ML-Coursework_Student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to TensorFlow and Deep Learning\n",
    "\n",
    "## Structure of this notebook\n",
    "1. **Sanity check** \\[Essential\\] <br/> _Run some simple \"Hello World\" code (1 minute)_\n",
    "2. **Everything in TensorFlow is part of a graph** \\[Essential\\] <br/> _Set up some basic calculations to get a feel for what a graph is (20 minutes)_\n",
    "3. **A simple logistic regression model** \\[Essential\\] <br/> _Learn one of the fundamental machine learning models for classification and understand how TensorFlow optimises model parameters (30 minutes)_\n",
    "4. **Extending to muliple classes** \\[Essential\\] <br/> _Adapt the simple logistic regression model to predict multiple classes and produce a reasonable handwriting recognition tool (30 minutes)_\n",
    "5. **Generalising to a Multilayer Perceptron (MLP) model** \\[Essential\\] <br/> _Improve the handwriting recognition tool using a multilayered neural network (30 minutes)_\n",
    "6. **Neural networks for image data** \\[Recommended\\] <br/> _Understand the basic components of convolutional neural networks and how they're used on imaging data; implement the LeNet architecture for handwriting recognition (45 minutes)_\n",
    "7. **Introducing higher-level TensorFlow APIs** \\[Recommended\\] <br/> _Learn how to apply higher-level APIs to handle data pipelines, training, evaluation and inference (45 minutes)_\n",
    "8. **\\[Bonus material\\] Neural networks for sequence data: Ratings from IMDB reviews** \\[Optional\\] <br/> _A prepared example covering the recurrent neural networks and their application to a text analysis task_\n",
    "\n",
    "## Setup\n",
    "Normally, TensorFlow would be loaded by running `import tensorflow as tf` alongside any other packages that are necessary for your application. To help keep this workshop focussed on implementing TensorFlow models, we have wrapped up our dependencies and some helper functions in a handy module called `TutorialSupport`. We've also slightly modified TensorFlow's behaviour so the results are reproducible (by resetting the random seed each time a session is created) and by restricting the number of threads used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load all libraries and helper functions\n",
    "%matplotlib inline\n",
    "from TutorialSupport import *\n",
    "print(\"TensorFlow version %s is loaded.\" % tf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sanity check\n",
    "Before we get stuck into the nitty gritty, let's just check that everything is working as expected with the customary \"Hello World\" code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "hw = tf.constant(\"Hello World!\", name=\"sanity_check\")\n",
    "print(hw)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that printing the `hw` variable, when printed, did not show anything at all related \"Hello World!\". After all, we did just create what _looks_ like a variable that apparently contains \"Hello World\". It turns out that we can only evaluate values in TensorFlow when running within a \"session\". Sessions are created as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    print(sess.run(hw))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By using `sess.run()` and passing in the `hw` variable, we get the expected output: \"Hello World!\"\n",
    "\n",
    "**<span style=\"color:red\">If you get any errors with the above code, stop now and check that you have the required TensorFlow dependency installed correctly.</span>**\n",
    "\n",
    "_**What did we actually do?**_ The above code created a tensor that, when evaluated, always returns a value of \"Hello World\".\n",
    "\n",
    "_**What's a tensor?**_ The [TensorFlow documentation](https://www.tensorflow.org/programmers_guide/tensors) describes tensors as \"...a generalization of vectors and matrices to potentially higher dimensions.\" The dimensionality of the tensor is known as its \"rank\". A rank of zero corresponds to a scalar value, rank one is a vector, rank two is a matrix, rank three is a cube of data, and so on. Vectors and matrices can be operated on using normal mathematical operations, but TensorFlow tensors can contain any data type including text and boolean values. The _shape_ of the tensor can convey both the rank and size of the data, e.g. a tensor of (100, 800, 600, 3) is a rank four tensor that might contain 100 RGB images each with a size of 800x600 pixels.\n",
    "\n",
    "_**What is a session?**_ A session in TensorFlow defines the context in which computations operate. This might include providing access to GPU (or Google's TPU) accelerators, or even multiple compute nodes, in addition to creating traditional CPU-based threads.\n",
    "\n",
    "## Everything in TensorFlow is part of a graph\n",
    "Computation in TensorFlow is performed using a directed graph architecture; dependent operations are linked so that the path to any desired output is known and can be computed on-demand. Let's implement the following graph that adds two integers as an example:\n",
    "<img 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\" style=\"width:300px\">\n",
    "\n",
    "**Remember:** A graph describes _how_ a calculation should be performed but does not actually perform the calculation until required.\n",
    "\n",
    "Every edge of this graph represents a _tensor_ and every node represents an _operation_. Operations act on tensors, and most operations consume one or mode input tensors to produce an output tensor. Generally, when operations are created, the variable returned is a reference to the output tensor. Evaluating the output tensor triggers all dependent operations to be executed.\n",
    "\n",
    "### Common types of tensors\n",
    "#### [tf.Variable](https://www.tensorflow.org/api_docs/python/tf/Variable)\n",
    "This kind of tensor represents a value that can change, such as the weights in a regression or neural network. We will use these extensively in the exercises in this notebook.\n",
    "\n",
    "#### [tf.constant](https://www.tensorflow.org/api_docs/python/tf/constant)\n",
    "This tensor represents a value that strictly cannot change, such as the value of $\\pi$.\n",
    "\n",
    "#### [tf.placeholder](https://www.tensorflow.org/api_docs/python/tf/placeholder)\n",
    "This tensor represents a fixed value that we are yet to obtain, such as the training data used to fit a model, or the input data for when a model is used for inference. Placeholder values are supplied on-demand in cases where the graph requires them to compute an output.\n",
    "\n",
    "#### [tf.SparseTensor](https://www.tensorflow.org/api_docs/python/tf/SparseTensor)\n",
    "This tensor represents a value where most of its components are zero; it saves space by only storing the overall shape of the data and the indices of the non-zero elements.\n",
    "\n",
    "### Creating a simple graph\n",
    "**Challenge:**\n",
    "\n",
    "Use [`tf.placeholder()`](https://www.tensorflow.org/api_docs/python/tf/placeholder) to create `x` and `y`, and [`tf.add()`](https://www.tensorflow.org/api_docs/python/tf/add) to create `z`, according to the above graph. Make sure that `x` and `y` accept `tf.int32` as their [data type](https://www.tensorflow.org/api_docs/python/tf/DType), and their names are \"Placeholder_X\" and \"Placeholder_Y\", respectivley.\n",
    "\n",
    "**Note on naming:** Nodes in a TensorFlow graph all have names, and TensorFlow will assign a default when none is specified. This is usually okay for small projects, but it can make things difficult in more complex cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = None\n",
    "y = None\n",
    "z = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(x)\n",
    "print(y)\n",
    "print(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**<br/>\n",
    "```\n",
    "Tensor(\"Placeholder_X:0\", dtype=int32)\n",
    "Tensor(\"Placeholder_Y:0\", dtype=int32)\n",
    "Tensor(\"add:0\", dtype=int32)\n",
    "```\n",
    "\n",
    "Let's now evaluate the tensor `z` as constructed above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with tf.Session() as sess:\n",
    "    print(\"The value of z when x = 1 and y = 2 is:\",\n",
    "          sess.run(z, feed_dict={x: 1, y: 2}))\n",
    "    print(\"The value of z when x = [1,2,3] and y = 1 is:\",\n",
    "          sess.run(z, feed_dict={x: np.array([1,2,3]), y: 1}))\n",
    "    print(\"The value of z when x = [1,2,3] and y = [4,5,6] is:\",\n",
    "          sess.run(z, feed_dict={x: np.array([1,2,3]), y: np.array([4,5,6])}))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By evaluating the output tensor `z` using the `sess.run()` function and feeding in placeholder values with the `feed_dict` dictionary, the correct output of `3` is obtained when requesting the value of `z`. The `feed_dict` dictionary maps the input placeholder tensors with values they should hold for the computation.\n",
    "\n",
    "Notice that the session exists within a python \"`with`\" block, meaning that the session is automatically closed after the block exits. When running TensorFlow code interactively, it's often useful to create a [`tf.InteractiveSession`](https://www.tensorflow.org/api_docs/python/tf/InteractiveSession) object. By creating this object, we can evaluate the result of operations much more rapidly by calling [`eval`](https://www.tensorflow.org/api_docs/python/tf/Tensor#eval) directly on the tensor. `InteractiveSession` objects should only be used when prototyping TensorFlow models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the interactive session, which becomes the default session when calling `eval`\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Once the session is instantiated, we can simply evaluate the tensor as follows:\n",
    "z.eval(feed_dict={x:1, y:2})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Inspecting the graph\n",
    "You can access the current [graph object](https://www.tensorflow.org/api_docs/python/tf/Graph) using [`tf.get_default_graph()`](https://www.tensorflow.org/api_docs/python/tf/get_default_graph) and a serialised ([what is serialisation?](https://en.wikipedia.org/wiki/Serialization)) version of the graph by calling [`as_graph_def()`](https://www.tensorflow.org/api_docs/python/tf/Graph#as_graph_def) on the graph object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Use tf.get_default_graph() and as_graph_def() to print out a serialised version of the graph\n",
    "graph = None\n",
    "graph.as_graph_def()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each `node` entry describes an object in the graph along with its dependencies. The final `node` in the graph shows that it requires the values provided by `Placeholder_X` and `Placeholder_Y`:\n",
    "```\n",
    "...\n",
    "node {\n",
    "  name: \"add\"\n",
    "  op: \"Add\"\n",
    "  input: \"Placeholder_X\"\n",
    "  input: \"Placeholder_Y\"\n",
    "  attr {\n",
    "    key: \"T\"\n",
    "    value {\n",
    "      type: DT_INT32\n",
    "    }\n",
    "  }\n",
    "}\n",
    "...\n",
    "\n",
    "```\n",
    "\n",
    "By far the better method to visualise the graph is to use [TensorBoard](https://www.tensorflow.org/programmers_guide/graph_viz), a specialised tool for visualising TensorFlow data, but that is overkill for now! We've written a helper function that can visualise the current graph right here in the notebook. Just run `plot_graph()` to see what we've created so far:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plot_graph()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the current graph contains everything we've created in TensorFlow so far. Even if we've lost the Python reference to the tensor object, we can always find it again by name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use `get_default_graph()`\n",
    "graph = None\n",
    "# Use `get_operation_by_name()`\n",
    "operation = None\n",
    "# Use `.outputs[0]` on the operation\n",
    "first_output = None\n",
    "first_output.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code, we fetch the operation called \"sanity_check\", fetch its first (and only) output tensor, and evaluate it using the default session. The expected text, \"Hello World!\", is printed!\n",
    "\n",
    "**Challenge:**<br/>\n",
    "Using the above code as a template, use the `get_operation_by_name()` function to print the result of the `add` operation on a pair of integers of your choice. You will need to reference the placeholders for X and Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "graph = None\n",
    "operation = None\n",
    "first_output = None\n",
    "feed_dict = {x: None, y: None}\n",
    "\n",
    "first_output.eval(feed_dict=feed_dict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Challenge:**<br/>\n",
    "Create a graph that calculates the area of a circle from its radius (remember that $A=\\pi r^2$) as well as its circumference (remember that $C=2\\pi r$). Use the `plot_graph()` function to visualise the graph. Ensure that the placeholders use data type `tf.float32` and that every operation is given a name.\n",
    "You can get the value of $\\pi$ from the [`math`](https://docs.python.org/3/library/math.html#math.pi) module. Take a look at the [TensorFlow Python API](https://www.tensorflow.org/api_docs/python/tf) to find an operation that can square $r$ - there are more than one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# Convert math.pi to a TensorFlow constant\n",
    "pi = tf.constant(math.pi, name=\"pi\")\n",
    "\n",
    "r = None # Radius: A placeholder of type tf.float32 with name \"radius\"\n",
    "r2 = None # Some TensorFlow operation accepting `r` as in input that calculated r^2 (hint: tf.square)\n",
    "two_pi = None # Some TensorFlow operation multiplying pi by 2 (hint: tf.multiply)\n",
    "a = None # Area: Some TensorFlow operation multiplying r2 and pi\n",
