output()` has been replaced by the property `output`). But what if a layer is connected to multiple inputs?\n",
    "\n",
    "As long as a layer is only connected to one input, there is no confusion, and `.output` will return the one output of the layer:\n",
    "\n",
    "```python\n",
    "a = Input(shape=(280, 256))\n",
    "\n",
    "lstm = LSTM(32)\n",
    "encoded_a = lstm(a)\n",
    "```\n",
    "\n",
    "So then:\n",
    "```python\n",
    ">>>lstm.output == encoded_a\n",
    "True\n",
    "```\n",
    "Not so if the layer has multiple inputs:\n",
    "```python\n",
    "a = Input(shape=(280, 256))\n",
    "b = Input(shape=(280, 256))\n",
    "\n",
    "lstm = LSTM(32)\n",
    "encoded_a = lstm(a)\n",
    "encoded_b = lstm(b)\n",
    "\n",
    "lstm.output\n",
    "```\n",
    "```\n",
    ">> AttributeError: Layer lstm_1 has multiple inbound nodes,\n",
    "hence the notion of \"layer output\" is ill-defined.\n",
    "Use `get_output_at(node_index)` instead.\n",
    "```\n",
    "\n",
    "Okay then. The following works:\n",
    "\n",
    "```python\n",
    ">>> lstm.get_output_at(0) == encoded_a\n",
    "True\n",
    "\n",
    ">>> lstm.get_output_at(1) == encoded_b\n",
    "True\n",
    "```\n",
    "\n",
    "Simple enough, right?\n",
    "\n",
    "The same is true for the properties `input_shape` and `output_shape`: as long as the layer has only one node, or as long as all nodes have the same input/output shape, then the notion of \"layer output/input shape\" is well defined, and that one shape will be returned by `layer.output_shape`/`layer.input_shape`. But if, for instance, you apply the same `Conv2D` layer to an input of shape `(32, 32, 3)`, and then to an input of shape `(64, 64, 3)`, the layer will have multiple input/output shapes, and you will have to fetch them by specifying the index of the node they belong to:\n",
    "\n",
    "```python\n",
    "a = Input(shape=(32, 32, 3))\n",
    "b = Input(shape=(64, 64, 3))\n",
    "\n",
    "conv = Conv2D(16, (3, 3), padding='same')\n",
    "conved_a = conv(a)\n",
    "\n",
    "# Only one input so far, the following will work:\n",
    "conv.input_shape == (None, 32, 32, 3)\n",
    ">>>True\n",
    "\n",
    "conved_b = conv(b)\n",
    "# now the `.input_shape` property wouldn't work, but this does:\n",
    ">>> conv.get_input_shape_at(0) == (None, 32, 32, 3)\n",
    "True\n",
    "\n",
    ">>> conv.get_input_shape_at(1) == (None, 64, 64, 3)\n",
    "True\n",
    "\n",
    "```\n",
    "\n",
    "-----"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time to work!\n",
    "### As always: MNIST\n",
    "- Also known as digits dataset in scikit-learn\n",
    "- url: http://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from tachles import load_mnist, show_mnist_teaser, PlotLearning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(x_train, y_train), (x_test, y_test) = load_mnist()\n",
    "show_mnist_teaser()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Slow start: Single input & output\n",
    "- Model with only single input & output\n",
    "- Such structure is able to create using Sequential API as well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.utils.vis_utils import plot_model\n",
    "from IPython.display import Image\n",
    "from keras.layers import Input, Activation, Dense, Flatten, MaxPooling2D, Conv2D, LSTM\n",
    "from keras.models import Model\n",
    "from keras.utils import plot_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x shape:  (60000, 28, 28, 1)  y:  (60000, 10)\n"
     ]
    }
   ],
   "source": [
    "print 'x shape: ', x_train.shape, ' y: ', y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7ffa67c9ff50>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "input_layer = Input(shape = x_train.shape[1:])\n",
    "\n",
    "activation_1 = Activation('relu')(input_layer)\n",
    "hidden_layer = Dense(50)(activation_1)\n",
    "activation_2 = Activation('relu')(hidden_layer)\n",
    "flat = Flatten()(activation_2)\n",
    "output_layer = Dense(10, activation = 'softmax')(flat)\n",
    "\n",
    "model = Model(inputs = input_layer, outputs = output_layer)\n",
    "model.summary()\n",
    "model.compile(optimizer='rmsprop', loss='categorical_crossentropy')\n",
    "model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=10, callbacks=[PlotLearning()])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging layers\n",
    "- Sometimes, it is necessary to merge layers (e.g., GoogleNet or ResNet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. concatenate\n",
    "- concatenate() simply merges results of two or more layers\n",
    "- For instance, assume there are two layers to be concatenated, whose results are\n",
    "**[x1, x2, ..., xn]** and **[y1, y2, ..., yn]**. Then, concatenated layer would be **[x1, ..., xn, ..., y1, ..., yn]**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 50)\n",
      "(?, 50)\n",
      "(?, 100)\n"
     ]
    }
   ],
   "source": [
    "input_layer = Input(shape = X_data.shape[1:])\n",
    "activation_1 = Activation('relu')(input_layer)\n",
    "hidden_layer_1 = Dense(50, activation = 'relu')(activation_1)\n",
    "hidden_layer_2 = Dense(50, activation = 'relu')(activation_1)\n",
    "concat_layer = concatenate([hidden_layer_1, hidden_layer_2])\n",
    "print(hidden_layer_1.shape)\n",
    "print(hidden_layer_2.shape)\n",
    "print(concat_layer.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. add, subtract, multiply, average, maximum\n",
    "- Such layers perform element-wise operations over all corresponding elements of two or more layers\n",
    "- Hence, dimensionality of the input layers are preserved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 50)\n",
      "(?, 50)\n",
      "(?, 1)\n"
     ]
    }
   ],
   "source": [
    "# creating layers\n",
    "input_layer = Input(shape = X_data.shape[1:])\n",
    "activation_1 = Activation('relu')(input_layer)\n",
    "hidden_layer_1 = Dense(50, activation = 'relu')(activation_1)\n",
    "hidden_layer_2 = Dense(50, activation = 'relu')(activation_1)\n",
    "add_layer = add([hidden_layer_1, hidden_layer_2])\n",
    "print(hidden_layer_1.shape)\n",
    "print(hidden_layer_2.shape)\n",
    "print(add_layer.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. dot\n",
    "- dot() performs inner product operation between two layer results\n",
    "- 'axes' should be defined to perform the operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(?, 50)\n",
      "(?, 50)\n",
      "(?, 1)\n"
     ]
    }
   ],
   "source": [
    "# creating layers\n",
    "input_layer = Input(shape = X_data.shape[1:])\n",
    "activation_1 = Activation('relu')(input_layer)\n",
    "hidden_layer_1 = Dense(50, activation = 'relu')(activation_1)\n",
    "hidden_layer_2 = Dense(50, activation = 'relu')(activation_1)\n",
    "dot_layer = dot([hidden_layer_1, hidden_layer_2], axes = -1)\n",
    "print(hidden_layer_1.shape)\n",
    "print(hidden_layer_2.shape)\n",
    "print(dot_layer.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hands On! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### My turn:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 2)                 0         \n",
      "_________________________________________________________________\n",
      "dense_4 (Dense)              (None, 2)                 6         \n",
      "=================================================================\n",
      "Total params: 6\n",
      "Trainable params: 6\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "visible = Input(shape=(2, ))\n",
    "hidden = Dense(2)(visible)\n",
    "model = Model(inputs=visible, outputs=hidden)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n",
    "Image('model_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your Turn: \n",
    "\n",
    "#### Exe1:\n",
    "- Build the following network: Input -> Dense(10) -> Dense(2) -> Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = Input(shape=(2, ))\n",
    "\n",
    "# x = ...\n",
    "\n",
    "out = Dense(2)(x)\n",
    "model = Model(inputs=inp, outputs=out)\n",
    "model.summary()\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n",
    "Image('model_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exe2: \n",
    "- Build the following network: Input -> Dense(2) -> Dense(5) -> Dense(7) -> Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Your model goes here..\n",
    "# ...\n",
    "# .. \n",
    "\n",
    "model = Model(inputs=inp, outputs=out)\n",
    "model.summary()\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n",
    "Image('model_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Exe3: \n",
    "- Build the following network: Input -> Dense(3) -> Dense(5) -> Dense(3) -> Output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Your model goes here..\n",
    "# ...\n",
    "# .. \n",
    "\n",
    "model.summary()\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n",
    "Image('model_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### My Turn:\n",
    "### Convolutional Neural Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_5 (InputLayer)         (None, 64, 64, 1)         0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 61, 61, 32)        544       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2 (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 27, 27, 16)        8208      \n",
      "_________________________________________________________________\n",
      "max_pooling2d_4 (MaxPooling2 (None, 13, 13, 16)        0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 13, 13, 10)        170       \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 13, 13, 1)         11        \n",
      "=================================================================\n",
      "Total params: 8,933\n",
      "Trainable params: 8,933\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visible = Input(shape=(28, 28, 1))\n",
    "conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible)\n",
    "pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "conv2 = Conv2D(16, kernel_size=4, activation='relu')(pool1)\n",
    "pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "hidden1 = Dense(10, activation='relu')(pool2)\n",
    "output = Dense(1, activation='sigmoid')(hidden1)\n",
    "model = Model(inputs=visible, outputs=output)\n",
    "model.summary()\n",
    "plot_model(model, to_file='cnn.png', show_shapes=True)\n",
    "Image('cnn.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your Turn: \n",
    "Build the following network: (LeNet5)\n",
    "- Conv2D(filters = 6, kernel_size = 5, activation = 'relu')\n",
    "- MaxPooling2D(pool_size = 2, strides = 2)\n",
    "- Conv2D(filters = 16, kernel_size = 5, activation = 'relu')\n",
    "- MaxPooling2D(pool_size = 2, strides = 2)\n",
    "- Flatten\n",
    "- Dense(84)\n",
    "- Dense(10, activation='softmax')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Your model goes here..\n",
    "# ...\n",
    "# .. \n",
    "\n",
    "model.summary()\n",
    "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n",
    "Image('model_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Recurrent Neural Network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Architecture of the network \n",
    "We now define the network, we do not consider the output nodes yet.\n",
    "A single RNN cell is shown in the figure below in the middle:\n",
    "\n",
    "<img style=\"width: 70%;\" src=\"http://colah.github.io/posts/2015-08-Understanding-LSTMs/img/LSTM3-SimpleRNN.png\"/>\n",
    "\n",
    "The joining of the two lines coming from the previous state $h_{t-1}$ and the current x-values $x_t$ is a concatenation to a vector  $[h_{t-1}, x_{t}]$ of size `state_size + num_classes_in`. Alternatively, instead of concatenating, one could also use two matrices $W_x$ and $W_h$ and keep the states separate. This is mathematically completely identical. The new state $h_t$ is \n",
    "then calculated as:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "    h_{t} = \\tanh([h_{t-1}, x_{t}] \\cdot W + b) = \\tanh(h_{t-1} \\cdot W_h + x_{t} \\cdot U + b)\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_8 (InputLayer)         (None, 100, 1)            0         \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 10)                480       \n",
      "_________________________________________________________________\n",
      "dense_11 (Dense)             (None, 10)                110       \n",
      "_________________________________________________________________\n",