    "c = None # Circumference: Some TensorFlow operation multiplying two_pi and r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plot_graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "area, circumference = sess.run([a, c], feed_dict={r: 10})\n",
    "print(\"A circle with a radius of 10 has an area of\", area, \"and a circumference of\", circumference)\n",
    "print(\"The value of 2 * pi is\", two_pi.eval())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**<br/>\n",
    "```\n",
    "A circle with a radius of 10 has an area of 314.15927 and a circumference of 62.831856\n",
    "The value of 2 * pi is 6.2831855\n",
    "```\n",
    "\n",
    "Notice that we can use our session object (`sess`) to request the value of multiple tensors by providing a list as the first argument. Only one `feed_dict` is required if any of the values requested depends on a placeholder value.\n",
    "\n",
    "## A simple logistic regression model\n",
    "<hr/>\n",
    "**TL;DR** Logistic regression is a method of squashing a multiple linear regression that can produce any value from $-\\infty$ to $+\\infty$ to values between 0 and 1. We can interpret the output of logistic regression if optimise our parameters using a maximum likelihood estimation to evaluate our model.\n",
    "<hr/>\n",
    "While in traditional multiple linear regression model esimates a target value $y$ as $\\hat{y} = w_0 + w_1x_1 + \\cdots + w_nx_n$ where $w$ is a set of trainable weights (note that $w_0$ acts as a bias term), a logistic regression model extends this formulation to constrain $\\hat{y}$ to take values between $[0, 1]$ such that $\\hat{y}$ can be interpreted as the probability of some class membership, or more formally $\\Pr(Y=1\\vert X=x;w)$ (one way of reading this might be: the probability datapoint drawn from $X$, described by a set of values, $x$, belongs to class $Y=1$ given parameters $w$).\n",
    "\n",
    "Logistic regression is based on the sigmoid function, $\\sigma(t)$:\n",
    "$$\\sigma(t)=\\frac{1}{1+e^{-t}}$$\n",
    "\n",
    "In logistic regression, $t$ is known as the _logit_ and is defined as the logarithm of the odds ratio, or $\\log\\left(\\frac{p}{1-p}\\right)$. It is this component that is modelled using the standard multiple linear regression model:\n",
    "\n",
    "$$t = \\log\\left(\\frac{p}{1-p}\\right) \\approx w_0 + w_1x_1 + \\cdots + w_nx_n = \\textbf{w}^\\texttt{T}\\textbf{x}$$\n",
    "\n",
    "In short, the sigmoid function, $\\sigma$, acts on the logit function $t$ to recover the probability, $p$.\n",
    "\n",
    "Now that we have a method of approximating the probability of a data point ($X$) belonging to a class ($Y=1$), we need to find suitable parameters $w$ that _increases the probability of our observed data given the classes $y$_, or more formally, maximises $\\Pr(X|Y)$. Since logistic regression assumes only two mutually exclusive outcomes are possible, the [_Bernoulli distribution_](https://en.wikipedia.org/wiki/Bernoulli_distribution) can be used. The probability for an individual data point, $y_i$, can therefore be described as:\n",
    "\n",
    "$$\\Pr(Y=y_i|X=x_i) = \\sigma(t_w(x_i))^{y_i}(1-\\sigma(t_w(x_i)))^{1-y_i}$$\n",
    "\n",
    "In terms of the Bernoulli distribution, $\\sigma(t_w(x_i))$ might be defined more generally in other texts as the \"probability of success\". It can be seen that for $y_i=1$, the probability reduces to $\\Pr(y_i=1|X=x_i)=\\sigma(t_w(x_i))$ and for $y_i=0$ it reduces to $\\Pr(y_i=1|X=x_i)=1-\\sigma(t_w(x_i))$.\n",
    "\n",
    "With this information we can estimate the likelihood of the entire dataset's observations, $x$, having their associated classes $y$ as the product of each individual datapoint's probability:\n",
    "\n",
    "$$L(w)=\\Pi^n_{i=1}\\sigma(t_w(x_i))^{y_i}(1-\\sigma(t_w(x_i)))^{1-y_i}$$\n",
    "\n",
    "We want to maximise the value of $L(w)$, and this approach is called the [_maximum likelihood estimation_](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) (in practise, we minimise $-L(w)$).\n",
    "\n",
    "An equivalent and more convenient expression used in optimising this model is obtained by taking the log of the likelihood function above, $L$, to transform the product over all datapoints to a sum:\n",
    "$$\\ell(w) = \\log(L(w)) = \\sum^n_{i=1}y_i\\log(\\sigma(t_w(x_i))) + (1-y_i)\\log(1-\\sigma(t_w(x_i)))$$\n",
    "\n",
    "Maximising $L$ is equivalent to maximising $\\ell$!\n",
    "\n",
    "All that is left is to find the derivative of $\\ell$ with respect to parameters $w$ and iteratively solve through some form of [_gradient descent_](https://en.wikipedia.org/wiki/Gradient_descent). Finding the derivative is not covered here because TensorFlow can automatically differentiate and optimise objective functions! We call these objective functions _loss functions_.\n",
    "\n",
    "Gradient descent is an iterative procedure where the derivative of the loss function (shown as $J$ here) is used to update the weights by a tiny amount (shown as $\\eta$) so that the loss function is minimised until some stopping criterion is met:\n",
    "\n",
    "$$\\textbf{w}_{t+1} = \\textbf{w}_{t} - \\eta \\frac{\\partial J}{\\partial \\textbf{w}}$$\n",
    "\n",
    "### Classifying red or white wine with logistic regression\n",
    "#### Building the logistic regression graph\n",
    "\n",
    "Your challenge is to create the compute graph for the logistc regression model and then apply it to a dataset of features belonging to red and white wines. The model should be able to use these features to classify the wine as red or white.\n",
    "\n",
    "There are 12 variables in [this dataset](http://archive.ics.uci.edu/ml/datasets/Wine+Quality):\n",
    "1. fixed acidity \n",
    "2. volatile acidity \n",
    "3. citric acid \n",
    "4. residual sugar \n",
    "5. chlorides \n",
    "6. free sulfur dioxide \n",
    "7. total sulfur dioxide \n",
    "8. density \n",
    "9. pH \n",
    "10. sulphates \n",
    "11. alcohol \n",
    "12. quality (score between 0 and 10)\n",
    "13. red (1) or white (0)\n",
    "\n",
    "<span style=\"font-size:x-small\">Cortez, Paulo, et al. \"Modeling wine preferences by data mining from physicochemical properties.\" _Decision Support Systems_ 47.4 (2009): 547-553.</span>\n",
    "\n",
    "All variables apart from the quality measure will be the predictors ($x$) and only the final variable will be the dependent variable ($y$).\n",
    "\n",
    "### Take a look at the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_wine_data().head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the model\n",
    "**Steps:**<br/>\n",
    "1. Create two [placeholders](https://www.tensorflow.org/api_docs/python/tf/placeholder) for the `x` and `y` variables - both should have their data type set to `tf.float32`, and they will have shapes of `(None, 11)` and `(None, 1)`, respectively.$^\\textbf{*}$\n",
    "2. Create two `tf.float32` variables (matrices) using [tf.get_variable](https://www.tensorflow.org/api_docs/python/tf/get_variable) to create the weights ($w_1 \\cdots w_n$) and the bias ($w_0$). Ensure that the weights and bias are both initialised with zeros (using [tf.zeros](https://www.tensorflow.org/api_docs/python/tf/zeros)) having shapes `(11, 1)` and `(1,)`, respectively\n",
    "3. Calculate the logits by multiplying your `x` placeholder and weights using [tf.matmul](https://www.tensorflow.org/api_docs/python/tf/matmul) and add the bias\n",
    "4. Transform the logits back into probabilities using [tf.sigmoid](https://www.tensorflow.org/api_docs/python/tf/sigmoid) (this will provide our final predictions)\n",
    "5. Create the MLE objective function (also our [\"loss function\"](https://en.wikipedia.org/wiki/Loss_function)), $\\ell$ in the notes above, using [tf.losses.sigmoid_cross_entropy](https://www.tensorflow.org/api_docs/python/tf/losses/sigmoid_cross_entropy)\n",
    "6. Set up a gradient descent optimiser with a learning rate of 0.001 that minimises your loss function using [tf.train.GradientDescentOptimizer](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer)\n",
    "7. Create an \"init op\" that can be called to initialise the weights and bias using [tf.global_variables_initializer](https://www.tensorflow.org/api_docs/python/tf/global_variables_initializer)\n",
    "\n",
    "$^\\textbf{*}$Remember that when `None` is specified as part of a tensor shape, it represents a dimension of unknown size. We use that for the first dimension of our input data, which represents the index of each example fed into the model for training/inference. Since the amount of data we input is not known in advance (and can change), we leave it as `None`.\n",
    "\n",
    "**Things to note:**\n",
    "* `tf.get_variable` is the recommended way to create `tf.Variable` tensors; it will create the variable if none exists by the name given, and either raise an error that the variable exists or return a reference to that variable if it does. This is not so important now, but useful in cases [where variables need to be shared](https://www.tensorflow.org/programmers_guide/variables#sharing_variables).\n",
    "* The initialisation operation (`tf.global_variables_initializer`) must be created and run before any training takes place. Its job is to set the values of the variables created in step two to their defaults. If this step is missed, you will encounter an error because the variables will have no value assigned to them.\n",
    "* We have supplied a tensor that calculates the model accuracy for you. It might look complicated, but it just adds up all cases where both $y$ and $\\hat{y}$ are $ > 0.5$. Even though $y$ can take on only 0 or 1, we use the $> 0.5$ condition to avoid any issues with comparing floating point numbers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Start with a fresh graph and session\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = None # [Step 1] Hint: tf.placeholder(dtype=DATA TYPE, shape=SHAPE OF DATA, name=NAME OF TENSOR)\n",
    "y = None # [Step 1]\n",
    "\n",
    "weights = None # [Step 2] Hint: tf.get_variable(NAME, dtype=DATA TYPE, initializer=tf.zeros(SHAPE OF DATA))\n",
    "bias = None # [Step 2]\n",
    "\n",
    "logits = None # [Step 3]\n",
    "\n",
    "y_hat = None # [Step 4]\n",
    "\n",
    "loss = None # [Step 5]\n",
    "\n",
    "# The first dimension is the number of training examples\n",
    "total_examples = tf.shape(y)[0]\n",
    "# This is a boolean tensor where correct = True, incorrect = False\n",
    "correct_predictions = tf.equal(y_hat > 0.5, y > 0.5)\n",
    "# After converting boolean (True/False) to integer (1/0) values, add them using `tf.reduce_sum`\n",
    "# and divide by the total number of examples\n",
    "accuracy = tf.reduce_sum(tf.cast(correct_predictions, tf.int32)) / total_examples\n",
    "\n",
    "# Add in the training and initialisation operations\n",
    "train_op = None # [Step 6] Hint: tf.train.GradientDescentOptimizer(learning_rate=LEARNING RATE).minimize(TENSOR TO MINIMISE)\n",
    "init_op = None # [Step 7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print(\"This model has %i parameters.\" % count_parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**<br/>\n",
    "```\n",
    "This model has 12 parameters.\n",
    "```\n",
    "\n",
    "### Preparing the data\n",
    "In order to run this model, the data needs to be transformed in the appropriate data structure. Numpy arrays/matrices are the best for this. We've provided a function that does this conversion for you for this dataset, but a common way to load data into numpy arrays is to first create a [pandas data frame](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html) using a function such as [`pd.read_csv()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html), and then calling [`as_matrix()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.as_matrix.html) on that object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the data in a form suitable for the model, with 25% held out for testing\n",
    "x_data_train, y_data_train, x_data_test, y_data_test = load_wine_data(test_frac=0.25)\n",
    "print(\"Train data (x):\", x_data_train)\n",
    "print(\"Train data shape (x):\", x_data_train.shape)\n",
    "print(\"Train data (y):\", y_data_train)\n",
    "print(\"Train data shape (y):\", y_data_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run the optimisation\n",
    "Here we begin running the logistic regression model. Normally this would be done within a single python `with` block using `tf.Session()`, but we're using the convenient `tf.InteractiveSession` so we can use the `run()` and `eval()` operations directly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_op.run() # Initialise the variables (weights and bias)\n",
    "\n",
    "# We'll keep a list of all the losses and accuracies as we go for plotting later\n",
    "losses_train = []\n",
    "accuracies_train = []\n",
    "losses_test = []\n",
    "accuracies_test = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run for 200 iterations\n",
    "for i in range(200):\n",
    "    batch_train = {x: x_data_train, y: y_data_train}\n",
    "    batch_test = {x: x_data_test, y: y_data_test}\n",
    "    train_op.run(batch_train)\n",
    "    accuracies_train.append(accuracy.eval(batch_train))\n",
    "    losses_train.append(loss.eval(batch_train))\n",
    "    accuracies_test.append(accuracy.eval(batch_test))\n",
    "    losses_test.append(loss.eval(batch_test))\n",
    "\n",
    "plot_loss_accuracy(losses_train, accuracies_train, losses_test, accuracies_test)\n",
    "\n",
    "print(\"Final training set accuracy: %0.2f%%\" % (accuracies_train[-1] * 100))\n",
    "print(\"Final test set accuracy: %0.2f%%\" % (accuracies_test[-1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**<br/>\n",
    "```\n",
    "Final training set accuracy: 92.51%\n",
    "Final test set accuracy: 93.35%\n",
    "```\n",
    "\n",
    "## Extending to multiple classes: Simple handwriting recognition using softmax regression\n",
    "Logistic regression can be generalised to multiple classes. Multi-class logistic regression is often referred to as [_softmax regression_](https://www.kdnuggets.com/2016/07/softmax-regression-related-logistic-regression.html) and rather than outputting the probability of belonging to a single class, it outputs probabilities of belonging to arbitrarily many classes under the constraint that the sum over all classes equals 1. This is achieved using the [softmax function](https://en.wikipedia.org/wiki/Softmax_function):\n",
    "\n",
    "$$\\sigma(t)_k = \\frac{e^{t_k}}{\\sum_{i=1}^Ke^{t_i}}$$\n",
    "\n",
    "Here, $k$ is the class in question, $K$ is the total number of classes, and $t$ defined as in the case of standard logistic regression but now includes parameters for each output class.\n",
    "\n",
    "In this exercise, we will use softmax regression to convert a handwritten digit into a machine-readable form. For this task, the [MNIST dataset](http://yann.lecun.com/exdb/mnist/) will be used. It consists of 70,000 grayscale 28x28 images of handwritten digits with 60,000 of these used in training and 10,000 held out for testing.\n",
    "\n",
    "### Examining the data\n",
    "Running our helper function `load_mnist()` will automatically download and read in the MNIST handwriting dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_img, train_labels, test_img, test_labels = load_mnist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run this cell a few times to see what the data looks like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(20,10))\n",
    "for i in range(5):\n",
    "    char_idx = np.random.randint(len(train_img))\n",
    "    plt.subplot(150 + i + 1)\n",
    "    plt.title(\"%s: %s\" % (np.argmax(train_labels[char_idx]), train_labels[char_idx]))\n",
    "    plt.imshow(train_img[char_idx])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the value used to encode each digit is a vector of 10 elements, with a 1 in the index corresponding to that digit, e.g. 3 is encoded as \\[0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0\\]. This kind of encoding is known as [one-hot encoding](https://en.wikipedia.org/wiki/One-hot), and while not used in this example, is a case where `tf.SparseTensor` tensors are useful.\n",
    "\n",
    "### Creating the model\n",
    "Using the logistic regression model as a starting point, we only need to make a few changes:\n",
    "1. Update the shape of the input tensor, `x`, to `(None, 28, 28)`\n",
    "2. Normalise the input tensor so that the values range from 0 to 1 by dividing by 255\n",
    "3. Flatten the image using [`tf.reshape`](https://www.tensorflow.org/api_docs/python/tf/reshape) so that the input changes from `(None, 28, 28)` to `(None, 784)`\n",
    "4. Update the dimensions of the weight matrix to be `(28 * 28, 10)`, or equivalently `(784, 10)`\n",
    "5. Update the dimensions of the bias vector to be `(10,)`\n",
    "6. Swap the loss function for [`tf.losses.softmax_cross_entropy`](https://www.tensorflow.org/api_docs/python/tf/losses/softmax_cross_entropy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Start with a fresh graph and session\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = None # [Step 1]\n",
    "y = tf.placeholder(dtype=tf.float32, shape=(None, 10))\n",
    "\n",
    "x_norm = None # [Step 2]\n",
    "x_flat = None # [Step 3]\n",
    "weights = None # [Step 4]\n",
    "bias = None # [Step 5]\n",
    "\n",
    "logits = tf.matmul(x_flat, weights) + bias\n",
    "\n",
    "y_hat = tf.nn.softmax(logits)\n",
    "\n",
    "loss = None # [Step 6]\n",
    "\n",
    "total_examples = tf.shape(y)[0]\n",
    "accuracy = tf.reduce_sum(tf.cast(tf.equal(tf.argmax(y_hat, axis=1), tf.argmax(y, axis=1)), tf.int32)) / total_examples\n",
    "\n",
    "train_op = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)\n",
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This model has %i parameters.\" % count_parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**<br/>\n",
    "```\n",
    "This model has 7850 parameters.\n",
    "```\n",
    "### Run the optimisation\n",
    "Since our model is starting to get a bit large, a good strategy is to use \"mini-batch gradient descent\" for training. Whereas batch gradient descent (as used in the previous example) updates the weights of the model based on the average gradient of the entire dataset, mini-batch gradient descent involves partitioning the entire dataset into smaller chunks. By rapidly updating the weights after each small chunk is processed, we reduce the memory requirements and often speed up convergence.\n",
    "\n",
    "The size of each chunk is known as the \"batch size\", and one iteration over all batches is known as an \"epoch\". For the extreme case of a batch size of 1, this is known as stochastic gradient descent. There is no hard and fast rule for setting the batch size; the best value will quality of weight updates (i.e. reduce oscillation) while minimising calculation time. An excellent discussion on batch learning is available in [this paper](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise the variables, set up lists to keep record of the losses and accuracies for each iteration\n",
    "init_op.run()\n",
    "losses_train = []\n",
    "accuracies_train = []\n",
    "losses_test = []\n",
    "accuracies_test = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "for epoch in range(20):\n",
    "    shuffle_idx = np.random.permutation(len(train_img))\n",
    "    train_img_shuffled, train_labels_shuffled = train_img[shuffle_idx], train_labels[shuffle_idx]\n",
    "    \n",
    "    epoch_acc_train = []\n",
    "    epoch_loss_train = []\n",
    "    epoch_acc_test = []\n",
    "    epoch_loss_test = []\n",
    "    \n",
    "    for i in range(0, len(train_img), batch_size):\n",
    "        batch = {x: train_img_shuffled[i:i+batch_size], y: train_labels_shuffled[i:i+batch_size]}\n",
    "        train_op.run(batch)\n",
    "        epoch_loss_train.append(loss.eval(batch))\n",
    "        epoch_acc_train.append(accuracy.eval(batch))\n",
    "        \n",
    "    for i in range(0, len(test_img), batch_size):\n",
    "        batch = {x: test_img[i:i+batch_size], y: test_labels[i:i+batch_size]}\n",
    "        epoch_loss_test.append(loss.eval(batch))\n",
    "        epoch_acc_test.append(accuracy.eval(batch))\n",
    "        \n",
    "    losses_train.append(np.mean(epoch_loss_train))\n",
    "    accuracies_train.append(np.mean(epoch_acc_train))\n",
    "    losses_test.append(np.mean(epoch_loss_test))\n",
    "    accuracies_test.append(np.mean(epoch_acc_test))\n",
    "    \n",
    "plot_loss_accuracy(losses_train, accuracies_train, losses_test, accuracies_test)\n",
    "\n",
    "print(\"Final training set accuracy: %0.2f%%\" % (accuracies_train[-1] * 100))\n",
    "print(\"Final test set accuracy: %0.2f%%\" % (accuracies_test[-1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**<br/>\n",
    "```\n",
    "Final training set accuracy: 92.89%\n",
    "Final test set accuracy: 92.25%\n",
    "```\n",
    "### Inspecting the output\n",
    "We can plot the actual versus predicted labels in a joint frequency distribution heatmap known as a cross-tabulation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "true_labels = np.argmax(test_labels, axis=1)\n",
    "predicted_labels = [np.argmax(y_hat.eval({x: img.reshape(1,28,28)})) for img in test_img]\n",
    "plot_misclassifications(true_labels, predicted_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we know, most values are predicted correctly (as shown in the diagonal), but some are much more likely to be misclassified, e.g. 3 is most often misclassified as a 5.\n",
    "\n",
    "By running the code below, we can visualise the weights and see how the model has learnt to identify each character. Regions that are light in colour positively influence the likelihood of the input being a given character, whereas dark regions negatively influence the prediction. **Can you explain based on the weight visualisation how misclassifications are occurring?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(20,10))\n",
    "w = weights.eval().T\n",
    "for i in range(10):\n",
    "    ax = plt.subplot(2, 5, i+1)\n",
    "    plt.title(i)\n",
    "    sns.heatmap(\n",
    "        w[i].reshape(28, 28),\n",
    "        cmap=plt.cm.PuBu_r,\n",
    "        vmin=w.min(),\n",
    "        vmax=w.max(),\n",
    "        ax=ax,\n",
    "        square=True,\n",
    "        cbar=False,\n",
    "        xticklabels=False,\n",
    "        yticklabels=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generalising to a Multilayer Perceptron (MLP) model\n",
    "The softmax regression model can be considered as a single-layer [MLP neural network](https://en.wikipedia.org/wiki/Multilayer_perceptron); the 784 input values (28x28 pixels) are used to form ten weighted sums (including a bias term for each) representing each digit from zero to nine. The resulting values are then transformed by a nonlinearity (the softmax function, in this case) to form an \"activation.\" A general equation for a layer of a network of this type is therefore:\n",
    "\n",
    "$$\\textbf{A}^{<l>}=\\phi^{<l>}(\\textbf{W}^{<l-1>}\\textbf{A}^{<l-1>} + \\textbf{b}^{<l-1>})$$\n",
    "\n",
    "Where the superscript $<l>$ refers to the layer number (the input data takes the place of $\\textbf{A}^{<0>}$) and $\\phi$ refers to the nonlinear [_activation function_](https://en.wikipedia.org/wiki/Activation_function) of which there are many different types. An alternative representation of the above formula for a two layer network (usually input layers are not counted) is shown in the following figure:\n",
    "\n",
    "<img style=\"width:60%\" src=\"data:image/png;base64,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\"/>\n",
    "\n",
    "Notice that the bias node always represents the value 1, which is scaled by the weight matrix. This is equivalent to adding a bias vector that is calculated separately, as we did in the previous exercise.\n",
    "\n",
    "A key skill to develop is to keep track of the matrix shapes (dimensions) when constructing networks. Excluding the bias term, the weight matrix shape follows the following rule for each layer:\n",
    "\n",
    "<img 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\"/>\n",
    "\n",
    "**Remeber:** For a given layer in a network with $i$ input examples, each with $m$ input features and $n$ output activations, the weight matrix $\\textbf{W}$ should have a shape of `(m, n)`, the bias vector $\\textbf{b}$ should have a shape of `(n,)`, and the resulting activations $\\textbf{A}$ will have a shape of `(i, n)`.\n",
    "\n",
    "**Challenge:**<br/>\n",
    "TensorFlow provides a convenient abstraction that allows us to create weights and biases in one go. Modify the softmax regression example to use the [tf.layers.dense](https://www.tensorflow.org/api_docs/python/tf/layers/dense) function to take the 784 element image and produce a three-layer network with layers of size 100, 50 and 10. Use the [tf.tanh](https://www.tensorflow.org/api_docs/python/tf/tanh) activation function for the first and second layer, and set to `None` for the last layer (an activation of `None` is just a weighted sum with no transformation). Train this model and compare the accuracy with the softmax regression model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Start with a fresh graph and session\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = tf.placeholder(dtype=tf.float32, shape=(None, 28, 28))\n",
    "y = tf.placeholder(dtype=tf.float32, shape=(None, 10))\n",
    "\n",
    "x_norm = x / 255.0\n",
    "x_flat = tf.reshape(x_norm, (-1, 28 * 28))\n",
    "layer1 = None # Hint: tf.layers.dense(INPUT, NUMBER OF UNITS, activation=ACTIVATION FUNCTION)\n",
    "layer2 = None\n",
    "layer3 = None\n",
    "\n",
    "y_hat = None # Use tf.nn.softmax\n",
    "\n",
    "loss = None # Use tf.losses.softmax_cross_entropy\n",
    "\n",
    "accuracy = tf.reduce_sum(tf.cast(tf.equal(tf.argmax(y_hat, axis=1), tf.argmax(y, axis=1)), tf.int32)) / tf.shape(y)[0]\n",
    "\n",
    "train_op = tf.train.GradientDescentOptimizer(learning_rate=0.1).minimize(loss)\n",
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How does this models compare to the softmax regression in terms of the number of trainable parameters?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This model has %i parameters.\" % count_parameters())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**\n",
    "```\n",
    "This model has 84060 parameters.\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_op.run()\n",
    "losses_train = []\n",
    "accuracies_train = []\n",
    "losses_test = []\n",
    "accuracies_test = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "for epoch in range(20):\n",
    "    shuffle_idx = np.random.permutation(len(train_img))\n",
    "    train_img_shuffled, train_labels_shuffled = train_img[shuffle_idx], train_labels[shuffle_idx]\n",
    "    \n",
    "    epoch_acc_train = []\n",
    "    epoch_loss_train = []\n",
    "    epoch_acc_test = []\n",
    "    epoch_loss_test = []\n",
    "    \n",
    "    for i in range(0, len(train_img), batch_size):\n",
    "        batch = {x: train_img_shuffled[i:i+batch_size], y: train_labels_shuffled[i:i+batch_size]}\n",
    "        train_op.run(batch)\n",
    "        epoch_loss_train.append(loss.eval(batch))\n",
    "        epoch_acc_train.append(accuracy.eval(batch))\n",
    "        \n",
    "    for i in range(0, len(test_img), batch_size):\n",
    "        batch = {x: test_img[i:i+batch_size], y: test_labels[i:i+batch_size]}\n",
    "        epoch_loss_test.append(loss.eval(batch))\n",
    "        epoch_acc_test.append(accuracy.eval(batch))\n",
    "        \n",
    "    losses_train.append(np.mean(epoch_loss_train))\n",
    "    accuracies_train.append(np.mean(epoch_acc_train))\n",
    "    losses_test.append(np.mean(epoch_loss_test))\n",
    "    accuracies_test.append(np.mean(epoch_acc_test))\n",
    "    \n",
    "plot_loss_accuracy(losses_train, accuracies_train, losses_test, accuracies_test)\n",
    "\n",