      "dense_12 (Dense)             (None, 1)                 11        \n",
      "=================================================================\n",
      "Total params: 601\n",
      "Trainable params: 601\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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pZWVlvfLKKyxv/WVTXl6+e/fu7OxsZ2dnDofj6OhInjYQBQUFCxcu5HA406ZNIzduLOPxeBkZGX5+fkJ5QiiKOnr0qK+vb0JCQkhICP0mkOhZ0m2Oz+fHxsaSX4T0mPdaeA/8ElJ0n86DBw+UVYlE53NRUdHy5cufPn1KURQZ+gAAoaGhzGWqqqoA4O7du1I0Ri7oN5CYhUlJSVwut7m5maKo5uZmLpe7b9++YWdJvbmmpqalS5eKk5KdAEzMjoQoNOhUVla6ubkpqxLxz+fbt29bWFgwnxUCAPks75kzZ+hCMi6MBCZlEYoC1dXVGhoazE/B7NixQ0dHp6mpScQsqTdH3Lp1y9HRUfQ7fcwaRlhHMhq55JLfg4UkIQKBIDAwcM2aNUZGRszy7OxsU1PTsLCwyspKUqKpqQnPBqOqiMzMTD6f//bbb9Ml8+bN6+7uPnLkiIhZMm7U2dnZxsZG6tyhGHSQWNra2mJiYuLi4qKiohYsWBAVFUUyZoiZ34OSX5IQ8ZOWiOn8+fNlZWXknWEmMzOznJycrq6ugIAA+uvswx4TauhMKWStwQlJZGl8SUkJAJibm9Ml5AWdX3/9VcQsWbZILFy48PDhwxUVFdKszLzswdurl5A4t1ft7e12dnbbtm0jkw0NDXZ2dlZWVuQ3Jk5+DzkmCRk2aQlNzPOZjGInX+ak0SumpaUBQHR0tFD5UMekubl5qEwpZMnBCUnIFz7FJHQ0nJ2dAaC7u5suIQ9bZ8yYIWKW1Juj3bx5E/ALn0g64gSd+Ph4AKC/n0tRFMkiFhMTQ0mS30NeSUJEJy2hiXk+c7lcfX19oUJ6RYFA4OPjAwD5+fnMctHHZKhMKSISkohJ6GiQjid6ID5FUeSJm4uLi4hZUm+OxuPxAMDT01OcGrBPB0mMfL5izJgxdMmcOXMA4IcffpConsFJQgBAiiQhEiUtGVZ9fT35kvpzcTicjIwMe3v7oKAg8gIdIfqYDM6UQm7QSEISoZ+lt7e31I0nb/+Rl/UIcts7fvx4EbOk3hzNwMAAABoaGqRYF4MOGh4JFtXV1XSJiYkJAOjr68tSrdyThEhHXV19YGBAxAJjxozJzc3t6elZuXIlXSjdMaETkjALRW9dNEdHR3j2OjFRV1cHAG5ubiJmSb05GomqlFRjdzHooOGRv+H5+fl0CXn9Xcb8HlInCZFv0hIzMzPm5QA8iwLMWODg4JCRkUE+AUCIPiZDkXtCklWrVunr6xcVFdElhYWFmpqaK1asEDFL6s3RyEWTmZmZNCszL/OwT+clJE6fTldXl6Oj44QJE+gujPDw8FmzZpHO1yVLlgBAYmJieXl5WloauVW5dOkSn8+3sbHR0dGpqakha5GeGrpH5sSJEy4uLpJWkpeXN3r06IsXLw67a2KezyRFUXt7O11Crg54PJ7QkhEREXSFoo8Jl8sFAIFAQGaRa7q+vr6enh4rKysAWLt27alTpxISEjw8PEhH8q5du1599VWSsnYo5BLJ1taWWfjxxx/b2dmR9re1tdna2iYlJQ07S+rNEeQRGHYkI2mIOTiwvb09JibGw8MjKioqJibmo48+6u3tJbPEz1Iil1QnIpKWCBHzfCbXL5cvXyaTubm5JJWal5fXtWvXmEv29fXNnj172GMiOlPK4IQkpLb169dzOJzx48cP1c7CwsLQ0FAA0NDQSE1NLSsrI+UCgeDIkSOBgYHx8fE+Pj6HDh2ig52IWVJvjvj888/V1NTEGZeMQQcJYzO1BctJQsQ/nz09PT/44ANFt2dYJPKOiM15e3uHhISIs+TgoIN9OgjBsWPH8vPz6+vrldiGrq6u/fv3yz5cmIXNlZaWlpeX79mzR7pNY9BB7KGThCi7IcJMTExyc3MjIyOFniuxqbKyMiUlZcqUKSq+OR6Pl5ycXFBQoKenJ92mMeggNig9Sciwpk6dmpycnJ6erqwGTJkyReqfMWub6+/vP3nyZFZWliwDHVj9BA16aenq6qakpNAZtVWTtbU1nTwbPZempiadIl1qeKWDEGIVBh2EEKsw6CCEWIVBByHEqud0JJ89e5b9diBlIU+UXsj/dJJH4oXctZGNOVLw2rVr8k0agBB6yWloaAi9TcKhVODDUmjE8fPzA4CcnBxlNwSNPNingxBiFQYdhBCrMOgghFiFQQchxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoIMQYhUGHYQQqzDoIIRYhUEHIcQqDDoIIVZh0EEIsQqDDkKIVRh0EEKswqCDEGIVBh2EEKsw6CCEWIVBByHEKgw6CCFWYdBBCLEKgw5CiFUYdBBCrMKggxBiFQYdhBCrMOgghFiFQQchxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoIMQYpWGshuARoaWlpajR48ODAyQybt37wJAamoqmVRXVw8ODjY0NFRa+9DIwaEoStltQCPAtWvX5s6dq6mpqaYmfHUsEAj6+/uLi4vnzJmjlLahkQWDDhKLQCAwNTV9/Pjxc+eOGzeuvr5eXV2d5VahkQj7dJBY1NTUVqxYoaWlNXiWlpZWYGAgRhwkJgw6SFzLly/v6+sbXN7X17d8+XL224NGKLy9QhKwtLSsqakRKrSwsKipqeFwOEppEhpx8EoHSWDVqlWamprMEk1Nzb/97W8YcZD48EoHSeDu3buvvvqqUOHvv//u6OiolPagkQivdJAE7O3tHRwcmNc1Dg4OGHGQRDDoIMmsXr2aflClqakZFBSk3PagEQdvr5BkamtruVwuOW04HE5lZaWlpaWyG4VGErzSQZKxsLCYPn26mpqampra9OnTMeIgSWHQQRJbtWoVh8NRU1NbtWqVstuCRh68vUISa2pqMjU1BYBHjx4ZGxsruzlopKFkdu3aNQ0NfFsdoRechobGtWvXZI8YcggWdXV1fD4/JydH9qqQLPbu3QsAkZGRLGyrp6cHAEaNGsXCtgCgtLR07969eI4pl5+fX11dnez1yO0KxdfXV15VIemcPXsWXtD/CIqi4AXdtZcQdiQjhFiFQQchxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoPOymz59ekxMjLJbIWfl5eW7d+/Ozs52dnbmcDiOjo7d3d303IKCgoULF3I4nGnTpmVnZ7PfPB6Pl5GR4efnN3PmTGY5RVFHjx719fVNSEgICQnJysoSZ5Z0m+Pz+bGxsQ8fPpR9dyQm+/hC8t8mez1IRr6+vr6+vpKu5e/vn5iYqIj2EA8ePJC9EonOsaKiouXLlz99+pSiqLa2NnKeh4aGMpepqqoCgLt378reNumQrK+TJ09mFiYlJXG53ObmZoqimpubuVzuvn37hp0l9eaampqWLl1aUVEhZiUAkJ2dLf5Gh6xH9iow6KgI6YKOQlVWVrq5uclej/jn2O3bty0sLJqamugSAHB3dweAM2fO0IUkwzwJTMoiFAWqq6s1NDRSUlLokh07dujo6DQ1NYmYJfXmiFu3bjk6OnZ0dIhZg1yCDt5eIUV5+PCht7f3UJ/KUgSBQBAYGLhmzRojIyNmeXZ2tqmpaVhYWGVlJSkhmZ6f+0UdZcnMzOTz+W+//TZdMm/evO7u7iNHjoiYJeNGnZ2dbWxsWL6/xqDz8hoYGMjJyQkKCpozZw5FUd98801YWJi5uXlLS0tQUJCRkZGTk9PPP/9MUVRpaWl0dLSlpWV9ff2yZcvGjh3r5OSUm5sLAAcPHuRwOCSBaXt7+549e+jJ48eP37lzp76+/r333iNbLCwsNDc3Ly4uVtAenT9/vqys7J133hEqNzMzy8nJ6erqCggIeO5XdNra2mJiYuLi4qKiohYsWBAVFdXS0iLimJC1enp6UlNTg4ODp02bNn/+/N9++02WxpeUlACAubk5XWJhYQEAv/76q4hZsmyRWLhw4eHDhysqKmSvSlyyXyzh7ZWKkOL2ir7VFwgEtbW1o0ePBoAdO3ZUV1efPHkSAFxdXfl8fl5enra2NgBs2rSpuLg4MzNTV1cXAEpKSiiKsra2Zp4AzEn48yX9uXPnRo0adf78eUl3TcxzLCAgAAD6+vqYhfSKaWlpABAdHS1U3t7ebmdnt23bNjLZ0NBgZ2dnZWXV3Nw81DEhS4aEhPzxxx/k3x4eHsbGxm1tbeLvlNDBcXZ2BoDu7m66pKurCwBmzJghYpbUm6PdvHkTAJj3biJqwD4d9CdSBB2BQMA8FydNmkT/VwoEAmNjYy0tLTJpZ2cHAJ2dnWSSvNHu7+9PUdTkyZOZJwBzcvCJ3t/fL/GOiX2OcblcfX19oULmHvn4+ABAfn4+szw+Ph4AHj16RK9y4sQJAIiJiaGGPiY//vjj4D/heXl54u+U0MEhHU89PT10CXni5uLiImKW1Juj8Xg8APD09BSnBuzTQbIS+l4Vc5LD4RgaGtI3I2pqagBA/uwDwOLFiwHg/v37km5RoamX6uvrDQ0Nh5rL4XAyMjLs7e2DgoIePXpEl1+/fh0AxowZQ5fMmTMHAH744QcY+pjcuHHDwcFB6Ofk7e0tdePt7e0BoLW1lS5paWkBgPHjx4uYJfXmaAYGBgDQ0NAge1ViwqCDpEFOd9KzoDrU1dUHBgZELDBmzJjc3Nyenp6VK1fShSSeVldX0yUmJiYAoK+vL6KqJ0+eVFVVkdscmuiti0a+5MOMhiR5jZubm4hZUm+ORqIqxWIGUQw6SBpPnjwBgPnz58Ozs/bp06cAIBAIyNAY+iTm8/nMFYUm5cvMzIx5OQDPogAzFjg4OGRkZHz33Xd0Cbmuyc/Pp0tqa2vh2d4Nxd7ennQk0yV37tw5cOCA1I1ftWqVvr5+UVERXVJYWKipqblixQoRs6TeHI1cNJmZmclelbhkv0PDPh0VIUWfTnt7OznhyCSXywUAgUBAJsnlDOmXJT01dI/MiRMnXFxcyKwlS5YAQGJiYnl5eVpaGrnBuXTpEp/Pt7Gx0dHRqampIWvl5eWNHj364sWLku6amOfY2rVrAaC9vZ0uIVcHPB5PaMmIiAi6wq6uLkdHxwkTJtDdOuHh4bNmzSJ7N9Qx6enpsbKyAoC1a9eeOnUqISHBw8ODdCTv2rXr1VdfzcrKEtFUcolka2vLLPz444/t7OxI+9va2mxtbZOSkoadJfXmCPIIDDuSkTQkDTqdnZ1xcXHkb8+ePXtSUlLIv7dv397a2kq6igFg8+bN3d3dJOjs2rXr8ePHDQ0NO3fupEeU3bt3z9XVVUdHx8PD4969e25uboGBgadPn+7t7Y2LizM1Nf3qq6/IkleuXDEzM7t69aqkuybmOUauXy5fvkwmc3NzPT09AcDLy0souW9fX9/s2bPpyfb29piYGA8Pj6ioqJiYmI8++qi3t5eiKPrK5bnHpKqqatGiRYaGhiYmJqGhoY2NjaS29evXczic8ePHD9XOwsLC0NBQANDQ0EhNTS0rKyPlAoHgyJEjgYGB8fHxPj4+hw4dooOdiFlSb474/PPP1dTUxBmXjEEHCVPoiGShR1QsE/8c8/T0/OCDDxTdnmGRQDwiNuft7R0SEiLOkvIKOting14ox44dy8/Pr6+vV2Iburq69u/fL/twYRY2V1paWl5evmfPHrm3SgT2gk5DQ0NOTk5ycjJrW0RyRDoFOjs7ld2QYZiYmOTm5kZGRgo9V2JTZWVlSkrKlClTVHxzPB4vOTm5oKBAT09PEQ0bCktB548//vjoo4/8/f3JmE6WURR15MiR1157TVdX19nZOSMjgxLjAWFRUZGvry8Z1L9u3ToymmNwzUePHnV0dHR2dp4wYQJZuLCwkKzO4XD09PSmTp06ffp0Doejra09ffp0JycnbW1tDoeTnp5O1898MEG7fv06mbts2bLnLsCOzs7O+Ph4kgMhPDy8tLRUWS0R09SpU5OTk9PT05XVgClTprD5M5Zuc/39/SdPnszKylLCuAfZ79DEvN8mX0p67phIJrlkQhCyefPmlStXHjhwIDw8nAzn379/vzgrkr+WEydOHGqBo0ePAsisKs8AACAASURBVMDp06fJ5Ndff62np/fll19SFHXhwoU333yTHsLL3PempiZbW9uKigr6r/GiRYsGVx4QEEA+LFVXVydOa1XwLXN5wX5DVQAjrk+H/NpFq6qqksvQA6ba2tra2tpTp05t3Ljx008/PXfuHAB8+umn4qyro6MDIj8p9+WXXwIAeUQCAEuXLj106BC5KOjp6YmNjaWH8DIZGRlt2LChp6eH1D9r1qwLFy4Ije6tq6trbm6eOHEiAJBv+CL0YlChjmQFZUKoqalh9pMtWLBg3LhxjY2NcqmcvLu0d+9e6tn92rJly8ig9b/85S8eHh5Drbh+/XryNhMAREREUBQlFAcPHTq0fv16uTQSIZWitKBz48aN6dOnb9y4cevWrRoaGh0dHUKZELq6uk6dOrV8+fJZs2aVlpa+/vrrXC63pKTk3r17S5YsGTdunL29PZ1kQAQ3NzehK4W+vj7yBh3InGzh/fffB4CkpKQlS5aQJyYaGhpLly4FAB0dHRHvGWlra9PJXJYuXTpx4sRjx46RsaGkhZcvX160aJF0rUJIpcl+hyb+/TYw+jXs7OwMDQ3JACc/P7+GhgahBQYGBsgdh56e3oULF27fvg0AXC73k08+aW1tJe/jz507V9LWlpSUaGtr//LLL2Ry2GQLMFw/1Jdffkle0jE0NPziiy/4fL5E9ZBDt2vXLgBITU0lhadPn961axcl4egY7NNBCgVy6tNR4Cu/orW0tLS0tOzfv//999/funXr4B4fNTU1GxsbADAzM/Py8gKA8ePH19TUkCxnr7322rhx427duiXRRvl8fnx8fEZGhouLCyl5991329vbZXn1edWqVZ6enh9++OHBgwfXr19/4cKFM2fOkHQz4gsJCfmf//mfzz77LDIyUlNTMyMjQ7qE4Q8fPiRfNH/BkDwSL+SuvYxkj1vSXemcPXuW/DL/+7//u7S0dPACg0tE5G0RU2JiIv3GipgGN2koZWVl5Onj+vXrxa+H3oVNmzYBwOnTp2/evLlu3TpSKOmVjlJOIfTyGGFPr4T4+PjcunVrwYIFv/zyi7u7+/HjxxW9RfK24YcffiiX2hobGwsKCshdHvHaa6+Rd3/OnDkjRYXh4eEcDmfv3r3p6enh4eHStQpvr5DiSHdODqa0oPPhhx/a2Nhcvnw5KyuLz+cnJiaScgWlPrhy5QqPx6Pfb4RnKZqk2yJFURs2bDAwMIiKimKmTbC2tjYxMTE2Nh68/HPrYSZesLOz8/b2/umnn3g8noODg+gVERq52As6JMFib28vmdy9ezd5WOPj46OnpzdhwgQAsLGxqaure/DgAVmGjCekf3j9/f0A0NHRQSZJVeKkTSooKPj444/5fP6BAwcOHDhAuk4uXrwIABcuXDAwMLh06dJzVySJETo6OsijcaKtrW3dunXa2tqTJk0qLi4ODg6mm5SXl9fQ0LB582ahesggQOb33gjy5J5O2hYZGQkAGzZsEFqRHAeEXhCyX3SJc+lbUVFBni4DwN69e5ubmwHg9ddf37lz54oVK7y8vCorKymKYmZCqK+vJz9CLS2tb7/99p///Ke6ujoAvP/++01NTfv37ye1paamPn78WMSmr1+//tzRfeRdfhHJFq5evUqScgLA5MmT33zzzTfffHPSpEnkUffx48cpiiIP48eOHTt//vz58+fPnDnz66+/Fqrnn//859/+9jdSz7p164qKikj5uXPnSHZLLy+vgoICiqIEAsHSpUvJ86/bt2+T3L0A4OvrW1hYOOx/BD69QgoFcurT4VAyX8Dn5OSQBN0y1oNk5OfnBwA5OTnKboj84TmmCjgcTnZ2NjnNZKFCI5JlwRna3bt3ld06hND/Udo4HfnCv4EIjRQvyJUOQmikwKCDXmrl5eW7d+/Ozs52dnbmcDiOjo7Mh4wFBQULFy7kcDjTpk2Tboy4jHg8XkZGhp+f38yZM5nlFEUdPXrU19c3ISEhJCQkKyuLlPP5/NjYWJLnQHXJ3heNTxZUhKKfXskl1ZF0lSjoHCsqKlq+fPnTp08piiJfzgGA0NBQ5jJVVVUAcPfuXblvXUz0p5+ZhUlJSVwut7m5maKo5uZmLpe7b98+MqupqWnp0qXiJFqXFIz0EcloZJFLqiNF5EuS2p07d1avXv3ZZ5+RMRAk+Z67u/vhw4eZFzVkBBn52oxSkJxKTDU1Ndu3b1+3bh352o+hoWFoaGh8fDz5GJmRkdG2bdsWL16ssrllMeig4ckl1ZGC8iVJRyAQBAYGrlmzxsjIiFmenZ1tamoaFhZWWVlJSjQ1NQGAzkOiCjIzM/l8/ttvv02XzJs3r7u7m87N7uzsbGNjQ16NVkEYdF46bW1tMTExcXFxUVFRCxYsiIqKIkPDDx48SAYZAEB7e/uePXvoSWaqI4qiSktLo6OjLS0t6+vrly1bNnbsWCcnp9zcXPErIS2RMZmRLM6fP19WVvbOO+8IlZuZmeXk5HR1dQUEBNDfcWd67tGjKOqbb74JCwszNzdvaWkJCgoyMjJycnKi8z2Rb4EGBwdPmzZt/vz5v/32myyNLykpAQBzc3O6hLxpTD6bRyxcuPDw4cMVFRWybEhRZL9Dwz4dFSFOn057e7udnd22bdvIZENDg52dnZWVFfnlWFtbM/8rmZPwrFuBz+fn5eWRVCSbNm0qLi7OzMwkCQNKSkrErIQYNpkRTe7nWEBAADz7eCmN3kRaWhoAREdHC5UPdfSam5tra2tJatodO3ZUV1eTDxDQ36IKCQn5448/yL89PDyMjY3Jt0DFJHTcnJ2dAaC7u5suIa/LzJgxgy4hryKL891OiZqBH9tDfyJO0CGvVtDfz6Uo6sSJEwAQExNDiUweInTek1yrdNp58ulLMmhY/EooiqK/Uyya3M8xLperr68vVEhvQiAQ+Pj4AEB+fj6zXPTRmzRpErMGY2NjLS0tiqJIMiAheXl54rdW6LiRvJc9PT10CXni5uLiQpfweDwA8PT0FH8r4jQDO5KRxMiHdMaMGUOXzJkzBxjv3ItJTU0NAOi08+QlNaHc8uKQJX2aLOrr60kv7HNxOJyMjAx7e/ugoCDy0i8h+uiRu0i6BkNDQ3KDduPGDQcHB6EfHnntTjokCXdraytdQm6QyXfWCQMDA2C8S6xSMOi8XEiwqK6upktMTEwAgGRclRo53ZXwBSVpqauri85PMGbMmNzc3J6enpUrV9KF0h29J0+eVFVVCX38T5zsCENxdHSEZykQiLq6OgBwc3OjS0gEpFRypD4GnZcL+cucn59Pl9TW1gLA/Pnz4dmZ+vTpUwAQCARk6Ap94opIPEQe1kpRiYLSJw3LzMyMeaUAf85tRDg4OGRkZJDEbIToozcUe3t70pFMl9y5c+fAgQNSN37VqlX6+vrM7y8WFhZqamoyhyOQax8zMzOpt6JAst+hYZ+OihCnT6erq8vR0XHChAl0x0R4ePisWbNIl+qSJUsAIDExsby8PC0tjdyAXLp0ic/n29jY6Ojo1NTUkLVITw3dI3PixAkXFxdJKyG5HC9evDjsrsn9HFu7di0AtLe30yXkwoHH4wktGRERQW9a9NHjcrkAQL41QFEUufrr6+vr6ekhw3zWrl176tSphIQEDw8P0pG8a9euV199NSsrS0RTySWSra0ts/Djjz+2s7Mj7W9ra7O1tRXKw0ueZGFHMlIsMUckt7e3x8TEeHh4REVFxcTEfPTRR729vWTWvXv3XF1ddXR0PDw87t275+bmFhgYePr06d7eXmaqI+pZ0Nm1a9fjx48bGhp27tzZ0dEhaSUikhkJkfs5Rq5fLl++TCZzc3PJFxO9vLyuXbvGXLKvr2/27Nn05FBHj75y2b59e2trK+lZB4DNmzd3d3dXVVUtWrTI0NDQxMQkNDS0sbGR1LZ+/XoOhzN+/Pih2llYWBgaGgoAGhoaqampZWVlpFwgEBw5ciQwMDA+Pt7Hx+fQoUN0sCM+//xzNTU1+Y5LxqCDhLGZxEuKrPiyUMQ55unp+cEHH8i3TimQGC33ar29vUNCQuRbp7yCDvbpoJfUsWPH8vPzyScSlaWrq2v//v30SGJ5KS0tLS8vZ37YVqVg0EHSIB0NKvt2jzhMTExyc3MjIyOFniuxqbKyMiUlZcqUKXKsk8fjJScnFxQUkLfJVBAGHSSZzs7O+Ph4kjwhPDy8tLRU2S2S3tSpU5OTk9PT05XVgClTpsg3NPT39588eTIrK0uVhy+8IJkDEWt0dXVTUlJSUlKU3RD5sLa2jo2NVXYr5EZTU5P5nSXVhFc6CCFWYdBBCLEKgw5CiFUYdBBCrJJbR7Lsn+BCMiIPkl7I/wjyitMLuWsvITl84bO6unrLli2yvDWLRhyS+06+A0yQilNXV9+5c6elpaWM9cgh6KCX0Av8CWOkaNingxBiFQYdhBCrMOgghFiFQQchxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoIMQYhUGHYQQqzDoIIRYhUEHIcQqDDoIIVZh0EEIsQqDDkKIVRh0EEKswqCDEGIVBh2EEKsw6CCEWIVBByHEKgw6CCFWYdBBCLEKgw5CiFUYdBBCrMKggxBiFQYdhBCrMOgghFiFQQchxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoIMQYhWHoihltwGNAL///vvq1av7+/vJZFNTEwCMGzeOTGpqan755ZdOTk5Kax8aOTSU3QA0MmhpaZWVlQkV1tfXMxdgt0VopMLbKySWSZMmTZ06lcPhDJ7F4XCmTp06adIk9luFRiIMOkhcq1evVldXH1yuoaERFBTEfnvQCIV9Okhcjx49srCwEAgEQuUcDqe2tnbChAlKaRUacfBKB4lr/Pjxs2bNUlP70zmjpqY2e/ZsjDhIfBh0kARWrVolVMLhcFavXq2UxqARCm+vkARaWlqMjY35fD5doq6u3tDQYGRkpMRWoZEFr3SQBAwNDRcsWEB3J6urq7/zzjsYcZBEMOggyQQGBtJ9yRRFBQYGKrc9aMTB2yskme7ubiMjo97eXgDQ1tZuamoaPXq0shuFRhK80kGS0dHRWbp0qaampqam5tKlSzHiIElh0EESW7FiRX9/f39//4oVK5TdFjTyKOrdq+rq6hs3biiocqRcAwMDOjo6FEV1dHScPXtW2c1BCvHGG29YWloqpGpKMQICAhTSXIQQKwICAhQUHBR1pTMwMODr65uTk6Og+pHc5eTk+Pv7Uy/ogwUOh5Odne3n56fshowMfn5+AwMDCqoc+3QQQqzCoIMQYhUGHYQQqzDoIIRYhUEHIcQqDDoIIVZh0EEIsQqDDpLV9OnTY2JilN0KeSovL9+9e3d2drazszOHw3F0dOzu7qbnFhQULFy4kMPhTJs2LTs7m/3m8Xi8jIwMPz+/mTNnMsspijp69Kivr29CQkJISEhWVhYp5/P5sbGxDx8+ZL+pz6egQYe+vr6+vr4KqhwpAvn9SLGiv79/YmKi3NtDe/DggeyVAEB2drY4SxYVFS1fvvzp06cURbW1tZGfSWhoKHOZqqoqALh7967sDZNOTU0NAEyePJlZmJSUxOVym5ubKYpqbm7mcrn79u0js5qampYuXVpRUSFm/Qr9/WLQQf+f1EFHoSorK93c3GSvR8ygc/v2bQsLi6amJuaK7u7uAHDmzBm6sK+vDwBIYFIWoaBTXV2toaGRkpJCl+zYsUNHR4fel1u3bjk6OnZ0dIhTuUJ/v3h7hVTXw4cPvb29Hz9+zM7mBAJBYGDgmjVrhHIhZmdnm5qahoWFVVZWkhJNTU1Qse8LZmZm8vn8t99+my6ZN29ed3f3kSNHyKSzs7ONjY0q3Ahj0EHSGxgYyMnJCQoKmjNnDkVR33zzTVhYmLm5eUtLS1BQkJGRkZOT088//0xRVGlpaXR0tKWlZX19/bJly8aOHevk5JSbmwsABw8e5HA45DN+7e3te/bsoSePHz9+586d+vr69957j2yxsLDQ3Ny8uLhYEbtz/vz5srKyd955R6jczMwsJyenq6srICCAXOMIaWtri4mJiYuLi4qKWrBgQVRUVEtLi4gDQtbq6elJTU0NDg6eNm3a/Pnzf/vtN1kaX1JSAgDm5uZ0iYWFBQD8+uuvdMnChQsPHz5cUVEhy4bkQEFXUHh7NeJId