    "print(\"Final training set accuracy: %0.2f%%\" % (accuracies_train[-1] * 100))\n",
    "print(\"Final test set accuracy: %0.2f%%\" % (accuracies_test[-1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:** (might vary slightly)<br/>\n",
    "```\n",
    "Final training set accuracy: 99.88%\n",
    "Final test set accuracy: 97.74%\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_labels = np.argmax(test_labels, axis=1)\n",
    "predicted_labels = [np.argmax(y_hat.eval({x: img.reshape(1,28,28)})) for img in test_img]\n",
    "plot_misclassifications(true_labels, predicted_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A note on activation functions\n",
    "Neural networks are said to be [universal approximators](https://en.wikipedia.org/wiki/Universal_approximation_theorem) that, with an appropriate number of parameters, can map their inputs to an approximation of any continuous function of a specified error threshold for any set of inputs within a finite range. This relies upon the selection of a nonlinear activation function, and there are several popular functions to choose from. Below are five popular nonlinear activation functions:\n",
    "\n",
    "<img 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\" />\n",
    "\n",
    "So far we have used the sigmoid and tanh functions exclusively, but there are some important considerations when choosing the most suitable function. Below is a short introduction to five of the more commonly used functions:\n",
    "\n",
    "#### Linear\n",
    "This function maps an input value to itself (i.e. $f(x)=x$); this function is often used in the final output of a regression network if the output should take on any real value from $-\\infty$ to $+\\infty$. A network entirely composed of linear activation functions simply reduces to a linear regression.\n",
    "\n",
    "#### Sigmoid\n",
    "This function maps any input value from $-\\infty$ to $+\\infty$ to a value between 0 and 1. This has historically been the activation function of choice for many networks, but is essential in the final layer if the network should output a probability. In the multiclass approaches, the equivalent softmax function is used. A key issue encountered with the sigmoid function is known as the [vanishing gradient problem](https://en.wikipedia.org/wiki/Vanishing_gradient_problem) where very large or very small inputs cause a very shallow gradient, which can take a long time to optimise with gradient descent methods.\n",
    "\n",
    "#### Hyperbolic tangent (tanh)\n",
    "Whereas the sigmoid function maps inputs to an output between 0 and 1, the tanh function maps inputs from -1 to 1. Tanh similarly has issues with vanishing gradients at the extremes, but is said to [converge faster than the sigmoid function](http://yann.lecun.com/exdb/publis/pdf/lecun-98b.pdf) with properly normalised inputs.\n",
    "\n",
    "#### Rectified Linear Unit (ReLU)\n",
    "The ReLU function maps the input to zero for all inputs less than zero, otherwise it is linear. This makes the derivatives easy to calculate; there is no vanishing gradient since it is constant for all values $> 0$. ReLU units can suffer from becomming \"dead\", which occurs when the ReLU is stuck with weights producing values $< 0$. Since the derivative is zero in this regime (i.e. changing its input does not change its output), no amount of optimisation steps will alter the weights. ReLU can be used as the final output layer if the network is expected to produce values from 0 to $+\\infty$.\n",
    "\n",
    "#### Leaky Rectified Linear Unit (ReLU)\n",
    "The Leaky ReLU is a modification of the standard ReLU that retains the linear mapping for all positive outputs, and remaps to a shallower linear relationship (e.g. $f(x) = 0.1x$) for values $< 0$. This function eliminates the \"dead neuron\" problem by ensuring the gradient can be evaluated for all values of $x$ but doesn't vanish.\n",
    "\n",
    "## Neural networks for image data\n",
    "\n",
    "### Convolution\n",
    "The convolution operation is a process where a matrix of weights (the _convolution kernel_ or _filter_) is scanned across an input matrix. At each step of the scan, the kernel is multiplied element-wise with the corresponding region of the input matrix and summed to produce a single value. The result of the convolution operation is called an \"activation map\".\n",
    "<img 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\" />\n",
    "\n",
    "In convolutional neural networks, the convolution layers are defined in terms of the number of filters, size of the filters, stride and padding. Here, stride refers to the gap between each step (the larger the stride, the smaller the activation map). Padding is usually set to \"valid\" or \"same\", where the latter forces the activation map size to equal the input size by padding the input with zeros.\n",
    "\n",
    "The activation map size in each dimension can be calculated as:\n",
    "\n",
    "$$L_\\text{output}=\\frac{L_\\text{input}-F+2P}{S}+1$$\n",
    "\n",
    "Where for the given dimension $L_\\text{input}$ and $L_\\text{output}$ are the size before and after convolution, $F$ is the filter size, $P$ is the padding and $S$ is the stride length. The activation map will have the same number of channels as there are filters.\n",
    "\n",
    "#### Playing with convolutions\n",
    "Below is an example of a hand-crafted convolution kernel applied to a few different inputs. **What is this convolution designed to do?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "kernel = np.array([\n",
    "    [0, 0, 0],\n",
    "    [-1, 2, -1],\n",
    "    [0, 0, 0]\n",
    "])\n",
    "\n",
    "v_edge = np.zeros((12, 12))\n",
    "v_edge[:, 6:] = 1\n",
    "h_edge = v_edge.T\n",
    "diag = np.tri(*v_edge.shape).T\n",
    "\n",
    "# The \"do_convolution\" function is not part of TensorFlow; we've just provided it for convenience\n",
    "v_conv = do_convolution(kernel, v_edge)\n",
    "h_conv = do_convolution(kernel, h_edge)\n",
    "diag_conv = do_convolution(kernel, diag)\n",
    "\n",
    "plt.figure(1, figsize=(15,10))\n",
    "ax = plt.subplot(2,3,1)\n",
    "ax.set_title(\"Vertical edge\")\n",
    "sns.heatmap(v_edge, annot=True, cmap=\"gray\", cbar=False, square=True, ax=ax)\n",
    "ax = plt.subplot(2,3,2)\n",
    "ax.set_title(\"Horizontal edge\")\n",
    "sns.heatmap(h_edge, annot=True, cmap=\"gray\", cbar=False, square=True, ax=ax)\n",
    "ax = plt.subplot(2,3,3)\n",
    "ax.set_title(\"Diagonal edge\")\n",
    "sns.heatmap(diag, annot=True, cmap=\"gray\", cbar=False, square=True, ax=ax)\n",
    "ax = plt.subplot(2,3,4)\n",
    "ax.set_title(\"Vertical edge (Conv)\")\n",
    "sns.heatmap(v_conv, annot=True, cmap=\"gray\", cbar=False, square=True, ax=ax)\n",
    "ax = plt.subplot(2,3,5)\n",
    "ax.set_title(\"Horizontal edge (Conv)\")\n",
    "sns.heatmap(h_conv, annot=True, cmap=\"gray\", cbar=False, square=True, ax=ax)\n",
    "ax = plt.subplot(2,3,6)\n",
    "ax.set_title(\"Diagonal edge (Conv)\")\n",
    "sns.heatmap(diag_conv, annot=True, cmap=\"gray\", cbar=False, square=True, ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lion = imread(\"http://farm1.static.flickr.com/132/417975520_5e8053ca8c.jpg\", as_grey=True)\n",
    "lion_emboss = do_convolution(kernel, lion)\n",
    "fig = plt.figure(1, figsize=(10,10))\n",
    "fig.add_subplot(1, 2, 1)\n",
    "plt.imshow(lion, cmap=\"gray\")\n",
    "fig.add_subplot(1, 2, 2)\n",
    "plt.imshow(lion_emboss, cmap=\"gray\", vmin=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While this filter was hand-crafted for a certain feature, convolutional neural networks can learn these features as needed. The ensemble of these learnt features is then used perform the desired task, e.g. handwriting recognition. Generally, as the layers get deeper, the number of filters increases while the activation map size reduces.\n",
    "\n",
    "**Why convolve?**\n",
    "1. Fewer parameters: rather than associating multiple weights per pixel, multiple filters consisting of only a few parameters are associated with each pixel.\n",
    "2. Limited possibility of over-fitting: Since the number of weights is reduced, the kernels are forced to respond to common features in a spatially invariant way\n",
    "\n",
    "See a simple CNN in action on the [Stanford CS231n website](http://cs231n.stanford.edu)!\n",
    "\n",
    "### Max pooling\n",
    "\n",
    "Max pooling is a common downsampling technique where only the largest value (most important) within a given region is carried forward. Along with reducing the size of the data, they introduce some degree of translational invariance; a small shift in the $x$ or $y$ direction will not affect the output of the pooling operation.\n",
    "\n",
    "<img src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAATIAAACyCAYAAADBAUOpAAAACXBIWXMAAAsSAAALEgHS3X78AAAbtUlEQVR4nO3df1BU97038PeBFbOFZVcTJAZYwMYiEpzJfdKGqH2exx9Xs5qqdIlCvM3gWEXwajQmAeI1mTSjgOZaGx9U1DxmsApaqTStQY01dyoiaWxTQWK0DewuC0TQdZdduiIs5/7B3S2aaOOy57N71s9rhj9ghn1/zznw5pzD2e9XEEURjDEmZwKANQB+7u+BUFAoRqC/v++ByaWmVCrhdDr9Eb1VFMV1/ghmgUEBAM8vzOw/fKhCQRksCAKozwZ/8mI2wsPDt+7aWXqQKjO/YP2U6qNVOZcvf/EiVSYACIJwXhTFpygzk5Mnlj/3o3llWzYXn6XKTE/XL6+u/jVVHAtQIf4eAGOMDRcXGWNM9rjIGGOyx0XGGJM9LjLGmOxxkTHGZI+LjDEme1xkjDHZ4yJjjMme10W2ddt2CIIAQRDws7c3+XJM/1RtXT2yshZDEASyzMrDVRAEAbV19ZJnNTQ2ebYvKWkCKg9XSZ4J+O+Y+mt7WfDwqsj2vPc+ynaW4kLDRbQYjKg/V4et27b7emx3tb+8HJFqNVme1WbDmxvWAwDM5jaSzPnpP8YNqxVvvb0RWYsyYDCaJM3z9zGl3l4WXLwqsqNVR5CTuxKTUlOQEK9Fuj4DZTtLfT22uyrbtQPTps8gy3v33VLMm7+ALG9SagoyF+qhUauRuVAPADC3tUua6c9j6o/tZcHFqyKrqTmGCROSPJ+rVCpcuXLZZ4MKJA2NTTjwy3KsX1/ol/wPj58EADyRkixpTqAcU6rtZcHF63tk3d12X47jrmrr6j33bSjvibkV5Ofjrbc3QkN4KetmMJqw9qXV2L13H0m+v48p9fay4OFVkel0c9He/o9T//b2duh0c302qKGmTk6DKIqeD0q1dfWoqTmGrEUZnl+4rEUZJDejrTYbCgsKMW/+Aixbmi15nr+PKfX2suDiVZHNnDUbZTtLYTCaYDCacOrkCcycNdvXY/O7b/qFqzh0xHMfRyoGowm5K/IQGxeHLZuLJc1y8+cx9cf2suDiVZG9vGYVFv/bi0hMiEdiQjzSnpmMl9es8vXY7ik2NoY0jzK3sKAQlZUH8c6WEs/ll9Rngf48pv7YXhZcvJ4V9o0Nr+ONDa/7ciz3xX22RIkqr6LiACoqDpBkDeWvY+qv7WXBg5/sZ4zJHhcZY0z2uMgYY7LHRcYYkz0uMsaY7HGRMcZkj4uMMSZ7XGSMMdnjImOMyZ4AYEta2jOvPBI1hjTYaGhBfEIiaWbfrVu4eLHhenh4xFWqzN7eXtWtW71RKlVkM1UmAHR2Xp04Zkz055SZvb293wXQOXLkSJppNADY7d1RHR0dvxJFcSVVJgs8CgCW0aMfHli/fj3p2dkzaT/A7j17KSNRUlyMp576wbmkCckfUWW2tDSn1J+r0y1I15dRZQLA5pJNv1iek0eaeajy4Cs/eDqtJjFxXBNVZu2ZP+g6Ojr6qPJYYFIAcIZHRAykPf198svMtKe/T5oXoVIhPDz8SknxxrNUmfkF63HhL59NpcwEgM0lm0CdWX20KicxcVwTZW56uj4FgIsqjwUmvkfGGJM9LjLGmOxxkTHGZI+LjDEme1xkjDHZ4yJjjMkeFxljTPa4yBhjsudVkRmMJuSsyPOseLN123Zfjyugsv25ve5cSpSZBqMJr75WAEEQUFtXT5LJgo9XRZYQr8W06TNww2rFmbPnsG7tarIfQn9k+2t797z3PnaX7ZQ8Z6jKw1WkmR+dOg2VKhIAYDa3keWy4OL1pWXmQj00arXnhy825jGfDSoQs6kza+vqsfynS7B77z5Jc4YyGE3IWpSB//z5u2SZy5Zm+3VZQRYcvF7XsvJwFbIWZQAYXH07IV7rs0EFYvbQTJ1uLjQatWRZVpsNS5dk41jNCXR3k00kgcKCQuzeuw8qlYosM9hZ4sYvAjCWOHa0O54wU/k/ueSn1aNb/7rN6yLLXKhH5kIRW7dtx5sb1uPZ2TOhUfv+l7u2rh4/nPKM53NRFCXPvldmQ2MTSktLkbsiT7JFZfPzC5GTuxJznp1FtuL21m3bEalWY9nSbF7l24eEUWF7BG14eEiEcoAqs//SNYUwOgyh0ZH9VJmua/ZQseOmoEiNIssEgP6z7QoA3heZ28trVmHd2tU4fuIUMhfqfTC0291rRXGpsu+VOSk1BdOmz0DWogxJiqy2rt5zj2rd2tWerwuCgBaDUbKzT3fW0PtjUmc+EMJChIgNb4Yonp5L9oSAfbkOiiefhDJ307B/v7+tm/uL0Xv8BCIPfEyWCQCWuPEAvLxHVltXj4bGwSmn9rz3PgDg2dkzfTOyAM2uPFwFg9EEq82GP53/FJmZL0iS4y5R90fFoSMABs8KpSwUf2Qy5iteFdmlS1/g+Qw9BEHA0aojOFZzQpLLykDK/vj075GYEI9RGg3Mra0oKi6SPBMAYmNjSHL8nenPXCZ/Xp0GLluajWVLs306kEDPLtu1A2W7dpDn3usyN5gyqfNYcOEn+xljssdFxhiTPS4yxpjscZExxmSPi4wxJntcZIwx2eMiYyzAfHj8JLKyFkMQBCQlTcCHx0+S5je3GDwPncslk4uMsQAzMXkCVq5aBVEUkZO7EnN1s8myr3Z2weHoIcsDgOsWy7AzucgYCzAJ8VpMnZwGq80Gh90OnW4uSa7d7sDVq52Ijh5DkgcAvbduoa2tA1GPPDys11EACG0zm0Pf2foL34zsW9JoNKDO7OrshMnpnKbXP082rYrNZovt6+sbq9c/v44qExjcv9SZISEhYz/9Y32GXv98GlVmS0tzKoD/osqjkrMiz/MG/gsNFyXPc7lcMLW2QquNhcvlkjzPzWRqRXT0GCgUocN6HQUAy4ULn3WHh4d/6ZuhfTtWq/VfTh6v+TNl5mef/XlCfHzCTbVaTTalSk+PY8Bms6Kvr48sEwCsVvpMm80KjUbjioigy+3u7h4AEHTvbyrbtQMlJUXIzy9E0aZNkk0Z5WY0tWL0qFHQqNW4bqGZxqyj4yuEhoYiekzUsDMVAK5qNJq/nTx5PMc3w/t2BEE4f/Lk8eWUmcnJE8unTZ9euWVz8VmqzPyC9VMsFkvUBx9U/5wqEwAEQVhMnZmUNOHJ//1/ph0tKd5Itn/T0/XLW1qa+6jyKGnUakmnjHKz2x1wOHrgcPSg69p1z9cbGpswKTVFslx31tCb/A2NTUhKGo+RYWH39Vqkcwcxxv65ysNVSHv6+9Bo1JJOGeWmUkXcVljXLRa0tXVIWmIAfJrJN/sZCzD+mjLKLWzE/Z0NBUImn5ExFmD8NWWU251naHLI5DMyxpjscZExxmSPi4wxJntcZIwx2eMiY4zJnldF1tDYdNu786kXdK2tq/fkS81gNCFnRR4EQUBW1mKyWQEot3EoymM7dN8KgoCt27ZLlsWCm9dnZPPTf4wbViveensjshZlwGA0+XJc97S/vByRRMvPFRUVIzIyEjesVsTGxaFo0yaSXMptvBPVsU2I12La9Bm4YbXizNlzWLd2NWrr6iXJYsHNqyKblJqCzIV6aNRqzwrf5rZ2nw7sXsp27cC06TMkzzEYTdhdthPzFyyARq3GT37yE1RWHiQpbaptvBP1sXVnmc1tAIDYmMcky2LBa9j3yNyTvj2RkjzswQQa9y/w1MmDkzm4H9ir/+RTv42JEsWxrTxcNXjZvigDOt1caDT+OQtl8jasJ/sNRhPWvrQau/fuk2y179q6evxwyjOezwNhIddgWhF76D24M2fPeUqb4tgCg2dkmQtFNDQ2obS0FLkr8iSf6YHSQL/4ne6MNQDWkOb21fwNzk2/Is0EAEvcePJMYBhFZrXZUFhQiHnzF0i68rc/Vr12c1/m1NbVY+rkNM/9m2C6/PmmfUt1bIealJpCMtMDtRCF8HfVkW0RiqdpJkcEAPtyHRRPPgllLs39XAC4ub8YvcdPQH3gY7JM4B/F6dWlpcFoQu6KPMTGxWHL5mKfDiyQJMRrkZn5An5TXQ2rzYbfVFcjM/MFJMRr/T00yVAf28rDVTAYTbDabCQzPbDg5FWRFRYUorLyIN7ZUuL51zn1IxhUl3dFxUUwt7b6ZSYCf1zCUh9bf8/0wIKDV5eWFRUH/H76T3XJmRCv9dv2+uOymnpb/T3TAwsO/GQ/Y0z2uMgYY7LHRcYYkz0uMsaY7HGRMcZkj4uMMSZ7vPgIYwHGarOhq+sanM6bCA0NQUzMY5K+TexOzS0GOBw9ki9A4svt5DMyxgKMUqnEo9HRmJSagtGjRsFkMpNlX+3sgsPRQ5Lly+3kImMswIwMC4NKFQGXy4V+lwthYSNIcu12B65e7UR09BiSPF9upwJAiNPpjMgvWD/Fd0P8dqgznU6nqqW5OSW/YD1ZZktLc0pPj0P16msF5PuXOtPpdEY2N39Jun87Ozu1AC6TBRJxX94BwPjHx0me53K5YGpthVYbC5fLJXme29Dt1GpjvX4dBYARvb29UdVHq3J8NLZvRalUgjrTbu+Oqq+v0zU2NkylyuzpcUQ6nc5HfvfbD0i3VaEYAerM/v6+R87V1eoaLvyFbP9aLJYoAM1UeVTGJSbA5XLBaGqFua0d4x//rqR5RlMrRo8aBY1ajesWi6RZQ7m3s7OzC21t7VBFRCA0NPS+X0cBoFej0bRcvvwF6Q+9IAjnL1/+4kXKzOTkieXP/Whe2ZbNxWepMl99rWDK7377Qc6lS5+TbqsgCOepM/2xf9PT9curq399kyqPUmhoKNTqSLS1dUiaY7c74HD0wOHoQde1656vNzQ2kaw4HhoairFjH0XXteuw2mx4ePTo+34N/q8lYwHmusWCiIgIKEJDYbc7oFQ+JGmeShVxW2Fdt1jQ1tYheYnZ7Q4oFKFQKpW42tkFAF7/15KLjLEAY7N1e87ClMqHoNXGkeaHjQgjyfm704lr167B5RpAWNgIaLWxXl1WAlxkjAWccYkJfs2/8wxNKtFjohA9Jsonr8WPXzDGZI+LjDEme1xkjDHZ4yJjjMkeFxljTPa4yBhjsjesImtobEJW1mIIgoCkpAmSLhtmMJqQsyLPs0TZ1m3bJcv6JpWHqyAIgmeRXil9ePzkbfv1w+MnJc8cqrau3pMvNYPRhFdfKyDbtyw4DfuMbH76j3HDasVbb29E1qIMGIwmX4zraxLitZg2fQZuWK04c/Yc1q1dTfaDb7XZ8OaGwTdCm81tkudNTJ6AlatWQRRF5OSuxFzdbMkzh9pfXo5IovmvPjp1GipVJACafcuC07CKbFJqCjIX6qFRq5G5UA8AMLe1+2Rg38Sd5f6Bj415TLKsod59txTz5i8gyQIGS3vq5DRYbTY47HbodHPJsoHBtSanTZ9BkrVsaTbe2PA6SRYLXj57st99+fNESrKvXvJrKg9XIWtRBgBAp5sLjUb6s4aGxiYc+GU5PvnjJ3hnS4nkeW45K/Kwu2wnAOBYzQmyXOZbAyGC0vHqekCxgSxTvNaL/ovt6P3VcbpMRx/gAqzT/4UsEwCEUYNvp7qvIht6z+TM2XOYOjkNwOB9jrUvrcbuvfsknZI3c6EemQtFNDQ2obS0FLkr8iRfFbsgPx9vvb2RdKphYPCsqKSkCP9/XznWvrQak//4iSRjuNsxZb4hhODWQ8sylYrvpZJl9mwtgSL5cYzU6ckyb338IW6d+xMiXqcrbADozlgD4D6LTBTFr33NarOhsKAQ8+YvwLKl2b4Y2z81KTUF06bPQNaiDEmLrLauHjU1x1BTcwxZiwa/NnhGeMRzKS0ljVqNl9eswrq1q3H8xClJMr/pmDLfEfpFl+J7qVA8TXd7IET9/xAS/ShpZv+VRggRn5NmDhossmHdIzMYTchdkYfYuDhs2Vzsk2HdS+XhKhiMJlhtNvzp/KfIzHxB0rypk9MgiqLnAwAqDklfYrV19WhobAIA7HnvfQDAs7NnSprJmJwNq8gKCwpRWXkQ72wp8TwWIeUjGB+f/j0SE+IxSqOBubUVRcVFkmXdTWxsjOQZly59gecz9BAEAUerjuBYzQnyS1uK7QyETBYchnWzv6LigOT3qIYq27UDZbt2kOXdieoybNnSbLLL9Ltxn41S4UtcNhz8ZD9jTPa4yBhjssdFxhiTPS4yxpjscZExxmSPFx9hLMBYbTZ0dV2D03kToaEhiIl5jOTxG7vdga+uXoXTeZNk8RGn0wlzW7tnOx99NNqrNS0BLjLGAo5SqcSj0dFQqSLQ0fEVTCYzNKnSF1nXtWteL8fmrdGjB1c3t9psaGvrQEREBEaG3f9ydHxpyViAGRkWBpUqAi6XC/0uF8LCRpDkjktMgFodSZIFDBb2w6NHIzQ01HMmdqv3llevxWdkjAWg5hYDHI4eAMD4x8f5eTTSs9psAIDvfEfp9WvoAfQAEIP9Q6lUPlC51B/h4eH+yi4b+p7YQPqw/K8n7H31vxO90d/fL37Z3CJe+evf7uv7upc9K/59R6FXmdeuXxcvNFy87+9zlheJ1hf+r1eZN3t7xYtNn4tfXe287++9Hvu4KIoiFACcMTExl8xmcw4ICYJwXhTFpygzk5ImlC9I15eVFG88S5WZX7B+SvXRqpzLl794kSoTeHD2b3q6fnl19a8dVHmUQkNDoVZHoq2tw99DkYzL5YLJ1Irw8PBhrTrO98gYCzDXLRb03roFl8sFu90BpfIhfw9JEr23bqG5xYARI0YgIV47rNfie2SMBRibrdtzFqZUPgStNo4sO2zE/f/H0FsmUyuczptwOm96pq2KiRnr1SMYXGSMBZhxiQl+y1apIkieIQOA8Y9/12evxZeWjDHZ4yJjjMkeFxljTPa4yBhjssdFxhiTPS4yxpjseV1kW7dt96yc9LO3N/lyTHdVebgKc+Y8B0EQMGfOc55nT6RkMJrw6msFEAQBtXX1kue5uTOzshaTbOc3yVmRd9sCvlLwxzFlwcerItvz3vso21mKCw0X0WIwov5cHbZu2+7rsX1NZKQKr//Hf+CG1Yq0Zybj+QzpF8n96NRpqFSDMwKYzW2S5wGDfySaLg7u2+SJKSjIzyfJHWrPe+9jd9lOyXP8cUxZ8PGqyI5WHUFO7kpMSk1BQrwW6foMlO0s9fXYvmbOs7MwdXIaNGo1vpeUhCtXLkueuWxpNt7Y8LrkOUOdOnkC6foMJMRrsWDBfNTUHIPBaCLLr62rx/KfLsHuvfskz/LHMWXBx6sn+2tqjuHfV6/2fK5Sqch/AK9cviz5SuP+UlNzDC9mLwEAz1PW9Z98Ouz3o30bVpsNS5dk41jNCXR32yXPGypYj2lvTRX6/kz2Pnq4TF0APoNzJ90f4P7PmzDQZiXNHMrrtyhR/5APVVtXjzffWI8zZ8/5bQzUpFqFe+g9sDNnz2F/eTlycldizrOzJF01/k5Be0wHxKP9f7k0CbhEFtkbNkIj9PSGjDzxsYUqs0+Esj9arQk58THpVB0hyZH9gJdFptPNRXt7u+fz9vZ26HRzfTS0e2tobMLSJdmoOHQEUyenkWRS0+nmwm4f/EPhvvkdG/OYJFnikBW+a+vqPffF1q39xxm3IAhoMRglOyMM5mM66s8XSadvAgDpJ8UOPF7dI5s5azbKdpbCYDTBYDTh1MkTmDlrtq/H9jW1dfV4PkOPV14rQObC4L0pPHPWbJw+9REMRhOqq3+DzMwXSC4rp05Ou21SwIpDRwAMlp1U+Q/KMWXS8qrIXl6zCov/7UUkJsQjMSEeac9MxstrVvl6bF/zwynP4MqVy1j+0yWeRz8ob4JLdXl3p5fXrEJsXBwSE+Jx6fMmFBUXkeTeiWJ7/X1MWXDw+h7ZGxteJ/9v3tDLIGrU2Vs2F2PL5mLSzDu5z9Ck5M9jyoIHP9nPGJM9LjLGmOxxkTHGZI+LjDEme1xkjDHZ4yJjjMkeFxljTPa4yBhjssdFxhiTPQFAukaj2TtmTPRXlMGdnVcnjhkT/TllZk+PY1xISGhXeHg42dQdPT09qoEBV1R4eEQzVSYAdHR0TBw7dizp/u3r6xs3MDDQNXLkSLL9a7d3R3V0dPxKFMWVVJks8CgA9IWEhHQtSNeXUQZvLtn0i+U5eaSZFQd/+cpTT32/5ruPP042n3Lzl1+mfPrpH3X+2L+5ef9Omnmo8uArP3g6rSYxcRzZ/q098wddR0dHH1UeC0wCgDkxMTE/M5vNOaTBgnBeFMWnKDOTkyeWP/ejeWVbNheTzXL36msFU3732w9yLl36nHQ6F3/s36SkCeUL0vVlJcUbyfZverp+eXX1rx2iKK6jymSBh++RMcZkj4uMMSZ7XGSMMdnjImOMyR4XGWNM9rjIGGOyx0XGGJM9LjLGmOz5rMhyVuTdttirrxmMJk+GIAjYum27ZFl35r76WgEEQUBtXT1JJjC4TFpW1mJJ9+k3aWhs8uQmJU2QfJFeg9HkyctZkQerzSZpHgtOPimyPe+971nYVSoJ8VpMmz4DN6xWnDl7DuvWriYplo9OnYZKFQkAMJvbJM9z219ejki1f5ZanZ/+Y9ywWvHW2xuRtShD0uXZCgsKERsXhxtWK7ptNrz7bqlkWSx4DbvIauvqsfynS7B77z5fjOeeMhfqoVGrPYUi1erbQy1bmk2+7B0AlO3agWnTZ5DnTkpN8exn94K55rb2f/Jd3jEYTaisPIj5CxZAo1Zj+sx/xYFflkuSxYKb1+taAoDVZsPSJdk4VnMC3d3ST3hQebgKWYsyAAA63VxoNA/i4vB0Pjx+EgDwREqyJK/vLsipk9MAACqVCleuXJYkiwW3+zojc9+fct8vys8vRE7uSsx5dpZU47tN5kI9RFHEhYaLiNNqkbsijyQ3mN15TN0MRhPWvrQau/fug8ZPl7iMfVv3VWSiKHo+AGB32U6sW7sagiB4zpQolryflJqCadNnoLLyoKQ5D4Khx9R9ZmS12VBYUIh58xdg2dJsybLdtwYaGgdn/bHb7dDp5kqWx4KX1/fIpk5Ou+2XoOLQEQCDvxgJ8VqfDXCoysNVMBhNsNps+NP5T5GZ+YIkOQ8yg9GE3BV5iI2Lw5bNxZJmJcRrkZn5Avbv3w+rzYbTpz7CzFmzJc1kwclnj1/Exsb46qXu6uPTv0diQjxGaTQwt7aiqLhI8syhKLbRn3nA4H8RKysP4p0tJZ5LTikfwSgqLoK5tRWjNBrExsXh5TWrJMtiwWtYN/uHcp+hSals1w6U7dohacbdSL1t34Rin96pouIAKioOkOUlxGvJM1nw4Sf7GWOyx0XGGJM9LjLGmOxxkTHGZI+LjDEme1xkjDHZ4yJjjMkeFxljTPa4yBhjsqcAAKvV+lh6un45dTh1psViiao98wddero+hSrzq686tBaLJepB2L92ezf5/r14sfEpAP9FlccCkwBg/COPROWNGKGgnVMZGA/gr5SBISEhsQMDA9cBOAljlSEhIQ8PDAyYCTMBP+xfhWJEbH9/nwXA3ylzOzo6Toqi+CFlJgss/w3AEmKRgo5koQAAAABJRU5ErkJggg==\"/>\n",
    "\n",
    "Pooling layers are defined in terms of their stride and size and have no trainable parameters. They preserve the number of channels in the activation map.\n",
    "\n",
    "Recently, the necessity of pooling layers [has been questioned](https://arxiv.org/pdf/1412.6806.pdf) since the same downsampling effect can be achieved with an increased convolution size. Pooling [has also been criticised](https://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton/clyj4jv/) since it can lose valuable spatial information. Nevertheless, it remains an extremely common approach in image processing today.\n",
    "\n",
    "### Dropout\n",
    "Dropout is a regularisation technique to help reduce overfitting during training. When dropout is activated for a given layer, a random selection of activations are set to zero at each training step, with the remaining activations upscaled to preserve the net activation.\n",
    "\n",
    "### Batch normalisation\n",
    "Batch normalisation is used to accelerate training by normalising the activations of layers of the network in a similar way that one would when preparing the input data. The normalisation transformation (i.e. mean centring, unit variance) is learnt during training from each batch (hence the name \"batch normalisation\"). For more information, read the [original paper here](https://arxiv.org/pdf/1502.03167.pdf).\n",
    "\n",
    "## The LeNet architecture\n",
    "The LeNet architecture was one of the first applications of convolutional neural networks back in 1998 and was applied to handwriting recognition using the MNIST dataset. Take a look at the [original paper](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf); you can see that it consists of successive 5x5 convolution and pooling operations, followed by some fully connected layers just like in the softmax regression exercise.\n",
    "\n",
    "<img 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\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**We will implement a slightly modified version of LeNet.**\n",
    "\n",
    "The main differences compared to the original paper include utilising ReLU activations for all layers instead of Gaussian activations, and rather than selectively connecting activation maps between S3 and C3, we'll introduce dropout layers in the hope of achieving the an equivalent effect.\n",
    "\n",
    "### Data preparation\n",
    "The LeNet architecture specifies an input size of 32x32, but the MNIST is only 28x28 (**why?**). So we need to pad our dataset with zeros. The data also needs to be centred about zero and rescaled to unit variance.\n",
    "\n",
    "The following cells perform the padding, and also reshape the data to include a channel dimension, resulting in an image shape of 32x32x1. If this were colour data, the shape would be 32x32x3 where the last axis represents the red, green and blue colour values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_and_rescale(data):\n",
    "    train_padded = np.pad(train_img, ((0, 0), (2, 2), (2, 2)), mode='constant')\n",
    "    padded_data = np.pad(data, ((0, 0), (2, 2), (2, 2)), mode='constant')\n",
    "    return (padded_data - train_padded.mean()) / train_padded.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_img, train_labels, test_img, test_labels = load_mnist()\n",
    "\n",
    "# These lines add the extra channel dimension\n",
    "train_img_scaled = np.expand_dims(pad_and_rescale(train_img), axis=-1)\n",
    "test_img_scaled = np.expand_dims(pad_and_rescale(test_img), axis=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.reset_default_graph() # Start with a fresh graph and session\n",
    "sess = tf.InteractiveSession()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Challenge:**<br/>\n",
    "Implement the LeNet architecture as:\n",
    "1. Placeholder tensors `x` and `y` for the input data of type `tf.float32`. **Remember that the input `x` shape has changed to `(None, 32, 32, 1)`**\n",
    "2. A placeholder tensor `train` of type `tf.bool` and `shape=[]` (this is used later to switch dropout on and off)\n",
    "1. `conv_1` is 6 filters with a 5x5 kernel size and stride of 1, ReLU activation (use [`tf.layers.conv2d`](https://www.tensorflow.org/api_docs/python/tf/layers/conv2d) and [`tf.nn.relu`](https://www.tensorflow.org/api_docs/python/tf/nn/relu))\n",
    "2. `pool_1` is max pooling layer with a 2x2 window with a stride of 2 (use [`tf.layers.max_pooling2d`](https://www.tensorflow.org/api_docs/python/tf/layers/max_pooling2d))\n",
    "3. `conv_2` is 16 filters with a 5x5 kernel size and stride of 1, ReLU activation\n",
    "4. `pool_2` is max pooling layer with a 2x2 window with a stride of 2\n",
    "5. `conv_3` is 125 filters with a 1x1 kernel size and stride of 1, ReLU activation\n",
    "6. `flat` is the flattened output of conv_3 (use [`tf.layers.flatten`](https://www.tensorflow.org/api_docs/python/tf/layers/flatten))\n",
    "7. `dropout_1` is a dropout layer with a dropout rate of 0.5 and should be connected to `flat` (use [`tf.layers.dropout`](https://www.tensorflow.org/api_docs/python/tf/layers/dropout) and be sure to set the `training` argument)\n",
    "8. `fc_1` is a fully-connected layer connected to `dropout_1` with 84 units and ReLU activation (use [`tf.layers.dense`](https://www.tensorflow.org/api_docs/python/tf/layers/dense))\n",
    "9. `dropout_2` is a dropout layer with a dropout rate of 0.5 and should be connected to `fc_1`\n",
    "10. `fc_2` is a fully-connected layer connected to `dropout_2` with 10 units and linear activation (set `activation` to `None`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = None # [Step 1]\n",
    "y = None # [Step 1]\n",
    "train = None # [Step 2]\n",
    "\n",
    "# Conv (5*5*1*6 + 6 parameters, output volume = 28x28x6)\n",
    "conv_1 = None # [Step 3] Hint: tf.layers.conv2d(INPUT, NUMNER OF FILTERS, SIZE OF FILTER, activation=ACTIVATION FUNCTION)\n",
    "\n",
    "# Pool (0 parameters, output volume = 14x14x6)\n",
    "pool_1 = None # [Step 4] Hint: tf.layers.max_pooling2d(INPUT, WINDOW SIZE, strides=STRIDE LENGTH)\n",
    "\n",
    "# Conv (5*5*6*16 + 16 parameters, output_volume = 10x10x16)\n",
    "conv_2 = None # [Step 5]\n",
    "\n",
    "# Pool (0 parameters, output_volume = 5x5x16)\n",
    "pool_2 = None # [Step 6]\n",
    "\n",
    "# Conv (1*1*16*120 + 120 parameters, output_volume = 5x5x120)\n",
    "conv_3 = None # [Step 7]\n",
    "\n",
    "# Flatten (0 parameters, output_volume = 3000)\n",
    "flat = None # [Step 8]\n",
    "\n",
    "# Dropout (0 parameters, output_volume = 3000)\n",
    "dropout_1 = None # [Step 9] Hint: tf.layers.dropout(INPUT, rate=DROPOUT RATE, training=TRAINING MODE?)\n",
    "\n",
    "# FC (3000*84 + 84, output_volume = 84)\n",
    "fc_1 = None # [Step 10]\n",
    "\n",
    "# Dropout (0 parameters, output_volume = 84)\n",
    "dropout_2 = None # [Step 11]\n",
    "\n",
    "# FC (84*10 + 10 parameters, output_volume = 10)\n",
    "fc_2 = None # [Step 12]\n",
    "\n",
    "y_hat = tf.nn.softmax(fc_2)\n",
    "\n",
    "loss = tf.losses.softmax_cross_entropy(y, fc_2)\n",
    "\n",
    "accuracy = tf.reduce_sum(tf.cast(tf.equal(tf.argmax(y_hat, axis=1), tf.argmax(y, axis=1)), tf.int32)) / tf.shape(y)[0]\n",
    "\n",
    "train_op = tf.train.AdamOptimizer(learning_rate=0.005).minimize(loss)\n",
    "init_op = tf.global_variables_initializer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"This model has %i parameters.\" % count_parameters())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_op.run()\n",
    "losses_train = []\n",
    "accuracies_train = []\n",
    "losses_test = []\n",
    "accuracies_test = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Slow code warning:** This training can take a few minutes to complete! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "batch_size = 100\n",
    "for epoch in range(6):\n",
    "    print(\"Epoch\", epoch, \": \", end='')\n",
    "    shuffle_idx = np.random.permutation(len(train_img_scaled))\n",
    "    train_img_scaled_shuffled, train_labels_shuffled = train_img_scaled[shuffle_idx], train_labels[shuffle_idx]\n",
    "    \n",
    "    epoch_acc_train = []\n",
    "    epoch_loss_train = []\n",
    "    epoch_acc_test = []\n",
    "    epoch_loss_test = []\n",
    "    \n",
    "    for i in range(0, len(train_img_scaled), batch_size):\n",
    "        batch = {x: train_img_scaled_shuffled[i:i+batch_size], y: train_labels_shuffled[i:i+batch_size], train: True}\n",
    "        train_op.run(batch)\n",
    "        epoch_loss_train.append(loss.eval(batch))\n",
    "        epoch_acc_train.append(accuracy.eval(batch))\n",
    "        \n",
    "    for i in range(0, len(test_img_scaled), batch_size):\n",
    "        batch = {x: test_img_scaled[i:i+batch_size], y: test_labels[i:i+batch_size], train: False}\n",
    "        epoch_loss_test.append(loss.eval(batch))\n",
    "        epoch_acc_test.append(accuracy.eval(batch))\n",
    "    \n",
    "    print(\"loss (train) = %0.4f, acc (train) = %0.2f%%, loss (test) = %0.4f, acc (test) = %0.2f%%\" % (\n",
    "        np.mean(epoch_loss_train), np.mean(epoch_acc_train) * 100,\n",
    "        np.mean(epoch_loss_test), np.mean(epoch_acc_test) * 100))\n",
    "    losses_train.append(np.mean(epoch_loss_train))\n",
    "    accuracies_train.append(np.mean(epoch_acc_train))\n",
    "    losses_test.append(np.mean(epoch_loss_test))\n",
    "    accuracies_test.append(np.mean(epoch_acc_test))\n",
    "    \n",
    "plot_loss_accuracy(losses_train, accuracies_train, losses_test, accuracies_test)\n",
    "\n",
    "print(\"Final training set accuracy: %0.2f%%\" % (accuracies_train[-1] * 100))\n",
    "print(\"Final test set accuracy: %0.2f%%\" % (accuracies_test[-1] * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:** (might vary slightly)<br/>\n",