3tFdy4IBILa2lqS02vHjh3V1dUnT54EAFdXVz6fn5eXp62tDQCbNm0qLi7OzMzU1dUFgJKSEoqirK2tmZtmTsKfbyLOnTs3atSo8+fPS9pOEOP2iqRG6OvrE1qR/CMtLQ0AoqOjhcrb29vt7Oy2bdtGJhsaGuzs7KysrJqbm4c6IGTJkJCQP/74g/zbw8PD2Ni4ra1Noj1iHhlnZ2cA6O7upku6uroAYMaMGXTJzZs3AYB5CzYU7NNBbJAu6JB8yfTZTz4uTM8yNjbW0tIik3Z2dgDQ2dlJJvfu3QsA5L32yZMnMzfNnIRB3aX9/f2SNpISL+hwuVx9ff3BK9K74+PjAwD5+fnM8vj4eAB49OgRvcqJEycAICYmhhr6gPz444+DrwDy8vIk2iPmkSEdTz09PXQJeeLm4uJCl/B4PADw9PQctnLs00GqS+jr5sxJDodjaGhI34+Qr/TR6U0XL14MAPfv35d0ixoaisrHUl9fb2hoONRcDoeTkZFhb28fFBT06NEjuvz69esAMGbMGLpkzpw5APDDDz/A0Afkxo0bDg4OQr9Gb29vqRtvb28PAK2trXRJS0sLAIwfP54uMTAwAICGhgaptyIXGHSQcpAfA+l3UBHq6uqik8iMGTMmNze3p6dn5cqVdCEJptXV1XSJiYkJAOjr64uo6smTJ1VVVeQOiCZLChtHR0cAYEbDuro6AHBzc6NLSASklJ0yCYMOUo4nT54AwPz58+HZj+Hp06cAIBAIyOgY+rfB/Lbf4Ek5MjMzY14pwLMowIwFDg4OGRkZ3333HV1Crmvy8/PpktraWni2a0Oxt7cnHcl0yZ07dw4cOCB141etWqWvr19UVESXFBYWampqMvNYk2sfMzMzqbciFxh0kEw6OjoAoL29nUyST9PQ8YLM7e/vp5enQ8bVq1ddXFzWrVsHz24NduzYcf/+/U8//ZREn8uXLw8MDNjY2NTV1T148ICsdeHCBQMDg0uXLiliX+bOndvR0UHaTDQ2NsKg+xE/P7+IiAh6MjY21tHR8bPPPiNXFgCQnp4+a9asTZs2wdAH5N1337Wystq+fXtwcHBmZmZiYmJERMSaNWsAYPfu3Q4ODqdPnxbRVNJfw4yGY8eO3bJly8GDB+n/kUOHDiUmJjKvJZuamuDP1z5KgUEHSa+rqyslJQUA6urq0tLSdu7cSR5mJScnt7W17du3j1ztb926taenh6yyb9++pqamxsbGR48eFRcXkwEvqamprq6uaWlpGzdu9PLycnR0DAwMbG1t5fP5vr6+enp6dJL/V155RU9P75VXXlHE7pCPspeWlpLJr7/+Ojg4GADCwsK+//575pKffPLJ7Nmzyb91dHRKS0tXrFgRFBQUHR0dGxs7btw4cpWRnp4+1AGhKKqwsHDRokX/+Mc/oqOjGxsbMzMz9fT0AKCysvLu3bt///vfh2pnUVERiXrV1dWffPLJrVu3SHlsbOzmzZs3bNiQkJAQHBwcGxu7detW5orXr19XU1Pz9/eXz/GSmoI6qPHp1Yij6BHJQo+oWAbijUj29PT84IMPWGiPaPfu3aOfrMuRt7d3SEiIOEvi0yuEWHLs2LH8/Pz6+noltqGrq2v//v30SGJ5KS0tLS8v37Nnj3yrlYLKBR2hnjz0wiBPajo7O5XdEFFMTExyc3MjIyOFniuxqbKyMiUlZcqUKXKsk8fjJScnFxQUkDs45VKVoNPb25ucnDxz5kyh116UZajsAaJnDeXbb7/19PQko/vfeuutt956a9q0aYsXLz5y5AjpNH2xdXZ2xsfHk9QK4eHhdKeJapo6dWpycnJ6erqyGjBlyhT5hob+/v6TJ09mZWWpygAFBd22SXFP2N3dTYZmKahJknpu9oBhZw2F/OQsLS3J5MDAwDfffGNtbW1ra/v777/Lp8WyUc23zOUFxE5tgaiXp09n1KhRxsbGym7F/5k4caIUs4YyYcIEAKAfu6ipqS1evLikpKSzs/Pdd9+lH+4g9MJToaDzEjIzM9u+fXtFRYUqdO8hxA4lB53u7u6oqKiwsLDExMQtW7Ywe+8Gv/hPDZcr4MaNG9OnT9+4cePWrVs1NDTIKCn5JhAQQbqsCz4+PmpqaleuXCGTI26vEZKYgm7bxLkn7O/vd3V1DQkJEQgEFEX95z//UVdXp5s0+MX/1tZW0bkC7OzsDA0NSW1+fn4NDQ3PrUf8BAIwdMfN4FnDZl0YqjZTU9OxY8cqfa+xTwfRFNqnw6EU8/YX+VJ9Tk6OiGUOHDjw/vvv375928HBgZRMmjTp/v37FEX961//mjFjhtDyeXl53t7ekydPLi8vp57lFjA1NW1tbSXPgP7rv/6rqalp375977///p07dyZOnPjHH38MVY84e8HhcCZPnnz37l0xZ/H5fBHvQA9Vm4WFxcDAwKNHj5S71zk5Of7+/r6+viKWGbnOnj07Y8YMVXl8o/JKS0tnzpwp+vcrNWXeXpF7CisrK7qEvLALIl/8F5E84YsvvtDV1Y2IiHB1de3s7CTD5+WbQEA0KbIu9PX1NTQ0vPbaazBi9xohiSgqNYk4SEqhJ0+eMHMsEvSL/3T6FQAYGBgg919D8fHxef311zds2HDlyhV3d/fDhw9LVw+bCgsL+/v7SWpbVdhrBf1xUzoOhxMZGUkuwNGwFHqglHmlQ94tZuYEYM6S4sX/Dz/80MbG5vLly1lZWXw+PzExUe4JBESTNOvC06dP4+PjX3vttfDwcBixe42QZOTVOSREnI6osrIydXX1sWPHXrp0qbu7++rVqyT9WmVlZU9PD7ntWrt27alTpxISEjw8PEhXKJfLBQDSb0pRFMkFRfLajho1inz0p6+vT09Pz9XVVUQ9wyKP0mxtbcWclZeXN3r06IsXL4qojcvl0iW//PKLu7u7paXl7du3SYly9xo7khHtRc6RXFxcPGvWLF1dXWtr6507d7q7u69bt66goIDP51dVVS1atMjQ0NDExCQ0NLSxsZGiKPrP9fbt21tbW0meXQDYvHkzyTDy+uuv79y5c8WKFV5eXpWVlRRFPbeeYRUWFoaGhgKAhoZGampqWVnZsLOuXLliZmZ29erVwbV9//33a9euJU2dO3fuggULFi1a9Ne//vXAgQNC3yFS4l5j0EG0F/bpFVIp5OmVgs4HpeNwONnZ2dinIyaF/n5f0hHJnKE99wE5QkhelPn0Sole1L/nCKm+l/RKByERysvLd+/enZ2d7ezszOFwHB0dSd8ZUVBQsHDhQg6HM23aNNIRxrKhkqtQFHX06FFfX9+EhISQkJCsrCxSzufzY2NjSZ4DlaCgviJMVzriKLoj+cGDB0qsBMTuSC4qKlq+fPnTp08piiLfpQCA0NBQ5jJVVVUAcPfuXekaI7vnJldJSkricrnkUWZzczOXy923bx+Z1dTUtHTp0oqKCjHrf1lSW6AXWFVVFfNbKEqsRLQ7d+6sXr36s88+09LSAgCSTIsMuWRe1JBEJczB9CwbnFylpqZm+/bt69atI0mpDA0NQ0ND4+Pjyad+jIyMtm3btnjxYlXI3IhBByncw4cPvb29Hz9+rPRKRBMIBIGBgWvWrBFKX5mdnW1qahoWFlZZWUlKyEcsSGBSEZmZmXw+nwxtJ+bNm9fd3U3nWnZ2draxsYmJiVFSA/8PBh0kmba2tpiYmLi4uKioqAULFkRFRZFPuB08eJA8/gOA9vb2PXv20JPHjx+/c+dOfX39e++9R1FUaWlpdHS0paVlfX39smXLxo4d6+TklJubK34lpCXS5RIR4fz582VlZe+8845QuZmZWU5OTldXV0BAAP3K27DHhBouJ4l804+UlJQAAPONIvJ266+//kqXLFy48PDhwxUVFbJsSA4UdNuGfTojjjh9Ou3t7XZ2dtu2bSOTDQ0NdnZ2VlZW5DdmbW3NrIE5Cc86IPh8fl5enra2NgBs2rSpuLg4MzNTV1cXAEpKSsSshBg2lwgTiNGnExAQAM/GeTNXJP9IS0sDgOjoaKHyoY5Jc3Oz6JwksiRdoQYdDWdnZwDo7u6mS8gg+BkzZtAlN2/eBICUlJRhK3+RRyQj1SFO0ImPjweAR48e0SUnTpwAgJiYGGrQl62Yk0K/EDs7OwDo7Owkk2SMNRmaKH4lFEX19/eLuXfiBB0ul6uvrz94RfIPgUDg4+MDAPn5+cxy0cdk0qRJzBqMjY21tLQoivrxxx8HXwHk5eWJuTvUoKPh7u4OAD09PXQJeeLm4uJCl5BXrD09PYetHDuSkaq4fv06AJBX5AjyJe8ffvhBonpIDhP6JfjFixcDwP379yVtjxS5RESor68nvbDPxeFwMjIy7O3tg4KCyIc6CdHHZKicJHJPP0Jen2Z+wYnc9pLX9AgDAwMY9JVk9mHQQRIgwaK6upouMTExAQB9fX1ZqiU/DKVn2FJXV2d+HXywMWPG5Obm9vT0rFy5ki6U7pjQ6UeYhaK3LpqjoyMAMKMh+bY688vlJAJSyh4Zi0EHSYD8DWdmI6mtrQWA+fPnw7NzmuQzFAgEZJALfYqLyPtBHutKUYmkuUREMzMzE/rWI4kCzFjg4OCQkZHx3Xff0SWij8lQ5J5+ZNWqVfr6+kVFRXQJ+Z46c5ABufYxMzOTeivyoaDbNuzTGXHE6dPp6upydHScMGEC3YURHh4+a9Ys0vm6ZMkSAEhMTCwvL09LSyO3KpcuXeLz+TY2Njo6OjU1NWQt0lND98icOHHCxcVF0kpE5xIRAmL06ZBMAO3t7XQJuXDg8XhCS0ZERNDHSvQxGSon0jiL7QAAF89JREFUiYj0I7t27Xr11VezsrJENPW5yVU+/vhjOzs70v62tjZbW9ukpCTmAuRJFnYkI1Uh5ojk9vb2mJgYDw+PqKiomJiYjz76qLe3l8y6d++eq6urjo6Oh4fHvXv33NzcAgMDT58+3dvbGxcXZ2pq+tVXX5ElSdDZtWvX48ePGxoadu7cSaf4EL8SEblEBhMn6JDrl8uXL5PJ3NxcT09PAPDy8rp27Rpzyb6+vtmzZw97TETnJBkq/cj69es5HM748eOHaudQyVUEAsGRI0cCAwPj4+N9fHwOHTpEBzvi888/V1NTE2dcMgYdxAY28+kIPaJigThBh6IoT0/PDz74gIX2iEYir9yr9fb2DgkJEWdJfHqFEEuOHTuWn59fX1+vxDZ0dXXt37+fHkksL6WlpeXl5arwWUcMOkgJSJeEKrwHJMTExCQ3NzcyMlLouRKbKisrU1JSpkyZIsc6eTxecnJyQUEBeZtMuTDoIFZ1dnbGx8eTNAvh4eGlpaXKbpGwqVOnJicnp6enK6sBU6ZMkW9o6O/vP3nyZFZWltIHJRAvaRIvpCy6uropKSkpKSnKbogo1tbWsbGxym6F3GhqasbFxSm7Ff8Hr3QQQqzCoIMQYhUGHYQQqzDoIIRYhUEHIcQuBQ06JPmQEEIjVEBAgIKCg6K+8FldXX3jxg1F1IxUAXmNKDIyUtkNQYryxhtvWFpaKqJmRQUd9GLDz0YjqWGfDkKIVRh0EEKswqCDEGIVBh2EEKsw6CCEWIVBByHEKgw6CCFWYdBBCLEKgw5CiFUYdBBCrMKggxBiFQYdhBCrMOgghFiFQQchxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoIMQYhUGHYQQqzDoIIRYhUEHIcQqDDoIIVZh0EEIsQqDDkKIVRh0EEKswqCDEGIVBh2EEKsw6CCEWIVBByHEKgw6CCFWYdBBCLFKQ9kNQCNGTU3NwMAA+XdXVxcAVFZWkkl1dXUul6u0lqERhUNRlLLbgEaAwsLCt99+W8QCV69enTdvHmvtQSMXBh0kltbWVmNj4/7+/ufO1dTUbGxsNDAwYLlVaCTCPh0kFgMDA09PTw2N59yPa2ho/OUvf8GIg8SEQQeJKzAwkO7TYRIIBIGBgey3B41QeHuFxNXb2ztu3DjShcyko6PT1NQ0atQopbQKjTh4pYPEpa2t/de//lVTU5NZqKmp6ePjgxEHiQ+DDpLAihUrhPqS+/v7V6xYoaz2oJEIb6+QBPh8vomJSXNzM11iYGDw+PHj53YwI/RceKWDJKChobF8+XL6DktTUzMwMBAjDpIIBh0kmeXLl9N3WP39/cuXL1due9CIg7dXSDIURVlYWPB4PAAwMzPj8XgcDkfZjUIjCV7pIMlwOJxVq1ZpaWlpaWkFBQVhxEGSwisdJLF///vfzs7O5B9TpkxRdnPQCKOoLsCzZ8+ePXtWQZUjpdPV1QWA7du3K7shSFF8fX19fX0VUbOibq/Onj1bWlqqoMqRItTW1or/d+KNN9544403FNoe+Tp79mxtba2yWzFilJaWKu6iQYEPO2fOnJmTk6O4+pF85eTk+Pv7v6j/ZRwOJzIy0s/PT9kNGRkUeqCwIxkhxCoMOgghVmHQQQixCoMOQohVGHQQQqzCoIMQYhUGHSSr6dOnx8TEKLsV8lReXr579+7s7GxnZ2cOh+Po6Njd3U3PLSgoWLhwIYfDmTZtWnZ2NvvN4/F4GRkZfn5+M2fOZJZTFHX06FFfX9+EhISQkJCsrCxSzufzY2NjHz58yH5Tn49SDDKcUUGVI0Ugvx8pVvT3909MTJR7e2gPHjyQvRIAyM7OFmfJoqKi5cuXP336lKKotrY28jMJDQ1lLlNVVQUAd+/elb1h0qmpqQGAyZMnMwuTkpK4XG5zczNFUc3NzVwud9++fWRWU1PT0qVLKyoqxKxfob9fDDro/5M66ChUZWWlm5ub7PWIGXRu375tYWHR1NTEXNHd3R0Azpw5Qxf29fUBAAlMyiIUdKqrqzU0NFJSUuiSHTt2kPTVZPLWrVuOjo4dHR3iVK7Q3y/eXiHV9fDhQ29v78ePH7OzOfJZizVr1hgZGTHLs7OzTU1Nw8LC6C+akjRmWlpa7DRMHJmZmXw+n/lBxHnz5nV3dx85coRMOjs729jYqMKNMAYdJL2BgYGcnJygoKA5c+ZQFPXNN9+EhYWZm5u3tLQEBQUZGRk5OTn9/PPPFEWVlpZGR0dbWlrW19cvW7Zs7NixTk5Oubm5AHDw4EEOh0NSZLS3t+/Zs4eePH78+J07d+rr69977z2yxcLCQnNz8+LiYkXszvnz58vKyt555x2hcjMzs5ycnK6uroCAAHKNI6StrS0mJiYuLi4qKmrBggVRUVEtLS0iDghZq6enJzU1NTg4eNq0afPnz//tt99kaXxJSQkAmJub0yUWFhYA8Ouvv9IlCxcuPHz4cEVFhSwbkgMFXUHh7dWII93tFd25IBAIamtrR48eDQA7duyorq4+efIkALi6uvL5/Ly8PG1tbQDYtGlTcXFxZmYmeU+9pKSEoihra2vmppmT8OebiHPnzo0aNer8+fOSthPEuL0KCAgAgL6+PqEVyT/S0tIAIDo6Wqi8vb3dzs5u27ZtZLKhocHOzs7Kyqq5uXmoA0KWDAkJ+eOPP8i/PTw8jI2N29raJNoj5pEhyUa6u7vpEvKxoBkzZtAlN2/eBADmLdhQsE8HsUG6oCMQCJhn/6RJk+hKBAKBsbGxlpYWmbSzswOAzs5OMrl3714A8Pf3pyhq8uTJzE0zJ2FQd2l/f7+kjaTECzpcLldfX3/wivTu+Pj4AEB+fj6zPD4+HgAePXpEr3LixAkAiImJoYY+ID/++OPgK4C8vDyJ9oh5ZEjHU09PD11Cnri5uLjQJSTfo6en57CVY58OUl1CmQOZkxwOx9DQkL4fUVNTAwDylx8AFi9eDAD379+XdIuKywNfX19vaGg41FwOh5ORkWFvbx8UFPTo0SO6/Pr16wAwZswYumTOnDkA8MMPP8DQB+TGjRsODg5Cv0Zvb2+pG29vbw8Ara2tdElLSwsAjB8/ni4hn35uaGiQeitygUEHKQf5MZB+BxWhrq7+3O8m08aMGZObm9vT07Ny5Uq6kATT6upqusTExAQA9PX1RVT15MmTqqoqoc+lit66aI6OjgDAjIZ1dXUA4ObmRpeQCEgpO1koBh2kHE+ePAGA+fPnw7Mfw9OnTwFAIBCQ0TH0b4PP5zNXFJqUIzMzM+aVAjyLAsxY4ODgkJGR8d1339El5LomPz+fLiHZwsiuDcXe3p50JNMld+7cOXDggNSNX7Vqlb6+flFREV1SWFioqanJ/BQiufYxMzOTeitygUEHyaSjowMA2tvbyWRvby8w4gWZy/woKB0yrl696uLism7dOnh2a7Bjx4779+9/+umnJPpcvnx5YGDAxsamrq7uwYMHZK0LFy4YGBhcunRJEfsyd+7cjo4O0maisbERBt2P+Pn5RURE0JOxsbGOjo6fffYZubIAgPT09FmzZm3atAmGPiDvvvuulZXV9u3bg4ODMzMzExMTIyIi1qxZAwC7d+92cHA4ffq0iKaS/hpmNBw7duyWLVsOHjxI/48cOnQoMTGReS3Z1NQEf772UQoMOkh6XV1dKSkpAFBXV5eWlrZz507yMCs5ObmtrW3fvn3kan/r1q09PT1klX379jU1NTU2Nj569Ki4uJgMeElNTXV1dU1LS9u4caOXl5ejo2NgYGBrayufz/f19dXT07tx4wZZ/ZVXXtHT03vllVcUsTurV68GADrN7tdffx0cHAwAYWFh33//PXPJTz75ZPbs2eTfOjo6paWlK1asCAoKio6Ojo2NHTduHLnKSE9PH+qAUBRVWFi4aNGif/zjH9HR0Y2NjZmZmXp6egBQWVl59+7dv//970O1s6ioiES96urqTz755NatW6Q8NjZ28+bNGzZsSEhICA4Ojo2N3bp1K3PF69evq6mp+fv7y+d4SU1BHdT49GrEUfSIZKFHVCwD8UYke3p6fvDBByy0R7R79+7RT9blyNvbOyQkRJwl8ekVQiw5duxYfn5+fX29EtvQ1dW1f/9+eiSxvJSWlpaXl+/Zs0e+1UoBgw5iCXlS09nZqeyGiGJiYpKbmxsZGSn0XIlNlZWVKSkp8v2gGI/HS05OLigoIHdwyqVyQUfo8QF6AXR2dsbHx5PUCuHh4Sr+baKpU6cmJyenp6crqwFTpkyRb2jo7+8/efJkVlaWigxQUJWg09vbm5ycPHPmTKF37ZRFRMqSI0eOvPbaa7q6us7OzhkZGZQYox6+/fZbT09P8krRW2+99dZbb02bNm3x4sVHjhwhT2pebLq6uvTQ+4yMDKFDqoKsra1jY2OV3Qq50dTUjIuLU4VrHEJVgo62tnZUVNS9e/fIsHqlmzBhwvz588+ePUuGNtC2bNny3XffhYaGBgcHl5eXBwcHizO2wsPDg9yiW1paFhUVFRUV/fTTTyEhITt37nRycrp9+7aidgMh1aMqQQcARo0aZWxsrOxW/J+JEycKldTW1tbW1p46dWrjxo2ffvrpuXPnAODTTz8Vp7YJEyYAAP2sV01NbfHixSUlJZ2dne+++y79RBmhF54KBR3VV1NTw+z8X7Bgwbhx48j4MemYmZlt3769oqJCFZ4pIMQOJQed7u7uqKiosLCwxMTELVu2MB8ZDM42Qg2XoOTGjRvTp0/fuHHj1q1bNTQ0yNBMOWYtcXNzMzU1ZZb09fWRt3tB2lQvPj4+ampqV65cUdm9RkjOFDT+R5zBRf39/a6uriEhIQKBgKKo//znP+rq6nSTBmcbaW1tFZ2gxM7OztDQkNTm5+fX0NDw3HrEz1oCg/IqMJWUlGhra//yyy9kcthUL0PVZmpqOnbsWKXvtWqmK5UXEDtHMqJe4Hw6n332GQDcvn2bLiEpVyiR2UZEZGwZN24cAOzbt29gYOC3335ra2uTMWuJiKDT398/Z86crKwsoUIpajM3NzczM1P6XivlwwZIZSku6CgqNYk4yD2FlZUVXUKyBMCzbCPPfawzOEEJ3avyxRdfrFmzJiIi4uTJkwcOHCDv7AxVj4ySkpLefvvt5cuXMwulSPXS19fX0NBA3khWhb3OycmRYi3V5+fnFxkZqfpP61UESbGmIMoMOiSP2ZMnT5iJXQk62wid8wkABgYGyP3XUHx8fF5//fUNGzZcuXLF3d398OHD0tUzrLy8vNGjR8fFxclSCVFYWNjf30/yaavCXvv6+sqwNyptxowZL/DeydfZs2cVV7kyO5JJQgNmIhLmLCmyjXz44Yc2NjaXL1/Oysri8/mJiYlyz1oCAFeuXOHxeMyIQ3LEgeSpXp4+fRofH//aa6+Fh4eDau81QnKjoNs2cfp0ysrK1NXVx44de+nSpe7u7qtXr5Kcj5WVlT09PeS2a+3atadOnUpISPDw8CBdoVwuFwBIvylFUSQBHUmmPWrUKPKlsb6+Pj09PVdXVxH1DIs8SrO1tWUWfvvtt2+99dZnz+zfvz8iIiIhIYGiKHL5c/HiRRG1cblcuuSXX35xd3e3tLSke7WUu9fYkYxoL2xHMkVRxcXFs2bN0tXVtba23rlzp7u7+7p16woKCvh8flVV1aJFiwwNDU1MTEJDQxsbGymKov9cb9++vbW1lb7z3Lx5M0lr9Prrr+/cuXPFihVeXl6VlZUURT23nmEVFhaGhoYCgIaGRmpqallZGUVR169fHzVq1ODATT6ceOXKFTMzs6tXrw6u7fvvv1+7di1ZeO7cuQsWLFi0aNFf//r/2ju/kKbeMI4/p/+Epf0B21WpCOFSIcKLCrsJTVoGv8ykRBHFgmQ515ZIYaAbKCb+aRcSmBIzJqygkgjMFJJ1lV4NU1qWpfNP0UwpcO508dDh/KabO9PzJ3s+Vzvv3LN3rzvfnfO+z/t9/rt7965f8TMZPzWJDsEhqugwrDiGqdnZ2bB+ZyXXJZ2dnVibQe6OiALDMDabDb+WxIqIev7+oxnJTGCGhobk7h1BrGfkXL2SkfX6e04QyucfvdIhiNAZHh6uq6uz2WzJyckMw6jVapxKQ7q7u9PT0xmGOXLkiCwJlsvasHi9XqPRiB5GSoNEh5AILMyihCCC6O3tvX37tlarvXDhAtqzO51OfjWIkydPtrS0AIDVapXF83xZG5ZNmzbduHFDq9W6XC7puxQcEh1CCj58+MAvwCRjEEE4nc68vLzm5uYtW7YAAPpgYQYm/6IGfUv4ufUSs9SGBQD27NlTWVmZmZmpNItYEh1CdD5//qzRaKanp2UPIgifz5ebm1tQUODnZmmz2fbt21dcXMxdRGAhHRQmRZGcnBwXF2cwGOTuyP8g0SGE4fF4DAZDeXl5WVlZWlpaWVkZXtW3tLTg8h8AzM7O3rlzhztsa2tzOp1ut/vKlSssyzocDr1ef+DAAbfbfe7cud27dx86dMhut4ceBHsSnpdI6Dx58mRgYODUqVN+7SqVqrOzc35+Picnh6vUvuIQsStZlIjkRpKenn7v3r3379+vSbS1QaT8H6p79dcRSnLg7OxsfHx8ZWUlHk5OTsbHx8fExOBJFRsby4/AP4Q/O+y9Xu/Tp0+3bdsGACUlJX19fVarNSIiAgBev34dYhBkRS8RPiA8OTAnJwf+pH3z4+CD+vp6ANDr9X7tgYbo27dvwS1KVuPBwgb2MHj79i0AcB7VIbKeM5IJ5RCK6FRUVADA+Pg419Le3g4ABoOBXVJOj3/od0qgh8nc3BweYo41piaGHoRdyUuETxiis3///sjIyKVx8IHP58vKygKArq4ufnvwIQpkUbJKDxY2sOjgtuqMjIzQQ7FUbI9QDv39/QCAW+SQ1NRU4G15DRH0MOE2wWdmZgLAyMiI0P6E4SUSOm63e9euXYGeZRimtbX14MGD+fn5WCwYCT5ESy1K8AYN3Uj8Tk6NRrP6TxEVFQVLyrHLC4kOIQAUi9HRUa4lOjoaACIjI1cTFvevKqQqE8fGjRsXFxeD/MGOHTvsdvvPnz8vXbrENYY3RJwbCb8x+LuHCMocq6RsWBIdQgD4o813I8HEGTQhw+83VvLy+Xwejwd4X/cgvh9fv34NL4hQLxFBqFQqv9KPqAJ8LUhISGhtbe3t7eVagg9RIMRzI8FpfpVKtfpQawWJDiEAo9GoVqubm5snJiawxWKxHD16tKSkBP4YJFVXV4+MjDQ2NqJwvHjxYnFxMS4ubmJi4tOnT/xonGS8fPny8OHDly9fFhTk2bNnUVFRz58/F+nDnjhx4sePH2h0j6Bbo9+tSnZ2Nj9XMPgQ/fr1C3gaisEXFhbOnj0bExNTVVVVWFhotVpv3rxZWlpaUFAAAHV1dQkJCQ8fPgzSVcyQXvbKaGZmBgCOHz8ufADEgkSHEMD27dsdDsfFixfz8/P1er3RaNy7d29PTw8mqtTU1KSkpNTX11+9evX06dNqtTo3N/f79+9er/f8+fPoo8qP1tDQMDMzMzU1NT4+3tfXJzTI1q1bd+7cyZUSW3Py8vIAgCuC/OjRo8LCQgAoLi7G1GSO2traY8eOrThEFovl48ePAGAymTweT0NDA04G3bp1i2XZnp6eM2fOPH78WK/XT01NWa1WzEV0uVxDQ0PXr18P1M9Xr16h6o2OjtbW1g4ODvKf7e/v37Bhgyyp0gERaYKaVq/+OqT00/FbopIACMtPJyMj49q1a2L0RxDv3r3jVtaFotFoioqKhL6KVq8IQh7u37/f1dXldrtl7MP8/HxTUxOWpRaKw+EYHh5WWilHEh1CBnCZRml7gpYSHR1tt9t1Op3fupKUuFwus9mcmJgo9IVfvnwxmUzd3d14m6YcSHQISZmbm6uoqEDLBa1Wy82YKJakpCSTyWSxWOTqQGJiYhiqsbCw8ODBg46ODqUlIsA/a+JFyEVERITZbDabzXJ3RACxsbFGo1HuXghj8+bNa1IiSQzoSocgCEkh0SEIQlJIdAiCkBQSHYIgJEXEieSxsTFRKyITawu6K6zjf9mbN2/4m7yJIIyNjYm47CVS0qFOpxOrxwRBiI9OpxNJHMSq8EkQBLEsNKdDEISkkOgQBCEpJDoEQUgKiQ5BEJLyG+2LC2T0Oc5xAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "visible = Input(shape=(100, 1))\n",