    "```\n",
    "Epoch 0 : loss (train) = 0.2776, acc (train) = 91.11%, loss (test) = 0.0629, acc (test) = 98.01%\n",
    "Epoch 1 : loss (train) = 0.1505, acc (train) = 95.54%, loss (test) = 0.0468, acc (test) = 98.58%\n",
    "Epoch 2 : loss (train) = 0.1346, acc (train) = 95.89%, loss (test) = 0.0491, acc (test) = 98.51%\n",
    "Epoch 3 : loss (train) = 0.1242, acc (train) = 96.19%, loss (test) = 0.0584, acc (test) = 98.31%\n",
    "Epoch 4 : loss (train) = 0.1238, acc (train) = 96.38%, loss (test) = 0.0442, acc (test) = 98.71%\n",
    "Epoch 5 : loss (train) = 0.1160, acc (train) = 96.67%, loss (test) = 0.0487, acc (test) = 98.44%\n",
    "Final training set accuracy: 96.67%\n",
    "Final test set accuracy: 98.44%\n",
    "```\n",
    "### Inspect the output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "true_labels = np.argmax(test_labels, axis=1)\n",
    "predicted_labels = [np.argmax(y_hat.eval({x: img.reshape(1,32,32,1), train: False})) for img in test_img_scaled]\n",
    "plot_misclassifications(true_labels, predicted_labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at the learnt filters. Re-run these cells altering `img_idx` to see how different examples are viewed by this network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_idx = 50\n",
    "test_img = test_img_scaled[img_idx].reshape(1,32,32,1)\n",
    "layer1 = tf.get_default_graph().get_operation_by_name(\"conv2d/Relu\").outputs[0].eval({x: test_img})\n",
    "layer2 = tf.get_default_graph().get_operation_by_name(\"conv2d_1/Relu\").outputs[0].eval({x: test_img})\n",
    "layer3 = tf.get_default_graph().get_operation_by_name(\"conv2d_2/Relu\").outputs[0].eval({x: test_img})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are the first six convolution kernels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer1_kernel = tf.get_default_graph().get_operation_by_name(\"conv2d/kernel\").outputs[0].eval()\n",
    "plt.figure(1, figsize=(25,10))\n",
    "for i in range(layer1_kernel.shape[-1]):\n",
    "    feature_map = layer1_kernel[:,:,0,i]\n",
    "    ax = plt.subplot(2,3,i+1)\n",
    "    sns.heatmap(feature_map.squeeze(), annot=True, cmap=\"gray\", cbar=False, square=True, vmin=layer1_kernel.min(),\n",
    "                vmax=layer1_kernel.max(), ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And these are the activation maps they produce after being passed through the ReLU activation function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(25,10))\n",
    "ax = plt.subplot(3,3,2)\n",
    "plt.imshow(test_img.squeeze())\n",
    "for i in range(layer1.shape[-1]):\n",
    "    feature_map = layer1[:,:,:,i]\n",
    "    ax = plt.subplot(3,3,i+4)\n",
    "    plt.imshow(feature_map.squeeze(), cmap=\"gray\", vmin=layer1.min(), vmax=layer1.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(25,10))\n",
    "for i in range(layer2.shape[-1]):\n",
    "    feature_map = layer2[:,:,:,i]\n",
    "    ax = plt.subplot(4,4,i+1)\n",
    "    plt.imshow(feature_map.squeeze(), cmap=\"gray\", vmin=layer2.min(), vmax=layer2.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(1, figsize=(25,10))\n",
    "for i in range(layer3.shape[-1]):\n",
    "    feature_map = layer3[:,:,:,i]\n",
    "    ax = plt.subplot(12,10,i+1)\n",
    "    plt.imshow(feature_map.squeeze(), cmap=\"gray\", vmin=layer3.min(), vmax=layer3.max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Introducing higher-level TensorFlow APIs\n",
    "#### The Dataset API\n",
    "So far we have been hand-crafting out data into numpy arrays and batches, and feeding them into our model using the `feed_dict` parameter of the `sess.run()` call or on our tensors directly using `eval()` or `run()`. This has widely been recognised as cumbersome, and in response, TensorFlow has released a high-level [Dataset API](https://www.tensorflow.org/get_started/datasets_quickstart).\n",
    "\n",
    "The primary goal of the Dataset API is to enable developers to specify a data loading and transformation pipeline that automatically handles batching and shuffling and is compatible with a wide variety of data sources. While beyond the scope of this class, the API also handles more advanced concepts such as \"[sharding](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shard)\", where subsets of data are loaded on distributed hardware for truly large-scale calculations.\n",
    "\n",
    "One of the main objects that you will interact with in the Dataset API is called an [_iterator_](https://www.tensorflow.org/api_docs/python/tf/data/Iterator). The job of an iterator is to provide one or more data elements (e.g. training bacthes) as required, while keeping track of how far through the dataset we have traversed. In practise, you will periodically evaluate the \"get next\" tensors relating to the dataset, which will return different values each time. Let's look at a real example using the wine dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, load the data into some numpy arrays. 25% of the data is held out for testing\n",
    "x_data_train, y_data_train, x_data_test, y_data_test = load_wine_data(test_frac=0.25)\n",
    "print(\"This is the first three couple of rows of X:\\n\", x_data_train[:3])\n",
    "print(\"This is the first three couple of rows of Y:\\n\", y_data_train[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clear our current graph\n",
    "tf.reset_default_graph()\n",
    "sess = tf.InteractiveSession()\n",
    "\n",
    "# Create the dataset - this one is using our numpy arrays as a data source\n",
    "# `from_tensor_slices` treats each item in the first axis as a training example\n",
    "example_dataset = tf.data.Dataset.from_tensor_slices({\"x\": x_data_train, \"y\": y_data_train})\n",
    "\n",
    "# Instantiate the iterator - its job is to keep track of where we are in the dataset\n",
    "example_dataset_iterator = example_dataset.make_one_shot_iterator()\n",
    "\n",
    "# Create our \"get next\" tensors\n",
    "example_dataset_input_tensors = example_dataset_iterator.get_next()\n",
    "\n",
    "print(example_dataset_input_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice that the output of `get_next()` is a dictionary containing the same keys (`x` and `y`) that we provided when we created the dataset from the numpy arrays. Take a look at what happens when we evaluate these tensors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sess.run(example_dataset_input_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rerun the above cell a few times!\n",
    "\n",
    "Now, here's where the Dataset API gets powerful. Previously we had a lot of extra code to shuffle and batch our data. We can achieve the same result in just two additional lines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_dataset_shuffled = example_dataset.shuffle(buffer_size=1000)\n",
    "example_dataset_batched = example_dataset_shuffled.batch(3)\n",
    "\n",
    "# This is the same stuff as in the above example:\n",
    "# Create the iterator\n",
    "example_dataset_iterator = example_dataset_batched.make_one_shot_iterator()\n",
    "# Create our \"get next\" tensors\n",
    "example_dataset_input_tensors = example_dataset_iterator.get_next()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A subtlety regarding the shuffle operation: Since datasets can be arbitratily large, it's not a given that we can shuffle all elements at once. The shuffle `buffer_size` acts as a known-size pool of elements where shuffling takes place. For each element that taken out of the shuffled buffer, a new one from the remainder of the dataset is moved in and becomes \"shufflable.\"\n",
    "\n",
    "Run the cell below a few times to see what the new iterator is doing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "sess.run(example_dataset_input_tensors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each time the new iterator is run, we get a shuffled batch of three elements per batch!\n",
    "\n",
    "#### The Estimator API\n",
    "\n",
    "Whereas TensorFlow generally can be considered just a numerical computation framework, the [Estimator API](https://www.tensorflow.org/programmers_guide/estimators) aims to specifically cater for the machine learning lifecycle of train, evaluate, predict and export.\n",
    "\n",
    "To create an Estimator, the minimum requirement is a model function:\n",
    "```\n",
    "def model_fn(features, labels, mode):\n",
    "    \n",
    "    # define the model here, just as we always have\n",
    "    # * `features` is just like our `x` placeholder\n",
    "    # * `labels` is just like our `y` placeholder\n",
    "    # * `predictions` is just like out `y_hat` tensor\n",
    "    # * `mode` allows us to tweak our model depending on whether we're training, evaluating or predicting\n",
    "    \n",
    "    return tf.estimator.EstimatorSpec(\n",
    "        mode=mode,\n",
    "        predictions=predictions,\n",
    "        loss=loss,\n",
    "        train_op=train_op)\n",
    "```\n",
    "\n",
    "To actually _use_ the estimator, we need an input function. All the input function needs to do is return a dataset tuple of `(features, labels)`. Easy!\n",
    "\n",
    "Here is a full example of the wine dataset exercise using the Estimator and Dataset API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fn(features, labels, mode):\n",
    "    weights = tf.get_variable(\"weights\", dtype=tf.float32, initializer=tf.zeros((11, 1)))\n",
    "    bias = tf.get_variable(\"bias\", dtype=tf.float32, initializer=tf.zeros((1,)))\n",
    "    logits = tf.matmul(features, weights) + bias\n",
    "    y_hat = tf.sigmoid(logits)\n",
    "    loss = tf.losses.sigmoid_cross_entropy(labels, logits)\n",
    "    metrics = {\n",
    "        \"accuracy\": tf.metrics.accuracy(labels > 0.5, y_hat > 0.5)\n",
    "    }\n",
    "    \n",
    "    # This global_step parameter accepts a tensor that is incremented with each optimisation step\n",
    "    # It helps keep track of how many training steps have been completed\n",
    "    train_op = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(\n",
    "        loss, global_step=tf.train.get_or_create_global_step())\n",
    "    \n",
    "    return tf.estimator.EstimatorSpec(\n",
    "        mode=mode,\n",
    "        predictions=y_hat,\n",
    "        loss=loss,\n",
    "        train_op=train_op,\n",
    "        eval_metric_ops=metrics)\n",
    "\n",
    "def train_input_fn():\n",
    "    # Note that we're using `from_tensors` and not `from_tensor_slices`\n",
    "    # In this case, `from_tensors` treats the entire input as one big batch training example.\n",
    "    wine_dataset = tf.data.Dataset.from_tensors((x_data_train, y_data_train))\n",
    "    wine_dataset = wine_dataset.repeat(200)\n",
    "    return wine_dataset.make_one_shot_iterator().get_next()\n",
    "\n",
    "def test_input_fn():\n",
    "    wine_dataset = tf.data.Dataset.from_tensors((x_data_test, y_data_test))\n",
    "    return wine_dataset.make_one_shot_iterator().get_next()\n",
    "\n",
    "wine_model = tf.estimator.Estimator(model_fn)\n",
    "\n",
    "print()\n",
    "print(\"TRAINING...\")\n",
    "wine_model.train(train_input_fn)\n",
    "print()\n",
    "print(\"EVALUATING...\")\n",
    "wine_model.evaluate(test_input_fn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**\n",
    "(final line)\n",
    "```\n",
    "{'accuracy': 0.93349755, 'global_step': 200, 'loss': 0.2397592}\n",
    "```\n",
    "\n",
    "**Advanced challenge:**<br/>\n",
    "Adapt the LeNet code to use the Dataset and Estimator APIs.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fn(features, labels, mode):\n",
    "    x = features\n",
    "    y = labels\n",
    "    \n",
    "    train = None # Check that `mode` equals tf.estimator.ModeKeys.TRAIN\n",
    "\n",
    "    ### Create your LeNet model here ###\n",
    "    # Be sure to include all layers, the loss function, and y_hat\n",
    "    \n",
    "    train_op = tf.train.AdamOptimizer(learning_rate=0.005).minimize(\n",
    "        loss, global_step=tf.train.get_or_create_global_step())\n",
    "    \n",
    "    metrics = {\n",
    "        \"accuracy\": tf.metrics.accuracy(tf.argmax(y, axis=1), tf.argmax(y_hat, axis=1))\n",
    "    }\n",
    "    \n",
    "    return tf.estimator.EstimatorSpec(\n",
    "        mode=mode,\n",
    "        predictions=y_hat,\n",
    "        loss=loss,\n",
    "        train_op=train_op,\n",
    "        eval_metric_ops=metrics)\n",
    "\n",
    "def train_input_fn():\n",
    "    nist_dataset = tf.data.Dataset.from_tensor_slices((train_img_scaled, train_labels))\n",
    "    nist_dataset = None # Repeat for 6 epochs. Hint: nist_dataset.repeat(NUMBER OF EPOCHS)\n",
    "    nist_dataset = None # Shuffle each epoch. Hint: nist_dataset.shuffle(BUFFER SIZE)\n",
    "    nist_dataset = None # Batch the data. Hint: nist_dataset.batch(BATCH SIZE)\n",
    "    return nist_dataset.make_one_shot_iterator().get_next()\n",
    "\n",
    "def test_input_fn():\n",
    "    nist_dataset = tf.data.Dataset.from_tensor_slices((test_img_scaled, test_labels))\n",
    "    nist_dataset = None # Batch the data\n",
    "    return nist_dataset.make_one_shot_iterator().get_next()\n",
    "\n",
    "\n",
    "lenet_model = None # Create the estimator using tf.estimator.Estimator()\n",
    "\n",
    "print()\n",
    "print(\"TRAINING...\")\n",
    "# Train the model using lenet_model and the train_input_fn\n",
    "print()\n",
    "print(\"EVALUATING...\")\n",
    "# Evaluate the model using lenet_model and test_input_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Expected output:**\n",
    "(final line, might vary slightly)\n",
    "```\n",
    "{'accuracy': 0.9864, 'global_step': 3600, 'loss': 0.046120092}\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \\[Bonus material\\] Neural networks for sequence data: Ratings from IMDB reviews\n",
    "\n",
    "Now that you have a good idea of how layers fit together and how some of the higher-level TensorFlow APIs work, we're ready to take on some more complex concepts. We'll start out with some basics of numerically representing text and then pull it all together with a neat model that tries to guess a movie 10-star rating based on the text of a review.\n",
    "\n",
    "### Text preprocessing: Tokenisation, stemming and lemmatisation\n",
    "A common text preprocessing step involves tokenisation, followed by some form of stemming/lemmatisation process.\n",
    "\n",
    "Tokenisation is the process by which text is segmented into semantic units. These are usually words, groups of words (e.g. \"New South Wales\" rather than \"New\", \"South\", \"Wales\") or sentences."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_sentence = \"I spoke with my friend in the U. S. A. today on the phone. Afterwards, I went to the park in Hoppers Crossing and saw three geese.\"\n",
    "sentence_tokens = nltk.tokenize.sent_tokenize(example_sentence)\n",
    "print(\"Example sentence:\\n%s\\n\" % example_sentence)\n",
    "print(\"Sentence tokens:\\n\", '\\n '.join(sentence_tokens), '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[_Stemming_](https://en.wikipedia.org/wiki/Stemming) and [_lemmatisation_](https://en.wikipedia.org/wiki/Lemmatisation) are methods to reduce word tokens to a standardised representation, e.g. \"went\", \"gone\" and \"going\" all relate to the infinitive \"to go\", thus these three words might all be reduced to just \"go\". The puspose of this is to produce a simplified vocabulary without sacrificing much of the meaning. The difference between stemming and lemmatisation is essentially that the former uses simple heuristics whereas the latter attempts to consider the function of the word in the sentence. Consider the examples below generated using the [NLTK package](http://www.nltk.org):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tokens = nltk.tokenize.word_tokenize(sentence_tokens[1])\n",
    "print(\"Tokens:\", ' | '.join(tokens), \"\\n\")\n",
    "\n",
    "stemmer = nltk.stem.SnowballStemmer('english')\n",
    "print(\"Stemmed tokens:\", ' | '.join([stemmer.stem(t) for t in tokens]), \"\\n\")\n",
    "\n",
    "tagged = nltk.pos_tag(tokens)\n",
    "print(\"POS tagged tokens:\", ' | '.join([\"%s (%s)\" % (t, p) for t, p in tagged]), \"\\n\")\n",
    "\n",
    "lemmatiser = nltk.stem.WordNetLemmatizer()\n",
    "lemmatised = []\n",
    "for t, p in tagged:\n",
    "    if p.startswith('J'):\n",
    "        tag = nltk.corpus.wordnet.ADJ\n",
    "    elif p.startswith('V'):\n",
    "        tag = nltk.corpus.wordnet.VERB\n",
    "    elif p.startswith('N'):\n",
    "        tag = nltk.corpus.wordnet.NOUN\n",
    "    elif p.startswith('R'):\n",
    "        tag = nltk.corpus.wordnet.ADV\n",
    "    else:\n",
    "        lemmatised.append(t)\n",
    "        continue\n",
    "    lemmatised.append(lemmatiser.lemmatize(t, tag))\n",
    "print(\"Lemmatised tokens:\", ' | '.join(lemmatised))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have provided a tokeniser that is a hybrid of the above, along with some additional text cleaning steps:\n",
    "1. Any HTML tags picked up from IMDB are stripped\n",
    "2. Sentences are tokenised into sentence tokens\n",
    "3. Each sentence token is tokenised into word tokend\n",
    "4. The word tokens are POS tagged\n",
    "5. Each tagged word token is lemmatised\n",
    "6. For each word that cannot be lemmatised, stemming is used\n",
    "7. Any token that contains a digit or does not contain any a-z character is dropped\n",
    "8. All remaining tokens are converted to lower case\n",
    "\n",
    "Try it out!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_sentence = \"I am really looking forward to using this model!\"\n",
    "print(\"Tokens:\",' | '.join(tokenise(example_sentence)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Representing text with one-hot encoding \n",
    "\n",
    "The one-hot method of representing text is quite simple: for each input sentence, a vector for each word in the text with a length of all possible words (the vocabulary) is created. Each vector contains zeros everywhere except at the index of the given word in the vocabulary where a one is inserted.\n",
    "\n",
    "For example, if a vocabualry of:\n",
    "\\['.', 'I', 'cooking', 'enjoy', 'hate', 'movies', 'watching'\\]\n",
    "\n",
    "The sentence: \"I enjoy watching movies.\" would be represented as:\n",
    "\n",
    "\\[0, 1, 0, 0, 0, 0, 0\\]<br/>\n",
    "\\[0, 0, 0, 1, 0, 0, 0\\]<br/>\n",
    "\\[0, 0, 0, 0, 0, 0, 1\\]<br/>\n",
    "\\[0, 0, 0, 0, 0, 1, 0\\]<br/>\n",
    "\\[1, 0, 0, 0, 0, 0, 0\\]\n",
    "\n",
    "Using the IMDB dataset, a vocabulary of 9272 words was compiled after tokenising and accepting only words that occurred more than 50 times in the dataset. For the purpose of one-hot encoding, we reserved one position for an \"Unknown Word\" token. You can see some of the words in the vocabulary below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This dictionary maps the token to the word index:\n",
    "imdb_vocabulary = next(load_imdb_data('vocab'))\n",
    "imdb_vocabulary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this vocabulary, we can encode sentences as follows (notice that Ezequiel does not appear in the vocabulary, so it gets the unknown word token):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "encode_text(\"Hello, world! My name is Ezequiel.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This index representation of the sentence can then be easily converted into one-hot encoding as described above."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Understanding recurrent neural networks\n",
    "Recurrent neural networks (RNNs) are designed specifically to handle sequences of data, including data of variable lengths. RNNs are usually depicted using diagrams such as these:\n",
    "\n",
    "<img style=\"width:60%\" src=\"data:image/png;base64,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\"/>\n",
    "\n",
    "This kind of network is unique in that it can process arbitrary amounts of input data simply by updating the internal state based on previously seen data (shown by the horizontal arrows). In a basic RNN, the following equations govern the output and hidden state of each step of the network:\n",
    "\n",
    "$$h^{(t)} = \\phi_h(\\textbf{W}_{hh}h^{(t-1)} + \\textbf{W}_{hx}x^{(t)} + \\textbf{b}_h)$$\n",
    "$$o^{(t)} = \\phi_o(\\textbf{W}_{oh}h^{(t)} + \\textbf{b}_o)$$\n",
    "\n",
    "Here, $\\phi_x$ and $\\phi_o$ refer to the activation function of choice for the input, $x$ and output $o$ at step $t$, which is usually the tanh function. The output $o$ can subsequently be transformed into predictions using a softmax layer, for example. The $\\textbf{W}$ and $\\textbf{b}$ parameters are weight matrices and biases that correspond to the RNN unit's inputs.\n",
    "\n",
    "RNNs can take several forms:\n",
    "##### Many-to-many\n",
    "This style of RNN produces an output for each input step and follows the architecture in the above diagram. It is useful for problems such as [named entity recognition](https://en.wikipedia.org/wiki/Named-entity_recognition) or wake-word detection most famously used in devices like the iPhone (\"Hey Siri\"), Google products (\"Okay Google\"), and Amazon Alexa (\"Alexa\").\n",
    "\n",
    "##### Many-to-one\n",
    "<img style=\"width:60%\" src=\"data:image/png;base64,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\"/>\n",
    "\n",
    "Here only the last output is used for prediction. We will be using this style of RNN for the IMDB movie ratings prediction task; we only want the output once the entire text is parsed. These networks can also be used as a the first half of an encoder/decoder sequence-to-sequence model as used in machine translation tasks.\n",
    "\n",
    "##### One-to-many\n",
    "<img style=\"width:60%\" src=\"data:image/png;base64,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\"/>\n",
    "\n",
    "One-to-many networks generate sequences based on a single input. These architectures are used for tasks such as music generation where the first input could be random, or could indicate the style of music desired. They're also often seen as the decoder component of the encode/decode sequence-to-sequence model.\n",
    "\n",
    "##### Sequence-to-sequence\n",
    "<img style=\"width:60%\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABWQAAAHgCAAAAAAHiDuwAAAABGdBTUEAALGPC/xhBQAAACBjSFJNAAB6JgAAgIQAAPoAAACA6AAAdTAAAOpgAAA6mAAAF3CculE8AAAAAmJLR0QA/4ePzL8AAAAJcEhZcwAAGJsAABibAUl1g5QAAAAHdElNRQfiAw8DORcHisyLAAAv20lEQVR42u2dS66sPJemPYZSqT7a1c5kECnmUGISDKEkZlASUs2ACZREv6Si/TfoZoM+bXo0ojDEdUdwMV7LLOz3aXz55z5nE0/44NcXjK1uAAAA2FBnCwAAgM8gZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWADOG4WwD+F3KDyELwD7aqkhjdSdOi6o92+hSfkNb5mkUoh9CFoBtujJVP0jL7myza/g1RRKuH0IWgA36Kn71bu48e41x1cNvnTafO4hJWgbph5AFYJV26iPGef1V2fo6nypi2sr2y071qyeHpGi+pjpD8UPIArDCFGFR3i7++dQLOi9mt/yac/2GMl7vDYbgh5AFYJE+01WwXv9LU1coO2XQu8dvmP1OeaTfxNsJ5b8fQhaAJcpoO2I1uhpGpXC/yrler3vZ+Y7Wx3M/hCwAv+mT/dGp4y5x3Jk19UsddxYbXSYt/BCyAPxG18H9kwB64B41wv1al36FUe/PZz+ELAC/KMdKtWMk/qIeQ8/hlIGxXzWOjY1+wYph7GYnRotM/fVDyALwg9y0Dt5u3Vhvc8l+sTs/nWG54fDaWz+ELADf6AwznoIb3KWscL8uOtLr89UPIQvAF/nB2n709xx9zuDIbziUYd76IWQB+Mvxuu4mJYT76R7fwdlVL/0QsgD8obSo6bmDp1+WfuwLUo9nmJ9+CFkAPmmUSo7/9liDmVdyWfnpXlzL61fY5KSPfghZAD7oowPPlF6MtTBifSvB1i9WEeuq/8ZuxO+hH0IWgA/GkLTaRLSLbDrCDvyUShn1LNsAH/0QsgC8U1ovia9Zp2Xt/SrWac/Msg3w0A8hC8AbY0cns71GxjhhQOTHNiBvCRLSNz+ELABvUAQkRRBe1S9WsfU1fPNDyALwoiUZ6pdsT8il+1UkV/bMDyELwIuUoKNz0/0lpmc30v1imj6oX34IWQCetEQbQdVMXTHpfuN1SY6f9csPIQvAE6KOIltXjNCPZdaT7Gt75YeQBeBBT7aj6djVYVhgIN2vI+uAeuWHkAXgQaUiqktFHGs96fwGFr+cqqPtlx9CFoAHMd0WUHT1+UJ+kSrg9w1CFoA7dKPJ6REVySMWLr+Gwa8hHOP75IeQBeBOSdl7iunfrZXuV1Bu2uCRH0IWgDsp5YbROf36Aul+Cd1o3Cs/hCwAd0hPS60Ved0S7jeQ7qTrkR9CFoCZdnnKLp+fJXf38XqmtqtYT76e/gJ+S/umyPYr1YOPH3dFPP4oWyhzAz+ELAAz1fKUZz2vPS/vMTfW1u3FVDH1IiTpfuXylKdsv/EP0pmPH0cqLctURQvDh/1+CFkAZorlWbZ7JtwPh2rHv5huzsillDOA8GP0Kz/7pP3cd63nv77YtO33Q8gCMLNWaxK9YGdQeaR7ZMXYh9kOiYL6yY18v+Xn7aL9PkK2yZTK9Nxtdv/r7cIcyH4/hCwAM2vjv0rXuFrVme6R6cToNldJVtTL6aX7RVf1ew9ZfeZ3GWnbrXeY9/shZAGYWXuS0eluS66GasyHYV/taqkfj8OPya8cc3Wi1SsQet3l1rO3Y95GWbX4+sJ+P4QsADOrj4tjNdziZAyLcuyQPYbt5dqA0WmI/fRr8rRcPiflfL86zyoJfs/VBeX4odPDsXRah9Dk0cryAoQsAKashkSumk6HQ5zq/zn9aEhXqxn5i7Wmfq2K1pbfuw3ZH36N9lt+vcJlyD7/oJ2FHoq3rk6WZnIRsgAYMqyGRKOKSte8ccgb3ytNmzUbIatUQvf6gLFfmXVDtrzt6exXkR1ZaOxXZMOQLK/kcuf3FrLdvNYse//JwqmJK8uC/4CQBWBiPSRuKs50papV9dqvud0MWaXitYsy+41psRzyDz+q7vYRvyGW4Pf+4Cua5osT1fb5XTNeGJAgZAEwZT0kMhXr6bpeRa+Hzlsh2zaponsX1txv7Iatznm2hY4xqjX/5n5q7XwBd37vIZtpu1o/mrtPZSzuyoPpAgBMWQ+J6n5ObPL2cutWyOr/xmQpa+5Xr3305Kfnlam2uzrgV0bLjw7d+b3e+KpvTaTiWEXNdPC3StNo8Z8PIQuAKesh0afpNGys0tezmh0hexsSqpQ19iuitQ1R7n61oto3xdSv7XT0Ls4GuPOr0/QZsuP/l6Tzv1df6R8trs9AyAJgivnL8qsh+1isPlDtsGfq16yH08OP6gRc4xN3xu/TRcsPvk732wAvIwBgiunL8vPyynbpj5+vXXZE7+Cb+uU7/UqiraxTw3F9Nw7Fo+XcO91vA7xWC4Appi/L961m8cmSyw1YftIJ97sNK3YS/NbBBjEAmEL8srzLrQ5F+JWUp7v45IeQBWCmJTxnz/Gm3VL8yN4c8MoPIQvAHeHHu0j3w/EzCyBkAbgj/aBC6X44SPE3CFkA7kg/clu6H44E/w1CFoA7HeE04PceXPbzqZR+zV+/xn6+siGcNL6c3woIWQAexHTj8fxPr7NPCSo4o19BcemIbjx+Nb81ELIAPKi2z1Ddy9/Xi4aIYPhL5zf89Ssodr81SR7P/NZAyALwYOtYp/3UXyNTigrO6Ufx0indfMb1/FZAyALwJKXq6sRfz547igVEhH5/NxkkeZofUy0JuJ7fCghZAJ5QbUZS/+gyJSb10r1fTbFUvyY6cueCfisgZAF4QdRV/NVjIkkJPr+BZHdsox6eV34rIGQBeNGSbBFd/uroDBS9UEY/kqdCFcms5yX9lkHIAvBGpiLrpVZ99LO/lFGshefza0neOo0pnu9d0m8ZhCwAb5hWoF8sBKHRE2lmv18TFwnFU6GWYFR/Ub9FELIAvFNaj+rrhSG90dpKRr9qIWdIGgGdQJbPlq7qtwhCFoAPEsta2EVL0wIZyQIiaz+1pEHSCIxd7cTqAd91/ZZAyALwgWUtHJLFWVOS9QXWfvHiYJemEWiU1RuwF/ZbAiELwCdjLbR4QpUsP58hWV9g6acPz20X/oymEdBvwB7vcV7ZbwmELAB/KC36OvnaGquEZoMXS7/FhCFqBG42h6Bf2W8JhCwAf8kPp9j6b1Jt8MLlR9QIDMdT7Np+CyBkAfjiaIpt/B7ZBiUH/YaN36NqBIboWIpd3W8BhCwA34y1yfzpku4hrdfBmGpDUx6/jujVfr3C4kCKXd5vAYQsAD/QKWZYobvtOpiTHYByxC/e9CPbCnDKI8NWwAO/BWsaZwA8o1QqMurs1NH2vgJNTHa0gbFfpbZ7b2lK01OcU8ysFfDB7zcIWQB+0oyhme1+xajPxtAjPHGaxa916VeMH7j/QbzPfghZAH7Tj52daOeeV+WYeAnZKX08finFIlMDdCuQtPBDyAKwiK5a8Y4hYh3vj7vT/AheSTWkT8cRdr6j6fHcDyELwCJ6kLgZY7oKGgzcXfsNs5/jbuJMoz86bQP3Q8gCsEKreztR3i7+eR5t19MT/Zpz/YZSJ1RcLTZBIfghZAFYZYoxFef1V0Xs61xX0RMjdp9fdqrf1BFUSdF89QVD8UPIArBBX011TcVpUbV3qiK9/7A6ZaLgSn5zb38MsrQM0g8hC8A2XTn1F/+SllTrNj33a4okXD+ELAD7ePVuHr3GY5fhesQj3G9oyzyN/Pf7BiELgBn/+pfNb8cUx80y+pG9uRqo3y9lZmEAfCO3ejU2sz8IkdWPvxHw2+8HCFkAzIis9turCM6k5vTjbwT89vsBQhYAI5rl82X20JKcucrnV3I3Ap77/QAhC4ARx48lmCGvw7R+Lc1BWsH6/QAhC4ARkbLbnz9hnlS09Bu4nyx57vcDhCwAJjR6gY9NXzQnOdiaz4+5EfDe7xuELAAm5LoS2owna8Va6az9mBsB7/2+QcgCYMK0XN1mPEl2UBWTH9VphQv847vfNwhZAAzo5heCWotLHD6P2o0f7/IH//2+QcgCYEAxV0KbQ2dTqiNrmfxYlz8E4PctzKcLgH/cNwewWatZkB1Zy+PH+mTp3/33+wIhC8B+uscGIhbTqg3jky8KP84nSyH4fYGQBWA/xaMSWowne8aVnhR+nE+WQvD7AiELwH5eWwlaXCRSbIcGUvhxPln6twD8vkDIArCb52jSajzJ9+SLxo9v+UMYfl++TLYAeEjxqoQWOVmwTSrS+KXUL+8H5vcXhCwAu4lfldBiPMk3qUjjV7ItfwjD7y8IWQB2k2qUmv7P8XnBlm15AY0f3/KHMPz+gpAFwAzrKj6wHfMFPwl+XxfktAXAQ3g3eIHf2SBkATiZ4EICfpYXPPsbAXAxggsJ+Fle8OxvBMDFCC4k4Gd5wbO/EQAXI7iQgJ/lBc/+RgBcjOBCAn6WFzz7GwFwMYILCfhZXvDsbwTAxQguJOBnecGzvxEAF4OgErY54+EIBH5lyngMWQB+fy7I5wqAlxBUwprzMEACP9YTXgLw+3NBPlcAvISiJ6sU35uhBH6sJ7wE4PfngnyuAHgJQSUcGA9HoPDL2DYTDMPvzwX5XAHwEopKyHg4AsmcJ+MxWiH4/bkgnysAXkJRCTmPBSeZM2YMhgD8/lyQzxUAL6GohHyHI1DNGbMdoxWC358LsqkC4CcUlZDzxFUKP+FzxuL9Pi/IpgqAn1BUQs7lBRR+sew5Y/F+nxdkUwXATygqIefyggDmjMX7fV6QTRUAPyGphIzLCyj8OB/fh+D3eUE2VQD8hKQSMnbFQpgzlu73eUE2VQD8hKQSMi4vCGHOWLrf5wW5TAHwFJJKyNgVC2HOWLrf5wW5TAHwFJJKyNgVI5ozrpn0wvD7uCCXKQCeQlIJGbtiRHPGbFuwBOH3cUEuUwA8haYS8i0vCGHOWLzfxwW5TAHwFJpKyNcVI/ErVcykF4bfxwW5TAHwFJpKyNcVo5ozZtILw+/jglymAHgKTSWsU66FsiR+fcp2wksQfh8XZBIFwFeCOwgQfpYXPPsbAXAxggsJ+Fle8OxvBMDFCC4k4Gd5wbO/EQAXI7iQgJ/lBc/+RgBcjOBCAn6WFzz7GwFwMYILCfhZXvDsbwTAxQguJOBnecGzvxEAFyO4kICf5QXP/kYAXIzgQgJ+lhc8+xsBcDGoKmHMtA8X/GT4vS7IogmAv1BVQqUa4X4t/EguyKIJgL9QVcKEaR8uKj+ufbFD8XtdkEUTAH+hqoSpyoX7yW4EpPu9LsiiCYC/UFVCrmOt6UKMZ5+wUPxeF2TRBMBfqCoh11mKVH5cO96G4ve6IIsmAP5CVQm59p0Opact3e91QRZNAPyFqhL2TI/Hqfxq4T1t6X6vC7JoAuAvZJWQaQ1XKD1t6X6vC7JoAuAvZJWQaQ0XYYj18KO4IIclAB5DVgmZ1nAR9rRb+FFckMMSAI8hq4RMT26CCTHpfs8LclgC4DFklbBSsWg/4dMZ4v2eF+SwBMBjyCoh05MbwukM2SEm3e95QQ5LADyGrBL2SnWS/TKVMeiF4/e8IIclAB5DVwl5JhWDmTOW7ve8IIclAB5DVwl5JhUJQyxh0AvH73lBDksAPIauEvJscRLMnLF0v+cFOSwB8Bi6