    "hidden1 = LSTM(10)(visible)  \n",
    "hidden2 = Dense(10, activation='relu')(hidden1)\n",
    "output = Dense(1, activation='sigmoid')(hidden2)\n",
    "model = Model(inputs=visible, outputs=output)\n",
    "model.summary()\n",
    "plot_model(model, to_file='rnn.png', show_shapes=True)\n",
    "Image('rnn.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shared Input Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_30 (InputLayer)            (None, 64, 64, 1)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)                (None, 61, 61, 32)    544         input_30[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)               (None, 57, 57, 16)    1040        input_30[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2D)   (None, 30, 30, 32)    0           conv2d_9[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_10 (MaxPooling2D)  (None, 28, 28, 16)    0           conv2d_10[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "flatten_7 (Flatten)              (None, 28800)         0           max_pooling2d_9[0][0]            \n",
      "____________________________________________________________________________________________________\n",
      "flatten_8 (Flatten)              (None, 12544)         0           max_pooling2d_10[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_4 (Concatenate)      (None, 41344)         0           flatten_7[0][0]                  \n",
      "                                                                   flatten_8[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "dense_47 (Dense)                 (None, 10)            413450      concatenate_4[0][0]              \n",
      "____________________________________________________________________________________________________\n",
      "dense_48 (Dense)                 (None, 1)             11          dense_47[0][0]                   \n",
      "====================================================================================================\n",
      "Total params: 415,045\n",
      "Trainable params: 415,045\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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RY8Ib73nz5smnn34qp556qrr/J/pWdv78+aoV4GWEsIBzzjnHu6lIlhHy8Oabb4a0jQdK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\nlickGf5OPvlkX09Ys/H+M2O1cx89/PDDsRn3TiQLIJ6B8KLxR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71XZ20VesW8hSENaRLr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\nMiKNEaXjBNNQFGkhU9pJ0LQOMu8wCd7z5xLWlHnh0C+//NK0ShT1kpfmxy7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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "\n",
    "visible = Input(shape=(64, 64, 1))\n",
    "\n",
    "# first feature extractor\n",
    "conv1 = Conv2D(32, kernel_size=4, activation='relu')(visible)\n",
    "pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n",
    "flat1 = Flatten()(pool1)\n",
    "\n",
    "# second feature extractor\n",
    "conv2 = Conv2D(16, kernel_size=8, activation='relu')(visible)\n",
    "pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n",
    "flat2 = Flatten()(pool2)\n",
    "\n",
    "# merge feature extractors\n",
    "merge = concatenate([flat1, flat2])\n",
    "\n",
    "# interpretation layer\n",
    "hidden1 = Dense(10, activation='relu')(merge)\n",
    "\n",
    "# prediction output\n",
    "output = Dense(1, activation='sigmoid')(hidden1)\n",
    "model = Model(inputs=visible, outputs=output)\n",
    "\n",
    "model.summary()\n",
    "plot_model(model, to_file='shared_input_layer.png', show_shapes=True)\n",
    "Image('shared_input_layer.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Shared Feature Extraction Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_32 (InputLayer)            (None, 100, 1)        0                                            \n",
      "____________________________________________________________________________________________________\n",
      "lstm_17 (LSTM)                   (None, 10)            480         input_32[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "dense_55 (Dense)                 (None, 10)            110         lstm_17[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_56 (Dense)                 (None, 20)            220         dense_55[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "dense_54 (Dense)                 (None, 10)            110         lstm_17[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_57 (Dense)                 (None, 10)            210         dense_56[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_6 (Concatenate)      (None, 20)            0           dense_54[0][0]                   \n",
      "                                                                   dense_57[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "dense_58 (Dense)                 (None, 1)             21          concatenate_6[0][0]              \n",
      "====================================================================================================\n",
      "Total params: 1,151\n",
      "Trainable params: 1,151\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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rI4oeggUaZeLCCy+0gg9YA7GMheCZSvoPWTBQfrHg1loo4jH77LMXfcyll17q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hgw8+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\nkAhUTmB0D9r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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers.merge import concatenate\n",
    "\n",
    "visible = Input(shape=(100, 1))\n",
    "\n",
    "# feature extraction\n",
    "extract1 = LSTM(10)(visible)\n",
    "\n",
    "# first interpretation model\n",
    "interp1 = Dense(10, activation='relu')(extract1)\n",
    "\n",
    "# second interpretation model\n",
    "interp21 = Dense(10, activation='relu')(extract1)\n",
    "interp22 = Dense(20, activation='relu')(interp21)\n",
    "interp23 = Dense(10, activation='relu')(interp22)\n",
    "\n",
    "# merge interpretation\n",
    "merge = concatenate([interp1, interp23])\n",
    "\n",
    "# output\n",
    "output = Dense(1, activation='sigmoid')(merge)\n",
    "model = Model(inputs=visible, outputs=output)\n",
    "\n",
    "model.summary()\n",
    "plot_model(model, to_file='shared_feature_extractor.png', show_shapes=True)\n",
    "\n",
    "Image('shared_feature_extractor.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multiple Input Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_40 (InputLayer)            (None, 64, 64, 1)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "input_41 (InputLayer)            (None, 32, 32, 3)     0                                            \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)               (None, 61, 61, 32)    544         input_40[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)               (None, 29, 29, 32)    1568        input_41[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_25 (MaxPooling2D)  (None, 30, 30, 32)    0           conv2d_25[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_27 (MaxPooling2D)  (None, 14, 14, 32)    0           conv2d_27[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)               (None, 27, 27, 16)    8208        max_pooling2d_25[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)               (None, 11, 11, 16)    8208        max_pooling2d_27[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_26 (MaxPooling2D)  (None, 13, 13, 16)    0           conv2d_26[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "max_pooling2d_28 (MaxPooling2D)  (None, 5, 5, 16)      0           conv2d_28[0][0]                  \n",
      "____________________________________________________________________________________________________\n",
      "flatten_16 (Flatten)             (None, 2704)          0           max_pooling2d_26[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "flatten_17 (Flatten)             (None, 400)           0           max_pooling2d_28[0][0]           \n",
      "____________________________________________________________________________________________________\n",
      "concatenate_10 (Concatenate)     (None, 3104)          0           flatten_16[0][0]                 \n",
      "                                                                   flatten_17[0][0]                 \n",
      "____________________________________________________________________________________________________\n",
      "dense_68 (Dense)                 (None, 10)            31050       concatenate_10[0][0]             \n",
      "____________________________________________________________________________________________________\n",
      "dense_69 (Dense)                 (None, 10)            110         dense_68[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "dense_70 (Dense)                 (None, 1)             11          dense_69[0][0]                   \n",
      "====================================================================================================\n",
      "Total params: 49,699\n",
      "Trainable params: 49,699\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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7QvJIUsfD3FEoj/yXQ9F/9SGirgujn9f18tfkxZca/RzZTpAtRf9TcWrN4PRtvmX6UDcF\nq1PrDIfsK/pw7/r162d1al3gpk2b5vSh3/a7d+/ukBEEWS8057lDdpJwQaTsTTbZxNpqAEyn8R6c\nBvV022yzjdOH8aBpz549rQ0ywKjpqdUj64n6Uzp9EA/aFbMRN/o5xtCAnU7fHqQMN3HiRNetWzeb\nZ+/evZ2+NUo57nc0jWlKNhbUqyLBrkXTuLq77rrL+tbYG3YKsqtEYY7Gan3h1C3E5uDXTS1ErB//\n5/rrr7fjGlcjJzsw1UCnThUoToOSOmTVQRaZfGXkyJFZs7EkcTxkBhozZoxTyxljgbV8+OGHMy4P\nGVg0rZ1DZPY4hdlY4tBi23QClEfSiXC/2glQHom2gpRHUjmpW4rTOBWWsU5f3LhTTz3VIdNKuLz4\n4otOg+IHcg9kH//RlxROX6AFzQvJI0kcz0+e8ognUfT3WLzBq+tC4aKul78mL75U4aIUKF7ZgQd7\ntdhwSNOq1go5u1SXkOAY0qymF5yr7hEp1fo2PlY60JSTQzvFCBeYI5QKUPQUU7744ouM03CNGozU\nqfWLw7WVUlT7HyiFsvUD3sOHD892KKUON1cNPOrUhSilPu5OUscrdB1qGeN22mmnQs0yjlPZkYGE\nFTEIUB6JAYtNq4IA5ZFoy0R5JJMT5A+8oMgnQ2aelbumkDyS1PEoj+Re04hHmHpWtYAsJEACDUAA\nLh+Id5DP/QGxHnzJ5i6Ac1dYYQXfxL6Rhm6BBRZIqavUDuYIN5CTTz7ZXE7ijotYHOkF1whXlw02\n2MDcWtKPx9lv27ZtSnDR9HMnT54san2SXp2xD76rrbZahutNRsMCFUkdL9+0kQUHbkdI6ctCAiRA\nAiRQ/QQoj2SuYdLlEbj+LrPMMnllyMyryl1TSB5J4niUR3KvZ5wjzMYSh9b/2iKuwOmnny5ILYnA\nfdVe4N+vbgEZaS7hJ4kAP6+88opsvvnmom4HsR9+nnrqKYtlEGaE9JR9+vQJVzXKNuIsTJo0KWVs\nPHgOHDgwpY470Ql4/0oEkarVoi4rgn8biJFx3nnnxf430VhcfvzxRwuU2qNHj4pMoRrHQ5T+0aNH\nyzXXXCPNmzevCCcOQgKlEKgXecQz0je9ggeAYv4fozziKdbHN+WR5K5zNcoHcWiW4/ooj8Qhnr8t\nA5Tm55P1KLIlIKBiOAhj1oZVUKlxFmTIkCGCawoXNfcSjaVgaSj32WcfyyiBlE5qThZuVnAbChI8\nNCCgJD4ISlmMkFJwoCIaQGMMy4NDDz3U5nb33XdL165di+iJp4AAAijB4gFFY3TYv5FwMFM7UCN/\nYB2hsS0sgGe1XBIyw1Ty3141jgeLFgRoXWKJJaplWTnPOidQD/IIlhiZEaBgRpYqZF8oplAeKYZa\ndZ5DeSTZ61aN8kEcouW4PsojcYjnb0tlR34+WY9q8Ea78Ta2dYIG3Mk6v6iVyIJxyimnCKIYhwsU\nGv3795fOnTvLfvvtJ61bt7a3nepPLKNGjQo3LbiNf6zq+y6w5kCBYqUx35hCYELUZRSYrEH4QRYK\nlMGDB1tWC9vhn9gEkP9cg1paNhUNHCUavFSaNWsWu59qOQFpdPH7ZqkdAsgAlM/tqnaulFdSKwRq\nXR7x64SHV6SWTM/85Y9H+aY8EoVSbbShPFIb61jPV0F5pHyrT2VHkSyhAGjM8sQTT8RWPKTPVzNl\nyPHHH59eLTD1fOaZZ2T//fcPjiENJtJSIq0TlCRxCh4eFl10UTulZcuWcU4ta1sNPmQWHBCawsXP\nTaM6h6u5HZMABEkotcIfPjjGhMjmJEACJBCTQC3LIx7FhhtuKGuuuabfLfqb8kjR6KrqRMojVbVc\nnCwJNCgBxuwoAi8sHxDLQtMiCW7AKLNmzRJNn2kuEZoGUjSNpAUKhLUALAhQNK2jPPbYY4KHauSJ\nRhv42+IN+MYbb2xtNKWnvP/++9Y3rCo0Raflmob1BbR8mhJSoOiAtQRu2poWUqDB3mGHHez8qH9g\nBqpZJWSttdbKOMWbiMKyI1w0DakpOu6//37RlJR2CG1xXX4/3L7QNs7DtYAPrCtw7e+8847FzMDc\nUMrFDDEWsBbIyY2gTGAHtxwwjVveffdd0fSslsMbbi9YPxSsLX4HKAiiqalU7Xvq1KmC38Tiiy9u\n62YN9A/mMmXKFKvHumqqS3/IrCMQIPEf//iHPPDAAzbW//3f/0nTpvwnG0DiBgmQAAnUOYFal0fi\nLC/lEcojcX4vbEsCJFAnBCKmbanZZnFTvb3xxhtOzUYtl/Pll19uXDTWg9OsElY3ZswYt/fee7vt\nt9/e9s8880xrow/BTh9+rU4fsl3fvn2dPsg6fdh2+gDr/v3vfweMVQHhNPBpsK+BbizXtCoErO7l\nl192+pBtY6qywGE/TkHaTHUnsVN++OGHlGtBpbrnWB3ST4bLpEmTrF6DswbVminD6UN6sJ9rY6WV\nVrJzfSrLb7/91mkgUKtTJYTTeB7usMMOcxpHw5ggP3Y5mWnATKdZNGy8kSNHOnD77rvvbLqDBg2y\n+scffzzX9IN6rK/GPbBUWDNnznTt2rVzl112mR1HqlWsnf7X4VRhFZyDDX0j5VSRY3Xgqoosp8oM\np8Ff7fekb+YcflsoGjPAaeRw+12oW4hbZ511rM9XX33Vjhf605ip3grNLUnHi0n1lqT5cy7JI8DU\ns8lbk2qaEeWRTHkkvH64d+L+OmLEiHB1sE15hPJI8GOosg3KI1W2YFUwXcojwSKNlWCzTjfiChfA\n9Nprr9kN1ys7UHfsscdanb6tx66V9dZbz62//vp+17333nvWRq0ggjrkkIaiBMoNtd6weihTwsoO\nVKIvr+zAPh5ooUCIW/QtkMPDPcZFyabswFjqtpLRtVoo2PwPPvjg4JhaODh1eQn2c22kKzvQTn1v\nrb8tt9wyuHYojiDMqJWHdVVOZlAsoO+rr746ZZpxlB2ajtOFrx/rsN122wX9+flDseLLp59+agoN\nv68ZPJwG8vS7ptTBvLbZZpugDgog1Km1kNW99dZbwbFCG1Cm4Vx+CjPQmCLkxN9KWX8DW2yxRaF/\nojxOAlkJUB4pTdlBeYTySDXLPZRHCsts1by+jTF3yiN2qx1Lm3j99cUtcFFILz7oZtintGPHjvLQ\nQw8FTX1MiHXXXTeoQ0YQxMZQCxBRSwFzbwkOFtgoJh6CWiaIPtxb7upc3cM9J1tBzAsUBGb0xbvf\n+P043wsttJC5k6y66qqBewaYoXz88cf2XW5m6LQYbjYZ/aPWLeaGhH24psBtBSmmfFGLHstic8EF\nF8i+++5rY910000WWM23wbENNthAVGniq2SNNdYQtXYJ9uGahAJ3JZTw78oq8vzB9SFVsL79ytOK\nh5B9B/8+L730UsIggbIQGDduXFVl6CnLRbOTRiVQ6/JIHLiURyiPxPm9JKkt5ZEkrUZtzIXyyLx1\npLJjHouybyGop+qUCvbr41MgUwhieUQtcR/aEWtC3WUsfRvii6D4POTqCmMxRxA7Q60wBIoNxLkI\nC1KIH4LiFRK2U+Y/YIZSiFuxzNB3XG44xxc1k5WHH35Y7r33XunevbtAUYPMI76gb3WTEaTrRWwT\ndVey2BzqomNN1J1G1NLDstzki7Pi47z4b99/lG/MoU2bNkXFUYnSf620QaBaBOItJt5MrTDgdZSX\nAOL2zJgxo7ydsjcSKAOBapVHiomrVQZc1gXlkf+S9HKI/47Dl/JINFqUR6JxYqvoBCiPzGNFZcc8\nFo229dFHH9nYyB8fp8R9aJ89e7ZZTITf+Hulwm233Sb33XefqIuHWSZgHrBaULeNYEpff/21bTek\nsiMYrMBGsczQbVxuOAeWJlAgnHjiiRacFhYBsOa5/fbbcTilIBAq2p1//vnSrl07CwLrA4t6YWH6\n9Omxg8qmDMIdEiABEiABEigzgWLvrXHvq1HlkcZUdkRFWywz9B+XG86hPAIKLCRAAiQQjQBTz0bj\n1KCtNDCmaGyPwD0ED8a//fZb3jFxg/RuJXkbhg5utdVWAgEj/PFvIUePHm31GjfC3C9g0fHss8+G\nzhazYIALjreqSDlY4Z1imWGacblhLCiD4GakwVlFg7uaogN9IRJ+ekHKs8MPP9wyzcDKQwPWBk0W\nW2wxWXnllUXjvYjGLAnqsfGvf/0rcN9JOcAdEiABEiABEqgAgWLvrXHvq1HlkQpccslDFMsMA8fl\nRnmk5OViByRAAnVGgMqOIhYc7h0o3tIB2z5uwx9//IFdKziOtt56wtfjrb4vmhlFXnjhBTn77LN9\nlWy99dbW94QJEyzVK741O4mlqdUMItYObzs0yKjVIVWtZgIJzi91AzE5DjnkEDn33HODuUP5gtSw\nsPzw1gkY56CDDhLNpFJwSM/Hf+OEn3/+2fpPZ4Zj6YqAcjFD388995yNq4FmsSv+rUx4HnZA/yA1\n7NChQwVKIMwX5ZZbbrH1fvrpp+Wpp56yNLE45t180EajIEvLli1tHdPT+0IBAoUThD3EAIELkQYs\nFQ0Wa9YjON+vJ9adhQRIgARIgASyEah1eSR8zV7+yfUyiPII5ZHw74XbJEACJGAE9EG8rkvc6OeI\n9u1Tz3bq1Mlp7AanD6xOXVAsoj9Sin722WeWVlTf4lvdKaecYtlGUK/QncZ6cBq80h133HGWrUVd\nIVLWQB+a3SabbGJtO3ToYBk5kLYW2Tp8lg+kTkXK2latWrmLLroo5fw4O0iXijmFM8vgfGRtOeaY\nYyyFLvrHXK+//vqMrtdee21Lvzp37tyMY6h45JFHLM0qxsAH14HrVeWApY9DnSpXLPsKUuL269fP\n2iHd6rRp04wl2pSDGebTs2dP6x8ZYFSR4Y444ginlhhWh3moAsJSy2oAUZsXxkY6O180FodxR1aW\nK664wlIG43ych3S54TJ8+HCnwS/DVbYNtuCJ9UP/+EY2H33DY8evuuoqGxPHBgwYYPPM6CRPBVPP\n5oETOsRUbyEY3CwLAaZ6KwvGuu2E8kh2eQQ/CI2B5XbffXe7Zy699NImC0GmChfKI/82eYbySPhX\nUR3blEeqY52qaZaUR4LVYurZuMJFgK6IDa/sOOOMMxyUDB988IEpFXJ19eWXXwaHkKY1vWiwS6eW\nEunVZd2HEsOnqc3Wsb5hcfpmKduhstSVmxkUDWpVUdLc0pmDQbbSu3dvp2+ish2yOg0O6/D7w2+h\nnIXKjmg0KVxE48RW0QlQuIjOii0zCVAeyWQSp4byiHOUR+L8YpLTlvJIctaiVmZCeSRYSaae1bfn\njVJatGhhsRvyDb7UUksFh5GmNb3ATcIXBBfFJ19BJpHjjz8+X5OMY4hGjvS4uUo4W0uuNuWqLwcz\nxDoBh1LKoosumnJ6NgavvvqqIOCsWt6ktA3vIMBpuotL+Di3k01ALY8sMxEyunh3I6QI7tKlS8rE\nkYHngQceSKnbdtttZfHFF0+pS9oOXMnuuusuyx6EOD1Iq5yt4NqR4kytlbIdjlxX6fFwbXBPC//f\n+tJLL0nr1q0Dd7LIk2dDEqhiAuW4t1ZCHimEONu9uNA5xR4vBzPKI8XS53npBGpVHkGMGmQ2hOv+\nwIEDs8rvcCG/6aabLK4ekirAtR7/PospSRqP8kgxK5j9HGZjyc6lQWp9mlc8/JS7IOilumbk7TYs\njORtmKCDDcms3JeJFLRHH320dO7c2WJx3HnnneUegv0lhADi1zRr1sxuqF27dhV9KyOnnXaaxWmZ\nOnVqShBf/LtbY401ZK+99rJgdOralFcJloRLxG8XcWQQaBefcJye9Pmp657FwSlF2VHJ8aAUxrXh\n3+u3336bouxQM3g59NBDZdCgQdKtW7f0S+U+CdQMgYa8t1IeafyfCeWRxl+DSs2gVuURxDJE4P7N\nNttMbrzxRpOv7777bunbt2+A9p133pEePXoIXkIi/h5i75111lmCl1CIPxinJG08yiNxVq9A28DI\no043KmU2qpk8nGbxMH9TxPe45pprGtT9oxaWs9qY6UOu0/9wnT7cOs3e0mhL0NhuLNddd12DXnu5\n+i/WbFRTCmeNxeJjvyDOTrqrE4BoJh936qmnNiibcnR+1FFHObU6chrAt2B3atHh2rdv79T6q2Db\nXA0qOZ4KQw4fVWbY/8Wq7MiYFlz3+vTpE+n600+m2Wg6Ee7HIUB5JA6tyralPFIcb8oj0bhRHknl\npIkXnCYDCCoRyxCyda9evYI6bOBerZbUVgfXf8RN1Mdeh/h6cUpSx6M8EmcVc7Ydy2wsBZRB5Tq8\n/PLLy8UXX2yZO6Bx10Cc9ma4XP3XYj/VxmzDDTe0N8V4W7zbbrvV4pIUvCYNnCujRo0q2K7YBg3d\nf6F56cOIwDIDUf/TC8wnkUnprbfesgw++t9uSpMll1wy8a4rsLA477zzZOzYsWahlHIBaTvvvvuu\nZRLK5d6S1jzrbqXHa9OmjbmotGvXLut8UAnXvSOPPFIOOOCAnG14gASqmUC13VuTwLramFEeEWlo\neaGh+y/0u69leeTPP/8UDUgcIFhkkUXsuUkTPwR1eJYaPHiwwAICBa7/sLCFJerkyZODdlE2kjge\n5k15JMrqFW5DN5bCjMrSQt/6Cj4s0QlUIzPNrBL9AhPWEv6BSKcL8+b11lvPHtzhU6xBYuU///mP\n4GagQVctzghu8ohLgqIZduwBEnU77bST4Jwrr7xSIBzusMMOopppeeyxx2ThhRcWtQKwOBAanNdu\nXBtvvHHJ/SPFs2YpEtXk540vUw7cmqHI/EFxjekFa4+0xBAy8RCvlhxy4oknBs1wA87mDpKLO04E\nO3DFeZtuuqmlf4bZJnxXEUcjXD799FN58MEHLa0xXGs081D4cMFtpMHee++9pW3btqLZovK2x2/h\nhBNOsFTUcAkpplR6vDhz1LdH5r6D3z1+3ywkUEsEqvHe2tj8q5EZ5RHKI9Uqj8D1N1w0uYCo9YWM\nHj06qMZLC8iq4YLYHuuvv77E/e0ncTx/XZRHPIniv2nZUTw7nkkCNUMAb7LhrwjlBIJnIvaIpq+z\nwJu4eWiqP9E0vaKpl+2aER9GXTWs7u2337Y6BNyEhh1B4nDjWGmllezBG9p59HnuuefaQzSUJJrG\nWDbffHPRNMQWeKrY/jEwFAuwJlHXIZtHQ/3BWxQEysK15CpggPngLQSUAJqaOldTq8/HXTP5yJ57\n7mlKpwkTJsj+++9vsTEuu+wy6aE+qrAg8gUKEU1xbcFR1Y1G1HRYDj74YH840jeCqCKeEBRSCPCF\nQL5QfEBhA+VGuODtCWJ5pAfrDbcptF3p8QrNJ/04FEaaOSu9mvskQAIkQAINSCDffZHyyH/B17o8\nEv554cUIZCG88MF92RdYy2Z78TRr1ixR9xbfLPZ3UsYLT5zySJhGEds5PVzq5ID+h2H+XW+88Uad\nXDEvs9YJxPWRRQwMNQ10SGXsi1oP2L8LxJlB8f9OrrrqKt/EaaAoa/PQQw8FdRhblRzBPjbee+89\na6euPUE90hmryaFbccUVnT5Il9T/zz//7DR4VdY4GcGAWTbi+shiDP0v1lJGZ+nOqaInqFYljtOb\nsPmYgiWKWru4Sy65JGgThTtSTmNMVS4ZJ5zsuWtQMusLvqyIAwQOvqhlhp333HPP+aqC397X9eqr\nr7a2SGGoSiTrRxVdwfmTJk1yqlgJ9nGsmJgdlR4vmLBuaDBVu65sMTt8O3Xlcfp2KFZsJcbs8PT4\nXQwB//8s5ZFi6PGcJBKgPBJtVSiPZOf0yCOPOH15ZvdryELqtpK94f9qn3zySZMrIRcVU5I6HuWR\nYlYzOIcxO4rQD/EUEqgpAhdeeKEgZWo4Ww9cJBBRH5GwYcERp6Rr2uG+grLuuusG3SCdMSwVZs+e\nbenCggMRNrL1D0uEUqwMIgxrsTjQLkqEb7g+IM3zDz/8YFYWSI2WXqJwR1pUXO+qq64amGV27NjR\nuvr444/t++abbxakbYU1Dqw58FFlkp2jiqb0YXPuw50GGWaGDh1qbWCh889//lNgKYJ4QxgDlh+q\nsImdwjrboJUeL9sc8tXh3wPciOIwzNcfj5EACZAACeQnEOW+mL+H1KPZ5AW0oDySyikK90rKI352\ncOGA9bAGCLY1Q1YWZFTLVv766y856aSTBBlbYF1bTEnqeJRHilnNeefQjWUeC26RQN0RUL2nPcRn\nuzFsscUWxsO7qUSFky5c5DrPx5z46quvcjXJWh+1/6wnl1CJeWJs3PCjFLh6wC0oW8DSUrgjYBUK\n+kDRt8DmCoTAqf4D9xk8pKtljrWJ8gc3U3zCvq6IFYK4Knjoh78sXJngAwxhAvEs8JkxY4aoFYht\nI0d91FLp8aLOy7fz/yagkGMhARIgARJoWAKl3BdzzSyqvEB55K2sCoJCcmBDySPp64n4HFB0oHh3\n6vQ2mtnNgot36dIl/VDs/aSNR3kk9hKmnFC90RRTLoM7JEACxRCAIIA4Ey+88IJAK+5vXOgLsRtQ\ncBy5y6OWqMIFcqKjqAuGxQYpd/9R+4vaDtYvEMbmzJmTVShI7wccYBkDZQHieCCwqI+jEZV7ep/Z\n9rFm6BtxNWCZUWyBsIfYH7AYQdYSX2BVggLLGSh81MzTH7JvWK8gqO2IESMseC1ivUQplR4vypzC\nbRAzBQWxZ1hIgARIgAQalkDU+yLlETFr3FqWR7L90mDVisD32axrx40bZzHLdtxxx2ynFlWXpPEo\njxS1hMFJtOwIUHCDBOqTAB7G4Wbx8ssvpwCAmwECh0IZ4d/24w1+vgJhBUqTKAVWAIiajRtXQ/Qf\nZQ5x2nTq1Mmaay73jNMgdOCBP70gTRoUHbBigIVHuEThHm6fa3udddYxBcwVV1yR0gQuJwhmGrUM\nGzbMmqa/NXnzzTdFY6uYAgQWI7B0CH+Qhhcp31Cn8VuiDieVHi/yxP7XEFmI8HuGOxcLCZAACZBA\nwxOIcl9sCHmB8khhOTDK6pdLHsk2Fl62QK7ZeuutUw7fcccd9iLKu+D6gxq/w28W9Z2U8TB5yiNF\nLWFwEpUdAQpukEB9EjjrrLMsg8oNN9wQAECaLw1uKTgGywG8hW+nZoRIrQqLDLi2TJw40dpDSYL2\nKIiUjngRSC0LtwdYQfgyffp0vymIdg1rEmSAQSmlf+Ra32ijjUQDZ1pfDfUHKc5atGgh4evwY+FG\nhGvKpgxCZhqYX6annY3CXYOO2k08/CYLqXZREEMDBdluYH0AE05kvIFSBZlpDjjgAItgbo30D9YT\nnJCmN1tBpHMoIK699trARQbuK0hHjLniwT9OSdp44bn7tyTZ1su3+/DDD02oiuq25M/jNwmQAAmQ\nQHEEotwXS5EX/KzC93HKI2L3eMTpyicHVlIeefDBBy1rX/glkgZPN5nRWx1jLR999FGrg2Ur4onh\no8E8RYOFy2uvveaXu6D8k9Tx/AVQHvEkivzWN5J1XRj9vK6XvyYvPm70c0DQB1qnygyn6UTdXXfd\n5VRD7jT+QwofZGJp1aqVU99BN2jQIOejXuMcn3FE3SAsgwXaXXTRRXa+KgIsknb37t0dsoQgE4Za\ndDhkLAmXYvv3mU/0IT7cXcHtuNHP0aEG7DQ24c5V6eO6detm19i7d2+nb4jCh4NtTWOako0FB/Jx\nR3YVdQ2xftX6xSH7igplrl+/flanb1DctGnTrH+1vnAqAFq93gqcWqE4tcwJxsaGpvu141gbVWKk\nHPM7qNdAp04VKE6Dkjpk0EEWmXxl5MiRWbOxJHE8ZAEaM2aMU4slY4Hf+cMPP5xxeb///rvTtHYO\nkdnjFGZjiUOLbdMJUB5JJ8L9aidAeSTaClIeSeWkbikmayJToL64caeeeqrJnOFW+qLLaQD8QO6B\n7OM/+pLCffPNN0HzQvJIEsfzk6c84kkU/T0Wb/DqulC4qOvlr8mLL0a4AAi1znBqseHU4sLpG++s\nbJAKVbOz2DG1NnDqspLRDilsfRsc9MoOPOyrpYelbsVY2Uox/aMfjRuRrbu8dcUIF5gflApQOhRT\nvvjii4zTonDPOClHhWr/nVre5DjqnLrguOHDh+c87g/g5qqBR7Our28T5Tup4xWau1rGuJ122qlQ\ns4zjVHZkIGFFDAKUR2LAYtOqIEB5JNoyUR7J5AT5Ei8ocsmLmWfkrykkjyR1PMoj+dc1wtGxDFBa\npEUMTyOBWiMANwW4XOQrMOn3Zv25gmEiPkWuAjeQfDEQiu0fsTEqUTA/uIGcfPLJohYPGa4pheaA\nGCjpJQr39HNy7bdt2zbXIaufPHmyqPVJ3jY4uMACC8hqq61WsF2hBkkdL9+84aIFtyOk9GUhARIg\nARKoPIEo98Vi5QV/NZRHki2PwPV3mWWW8ctV8ncheSSJ41EeKXnZrQMqO8rDkb2QAAnkIOB9LhFY\nqhaKuqyIWj5YjIzzzjsvtsKjsRiotY0FSu3Ro0dFplCN4yEezejRo+Waa66R5s2bV4QTByEBEiAB\nEqgMAcojleFcaJRqlA8KXVP4eDmuj/JImGhp21R2lMaPZ5MACeQhgKBKsIJA0dga0qFDBxk8eLBZ\nDuQ5LfGHYB3RuXNnQQBPWEFUQ4H1S6UUHeBRjeNhLRGgNW4w1mpYf86RBEiABOqZAOWR5Kx+NcoH\nceiV4/ooj8Qhnr8tlR35+fAoCZBACQSQE10DXdrHd5PL/cUfr5bvbLneq2XunGd2AsgmxEICJEAC\nJFB7BCiP1N6a1vIVUR4p3+pS2VE+luyJBEggjQA009Vi+ZA2de6SAAmQAAmQAAnUCAHKIzWykLwM\nEohJYL6Y7dmcBEiABEiABEiABEiABEiABEiABEiABBJNgMqORC8PJ0cCJEACJEACJEACJEACJEAC\nJEACJBCXAN1Y/kfsyCOPtIB6cQGyPQkkjcDUqVOladOmMmDAgKRNLVHzeeedd0TzrpNTolaluifz\n4osvSrt27ar7Ijj7RidAeaTRl4ATKBMByiPRQFIeicaJraIToDwyj1UTp2Xebv1tzZ49WyBYsJAA\nCeQm8Mknnwjyfffs2TN3Ix4hARKQTp06yUknnUQSJBCbAOWR2Mh4Qh0SoDxSh4vOSy6KAOURw3ZR\n3Ss7ivr18CQSqDMCEyZMkBEjRshPP/1UZ1fOyyUBEiABEiABEkgKAcojSVkJzoMEqoLARYzZURXr\nxEmSAAmQAAmQAAmQAAmQAAmQAAmQAAlEJUBlR1RSbEcCJEACJEACJEACJEACJEACJEACJFAVBKjs\nqIpl4iRJgARIgARIgARIgARIgARIgARIgASiEqCyIyoptiMBEiABEiABEiABEiABEiABEiABEqgK\nAlR2VMUycZIkQAIkQAIkQAIkQAIkQAIkQAIkQAJRCVDZEZUU25EACZAACZAACZAACZAACZAACZAA\nCVQFASo7qmKZOEkSIAESIAESIAESIAESIAESIAESIIGoBKjsiEqK7UiABEiABEiABEiABEiABEiA\nBEiABKqCAJUdVbFMnCQJkAAJkAAJkAAJkAAJkAAJkAAJkEBUAlR2RCXFdiRAAiRAAiRAAiRAAiRA\nAiRAAiRAAlVBgMqOqlgmTpIESIAESIAESIAESIAESIAESIAESCAqASo7opJiOxIgARIgARIgARIg\nARIgARIgARIggaogQGVHVSwTJ0kCJEACJEACJEACJEACJEACJEACJBCVAJUdUUmxHQmQAAmQAAmQ\nAAmQAAmQAAmQAAmQQFUQoLKjKpaJkyQBEiABEiABEiABEiABEiABEiABEohKgMqOqKTYjgRIgARI\ngARIgARIgARIgARIgARIoCoIUNlRFcvESZIACZAACZAACZAACZAACZAACZAACUQlQGVHVFJsRwIk\nQAIkQAIkQAIkQAIkQAIkQAIkUBUEqOyoimXiJEmABEiABEiABEiABEiABEiABEiABKISoLIjKim2\nIwESIAESIAESIAESIAESIAESIAESqAoCVHZUxTJxkiRAAiRAAiRAAiRAAiRAAiRAAiRAAlEJUNkR\nlRTbkQAJkAAJkAAJkAAJkAAJkAAJkAAJVAUBKjuqYpk4SRIgARIgARIgARIgARIgARIgARIggagE\nqOyISortSIAESIAESIAESIAESIAESIAESIAEqoIAlR1VsUycJAmQAAmQAAmQAAmQAAmQAAmQAAmQ\nQFQCVHZEJcV2JEACJEACJEACJEACJEACJEACJEACVUGAyo6qWCZOkgRIgARIgARIgARIgARIgARI\ngARIICoBKjuikmI7EiABEiABEiABEiABEiABEiABEiCBqiBAZUdVLBMnSQIkQAIkQAIkQAIkQAIk\nQAIkQAIkEJUAlR1RSbEdCZAACZAACZAACZAACZAACZAACZBAVRCgsqMqlomTJAESIAESIAESIAES\nIAESIAESIAESiEqAyo6opNiOBEiABEiABEiABEiABEiABEiABEigKghQ2VEVy8RJkgAJkAAJkAAJ\nkAAJkAAJkAAJkAAJRCXQNGpDtiMBEqgPAjNmzJD+/fvL3LlzgwueM2eONGnSRDp27BjUYaNr164y\nfvz4lDrukAAJkAAJkAAJkECpBCiPlEqQ55MACVDZwd8ACZBACoH27dvL999/L7NmzUqpx85bb72V\nUjd8+PCUfe6QAAmQAAmQAAmQQDkIUB4pB0X2QQL1TYBuLPW9/rx6EshKYK+99pJmzZplPeYrYekx\nYMAAv8tvEiABEiABEiABEigrAcojZcXJzkig7ghQ2VF3S84LJoHCBAYNGiR//vlnzobzzTefdO/e\nXZZddtmcbXiABEiABEiABEiABEohQHmkFHo8lwRIgMoO/gZIgAQyCHTo0CEjPkd6o6FDh6ZXcZ8E\nSIAESIAESIAEykaA8kjZULIjEqhLAlR21OWy86JJoDCBYcOGSdOm2cP6wLJjl112KdwJW5AACZAA\nCZAACZBACQQoj5QAj6eSQJ0ToLKjzn8AvHwSyEVg4MCBKRlZfLv5559ftttuO2nZsqWv4jcJkAAJ\nkAAJkAAJNAgByiMNgpWdkkBdEKCyoy6WmRdJAvEJtGnTRjbeeGNLORs++++//5YhQ4aEq7hNAiRA\nAiRAAiRAAg1CgPJIg2BlpyRQFwSo7KiLZeZFkkBxBGA6CpeVcFlooYVk++23D1dxmwRIgARIgARI\ngAQajADlkQZDy45JoKYJpD7F1PSl8uJIgATiEthtt93EORechhge/fv3l+bNmwd13CABEiABEiAB\nEiCBhiRAeaQh6bJvEqhdAlR21O7a8spIoGQCrVu3lp49ewbWHXPnzpXBgweX3C87IAESIAESIAES\nIIGoBCiPRCXFdiRAAmECVHaEaXCbBEgggwBSzHrrjlatWkmvXr0y2rCCBEiABEiABEiABBqSAOWR\nhqTLvkmgNglQ2VGb68qrIoGyEdh5552lWbNm1t8ee+yRMx1t2QZkRyRAAiRAAiRAAiSQRoDySBoQ\n7pIACRQk0LRgixps8Nlnn8kzzzxTg1fGSyKBhiHQpUsXmTJliiy77LIyceLEhhmEvZJAjRFYZJFF\npE+fPjV2VbycchKgPFJOmuyrHghQHqmHVeY1lptAPcsjTdQ8fV70wXKTTWh/9913H7NJJHRtOC0S\nIAESqBUCq6yyirz//vu1cjm8jgYgQHmkAaCySxIgARIggRQCdSyPXFSXlh1+9efMmSMtWrTwu/wm\ngaoisOKKK8pRRx0lhx9+eIPP+48//pALLrhAjj322AYfq9wDDBgwwLq87bbbyt01+yOBnATOOuss\nGT9+fM7jPEACYQKUR8I0uF1tBCiPRFsxyiPROLFVeQnUuzzCmB3l/T2xNxKoSQILLLCAjBw5siav\njRdFAiRAAiRAAiRQHQQoj1THOnGWJJAUAlR2JGUlOA8SSDiB+eefP+Ez5PRIgARIgARIgARqnQDl\nkVpfYV4fCZSPAJUd5WPJnkiABEiABEiABEiABEiABEiABEiABBJAgMqOBCwCp0ACJEACJEACJEAC\nJEACJEACJEACJFA+AlR2lI8leyIBEiABEiABEiABEiABEiABEiABEkgAASo7ErAInAIJkAAJkAAJ\nkAAJkAAJkAAJkAAJkED5CNR16tnyYWRPJFCdBD744AM5/fTT5bTTThOkjmMpTGDu3LkydepUWXnl\nlWXSpEl2QpMmTaR///7SrFmzlA6efvppmT17dlC3wgorSLdu3YL9pG68+uqr8tRTTwmi3vft2zfn\nbwPpfNu1aycbbbRRSZdSyfE+//xzefvtt6VHjx4pc77jjjukX79+KXXcIQESIAESqAwByiPxOdeq\nPPL999/L1VdfLR9//LHJID179pRsQWknT54sDz/8sMlevXv3LloWSdp4lEfi/1vIdwYtO/LR4TES\nqHECL730kkyYMEGmT59e41dansv74Ycf5Nxzz5XOnTvLsssuK2uttZaccMIJMmjQIBkxYkTGIJ06\ndTJlxx577GEP2GuuuWZGmyRVfP3117LffvvJcccdJzvttJMceOCBORUd06ZNkyFDhgh+Q8WWSo73\n1VdfyVFHHSWrrLKKQJBIL8sss4zsv//+AuGRhQRIgARIoLIEKI/E412r8si3334rG2ywgeAlyOuv\nvy59+vSRzTbbLAPOYYcdJtttt53JsJDDNtlkEznnnHMy2hWqSOJ4lEcKrVq841R2xOPF1iRQUwR2\n3XVXwUMgbiaNWa6//vrGHD7S2J988onsueee8o9//EMWXXRRgTXH2muvLfvuu6+df8UVV9ibiHBn\niy++uD1gt2jRQk4++WRZeumlw4cTtf3hhx9Khw4d5Pfff5f7779f2rRpk3N+c+bMkVNOOUX+/PPP\nnG0KHWiM8YYOHSq//vpr1qlBmIJ1zgEHHJD1OCtJgARIgAQajgDlkehsa1kegcUorGchFz722GMm\na2D/2WefDQD95z//kfnmm0+++eYbgSzx6KOPCuSt448/XmAhFKckcTzKI3FWsHBbKjsKM2ILEqhp\nAq1bt27U63viiSdk1KhRjTqHKIMfeeSR5ubQsmXLlOZQeuABuWnTpnLwwQfLlClTMo7D1QM35qSW\nP/74QwYMGCBLLLGEQGlTqMDyA0JFsaXS42GeG264oRSyrNl2223l3XfflQcffLDYS+N5JEACJEAC\nRRKgPBINXK3KI5ANttlmG5NFPAm8pEBZbLHFfJU899xzct5555lrC2QwuLnsvvvuZpn5wgsvBO0K\nbSR1PMyb8kih1Yt+PLnSd/RrYEsSIIEiCfz9998CZUP45jBr1iwZO3as4BhMCM844wy54YYbbN8P\nA1P/hx56SJ555hn54osvZNy4cXLssccGD/qfffaZXHrppXLhhRfKG2+8YadhHOzjAz9MFNTBXeKn\nn36SK6+8Uu655x6rh3vD6NGjrW+raOQ/eKtw3333Cd48ZStbbbWVnH/++WYVscsuuwjiQoQLFCHp\nBdd866232lsL+KaCe7hEWQffHm81sE6XXXaZvenw9VG/objAb+Doo4+WhRdeOO9pcAFZffXVzYUn\nb8M8Bys9Xp6pZBw6/PDD7beM3z8LCZAACZBAZQhQHonGuZblEcQJQzy0cHnttddk++23N/dhXw9Z\nJT2GB9qgwMIjaknqeH7+lEc8idK+qewojR/PJoGqJfDmm2+aJhwP6i+++KJdB5QN66+/vuA/2Isu\nukguuOACef755wWa9bPPPtvaIOAmNOjQOiN+Bdw44FsJk8PNN99cbr/9dlluueXMZeOII46w83Hi\nlltuKT/++KOgDgEiUXBTgivIggsuKGussYastNJKVn/nnXeatQfMC5NQ4Ae66aabmvtKrvkgZsew\nYcPk008/NaVIPhcP8OratasF1YI1CIJjdezY0Rii/yjrgHZ4K4E4E1AO4UYP5RGsF7C2ccrNN99s\nlimI3YLfwyKLLGKBVNPjceDaYD56yCGHxOk+o22lx8uYQJ4KrAvW5957783TiodIgARIgATKRYDy\nSHSStS6PeBLOOYEMiBdpl19+ua+276WWWiplHzt4QQSZErE7iilJGs/Pn/KIJ1Hity5u3RUVYp1i\nc+p3XnfXzguuHQKa2cONGTOmpAtSjbn9W9AbSdCP3lisTq0Fgrr11lvPqRIk2H/vvfeszW677RbU\nqTWD0xuQ06wuTh/0nVqFWJurrroqaHP33XdbnVqFBHU777yzUyVHsI+Nn3/+2d14441OlSMp9cXs\nYI7heRbTR/v27Z0qfLKeeuaZZ7pbbrnFjv32229OM5PYNQ4fPjxorwqdYFtjYjhVSLiTTjopqMOG\nBjF1+pbBqSWM1UdZBzXjdBoLxNrjj97sbWw1Aw3qCm2o8srOWXfddZ36v1rzd955x6nCyqnSw+E4\nir51cxqI1WGdUTQ4mp0X/u3YgQJ/Kj1eeDpgj//7VTEVrs7YVoEpY30yGhWoUMskp8FQC7Ti4Xon\nQHmk3n8BtXH9lEeirSPlkWicIAPqixyn8c7snt2qVSunFi15T9YXak4th/O2yXUwyeNRHsm1apHr\nx9Kyo0RlEU8ngWomAIuK9NK8eXOrCsc3gNWBdz3BQe/qoA/Iwek+ejQsP2bOnBnUR9mAz2W4oH9k\nMEEg0MYusJ5AwCtYqxQq4AnLB2RqyRawFOcjHgQsW9LfPsBPFWPBpQUlyjrA8ubll1+2WCGwEIHr\nDyxkEF08avHWG6p0Cvxk4aaCvlUACN6oqGLNss5gnUsplR6vmLkiLstbb71VzKk8hwRIgARIoAgC\nlEcKQ6t1ecQTgAwI92i4+0L2wDeCw+cqd911l8loyNBSTEnyeJRHilnR1HMyHclTj3OPBEiABMw3\nUnWoBUngIRkFGV7SA3nmOzld2ZGvbaWPQXHw119/BcqHQuPrGy5z5YHbDhQQSD8bLt7FBK4i4bLF\nFlvYbr6HbPio+nWA6wvcSpAqdocddgh3FWvbr1N6YDi47aBAMYOgnf/+978tswyUOSi//PKLfUPZ\ngjq0j6IQqvR4NsmYf7A2UNqxkAAJkAAJJItA+D6Yb2aUR0SqTR5JX08Edodb9eTJk03OQLa4dKXY\njBkz5JprrjGXl/Tz4+4ncTzKI3FXMRzxRP4AAEAASURBVLM9LTsymbCGBEigSAIfffSRnanm+7F6\nSLKyA1YaakJpbxaiXhTShl188cVBwFKkavUFGU9QEE08XNq2bWsxPOBzGqX47C6Is1FK8QKhj9vi\n+0Lq2WbNmpl1DR78YdmDuCT+c8wxx1hT+NSiDkHEopRKjxdlTultvvvuuyB+TPox7pMACZAACSSf\nAOWR/65RNckjuX5VvXr1sngc6YoOvPQ55ZRTLN5Z+rFcfUWpT8p4mCvlkSgrlr8NlR35+fAoCZBA\nDAKPP/64BTiFgsBnINE4Fnl7gKIDlhNJLmuttZZ8+eWXWaeIQKR425BekI5W43aY9YXGtwgOb7zx\nxrb91FNPBXXYQOYb9OUtKlIOZtlBGjZELUfgrl9//TWlxb/+9a8Ut6OUg2k7WCu40CAQbbjgbQnm\ngwBZCFoKhUf4g+MocJ1BPfqIUio9XpQ5hdsgIwAyDK266qrham6TAAmQAAlUEQHKI/MWq1rkkXkz\nTt1CVr90C1ZYlyIrC7IHeotRnIVsgLBGLaUkYTzMn/JIKas471wqO+ax4BYJ1B0B/5CObB6+IGMK\nCnxDfcFxtPUuFL4+bFXwySefWPpSn7UFb/DbtWsnGrxT8IYF7hATJ060U+H6gP/EUeD6gFStiIvx\n/vvvC6wgYGWggT5l0qRJ1qax/8DFJHyt4fngQf/DDz8MVwXbyGiDDDXhss4661jWFig7wnFQkMZX\nA6EKhBKUKOswcuRIUzRAGQFW4KoBSwXKFVhmoMCCBCzHjx9v+9n+IG0uIpnDVNQXZHbp0KGD7LXX\nXr4q0ncSx/MTxxsSlHwKOPyOkVp5xx139KfxmwRIgARIoIEJUB6JBriW5RG8uDnjjDPs5Y+noYHT\nTbZB7A5f8CJm1113FbjfQsa85JJL7HPaaafJnnvuGaSvLSSPJHU8f52URzyJEr8jxzKtoYaMfl5D\ni1nHl1Jq9HN9k+/0ZmGRrjWuhMO/C31gtgwS+t+K01gQTjXkTtOEOrUisHZqLmiZVlCPNt27d3ea\netYdd9xxlq1F086mrAgysSCKNrJ6IJPHk08+adla1AfTIeMHij5UO7UCsXaqHLA69KMWH04f0G2/\nlD/liH6ucTvc0ksv7ZCFxhdVcrhRo0ZZtPAll1zSGOiN0x8OvpG9BJlOwgXtNJ6HU4sRd+211zpw\n6tu3r1PlhzWLug7IkAL24If1wDeyuKilTDCcpgS2Y1gHfYgP6tM3NN2q69mzp2UhUWHDaSpbpzFB\n0psF+8hmhTHTs7Ekdbz777/facpkmzPWEr8t/I7TCzLcqDVLenXsfWZjiY2sLk+gPFKXy15zF015\nJNqSUh7JL48gK0qXLl1M/ttwww3diSee6NRyw2mA0hTAAwcOtHs5ZJD0j1p7BG0LySNJHc9fAOUR\nT6Kk77F4U1t3hcJF3S15TV5wqcJFKVC8sgMPxXjoVasMS02arU882PsUsmotkvIg7tur32XQxtch\ntWk5SjmEC8xDs6uYgqKYOalbRNbTcN3PPvuspYzN2iBipZpzWqpfrEW2oi44LpwKN1sbX6dvEhyU\nO6WUJI+X77qgPNpggw2cWrjkaxbpGJUdkTDVfSPKI3X/E6gJAJRHoi0j5RHnosgHaoVpsmU0qvlb\nVet4lEfyr2uMo0w9qxpBFhIggRIIaB50MxnMFWR0oYUWClLIIuClD6wZHhL+lulpZhGTIklFc76L\nN6eMOy+1JMh6Cq4bwcNWXHHFrMejViJNLeKKYC2yFbin9O7dO9uhjLrll1/eAoFlHIhRkeTx8l3G\nkUceKWopEzluSr6+eIwESIAESKCyBCiPFOZdDfIIgsLnkmcKX2FqiyjySBLHozySuo6l7DH1bCn0\nGvlc5J2+6aabZObMmbLaaqvJHnvsUfA/B6S1vO+++wRxA6I+/PjLjDoe4hDo22p/mvm/40F25513\nDuribMCP85VXXhE1s7drRSwCxBJAoEekvBw8eHCc7sraNh8TxAd48MEHM8bDA+4yyyxj8RmS9kCf\nMdkcFT7tKCJh10uBkkZdTuTQQw8VKD7UxLIqLh2xP/Cb69GjR0XmW63jIdbM+uuvL7vssktFOHEQ\nEqglAvnuhbmusyHlEfji33nnnVmHXnjhhYuOyUN5JCvSRq2kPEJ5JNcPkPJILjJ1Vh/DDKRmmtaC\n2agGe3Sa1cBpQEO3wAILmM+aZg/I6oPuFw7xBg477DBrqzmpfXWk7zjjpfvSIfaCCjWRxklvNGXK\nFKeBLp0GWDQ3ApiXa6YJp1GZzadPlSjpp1RsvxATmKCpksZhXfS/FYuBcN1117nTTz/daQAlpw+g\nbtttt3UaQKmoOTeW2agq19yQIUPsmjTFrMNvSQXAoq6hEieVy2w0PFcNuBre5XYNEEAMlnIWurGU\nk2bt9kV5ROweEmeFC9170Zf31ce9N/0D+aGYQnkkNzXKI7nZhI9QHgnT4HYuApRHcpEpqp4xO4rC\nloCT+vTp4xBQEAX+aAgmiRv6Pvvsk3d2b775prWDIBCnRB1Ps1K4/v37OzwM+g8CNBZTbrzxRgu4\nqBYrTrMnZHSB4JDzzz9/Rn2lKqIyQQBPrM2tt96aMjXERgArdUFwaqGScizKTmMJF1BswJ8y/IFi\nJ6mlIYSLpF4r55UcAlR2JGctkjyTWlB2RL0Xpq9DQ8ojaqHlNPWoBTbEPct/NJOFBYVOn0uhfcoj\n+QlRHsnPxx+lPOJJ8LuSBOpcHmHMDn0IrbqCtJxw3Vh77bVt7ksttZQg3RLM7OGblq/4eAn+O19b\nfyzOeEgNpdYKAp9AuJvgA5eNuEUVOHLIIYcI3Dw024MsuOCCGV1oZhCLdaBCTMaxhq6IwySXqwpi\nI6gAJWussYal0NKsJw097bL0r5ZEAv/G8CdXvI6yDMhOSIAESIAEEkkgzr0w/QK8HOK/049n248y\nHtKma1Yq2XLLLUUzgQnuWfjAtXTq1KmxXVgoj2RbiWTUUR5JxjpwFiSQZAKM2RFjdTRFkfmAaspM\n6dy5s2yzzTbmC++7gM+qpjcU+KGutNJKsvXWW9u3P65pH0XTbJpSYtNNN5V77rlH0Je6fYi6agj6\n13SIghs1bv76tkQ0JajA50zdHwR+ifAnb9eunay33nq+W/tebrnlzN9cU0+m1GPnqaeekkmTJpnC\nwJ8X5+E06ngQJK6++mq7DigqEKPjnHPOMYVHeFJ33HGHxfFQDXe4OmVbXT1MMDnhhBNM4ZFy8H87\nCHZ54YUXiloVBIeTugbBBNM2oMQZN26cqJuOqDuIaHrWtBbcJQESIAESIIFUApRH5vFIl3/wAJwt\nphJifHXr1i0lADPlkXkcKY/MY8EtEiCB2iEwX+1cSsNeifqIyu67727WFCeffLIpPTQWg2jKTRsY\nwTO7du0qeAA/+OCDBYEbO3bsKOouYsehCNA4DaYAmTBhggU41FgNctlll1ngQE31aG8gNt98czn+\n+OPlscceM0UHToZlAG7es2bNsqCWSy65pGRTVuA4FCThgr5uuOEG+b//+z9TqsACBCXb+eHzwttR\nx/vzzz9FU5HaOLDsULcNCyT6wAMPhLuzAI8HHXRQSl36jvrGWtW6666bfihlHwoVZKJASeoapEw4\ny47mFLf1xe8BCjEWEiABEiABEshFgPJIkww02eSf9Eb//ve/RV1HU6oRcJryyDwklEfmseAWCZBA\njRCopM9QUsaK6yOrD6BOH7qdvoEPLkFNKS0wqFpnmC/ommuu6U466aTgODYQa0KVFO6NN96weo0O\nbrEb1LTSqWLA6u6++26rQz++DBs2zGnKJacKE19lMTkQDyNXefLJJ52mrzT/VN9GrUwspsUPP/zg\nqxwCZOpP12kWl6CumI1s44X7wfUhpoZaqFggVcR38OX55593zzzzjN/N+Eb8BzU9tXmCc5QCf9wk\nrgHmfsQRR9i1pMfsCF+XuiRZGwRAi1pat25t52A9+SED/gaS9xuAHzsLCeQjQHmk4eUR8P/iiy9M\nHkuPIUZ5JPPXSXkkefcS3t+5JqX+BupYHhmb6fOgNFlSCcA1BalP+/btGxyAOwhcJmBxoQoLwZuW\nTTbZJDiODbi5IDUsXDvOP/98WWihhcyiAhYh3t0E1h8oSNfqCyxD4LaiWUfMSgTj4NO2bVvfJOX7\nr7/+ElW02Dzgn+qLBqQx15ZwzAi4S6DEsezw/fnvXOP54/jG9cHKQzPGyIgRI8x9p1+/ftYEKWPz\nFcxNA49aE4wVpSDFaxLXIMrc0QYmyShIiRe1gLEqxlJ+l1HPrad2F1xwgV0ucpazkEClCCDtZTgF\nd6XG5Ti1TYDySOr6RpFHcAbcVSCjpccQozySyhN7lEcymZSrhvJIuUiynzgE6l0eobIjwq8F7hF4\nCEUg0HCBogNFI4rbd1jRgAqN+m31iOGRq/iHetWtB03ga4rPlVdeacqOW265xQKSBg3SNo466ijB\ngxzMD8MF8951113DVSUpOXxHucbzx8PfcP05/PDDZcaMGeHqgttQAsGtA+dl871N7yCpa5A+z2z7\ncHmCO5Sm0TW3n2xtstXhtwM3n3yxT7KdV291EydOtEsmp3pb+ca93vfff1/0rXHjToKj1xwByiOp\nSxpVHsF9IN2FJbWn3HuUR3Kz8Ucoj3gS+b8pj+Tnw6MNQ6De5RHG7Ijwu0IAzDlz5ph1QrbmSyyx\nhFXj4TxcYImBGB6LL754uDrSNqw7pk+fbg/8iHmx3XbbZT0PwS2h5Nhxxx1TjiP2AwKa+tgXKQd1\np1jLjlzjpffv96EgAh8EYI1TevToYc0feeSRSKclcQ0iTVwbIYAsCiyB4kSlt5P4hwRIgARIoG4I\nUB6Zt9RR5ZGvv/5a1PVWvHXpvB6ibVEeicaJrUiABEggiQSo7IiwKsi8ggKXlHD55ptvzDTSm0H6\nh1bf5vXXXxcE7UTmlbgFFhEIDKrxHiwoqrcACfcDs0xYhAwdOjRcbTd1uDh06NBBNF6IqK9qyvFi\nd/KNl6tPjc1h2VIQeDVOOe644wSpWRHgFW+ychWNY2JZW5K4BrnmHK6H5cp+++0nK6+8slxxxRXh\nQ9wmARIgARIggRQClEf+iyOOPIK2cD1GlrxiCuWRYqjxHBIgARJIBgEqOyKsA6wmYD2BOBrDhw+3\nTCljxoyRffbZxywu1llnHYudAGVHOPYGHvTbt28vBxxwgI0CP0goJ5Ba1he8cUDR4KW+yr4R32Pf\nffeVadOm2cNwykHdefTRR+Xss882Zcoll1wi+IwdO1YOPPBAee2116z5McccY9+INq4BPE3pgAwp\nKJgblDVRS5TxzjvvPHtgh0UJCq4VD/B4+6LBNIOhEPlcg7cG+9k24NKBLDKw2IBVS7oiCdcDc0CM\nCRejpK4Brg0KGZTwGsPyBmnwkJ4Y6d7uuusuU25ZQ/4hARIgARIggSwEKI9Ek3/C6PK5sFAeEcsC\nR3kk/IvhNgmQQE0R0AfSuitxo58D0OzZs13v3r2dun/YR80arc7DQ6YVdT1xa621lrv22mvdVVdd\n5TSgqVPlhzVRRYfTQJ2WNUODdjpkX/nkk0+cmlVanT6sO1Vs+O7se+bMmU5Tq6bUYQcZSvQB387T\nH2PKtypJnCoxgnPOPfdcy+yC+g022MCpcsCpxYjN9aWXXgra5duIOp6m1rW5qILCHXLIIZaFBJHO\n0wsifbdr184hy02h8tlnnxkjZGfB/Pfff3/Xq1cvp1YrThU8DplbfEnaGmAdjj32WKeKG+OigdGc\nKjfsg9/GXnvt5S6++GKnyiF/CbG+EVlZlW6xzqnHxhqrw+HDQgKVJKABot0qq6xSySE5VhUSoDzS\nMPKI/ynoCyWnlq7uvffe81Up35RHtjZZlfJIys+iwXYojzQYWnach0CdyyNjm4BNTWlvIlzMfffd\nJ9tvv73F4dAUrxHOmNcEwSThM+tjRMw78t8tTfNqriNt2rQRTQWbfjj2Pqwk4s4xfRBYEWi6NZsP\n3Gqw5D64anrbUve//PJLsxiBWwasU7IVWGUgZkicOSDiugorMmvWLAFbZLTJ5tqD8ZK4Btk4lFqH\n3xeCsyEALEtuAgMGDLCDt912W+5GPEICZSZw1llnyfjx4wWBwVhIIBcByiMNJ4+AOeKtffTRR+Iz\n36WvA+WRdCLF7VMeicaN8kg0TmxVXgJ1Lo9cxGwsMX9PrVq1yntGy5YtZbPNNsvbJs7BUhUdGAvx\nO7ziBQFTfYGQhU++otYDcvzxx+drknJs6aWXFnzyFbhtxC1QbKyxxhr2KXRuEteg0Jx5PLkE4EqG\nIMNTp061SUJRh6j+4X9LOPD000+LWoAFF4J/O926dQv2k7oB9yq4UX366acWSBiK4PSC4wigm0uB\nmd4+336lxnv88ccFaTqXW245GThwoGA90svkyZPl4YcftrVUyz3xqbnRTi3fzP0OylUWEkgiAcoj\nhVcFbq65FB04m/JIYYZskRwCtSqP5LsXe/pwZ0c6dzwXbbnllhbP0B+L+13p8SiPxF2h8rZnzI7y\n8qyq3mB9gf8w8n2ipH2tqovmZEkgBgF1NxPE1VF3BFEXNTnhhBNk0KBBoi5pGb106tTJlB2IR/P2\n22/LmmuumdEmaRXIvb7JJptYPBlYCKUrOqAMVfcxUXe6lJgzxV5HpcZDPKPDDjtMfvrpJ4vrA4VF\numIXxxEPaMKECbau4HDOOecEl6bm7aKmnxnxgoIG3CABEigbAcojZUPJjmqUQK3KI4XuxVhOdY23\nuIloixcveIGBWIXFlEqPR3mkmFUq8zl5XHxq9lAxPrI1C4MXVrUEGjtmhwbsbTB26g7lNOVyWfov\n1kf2/PPPd5deemnKHM4444wgRg7i8qQXxJDRtw5O3a7SDyVuX12gXPPmzZ0GNM46NzX9dviocseu\n+dtvv83aLmplpcZTtxF3yy23BNNShYdTay+L9eMrb7/9dqfKHYsbhDXTAMwOsYbg24/zfUFcoT59\n+uRk5Ntl+65zH9lsSFiXhQDlkSxQWFV1BCiPRFsyyiOpnKLci9FGLbBcWAZRq02TS9TSI7XDAnuV\nHo/ySIEFqczhsbTsKLPyiN2RQD0QeOKJJ2TUqFENcqmIzwLrCJ/FpkEGKdAp0karokMQqT9c4MKC\n7EpwDdOAxDJlypTwYYtFo8F3Zb75kv1fKywskMkIGZx8KsuUC9EdWEPgg+sptVRyPMQlQupuXzS4\nsWggaFlsscV8lTz33HN2/XCPw5r27NnTzkF8oxdeeCFoh+NHHnlkkFErOMANEiABEiCBRBCgPFK9\n8kiUezGyOkIOgTuxL97lFNaXcUqlx6M8Emd1Gq4tY3Y0HFv2TAKJIwCzfsQxeOutt2SllVay1Lf4\nRtHMN5YOF/85I34B3DYgRLz66qt2fJdddrGHX9TttNNO9pB45ZVXyvLLLy877LCDuXDcfffdpiB4\n8skn5aGHHrI4CUihrBYEkfrXjDUyePBgS62M2C94EEWqRcRdgDsJAj4i5TPaNWRB2mYoXDB+etlq\nq62MDUwhwUSzFYlmWAqaQRGSXvJxR1s8ZIMrlCSbbrqpwFz1nXfeMVPN1VdfPaU7xNZ48MEHjXfX\nrl3tQT2lQYEdzQIle++9t7Rt29bSWxdoXvLhSo+H2D7hgoDSCBIaFoqOPvrojADHcOG5/PLLUwQq\n9KPZnywIMFIzYr1ZSIAESIAESieQ775IeWQe31qWR6Lci+EWDBkyXDSrpMD17ZlnnglXF9yu9HiU\nRwouSUUaJPv1Y0UQcBASqA8CUFrg4RiBNWGVgMxCCNx2/fXXGwAoFKBgOOKII0RTBlsd4rn8+OOP\nVocbDgq064hngMBu+I8cypIbb7zR6pAd5h//+IfccMMNou4Rcuihh4qmaRYoUKL0/9tvv8m2225r\n4yCgJPr3NzlYB8CapKGzqsCqAwohPw+bTNofxOwYNmyYBfXcdddd7frSmgS7hbh/9913ommbTfGE\n+BGaXtksDy677DJjp6abQV9QiJxyyinSpUsX0fTLFksDaxmnqHuQrX379u1NoQPOUHyceOKJea8j\nzhjhtpUeLzw2FC1gCwUSfvu+LLXUUn4z+EamJ/y2EbsjveBcdWFKr+Y+CZAACZBAEQQK3RejyAsY\nlvKIWAyxapVHotyLEZB0xowZlmkx/FNDVkbIsVCaRS2VHi88L8ojYRoV3q6Mu0yyRqGPbLLWg7Mp\njkAcH1lNr+c0YKY76aSTUgZT6wWnKYDdG2+8YfX6oG9+kOF4FGqtYXVqqRGcqwErnSo5gn1sDBky\nxKklhEMfvugDtJ2rZohWFaX/V155xc65+uqrfTf2/fPPPztVqjhVvqTUF9qJ6yOLMfS/YffBBx9k\ndH3mmWcG8SBUMePUlNLaDh8+PGiriqBgOyp3zVBi/ahyyaliyM733NXKw/b1hu40UKoDB1/UasbO\nU9NMX1Xwe7/99rNzPF9chyqRrE4VXRnnH3fccXYs7C+b0ShPRaXH81N55JFHnCrLbO5YT7UY8oey\nfoP9hRdemPWYuvtYPA+sZ9TCmB1RSdV3O8oj9b3+tXL1lEeirSTlkcKc0u/F6k5s93HIROGiCRQs\n1la4rpjtSoxHeaSYlSnbOYzZUWHdEocjgUYhALcHWGakv7VGVOs//vhD9ME39rzSXTyQ4g8uHHB/\n8eXYY4+1OqQMi1uy9Q/XkkUXXTRuV7Haw8UHJeyakq0DWLbAtQHt4FOajWFU7kjpiuvFmwrvBuPT\nJX788cc2/M0332wZUWCGCWsOfD7//HM757333ss2xax1SKkK656hQ4facVzHP//5T7MUufjii8uS\ndSU8cKXH82PD/QS/+ZkzZ8q6665r1kfpGVl8W6TWxZtEuCZlK0hnDVejOJyz9cM6EiABEqh3AlHv\ni3E4ZZMXKI+kEozKvZLySOoMRbLdi08++WSTcxAv7ZprrjG5C/LP9OnTZZ111knvItZ+pcajPBJr\nWcreONO5vOxDsEMSIIHGJvDmm2/aFBCsMVy22GIL2/UP+OFjhbbThYts7WF+uOKKK8pXX32V7XDe\nuij95+2gyIOYK8bGDb9QgQuIRve29M24+SL9bLiUwh3BMVFUt23fan1jD+QInFpKwYM7Pl6pgr4Q\nK2TjjTe2WC6Ib5F+HdU0Xvpc22lgM7hZQQkH96y+ffumNIF5LASofO5R/t/N7NmzzfUrpQPukAAJ\nkAAJRCZQyn0x1yBR5AXKI8XLgQ0lj4TXM9e9GDHaEBsN7tFwf4IbNeKOwdVXrTLCXcTarvR4mBzl\nkVhLVLbG85WtJ3ZEAiSQWAKaVtPmhkjU4YJYDXjLH45yHT6ebzuKcKFm/2Z9oO4X+brKeixK/1lP\nLLFS3X1MwTBnzpxIPW222WYCiwhcKwJYhs8rJ3cIGwhaivgnpRQEPEWwV28x4vuCVQlKuS1nKj2e\nv57wN6xkEEg33VoH/r6IgYK4NbBwyVUQVwXFB/PN1Y71JEACJEAC+QmU877oR4oiL1AeKZ8cWC55\nxK9foXsxXtAccsghlkEOQe/HjRtnL9KQLa2YUunxwnOkPBKmUZltKjsqw5mjkECjEsBbe5R0dxIE\n48TDMwI4ovi3/QgUmq9AsECK2EIFyhX0hUwXKFH690JLlP4LjV/McW/V8OWXX2acDlYQmNILzCs1\nbocFLP3hhx+Cw1G5Byfk2YC5JhQpcJkJF9y08YYjakEgMxQfhNafh7dtsMJButlylkqPl23usNYB\np6233jo4/MsvvwhcgpB+F4KUL8gC8O677/pd+0YdfpeI/s5CAiRAAiRQPIGo98Uo8gJmQXkkdS2q\nSR7BzOPci9H+jjvusMx8559/vsB9Om6p9Hjp86M8kk6k4fep7Gh4xhyBBBqdAB6U8dAJZUf4jT7S\ndiErB26OKHgLDzO7W265RT766COLeTBx4kQ79vLLLwvSeKIgvgHiRWgQT0vr6a0ZENcg7BIDF4/u\n3bsHyo4o/aNvFChK4MKBrC4oMGNEbvVJkybZfkP9WW+99QTmrvAHTS9wY/jwww/Tq23/oosuks03\n3zzlWFTuGnTUrhXxU3yB9QWKBi+17913390sC5Dx5txzzzXOcL3A2iHjiC/Y32677eSLL77wVSnf\nUGzht3DttdcGLjJYt6efflrOOussExzDJ3irhlwKsKSNB79kWGpAoPEF8VTOPvts+62jDkorZNFp\n3bq1/dYvueQSwee0004zlulKDaw5FCVRXJv8mPwmARIgARLIJBD1vhhFXkDvlEcyGVeLPBL3XgyZ\nFbHgbr31VhkwYEDGhReSRyo9HuWRjCVqnIqyxTqtoo4Y/byKFotTzUkgTvRzdIKMHxpXwmnsAqcP\nug4ZVzR+gVPlR8oYqG/VqpXTOAVu0KBB7sknn3T6xt8dfvjhTt0orK2mQLXsFGinN1WrO/DAA52a\nNjo1NXQjR450AwcOdDvssENG9pQo/ffs2dOibyNKtipdrH9VnFi2l/Hjx6fMt9BO3Ojn6E8DdjoN\n4Bl0rUoOy1iiShCn+d0dMpSAZ3pRBZDTYJgp1YW4I7uKprK161U3C4fsK5qizPXr18/qVDB006ZN\nsz7V+sKpAGj1esdwaoXiNABoynjqjmLH9a1HSn14R5UbTq0anCpQnLrgODC68sorw00crmXMmDFO\n0xFbf+Dx8MMPp7TBTtLGU/NW++0utthiTgUfd+qpp9pvODxx/DbBL9sHXMJFLXlszRFNPU5hNpY4\ntOq3LeWR+l37WrpyyiPRVpPySCqnKPdifcnmpkyZ4vbZZx/L+qcvglI7Ce0VkkcqPR7lkdDiNN7m\nWLzZq7tC4aLulrwmLziucOEhqDm/e/bZZ92sWbN8VcY3HtB9ile1NnDqUpLRBv34NjgIZYfG/7B2\nUKCoO0fGOb6iUP+4uUHBkF7y9Zne1u8XI1xgflAqQOkQt6hFRdZTonDPemKWSrU0CJRA6YeRSlbf\nejiNMp5+KGMfD/IapCvr+mY0zlGRxPHwe4WyBr+jUotaz7iddtopdjdUdsRGVpcnUB6py2WvuYum\nPBJtSSmPROMUboWXPHjpphbE4eqs23HkkawdaGW5x6M8kot0xeqZerZx7Gk4Kgk0HgHEJ0BQTcRn\nyFVgru8DVSKAKbJ1pBf049ukH0MgR32znl4d7BfqHz64KjwF7f1Gvj59m3J8Y35qQSJIeeZdd6L2\nq5YQWZtG4Z71xCyVCCybK7YGYorABQiuLIXKAgssIKuttlrW9S10rj+exPHwe0UEd/yOSilIXYtM\nLkj7y0ICJEACJFBeAlHui4XkBcyI8kj2dakmeST7FYh06NBBunXrZu7Fudr4+jjyiD8n/bvc41Ee\nSSdc+f3MJ5jKz4EjkgAJ1AABxEhA7AfEn6iFgpsrfEIRIyOuwqMxr3/q1Kly5plnBsFgG3outToe\nYtaodYalpW3evHlDY2T/JEACJEACZSJAeaRMIEvsplblA4+lUtdHecQTL+67aXGn8SwSIAESmEcA\nb781noMFvDzmmGNk//33F41dMa9BlW717t1bOnfubEocWEFUQ+nVq1dFp1mr42G9EcS1VOuQii4G\nByMBEiCBOidAeSQ5P4BalQ884UpdH+URT7y4byo7iuPGs0iABEIEkFpWg50GNQsuuGCwXe0bGjS0\n2i+B8y+CgM8KVMSpPIUESIAESKCRCFAeaSTwHLbBCFAeKQ0tlR2l8ePZJEACSgD+siwkQAIkQAIk\nQAIk0JgEKI80Jn2OTQLJI8CYHclbE86IBEiABEiABEiABEiABEiABEiABEigBAJUdpQAj6eSAAmQ\nAAmQAAmQAAmQAAmQAAmQAAkkj0Bdu7EMGTKkYhkLkrf0nFG1E/j2228tgOLkyZOr/VIadP5Iw4qC\nzCosJFApAkhby0ICUQlQHolKiu2SSIDySLRVoTwSjRNblZdAvcsjdansQKCX3Xbbrby/JPZGAhUm\ngCBclSqffPKJ4D/Lnj17VmrIso2z6aablq0vdkQCUQmsueaasswyy0RtznZ1SoDySJ0ufI1dNuWR\naAtKeSQaJ7YqL4F6l0eaOC3lRcreSIAEao3AhAkTZMSIEfLTTz/V2qXxekiABEiABEiABKqEAOWR\nKlkoTpMEkkHgIsbsSMZCcBYkQAIkQAIkQAIkQAIkQAIkQAIkQAJlIkBlR5lAshsSIAESIAESIAES\nIAESIAESIAESIIFkEKCyIxnrwFmQAAmQAAmQAAmQAAmQAAmQAAmQAAmUiQCVHWUCyW5IgARIgARI\ngARIgARIgARIgARIgASSQYDKjmSsA2dBAiRAAiRAAiRAAiRAAiRAAiRAAiRQJgJUdpQJJLshARIg\nARIgARIgARIgARIgARIgARJIBgEqO5KxDpwFCZAACZAACZAACZAACZAACZAACZBAmQhQ2VEmkOyG\nBEiABEiABEiABEiABEiABEiABEggGQSo7EjGOnAWJEACJEACJEACJEACJEACJEACJEACZSJAZUeZ\nQLIbEiABEiABEiABEiABEiABEiABEiCBZBCgsiMZ68BZkAAJkAAJkAAJkAAJkAAJkAAJkAAJlIkA\nlR1lAsluSIAESIAESIAESIAESIAESIAESIAEkkGAyo5krANnQQIkQAIkQAIkQAIkQAIkQAIkQAIk\nUCYCVHaUCSS7IQESIAESIAESIAESIAESIAESIAESSAYBKjuSsQ6cBQmQAAmQAAmQAAmQAAmQAAmQ\nAAmQQJkIUNlRJpDshgRIgARIgARIgARIgARIgARIgARIIBkEqOxIxjpwFiRAAiRAAiRAAiRAAiRA\nAiRAAiRAAmUiQGVHmUCyGxIgARIgARIgARIgARIgARIgARIggWQQoLIjGevAWZAACZAACZAACZAA\nCZAACZAACZAACZSJAJUdZQLJbkiABEiABEiABEiABEiABEiABEiABJJBgMqOZKwDZ0ECJEACJEAC\nJEACJEACJEACJEACJFAmAlR2lAkkuyEBEiABEiABEiABEiABEiABEiABEkgGASo7krEOnAUJkAAJ\nkAAJkAAJkAAJkAAJkAAJkECZCFDZUSaQ7IYESIAESIAESIAESIAESIAESIAESCAZBKjsSMY6cBYk\nQAIkQAIkQAIkQAIkQAIkQAIkQAJlIkBlR5lAshsSIAESIAESIAESIAESIAESIAESIIFkEKCyIxnr\nwFmQAAmQAAmQAAmQAAmQAAmQAAmQAAmUiQCVHWUCyW5IgARIgARIgARIgARIgARIgARIgASSQYDK\njmSsA2dBAiRAAiRAAiRAAiRAAiRAAiRAAiRQJgJUdpQJJLshARIgARIgARIgARIgARIgARIgARJI\nBgEqO5KxDpwFCZAACZAACZAACZAACZAACZAACZBAmQhQ2VEmkOyGBEiABEiABEiABEiABEiABEiA\nBEggGQSo7EjGOnAWJEACJEACJEACJEACJEACJEACJEACZSLQtEz9sBsSIIEaITBjxgzp37+/zJ07\nN7iiOXPmSJMmTaRjx45BHTa6du0q48ePT6njDgmQAAmQAAmQAAmUSoDySKkEeT4JkACVHfwNkAAJ\npBBo3769fP/99zJr1qyUeuy89dZbKXXDhw9P2ecOCZAACZAACZAACZSDAOWRclBkHyRQ3wToxlLf\n68+rJ4GsBPbaay9p1qxZ1mO+EpYeAwYM8Lv8JgESIAESIAESIIGyEqA8Ulac7IwE6o4AlR11t+S8\nYBIoTGDQoEHy559/5mw433zzSffu3WXZZZfN2YYHSIAESIAESIAESKAUApRHSqHHc0mABKjs4G+A\nBEggg0CHDh0y4nOkNxo6dGh6FfdJgARIgARIgARIoGwEKI+UDSU7IoG6JEBlR10uOy+aBAoTGDZs\nmDRtmj2sDyw7dtlll8KdsAUJkAAJkAAJkAAJlECA8kgJ8HgqCdQ5ASo76vwHwMsngVwEBg4cmJKR\nxbebf/75ZbvttpOWLVv6Kn6TAAmQAAmQAAmQQIMQoDzSIFjZKQnUBQEqO+pimXmRJBCfQJs2bWTj\njTe2lLPhs//++28ZMmRIuIrbJEACJEACJEACJNAgBCiPNAhWdkoCdUGAyo66WGZeJAkURwCmo3BZ\nCZeFFlpItt9++3AVt0mABEiABEiABEigwQhQHmkwtOyYBGqaQOpTTE1fKi+OBEggLoHddttNnHPB\naYjh0b9/f2nevHlQxw0SIAESIAESIAESaEgClEcaki77JoHaJUBlR+2uLa+MBEom0Lp1a+nZs2dg\n3TF37lwZPHhwyf2yAxIgARIgARIgARKISoDySFRSbEcCJBAmQGVHmAa3SYAEMgggxay37mjVqpX0\n6tUrow0rSIAESIAESIAESKAhCVAeaUi67JsEapMAlR21ua68KhIoG4Gdd95ZmjVrZv3tscceOdPR\nlm1AdkQCJEACJEACJEACaQQoj6QB4S4JkEBBAk3TW8yePVuee+659GrukwAJ1DGBLl26yJQpU2TZ\nZZeViRMn1jEJXjoJkECYAAIW77DDDuGqsm1THikbSnZEAjVDgPJIzSwlL4QEykoglzzSRM3T50Uf\n1CHxIDNgwICyDs7OSIAESIAESIAEao8AFKCfffZZg1wY5ZEGwcpOSYAESIAESKDmCOSQRy7KsOzw\nV56mA/HV/CYBEqhDAn/88YdccMEFcuyxx1b06ldffXXZa6+9ZNSoURUdt9oGA6Ovv/5a7r333mqb\nOudbxQQuuugiGT16dINfAeWRBkfMAUigaghQHkn2UlEeSfb61Ors8skjjNlRq6vO6yKBMhJYYIEF\nZOTIkWXskV2RAAmQAAmQAAmQQDwClEfi8WJrEqh3AlR21PsvgNdPAhEJzD///BFbshkJkAAJkAAJ\nkAAJNAwByiMNw5W9kkAtEqCyoxZXlddEAiRAAiRAAiRAAiRAAiRAAiRAAnVMgMqOOl58XjoJkAAJ\nkAAJkAAJkAAJkAAJkAAJ1CIBKjtqcVV5TSRAAiRAAiRAAiRAAiRAAiRAAiRQxwSo7KjjxeelkwAJ\nkAAJkAAJkAAJkAAJkAAJkEAtEsiZerYWL5bXRAIkUH8EPvjgAzn99NPltNNOkxVXXLH+AES44mnT\npsniiy8uU6dOtdZNmjSR/v37S7NmzVLOfvrpp2X27NlB3QorrCDdunUL9pO68euvv8pdd90ln376\nqSCd8fbbb58x1c8//1zefvtt6dGjR8axuBWVGu/xxx+X+++/X5ZbbjkZOHCgYD3Sy08//SQ33XST\nzJw5U1ZbbTXZY489pEWLFtbspZdektatW0ubNm3ST+M+CZAACZBAmQlQHikMtFblkcmTJ8vDDz9s\nclXv3r1lo402yoDxzTffmKzy8ccfy9prry1bb721LLLIIhntolRUerwkyyO07Ijyi2EbEiCBqiWA\nB7oJEybI9OnTq/YaGnLi99xzj3z99deyyiqryFprrSUnnHCCDBo0SEaMGJExbKdOnUzZgQdmKAbW\nXHPNjDZJq7jzzjtlk002ESggDj/88AxFx1dffSVHHXWUXf8dd9xR8vQrNd7ZZ58thx12mECZcd55\n55nC4r777kuZ/zvvvGPKnfPPP1/GjBkj+++/vwlQUOygQJgaPXq0PPXUUynncYcESIAESKD8BCiP\n5Gdaq/II7tXbbbedyaKQsSCTnHPOOSkwXnnlFXvZ0rFjRzn66KPlvffek65du8pnn32W0i7KTqXH\nS7w84tLKbbfd5hRkWi13SYAESKDyBNq3b+/OOOOMkgfWB9qS+yi1g+uuu67ULnKeP2zYMNe3b9+c\nx3Md0Idgd+mll6YcBm/cA/C56qqrUo5h5++//3ZqGeD++uuvjGNJq1AlhmvevLl77bXXck5NrVnc\nq6++aterCp6c7aIcqNR477//vrvllluCKanCw7Vs2dL16tUrqMNGnz597Nqw/eWXX7r99tvPrnOf\nffZBlZW5c+dau3yMfNv077Fjx7pll102vbps+5RHyoaSHZEACZRIgPJINICUR1I53X777U5ftDjc\nayE/Pfroo26JJZZwTZs2dbiXo0CeWmeddZwqOVJOVusPp1YgKXWFdio9XhXII2Np2RFFRcY2JEAC\nVU0ApvqNWZ544gkZNWpUY04hY+zXX39dVNEhBx10UMoxuLAccMABojdiOfjgg2XKlCkZx9u1ayfz\nzZfs2wcsLGDxoA/k0rlz55RrCO9suOGGZbFQqeR4f/75p+y+++7BZcDMtV+/frLYYosFdS+++KIM\nHjzYrDdQudRSS5krF9YN5q2+zD///HLkkUfamvs6fpMACZAACTQMAcojmVxrWR557rnnTBbBvRby\nVc+ePe3+rcoPeeGFFwzG888/L/rSRbp06ZICB64ujzzyiOB+HrVUerxqkEeSLa1GXVm2IwESIIEc\nBFSTLlA2+JsKms2aNcsegnEMN1m1ZpAbbrhBsO8LbkQPPfSQPPPMM/LFF1/IuHHj5Nhjj015+IfJ\n5YUXXihqAWGnwaUACgTU3XrrrVaHsXfaaSdzN7jyyisF5/gCt4mJEyf63Yp+H3PMMRa/ATff9LLV\nVlsJXB9+//132WWXXcS7Pfh2UISkF1w7rvmUU06Rq6++2hiH24AnbtqPPfaY/PLLL9YWcVTefffd\ncDPbRmyNa665xh7O0T5u+eSTT2TvvfeWtm3byr777hv39NjtKz3eGmuskTJH/G717YopLfwBKKTg\nbhQuiO2x/vrrW3yWcL1ahNjv8z//z96ZwF03lf1/UZGkZIgoERUKTcJjzGOeh4gMoVeGlCFCMiVS\nQh5CPcYketVbeTVQSUVF3ihJRSFNKGnwvv6U9b++V63dPvvsc87eZ7rP8Fufz32fs9dee629vnvf\n9772ta7hv/4rX63vIiACIiACfSQgeaQc5iTLI7ikoOjIlxQ3jFhpFFxOKWa14Z/pF4sxFOTQqmXY\n442DPCJlR9W7R+1EQATGjsBPfvIT16Dz8p404ygbeOEjfsOcOXPCGWecEdCq77nnngG/QwpBOFk5\n32yzzcJpp53mL8xo3T/xiU+EddZZJ5iZoLfbeuutXdFx4okn+vZCCy3k/Rx//PGuTKGShxmxEeaf\nf/7AQ+FFL3qRt+XXO97xjibLimznAL+g4CGwJfNrVYjZYeaoHtTzjW98Y0B736rABt9SAppiDfLo\no48G/E7hRfnTn/4U9thjDw+2RfwUYkew+nDuuee6j+ojjzySdY1yCIUJKxwrrbRS2G677bzPrEGF\nL1/+8pf9HMzs2F/4CdyJ4uPYY49tO48KXZc2GfZ4+ZNA0QLbtdZay69B2rfooov6KlLaTp8o+sy9\nJW1mn1w/lH4qIiACIiAC/ScgeaSc6aTLI1hVFgvPYWRDYndQzN3WPwnOmi/LL7+8bxKwtGoZ9nj5\n8xpZeaTo+yMf2SIRbYuACMwUgX74yBKLwP4Zx/POOy+bhlloeB2+k6m85jWviaYESZvRgkN5m512\n2imrMwuHaA+SaFldor38e70pAnw7a2Rf6MtePrMqe2GPpuTIttMXU7JE09inza4/6/rIXn755T43\niwzfNOYpp5ySxYN4/PHHIz6j8Nt///2ztqa8yb6b9Ue0QKXxuOOOy+r4YlYFcb755ot33nmn11uA\nUO/nDW94Q8bu6quv9jpTQHkbYk9YoNT4t7/9zbf5ZZYZ3saUI1ldpy8pNoVZmHhT5mFuRN7PoYce\n2nQ4c2CO3cbsGPZ4aQJmKRNNgebnzvmb20raVfr5zW9+0+9VOBcL8TfwIYZF1aKYHVVJqZ0IiMC4\nE5A8Uu0KSh7pzAk5yCyAs4amzHB5CRnUrH+yegs67s93W5jL6rr5MozxRlgeUcwOExBVREAEJpgA\nFhXFkrTo+WwiWCLktecLLrigH/aqV70qO3yJJZZwqwQsP0jlWaeUuYusscYaDavxdfrrpe1dd93l\nh1twybbdwA7XBtqdf/757p5SPOArX/mKZ2ZJKxRp/6abbhqeeOKJ7JhnPvOZbmnASkVyg4E5JXG/\n4oorPGsKZphYiPCDCw3HEJm8aiHiPVYmWOtQmMdJJ53kliJnn322j1G1ryrthj1eOifcT8iKw73I\nfWpKrFDMyJLaWgC0YAqpYAqm0lR2FuA04GpUh3PqW58iIAIiIAKdCUgeaWY06fJIccZf+MIXPF08\nGVNSweL3/e9/v1sg44KL5S2uxFgJUyx4aWpa+3NY442yPNLseF0bow4QAREQgfEngE+lacw7TuRl\nL3uZtyFlKW4SVUuZsqPqsf1ux7lzPiggOhVcQHDbsZUBVz6QfjZfMM2lFHPBr7vuul6fBBnfKPxK\nfqyJu1mBuBBA3JNeCi/u/CSlCn0RmBPlEudDfIviPMZpvOK5Ep8DRQepg3HJssw8xSaeXpdApMUA\naKlhun4o8pISKu3TpwiIgAiIwPAISB4pZz2O8kh+JnfffbfHIzMviny1fz/iiCMCAUmvu+46j9Gx\nyy67+POcY1o9t5s6KVQMezyGH0V5RDE7CjeGNkVABESgHYH777/fd5u7RbtmTftGSdmBRQsKhsce\ne6zpPMsqZs2aFbCISAFL88dZCjU/hBgc+UKMDKwrUgCu/L5W3xHwCNTVLj5Iq2Pz9Sik/vCHP2QW\nI2lf8n8ltko/y7DHKzt3FBRLLbWUW+EU9xNcF2Fpm222Ke7KtomrQsnHlMl26osIiIAIiMDIEZA8\nskODHDOK8ki6aYhlRjwyYpmVWfjQbv311/fYWeZOHJBTsMQkkHs3Msuwx0vz5HPU5BEpO/JXR99F\nQAREoAOB66+/3gOcJhcQrAcsJkTbo1B04EYwKiVZNTz00ENNp4SiAaVGsZCO1uJ2eMDSP//5z9lu\nrCUo3/rWt7I6vhB0jL4InFm1YKqJIgWXmXzhoU0w06qFwKoUrBzyBSsUi7cSlllmmXx1z9+HPV7Z\nCWOtA6dNNtmkYTcZf1BsJZeetNPid6Sv/vm73/3OrX2WW265hnptiIAIiIAIjCYBySO/DaMuj3Dn\nkIEO91yLc+VWp+lu4rlblpEOF2CC5BPU/sADD0zNK38Oe7ziiY2aPCJlR/EKaVsERGCiCKQXd1b6\nU/nLX/7iX3mgpMJ+2iaXilR/xx13pK+BSNOksE1ZW9jByyXHkmWEF3U+//jHPwYL/ulZSGhDyk9i\nT1CHC0WyjDjggAOa0oPSftDFAqiGZz3rWSE/tzQmbgz33Xdf2mz4JHsN2WjyBQUFL/soO1LsDfaT\nKg03H5QkFAs66myLzNlnwUv58Ic7lgWHH364Z8HB5QRzT/og40gqbG+xxRaeEjjV5T9RsHBOl1xy\nSXY9iUfx7W9/O5x66qlNWUqSVUMrpdWojUecFFaHEGhSId0v92XetcoC8HodSqdzzjnHfxC29ttv\nv2CBe9Oh/sk1516u4trUcKA2REAEREAEKhGQPNKMadLlEZ6/ZLRbbLHFwpVXXpk9i7HYQK4pLjAg\nH5Kxjnqe4Xl3XOh1kkeGPd5YyCMm2DcUZWNpwKENERCBGSTQa/Rzsp2QLcWeD9GsGeI111wTb7jh\nBs/4QR1ZNEyzHi0wZnzOc57j7czM0LOFUE8bMyv0jCBHH320Z2ux+BUNRMhsYcE5va2lSo0W0DPu\nsMMO0QJ0xrlz53pbS6fqmS4WXnjhmI+qTVYT82+M9iLe0GfdDXuxjxanodZhFrAz2mp/dowpOTxj\niSlBoqUtjcyXDCrFQkYaC4bZUE07CyYaLWZENAVDvOCCC/x8iDBOIbsKmU7gaRYxkewrpjiK22+/\nvdeZwiRayjVva9YX0dxCvD5dNwsA6vvSL3NH8f0WwCtVNX3C1FZSoq2ORHPBiWTV+djHPtbUzgKB\neRvGev7zn+/XjGufL6M2nrmlRIux4fesCT7RUh9HMq3ki6VajhZkN+PI/NKPKTSiKeSy5iaA+zUn\nmnqdomwsdWiprQiIwDgTkDxS7epJHmnkZLE3smdveganT2SUVGzRLJJBztyGXY5M9cXPTvLIsMcb\nA3nkLFa9GoqUHQ04tCECIjCDBHoVLno59aTsOPnkk6Np2iNpWvMpwYp9m0tIVlWmJDAXg2gWJVkb\nvpASlRfNXks3wgXniFIBpUPd8uCDD5YewhxvuummaDnkS/fXqTRLg2j+yKWHwO3Tn/50tCjjpfvz\nlfC1IF3R3Ijy1bW+j+J4zAfFU7t7suokee5vu+22VZtn7aTsyFDoiwiIwIQTkDxS7QJLHqnGqdjK\nXE6jWf4Wq5u268gjTQfnKvo53ojLI0o9a9o1FREQARFoSwCXD0wK2wUZXXzxxbM+ylwByA5SDDJF\nkKr55psvO26YXzhHszzx1Gb2wlxraLOAKG3PHAlmSlyMXgsBTlvF1sAUmICouLJ0KvBdYYUVPBtL\np7at9o/ieGSXIRVyu3uy1Xzy9aSuJZMLaX9VREAEREAERpuA5JHG6zNO8kjjmTdubbfddqFK4Ps6\n8kjjCI1b/Rxv1OURpZ5tvPZ92cJfCv91M5kPG2+8cYNAfsYZZ7hPdDcBZ6qe3He+8x1PXUQmBMYn\nlVG7gl/8F7/4Rc/jTPu6pcp4+PLbim/WNf7zvPjxx1a1EIjJVpS9OQK+maWHlLqyrA/884k/kIqt\nXHqcgrTd7SfXltgN+cKLIy94ZGXgha/fpdU9RQwIcnPj+9ePF8yy8zY3jfCpT30q3Hvvvf7S+OY3\nvznjSKwD/PWKBQa8iNlKSDD3kOLusdhO8RAI+jipZb311vM4JcTI+PCHP9yTQmCYjG655ZZAtPKi\nL+ugzmFSxyOS/wc+8AFPhbfAAgsMCp/6nUECrZ4dnNK0ySNmzRY+//nPl14Nc/lqm62oeJDkkWYZ\nV/JI8S7p37bkkf6x7HdPkyofJE7Dmt9A5ZGcRYt/lRtLkUj9bXyl8aO2GyXz2U+94NNu2QvSZt8/\n8Yu3F81oK6I+vikFogWtaznOPffcEw8++GBve9FFF7Vs12pH1fGKPmSclylZWnVbWo/ZPf5scOUH\nM/ZWhRgBlvLS21nKxWiZIVo1rV2Przt+dpyDBZ70cyLOgwX3i8Q7IHYBZmb9LK3uqauuusrPg7gD\ngyi26usxFjDftBVyHwt/wRTTABP622+/PSYfwq222ipeeuml0RQw0QIv+b242WabRVuF7+r0Zsps\n1BQ7cffdd/f5mqY98reBO8Solm7MRvNz4XqO8vzy56rv/SPw29/+tic3GLmx9O9aDKqnVs8Oxps2\necSC+vr/9CRD5D+33nrrWpdA8kizjCt5pNYtVLmx5JHKqNRwjAkMUB5RzI5B3Rc//OEP/aGaAhSm\ncXgJNw1t2uzrJ4ETDznkEA92yEuoRfGNlnPaAyO28wMjICAPfQSBOqXqePje77jjju5/jw8+P/ia\nd1OInWCruX6+r3vd61p28dGPftSDDTIvAi32u6CooW9bGW/o2iwsvD4f+LGhQQ8bre4pS/HUQ6/t\nD918880j41KISUFAT+a9zz77NBz41re+1euLCijiQXDtbdW4bcClhs5yGzOl7ODF36xWGn76ERsh\nN7W+fu1V2dHXk1FnU0NAyo7xuNStnh3TJo8QONosMiJBpfkfn37WXXddD6xc92pKHmle0JM8Uvcu\n6txe8khnRmohAm3kEcXssBe3gZRkXl30p8ZUclDmwviwY4qOawfjzp4921M54jJCusxWBV8rSvps\n1a5YX3W8M888M9jqfsCvDh98fnBx6Kbgq7jiiiuGlVdeOVj2hmBZLpq6sT/5YFkXgr2Y+75inISm\nA7qoaOWaYVYdztEspEI+xWYXQzQd0uqeIp3VIIqtCIbddtstWMYQ756YFLjLcJ/gupQvrXgstdRS\nHg+AXOGk3hqXuADEebDMKQ0/xb/l/Pz1XQREQARGlUCrZ8c0ySM8j4866qjwhje8IVgmI4+VxP95\nXDEx095mm21qXz7JI6EpZpDkkdq3UccDJI90RKQGItCWQF9iduAHaVHx/WFhq7/BTOoDLzlmFugv\n3sQWMROSAABAAElEQVRZuPrqq/0liTgL+Rcjjr3hhhuCpRb0tuQcXnrppYNFdg22Spy9ML7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vFug+iO4ngo7T/1qU95RjGeHaT4JhMZhXhUuBOisFcJft0lj7S/Ewbx7BimPMLsuMbtFB20kTxS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\nIhRlpZVWChdddJHX23DDDd3QgbslgvslroqEeGBMQXi5JjxkzjnnDO94x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FRggb5CaIRl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\nkpoW+fvf/+7jh9CPe++91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pFGZ3z44YeJDl2s5Z9++qkFtnQP45sSlK5cudKC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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense, Flatten\n",
    "from keras.layers.convolutional import Conv2D\n",
    "from keras.layers.pooling import MaxPooling2D\n",
    "from keras.layers.merge import concatenate\n",
    "\n",
    "# first input model\n",
    "visible1 = Input(shape=(64, 64, 1))\n",
    "conv11 = Conv2D(32, kernel_size=4, activation='relu')(visible1)\n",
    "pool11 = MaxPooling2D(pool_size=(2, 2))(conv11)\n",
    "conv12 = Conv2D(16, kernel_size=4, activation='relu')(pool11)\n",
    "pool12 = MaxPooling2D(pool_size=(2, 2))(conv12)\n",
    "flat1 = Flatten()(pool12)\n",
    "\n",
    "# second input model\n",
    "visible2 = Input(shape=(32, 32, 3))\n",
    "conv21 = Conv2D(32, kernel_size=4, activation='relu')(visible2)\n",
    "pool21 = MaxPooling2D(pool_size=(2, 2))(conv21)\n",
    "conv22 = Conv2D(16, kernel_size=4, activation='relu')(pool21)\n",
    "pool22 = MaxPooling2D(pool_size=(2, 2))(conv22)\n",
    "flat2 = Flatten()(pool22)\n",
    "\n",
    "# merge input models\n",
    "merge = concatenate([flat1, flat2])\n",
    "\n",
    "# interpretation model\n",
    "hidden1 = Dense(10, activation='relu')(merge)\n",
    "hidden2 = Dense(10, activation='relu')(hidden1)\n",
    "output = Dense(1, activation='sigmoid')(hidden2)\n",
    "model = Model(inputs=[visible1, visible2], outputs=output)\n",
    "\n",
    "model.summary()\n",
    "plot_model(model, to_file='multiple_inputs.png', show_shapes=True)\n",
    "Image('multiple_inputs.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Multipe Output Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "____________________________________________________________________________________________________\n",
      "Layer (type)                     Output Shape          Param #     Connected to                     \n",
      "====================================================================================================\n",
      "input_45 (InputLayer)            (None, 100, 1)        0                                            \n",
      "____________________________________________________________________________________________________\n",
      "lstm_24 (LSTM)                   (None, 100, 10)       480         input_45[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "lstm_25 (LSTM)                   (None, 10)            840         lstm_24[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_78 (Dense)                 (None, 10)            110         lstm_25[0][0]                    \n",
      "____________________________________________________________________________________________________\n",
      "dense_79 (Dense)                 (None, 1)             11          dense_78[0][0]                   \n",
      "____________________________________________________________________________________________________\n",
      "time_distributed_1 (TimeDistribu (None, 100, 1)        11          lstm_24[0][0]                    \n",
      "====================================================================================================\n",
      "Total params: 1,452\n",
      "Trainable params: 1,452\n",
      "Non-trainable params: 0\n",
      "____________________________________________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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Pr03dL04ZMGCArV+/3rkJJNpPLOWB2tAmXAEXi6I8UDvKEKE4DbGUB7qvzbpcQmTxkAyp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IwOPXnvEsV79dVX9/ZwzUhJSr+E54HFi/A03fQl0lLpPmrWfZrHgOMXvU+5/+mLkl6UlK+mpPR+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\ngAiIgAiIgAgUTgL/B9xe8Uf2mVvUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from keras.utils import plot_model\n",
    "from keras.models import Model\n",
    "from keras.layers import Input, Dense\n",
    "from keras.layers.recurrent import LSTM\n",
    "from keras.layers.wrappers import TimeDistributed\n",
    "\n",
    "visible = Input(shape=(100, 1))\n",
    "\n",
    "# feature extraction\n",
    "# return_sequences=True\n",
    "extract = LSTM(10, return_sequences=True)(visible)\n",
    "\n",
    "# classification output\n",
    "class11 = LSTM(10)(extract)\n",
    "class12 = Dense(10, activation='relu')(class11)\n",
    "output1 = Dense(1, activation='sigmoid')(class12)\n",
    "\n",
    "# sequence output\n",
    "output2 = TimeDistributed(Dense(1, activation='linear'))(extract)\n",
    "\n",
    "model = Model(inputs=visible, outputs=[output1, output2])\n",
    "model.summary()\n",
    "plot_model(model, to_file='multiple_output.png', show_shapes=True)\n",
    "Image('multiple_output.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Advanced: Graph Neural Networks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "source": [
    "Mathematically, the GCN model follows this formula:\n",
    "\n",
    "$H^{(l+1)} = \\sigma(\\tilde{D}^{-\\frac{1}{2}}\\tilde{A}\\tilde{D}^{-\\frac{1}{2}}H^{(l)}W^{(l)})$\n",
    "\n",
    "Here, $H^{(l)}$ denotes the $l^{th}$ layer in the network,\n",
    "$\\sigma$ is the non-linearity, and $W$ is the weight matrix for\n",
    "this layer. $D$ and $A$, as commonly seen, represent degree\n",
    "matrix and adjacency matrix, respectively. The ~ is a renormalization trick\n",
    "in which we add a self-connection to each node of the graph, and build the\n",
    "corresponding degree and adjacency matrix.  The shape of the input\n",
    "$H^{(0)}$ is $N \\times D$, where $N$ is the number of nodes\n",
    "and $D$ is the number of input features. We can chain up multiple\n",
    "layers as such to produce a node-level representation output with shape\n",
    "$N \\times F$, where $F$ is the dimension of the output node\n",
    "feature vector.\n",
    "\n",
    "The equation can be efficiently implemented using sparse matrix\n",
    "multiplication kernels (such as Kipf's\n",
    "`https://github.com/tkipf/pygcn`). The above DGL implementation\n",
    "in fact has already used this trick due to the use of builtin functions. To\n",
    "understand what is under the hood, please read the tutorial on page rank specified in this repository."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "if [ ! -d \"keras-deep-graph-learning\" ] ; then git clone https://github.com/ypeleg/keras-deep-graph-learning; fi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "from tachles import fix_gcn_paths, load_mutag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "fix_gcn_paths()\n",
    "import keras_dgl\n",
    "from keras_dgl.layers import MultiGraphCNN, MultiGraphAttentionCNN\n",
    "from examples.utils import normalize_adj_numpy, evaluate_preds, preprocess_edge_adj_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "A, A_orig, X, Y, num_edge_features, num_graph_nodes, num_graphs, orig_num_graph_nodes, orig_num_graphs = load_mutag()\n",
    "print X.shape, Y.shape, A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "import keras.backend as K\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from keras.layers import Dense, Activation, Dropout, Input, Lambda\n",
    "from keras.models import Model, Sequential\n",
    "from keras.callbacks import Callback\n",
    "from keras.regularizers import l2\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "lines_to_next_cell": 0,
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "def plot_graph(adjacency_matrix):\n",
    "    rows, cols = np.where(adjacency_matrix == 1)\n",
    "    edges = zip(rows.tolist(), cols.tolist())\n",
    "    gr = nx.Graph()\n",
    "    gr.add_edges_from(edges)\n",
    "    fig, ax = plt.subplots(1, 1, figsize=(6, 6))\n",
    "    nx.draw_networkx(gr, ax=ax, with_labels=False, node_size=5, width=.5)\n",
    "    ax.set_axis_off()\n",
    "    plt.show()\n",
    "    plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "lines_to_next_cell": 0,
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "print X[0]\n",
    "# plot_graph(A)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "source": [
    "----\n",
    "\n",
    "<span style=\"float:right;\">[[source]](https://github.com/vermaMachineLearning/keras-deep-graph-learning/blob/master/keras_dgl/layers/multi_graph_cnn_layer.py#L9)</span>\n",
    "## MutliGraphCNN\n",
    "\n",
    "```python\n",
    "MutliGraphCNN(output_dim, num_filters, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)\n",
    "```\n",
    "\n",
    "MutliGraphCNN assumes that the number of nodes for each graph in the dataset is same. For graph with arbitrary size, one can simply append appropriate zero rows or columns in adjacency matrix (and node feature matrix) based on max graph size in the dataset to achieve this uniformity.\n",
    "\n",
    "__Arguments__\n",
    "\n",
    "- __output_dim__: Positive integer, dimensionality of each graph node feature output space (or also referred dimension of graph node embedding).\n",
    "- __num_filters__: Positive integer, number of graph filters used for constructing  __graph_conv_filters__ input.\n",
    "- __activation__: Activation function to use\n",
    ".\n",
    "If you don't specify anything, no activation is applied\n",
    "(ie. \"linear\" activation: `a(x) = x`).\n",
    "- __use_bias__: Boolean, whether the layer uses a bias vector.\n",
    "- __kernel_initializer__: Initializer for the `kernel` weights matrix\n",
    ".\n",
    "- __bias_initializer__: Initializer for the bias vector\n",
    ".\n",
    "- __kernel_regularizer__: Regularizer function applied to\n",
    "the `kernel` weights matrix\n",
    ".\n",
    "- __bias_regularizer__: Regularizer function applied to the bias vector\n",
    ".\n",
    "- __activity_regularizer__: Regularizer function applied to\n",
    "the output of the layer (its \"activation\").\n",
    ".\n",
    "- __kernel_constraint__: Constraint function applied to the kernel matrix\n",
    ".\n",
    "- __bias_constraint__: Constraint function applied to the bias vector\n",
    ".\n",
    "\n",
    "__Input shapes__\n",
    "\n",
    "* __graph node feature matrix__ input as a 3D tensor with shape: `(batch_size, num_graph_nodes, input_dim)` corresponding to graph node input feature matrix for each graph.<br />\n",
    "* __graph_conv_filters__ input as a 3D tensor with shape: `(batch_size, num_filters*num_graph_nodes, num_graph_nodes)` <br />\n",
    "`num_filters` is different number of graph convolution filters to be applied on graph. For instance `num_filters` could be power of graph Laplacian.<br />\n",
    "\n",
    "__Output shape__\n",
    "\n",
    "* 3D tensor with shape: `(batch_size, num_graph_nodes, output_dim)`\trepresenting convoluted output graph node embedding matrix for each graph in batch size.<br />\n",
    "\n",
    "\n",
    "\n",
    "<span style=\"float:right;\">[[source]](https://github.com/vermaMachineLearning/keras-deep-graph-learning/blob/master/examples/multi_gcnn_graph_classification_example.py)</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "source": [
    "## The model itself"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "num_filters = num_edge_features\n",
    "graph_conv_filters = preprocess_edge_adj_tensor(A, symmetric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "X_input = Input(shape=(X.shape[1], X.shape[2]))\n",
    "graph_conv_filters_input = Input(shape=(graph_conv_filters.shape[1], graph_conv_filters.shape[2]))\n",
    "\n",
    "# Your code here!\n",
    "\n",
    "output = Dense(Y.shape[1])(output)\n",
    "output = Activation('softmax')(output)\n",
    "\n",
    "nb_epochs = 200\n",
    "batch_size = 169\n",
    "\n",
    "model = Model(inputs=[X_input, graph_conv_filters_input], outputs=output)\n",
    "model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['acc'])\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "outputs": [],
   "source": [
    "model.fit([X, graph_conv_filters], Y, batch_size=batch_size, validation_split=0.1, epochs=nb_epochs, shuffle=True, verbose=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1295
    },
    "colab_type": "code",
    "id": "3lOBizVa4rVt",
    "outputId": "a3142dd2-4ff0-4bb6-a833-a7046f4e0596"
   },
   "source": [
    "__References__: <br />\n",
    "[1] Kipf, Thomas N., and Max Welling. \"Semi-supervised classification with graph convolutional networks.\" arXiv preprint arXiv:1609.02907 (2016). <br />\n",
    "[2] Defferrard, Michaël, Xavier Bresson, and Pierre Vandergheynst. \"Convolutional neural networks on graphs with fast localized spectral filtering.\" In Advances in Neural Information Processing Systems, pp. 3844-3852. 2016. <br />\n",
    "[3] Simonovsky, Martin, and Nikos Komodakis. \"Dynamic edge-conditioned filters in convolutional neural networks on graphs.\" In Proc. CVPR. 2017. <br />"
   ]
  }
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