Ssgz3g0mxKT7PS/IYQmAx9BVwrZsJfv1Jcuu2MH4PS/IYcnMMJxtAL+Q/YI7CBB+lhc8+xsZ0FZFGqs7cVpU7dlG8IOfHIT7DW2Zp1GIflcJ2a5M1Q/SkmOhIfzgBz9SmiIJ1+8SIdtX8at1ufNsteOKZSse+MEPfjS0+dxBTNIySL8LhGw7tdFxXn992b7Op4JIW/jBD35LftmpfvXkkBTN11R7KH7iQ3a6haJ88WvOrdB5tzn84CfZrznXbyjj9d5gCH7CQ7bPdBFsLNSYmqLslEEH/OAn3W+Y/U5ZstHE2wnlv5/skC2j7Vtco4sh4jmEHX7w88Ovcq7X6152vqP18dxPcsj2yf5bV99uiePOBPzgZ+vXtcd7SKZ+6YGPai38Gl0mbdh+E4JDVpfB/kGYHjhFLOfSwQ9+bH4W+3Ad8DP/LAu/wqj356nfLHlUkZ1y/FJGb83V403ncMgGP/jZ+yl19M1QY79KHfiwwyE2jN3sxGiRqZd+s+QxRX5y0zIYh15jubGcTAc/+DH5Hd7c/4hfbO4XH/TTGZYbDq999JuQGrL6HjKeAhnc1UL4wY/CLzv4hVz5HWwEuuhIr89DvwmhIZsfrE1Hfw9+8DvF7+Bmhwf9BuPfO3Zq+XAow3z0m5AZssfrkptaCD/40fxmpSLRfocaAd3jOzjV7JvfhMiQLS1qUu7g6Qj84Efkd2jfaUs/kwWfh0LseIZ55zchMWQbZXN2z1iCzCt94Ac/Kr/+wONxKz/dizP4xOZAI1AcyCFf/SYEhmwfHZjT/yiFiHXVOvzgR+dnXmVt/WIV7f/1Az3txm5Gxy+/CYEhO96kVps4dhHPIZbwgx+9n/mBtdZ+ymCIPf5lww+zbAM885uQF7KlxYTJTM06bQc/+BH6GR9Ya+9XmQyXjXvamWUb4Jnf5Gj3efSMDU1me42McUAJP/hR+hWG3SIiv919OdMZ8NZmwtNDv8nR9gOpoahAFDci/ODnwq80nPtw7We62j9Wsa2eV34aaSHbkgwFS4t9I+AHP4d+hk9unPsZhlhF8i/jkZ9GWsimBA3NTbdXh16kgR/8HPt1Spn0TJ375WYlHdOMMfzx0wgL2dZ6Vn+mZurqwA9+xH5G38S9n9lq/9r4Yb/vfhphIUvUULN1deAHP2K/yORBDKHfzh5dafSJZP8s3vhpZIVsT9RQT00NwwNo+MGP2s9kUvEEP6M5445sgOGNn0ZWyB7bLuMnRh0E+MHvLL/coE9E5zfs9TMKsZyqo+2Pn0ZWyMZ0WyzRlSf84Mfo17f7u0Qn+A1tu/+ikembFf77aUSFLF1vfmrhSKa44Qc/H/0aBr+GcA7HJz9RIWs2ib3B0YMp4Ac/+B2ioNxUwiM/USF7bJvzBQwX0MEPfvCzI6EbjXvlRxSyJclDT7Jnp5p6bUb8mK8Lv75sD18SfnbAz4qBdKdfj/yIvghJq9OSLstZ3Q75kK8Tv/b4KAl+dsDP2s9qD0Fv/SSFbEX7wDheWWRxyNeJn0WIwc8O+NlhutdNMH6EIWvdypru+rbB2k6d+pOMH1468ZtC7NhzVfjZAT/4sfiRheyQq6g06Y2XcfGnrhKX6lqZTr7xWp/sW8+N3xhibaqSzckj+NnVGfjZ+ams/lvbTXcdDMaPKmSjKK9io3nlUin1eSMRjw/WRlfbvt96bvxaFcdlEW3+u8DPbvQsx2+of9V9OX51+WuMOuqptPr4E+KX8HzxowtZnVet0QoTfRdNN1L7ukhr8PubrL1wN31Us+b7refGr53enMw32yv4HTpNW6Df72Oq5Pj9/thZTyVVt/EXg/cjDNmbXiKh+891tu+r3O+ikShvXBfClBT97Jv+nGH81nMVYjr5Kz2yafJsuekV4LfKWX57EeT383Pl+P1edf/Ue/W34bcA6eqC8b99HO2cq3jdRY8byWXIPnxv2cKnfuu5CrHy/t9aJcnyAOd8v3XO8tuLIL+fKSHH7/dU8LueiuG3xv4FtWu8QquOh3EY/p/KnOh/0hfCtu8trcdPbXfo5f9y4Ze+QizLhiEpym01F37t66cPv6Jus3KY/HaUuaPyOwz84Gfst/NBP3XIFvp/qP93pBTOCdnxrwkNWU3cyQ3ZNE4TVSBk4ResH3XItmu8QivVM3bq/7Tb/K/PImCZLvjPTd85ZIc9ek78uo+QreNbv7v4WP2G109fIXsb4nTye/7O4NbP4gUeJ8PxfX4/F8nL8fs9LP5IMK7huHS/nX+Tek420zsQ71rKde6Dr8+Q3aXnfs6zjJZX/Z/vlxajxeL0u5wHN+L9ml9/UY7f8PNz3xLs5AdL0v3IQjYt7v/Vta7b9Yr1qUu4nr5bIet8CU2X1vf/DvlKxgrwS8sdIStgCZJ4v59dNkF+P/eluSeYhCVS0v3Itzpso2rIdp2RcerLCO/GyyF77mLwai1jBfhth6yMxfTy/X7VfkF+P9+U0gkmZLG/dD/6/WTLSCXtrr945mu1L4upvfshfP5rjcmktih+ut9GyJ7ut44kv+hHV0yQ38+dUwW9tirdj2HT7mHnu7XfI6QzNnDoPx/rrOm59rs/o1vgfL9u7Cb0i53t8/3WkeT3q/YL8vv5YA5+zjeIIUH6Vm7wgx+LX7bvwNqz/PZOPp61laB0P1Ehe4FNieEHP3q/cl8X6yy/bueZgWdtii3dT1TISj9eA37wY/Gr1a5Hxaf57YyT8453ke5H96n2SD8oDn7w4/Db/5b+OX7JzidGZx1UKN1PVMhKP/IYfvDj8Ns78jzLb2+4n3XktnQ/USHbEU6ztTsnauAHv9P9do5jKf0aA7+dc8b6mmSTxj75iQrZsXUgGw/ltI9i4Qc/Pr+9492T/HZPP0Z043Gf/GSFbLXzCcAOiF/vgB/8+Pz2LpOn8xtM/Ha/QErX8nnlJytke7LnpzXpchf4wY/Tb+9fle5HN5/hlZ+skB2bdKKmJqZ/9gw/+Hnkt+v9h9O+tld+wkK2JWqqa/KV6vCDH/x2XJfkcaRffsJClqqpZupIwA9+8Fu/MEkP1C8/aSE7NtUEyx9LpoYQfvCD3xoVyZU985MWsrdMRdZPNPqIZ0YHfvCD3zoxQVfbNz9xIUtRwBQ3Ivzg57Ef4U4pn4xdbeu1db75iQtZ3RW3nNuvSYZU8IOfn34VQc4sMyaQ5bMl7/zkhewtsSyFLqLdNxJ+8PPKT3E9VZoYu9qJVT/UPz+BIWtZCkPCOFiDH/w4/Pp0f7219YsPDMazZH+uN8rq5V8P/QSGrC4Fi65AQrprJPzg58DPaD9pK7+xDTjwfN3oOxU2w30f/SSGrJ52OtzW5KwTYvCDH4ufUc219DuQMHt3sHn87ePTxj76iQxZ/U0O3kXHfxN+8DvPz6zaO/fLjH5rOJ5iXvrJDNnDd5GbOgg/+BH7GR5XffBzhqN+e3dsfXxOdCzFPPUTGrL6LjKf3dctlJM6CD/40fqZdcWc+5keqNYdSjFf/aSG7HQXGa5U6dzVQfjBj9TPsCt2zC8+7Gd8UO6UR4atgLd+YkNWz+5HRo1NHTl4JgI/+HH47T2w1sKvUhbPe4wfqesUM2sF/PWTG7K3Zrxps93tU5+NNx3z2h74wY/Jb/fm/lZ+7WG/2PyZejF+4P5f8tlPcMje+rGxiXb2DcrxjktY16jDD358fnsPrLXxSy1eYUgPnI6lW4Fk57fy2k9yyM5fLd7RRa/j/bcb/OAn0O/QFnxGflb7ARRHXnXt03GEne9oejz3kx2yUyd98zbSRWAwcIIf/OT5mS2nN/EbZj+7fa2qYzsENvqj0zZwP+Ehe7u1urWJ8sVyaPNou5zgBz/hfkfGu3v8Ghq/A3PGE0OpEyquFpugEPzEh+z9NlJxXn8VRF/nuohOrILwgx+N36Hx7k6/zN7vyJzxnakjqJKi+eoLhuJ3gZAdv2w1fVcVp0XV3qmK9P7D6pSBJPzgR+lXWuzY78LPZhfbubc/BllaBul3iZAd6cqpvf5LWpIcPgm/swne7+h415Wf4Xu/f2mKJFy/q4Ss5tW6PFrts43gBz8iOtN3lhz75dan3gxtmadRiH5XCtkH//rX2Qbwgx81dAe0sviVdMc9BOd3xZDNnb3ADj/4ufKLyA62YvFr7KYzgva7YshGpq95ww9+4v2KlOqlYRa/LiE7pTs4vwuGbMN+/gj84Ac/+FFxwZB1tXEz/OAHP/jZc8GQjZTxtnDwgx/8hBCe3/VCttELLASPN+AHP/jB743rhWyuC0HweAN+8IMf/N64XshOy4UFjzfgBz/4LfNPeH6XC9lufiGjPdsDfvCTCPzk+V0uZIu5EI7tCwc/+MEPfo79Lhey95ezj29ZBD/4we80/j1Av6uFbPfYwEHI7kzwgx8RDcWbRox+McUYOki/q4Vs8SgEoeMN+MHvIJabHbL72ezYGrbf1UL2tZXb2Sbwgx8pPUX3idHPcsfWmX8L0e9iIfvszQsdT8IPfocheKbN6We/Y2uofhcL2eJVCCLHk/CD32GOHVjrzI9ix9Yw/S4WsvGrEESOJ+EHv8McPbDWkR/Fjq1h+l0sZFONUtP/Of18PfjBj5Ly8IG1TvwIDsgJ1O9iITs7C5eGH/yOUBH1nrj8qN6DCs5P+P3sphDgJ4pQ/UjWcDH6xRSP70P0E34/uykE+IkiVL+O6JE2l19KtDlVcH7C72c3hQA/UQTrRzTe5fIjmDMO00/4/eymEOAnimD9iMa7XH410Q6AwfkJv5/dFAL8RBGsH8EaLk6/VqkBfkcuyPN9WQm2EsJPBGx+Bc14l8tvED6dIdZP+P3sphDgJ4pg/UqaNVxsfhHFFiwB+gm/n90UAvxEEawf0RouxukM0XPGYv2E389uCgF+ogjWj2gNF5tfLns6Q6yf8PvZTSHATxTh+tFMKgY7nSHVT/j97KYQ4CeKcP1iVUn2kz6dIdVP+P3sphDgJ4pw/cqylezX5zLnPKX7Cb+f3RQC/EQBP/h55Sf8+7opBPiJAn7w88pP+Pd1UwjwEwX84OeVn/Dv66YQ4CcK+MHPKz/h39dNIcBPFPCDn1d+wr+vm0KAnyjgBz+v/IR/XzeFAD9RwA9+XvkJ/75uCgF+ooAf/LzyE/593RQC/EQRsl9PsHkBp19LsGNrcH7C72c3hQA/UQTsR7K5P6MfyeYKwfkJv5/dFAL8RBGwH8nb94x+JDu2Bucn/H52UwjwE0XAfj1FV4zRj2TH1uD8hN/PbgoBfqII2U+pRrIfyY6twfkJv5/dFAL8RBGyX0LQFWP0I9mxNTg/4fezm0KAnyhC9qM4sDbgOWOZfsLvZzeFAD9RhOxXEox3eeeM7deYBecn/H52UwjwE0XIfhTj3YDnjGX6Cb+f3RQC/EQRsh/FeDfgOWOZfsLvZzeFAD9RhOxHcWAtp1+mMutrBOcn/H52UwjwE0XQfgQLZQOeM5bpJ/x+dlMI8BNF0H4EB9Zy+jXCpzNE+gm/n90UAvxEEbQfwTtLzHPGPfxML8j3fdkIuhLC73RY/QjeWQp5OkOkn/D72U0hwE8UQfuVKhHtF8vuaYv0E34/uykE+IkiaD+CSUXm6QzrV9KC8xN+P7spBPiJImi/VinbfaeZe9qypzMk+gm/n90UAvxEEbaf/aQiq19lv614cH7C72c3hQA/UYTtp6z3nebuadt/w8D8hN/PbgoBfqII26+zPqaK1W9oRL+RJtJP+P3sphDgJwr4wc8rP+Hf100hwE8U8IOfV37Cv6+bQoCfKOAHP6/8hH9fN4UAP1HAD35e+Qn/vm4KAX6igB/8vPIT/n3dFAL8RAE/+HnlJ/z7uikE+IkCfvDzyk/493VTCPATBfzg55Wf8O/rphDgJ4rQ/arcbj09t1+Z223ZGpyf8PvZTSHATxSh+9nu1sftZ3sibHB+wu9nN4UAP1GE7md7GCC3n+3hDcH5Cb+f3RQC/EQRul+pYtF+heVugsH5Cb+f3RQC/EQRup/tlrLcfrXlboLB+Qm/n90UAvxEEbrfYLmlLLdfZ3lYYXB+wu9nN4UAP1EE75fYTSqy+1k+WQrOT/j97KYQ4CeK4P0sT6wNvhGQ5if8fnZTCPATRfB+lkeoBN8ISPMTfj9/0FZFGqs7cVpU7dlG8IMfxwcdm1R05nesERjaMk+jEP2uErJdmaofpKX1YRPwkwD83jGfVHTqd6ARaIokXL9LhGxfxa/W5c6z1Y4ru5fo4Ac/aX6JKkT7GR722OZzBzFJyyD9LhCy7dRGx3n99WX7Op8KIm3hBz+P/AqVUPpl1H6pSSNQTw5J0Xwt/g3FT3zITrdQlC9+zbkVOq8awg9+5H7N/tcRtvwaDr/9jcBQxuu9wRD8hIdsn+ki2Oj7T01RdsqgEn7wY/Ab9k7K7vEbZj/bg8bf2d0INPF2QvnvJztky2j7FtfoYojsdoWAH/zk+O2clDXyq+j0dr6T1ute9p59Bz33kxyyfbL/1tW3W+K4swM/+DH57RrvmvqldJ3FXbsxNrpMWvhJDlldBvsHYXrgFNntIwk/+Anx2zPePeDXUvntWe5fGPX+fPaTG7Ll+KWMFmLU403ncEgJP/ix+e2YlDX2q5ThyqbVa20t9x/GbnZitMjUXz+xIZublsHt1o3llsMPfh745cvrBY77xWR+XboxZ6wzLDccXnvrJzVk9T1kPAUyuKuF8IMf/BbpoiO9Pl/9hIZsfrC0j/4e/OAXgN/gyG84lGHe+skM2eNl7eZfCX7wg98iusd3cHbVSz+RIVtalHTu4OkI/OB3YT/CBam/OZ5hfvpJDNlGmby6/ZfE9kRg+MHPXz/di2t5/QqbnPTRT2DI9tGBOf2PUohYV63DD35X9otVRPkK6xeN3YjfQz+BITvepFabOHaRTUcEfvDz3E9ZHpm9jmUb4KOfvJAtrZck16zTYvCD37X9KtZpz8yyDfDQT1zIjg1NZnuNjHHABj/4ufHril9zu0R+BAPy5ufWKi1BQvrmJy5kKW5QihsRfvA71y/7eRE5ftHPtIpVbH1l3/ykhWxLMtQq2Z5Qwg9+jvzqX5vECPLLf009VyT/Mp75SQvZlKChuen2imnuHH7wc+Q3/Jp8FeT3sxGIacYYfvkJC9mWaCOemqmrAz/4OfPLpq7Y8P6YRpLfvRHo3mcvxuuSHD/rl5+wkCVqqNm6OvCDnzO/RifCkLxfiNDPvkeX6vWmXfS+6JTsn8UrP1kh25PtKFmbH70OP/jJ8otV/vmCkSw/vWlr91/eL9SRDTC88pMVstt77e4m4lhrBz/4OfQr1T//od7foaXzGwj8xsj6v/9Vvb8/lVN1tP3ykxWyMd0WPHTlCT/4neM3dlwnnr1XYX7J3e/Zp4v2nf8Ymp+okKXrzU+PCEimuOEHv7P8hv8+h8SjOlP6NQR+//seYo/pyYZwDscnP1EhW1L2TnYdWAk/+In1Gx49sUdXVphf/dB7RP+uM3YD9BMVsinlhr17DqyEH/zE+r0y9pGtsvxeGfa4UkI3GvfKT1TI0p1WeZvKGH7wu6zfW8Y+vET5FW96c1dxxxG7YfpJCtl2bcokmv+suDdG29+wJ1+vDr9Q/boi1lN7/Z6fUvl16VtIxBvll8/Purt7nzdT2xFg65e/h1h69xsW/3LIfpJCtlqbcsrnVjyZp1WaHX31mHqRD/xC9YtUWpapiuodP6Xza5PPruxK+dXzw53yHsNjmmwv9rL16/M/XcVyecozbD9JIVus1ax6mo8a7gsyih1fMKWcgYFfwH71/AePkOv7Xz/l8Kvj967sSvndM+F+eFU7/sV0s5Wy93uLsRR+y0gK2VXruRhqVUzFkOxYi1FQPxmBX6B+2f1BcqtHm02mVNb8/SmX3zNm6/XyS/SCokHlUXabm6jtkKDw64t/3v2Wn7cH7ScpZNf731Mx5GM/PtfzITvWslTUy9XhF6jf+9us+kzoMtLVdesdVyK/e8zG6+VXaaNa1Vl0L8pucxUnjd9Q/vPwi+D3G0khuz6TXOkiipJbGk0dnu3LtdSPn+EXqt+YrFFW6c71ME1nDNPs3uunvH5zzNar5dfpbtXYRFV6T5l9tZ/K7x5j8FvkOiGri6Eb2xtdDPm0FqNNV1sbxyHx5dcXabEmeLZfN/qtDcrP9tvCpV+TR/NCgnZ+eJJOz6mfP+X20zEbr5dfPBrFyVSCryaqXBvQ0vkN5X875Nfkabl8jsv5fnWeVRR+1wnZsRjGCtiOxVDdIt2pLzb+vuOQ+Os3xGr9/eaz/SKVSvbbxHUjUCdjBWzn9wGercD8U36/MWbX9zAdjTodDnH6lBvSVQNKv6GMzP1aFa29HnC6X6P9ll//uGTIDhuVcPzuqdaNsm5acBF3myFLen67oV+T1rdqLSxO92tv5dpvnOzXpUOZrf2Ca78x/qNhvvVu2euvdkun6hH71f9j1a9RRaXDYRzyxvdK3WbNRsgS+g3lf5j6lVk3ZMvbsp7uV2TDkCzX4P1+FwrZsRgiPfzJVPV4A2b177uuhN9+43hDtl8u2G8cmOdbPTE3fn1+T4JYdbdo6vwnqv34qYDyU3GmP68eS+8ZXK2I8lv2G/+xl58dSvAbYgo/QSG7NZy8qemh7lgM97eDtkP2ZL8hWZtaPN2vXD/Z+GS/dkzarZ62I7/7oHHanyXTDUCt/9f7TwWUX6ZiPV3cj6X3DIatkD3bbxwGuJiTPeyn1s4/uOR0wWYlzObdxfrnNsbCQvavXxev7udxvl+Zri1HPdmv1fVPRsj2kVJpGk1LtppIxbGKms+fCii/6n6ObfL28q2kkP3lV68tgpPgV0bLjw79DNn6vkA4S+//NMJC9o9fE62vQzrZr2t1nq2sXD3ZbwoIGSF766t05P4kvE7u99/HT0/1m2zSebFNlb6adkkh+8OviASN9L792u42rXWx9pMUssYvK69XWseL6b/YyNiz/QqV9claCZ7stxmyZ//7biHAbzVkyf1MT4xp1hfqne43lne3ss7lmi8jmL2s3E4vcqxsnOv2tdAvauF+01Z6a0dunuy3GbIn+21yul/5vmG1Ez+zbbZz4X5dpFS0nMvXfK3WrFSHVrMyp+h2g5MveuF+Y4y1qxsEnOw3tLflfQEE+G1yut98By6W4Ol+nXA/HTEk95+kkCUeH7jdqg9+8Avcr6Q83cUnP0kh2xKec+Z802n4wQ9+hAtbPfKTFLKyjteAH/zgZ4Ks42cE+YkKWVkHxcEPfvAzQdZBinL8RIWssCOP4Qc/+Bkg7EhwMX6iQrYjnIZpldrcdhd+8AvVr2HwawgnjX3yExWyY+tANh7KqZcyww9+8FsnohuP++QnK2Sr7TMi92L6egf84BeQ38DiR5eMXvnJCtmtY5P2U5Mud4Ef/OC3Dd18hld+skL2llI1NTH9s0n4wc8jv8z+Ioxf2ys/YSHbEjXVNflKZvjBD347rkvyuMovP2EhS9VUM3Uk4Ac/+K1fmKQH6peftJBtFcXyuJKpIYQf/OC3RkVyZc/8pIXsLVOR9Yx3H/HM6MAPfvBbJyboavvmJy5kKQqY4kaEH/w89iM9ovCdsattvfbKNz9xIau74pZz+zXJkAp+8PPTryLImWXGBLJ8tuSdn7yQvSWWpdBFtPtGwg9+XvkprqdKE2NXO7Hqh/rnJzBkLUthSBgHa/CD39X9YsbBuKZRVi//eugnMGR1KVh0BRLSXSPhBz+v/PTRbi2vX2Ez3PfRT2LI6mmnw21NzjohBj/4Xd6PccJzJrGYNvbRT2TI6m9y8C46/pvwgx/8SBiOp5iXfjJD9nBZu7nH4Qe/S/oNjvyG6FiKeeonNGT1XWQ+u69bKCf3OPzgB78VukMp5quf1JCd7iLDlSqdu3scfvC7nl/szm/KI8NWwFs/sSGrZ/cjo8amjhw8c4Af/C7qVynS02430Clm1gr46yc3ZG/NeNNmu1cc9tl40zGvnYEf/C7t17r0K8YP3P8g3mc/wSF768fGJtrZNyjHOy5hXQMOP/hd2y9lXeP/jW4FkhZ+okN2/mrxji56He+/3eAHvyD92JefftGn4wg739H0eO4nO2SnTvrmbaSLwGDgBD/4heQ3zH6Ou4kzjf7otA3cT3jI3m6tbm2ifLEc2jzaLif4wS9Qv+Zcv6HUCRVXi01QCH7iQ/Z+G6k4r78Koq9zXUQn3uLwg598v+xUv6kjqJKi+eoLhuJ3gZAdv2w1fVcVp0XV3qmK9P7D6pSBGvzgB799zL39McjSMki/S4TsSFdO7fVf0pLk8En4nQ38/PZriiRcv6uErObVujxa7bON4Ac/OQj3G9oyT6MQ/a4Uss/SOOVBJPzgBz/4HeCKIQsAAJcBIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADACEIWAAAYQcgCAAAjCFkAAGAEIQsAAIwgZAEAgBGELAAAMIKQBQAARhCyAADAyP8HMHr9o+14QasAAAAldEVYdGRhdGU6Y3JlYXRlADIwMTgtMDMtMTVUMDM6NTc6MjMrMDA6MDDAHy8mAAAAJXRFWHRkYXRlOm1vZGlmeQAyMDE4LTAzLTE1VDAzOjU3OjIzKzAwOjAwsUKXmgAAAABJRU5ErkJggg==\"/>\n",
    "\n",
    "Sequence-to-sequence models typically follow and encoder/decoder architecture as shown above, where a sequence is encoded into a final hidden state, which is passed on as the initial state of a decoder network. The decoder network is effectivley a one-to-many RNN that continues to produce outputs until a final End of Sequence token is detected. There are many variants to this type of network, most notably the use of [\"attention\"](https://arxiv.org/abs/1409.0473).\n",
    "\n",
    "#### LSTM and GRU cells\n",
    "The plain vanilla RNN fomulation described above is not very well suited for complex or long sequences, and in practice is seldom used. [Long short-term memory](https://en.wikipedia.org/wiki/Long_short-term_memory) cells (LSTM) and [gated recurrent unit](https://en.wikipedia.org/wiki/Gated_recurrent_unit) (GRU) cells are most often used.\n",
    "\n",
    "The specifics of how these cells operate is beyond the scope of this document, except to say that both of these cells are able to learn how to selectively retain information in their hidden state over longer periods through the use of \"gate functions\". A very good explanation of how LSTM and GRU cells work [can be found here](http://colah.github.io/posts/2015-08-Understanding-LSTMs/). \n",
    "\n",
    "### Getting started\n",
    "This model will read the first 100 words (or less, should there be fewer words in the review) and try to predict what the rating was. It will be a many-to-one RNN using LSTM cells and accept one-hot encoded text, with the final RNN output used for rating prediction.\n",
    "\n",
    "#### Building the input function\n",
    "\n",
    "We've preprocessed the IMDB dataset for you, tokenising and converting to vocabulary indices. This dataset has been stored in a special [TFRecord file](https://www.tensorflow.org/programmers_guide/datasets#consuming_tfrecord_data) that can easily be used to create a dataset iterator. On this system, the TFRecord files are located here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_train, imdb_test = get_data_location(\"imdb_train.tfrecords\"), get_data_location(\"imdb_test.tfrecords\")\n",
    "print(imdb_train)\n",
    "print(imdb_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are the associated input functions for use with the Estimator API. This model will consider only the first 100 words of each review and pad the encoded sentence with zeros if the number of tokens is less than 100."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def pad_or_truncate(tensor, max_length):\n",
    "    \"\"\"Accepts the encoded text tensor and outputs a truncated or padded version\n",
    "    depending on the value of `max_length`\n",
    "    \"\"\"\n",
    "    truncated_tensor = tensor[:max_length]\n",
    "    pad_size = max_length - tf.shape(truncated_tensor)[0]\n",
    "    length = tf.shape(truncated_tensor)[0]\n",
    "    return {\n",
    "        'text': tf.pad(truncated_tensor, [(0, pad_size)]),\n",
    "        'length': length\n",
    "    }\n",
    "\n",
    "def imdb_input_fn(test=False, batch_size=1, max_length=100, first_n_records=None):\n",
    "    \"\"\"Returns an input function that iterates over the IMDB dataset\"\"\"\n",
    "    def _input_fn():\n",
    "        dataset = tf.data.TFRecordDataset(imdb_test if test else imdb_train)\n",
    "        dataset = dataset.shuffle(100000)\n",
    "        if first_n_records is not None:\n",
    "            dataset = dataset.take(first_n_records)\n",
    "        dataset = dataset.map(lambda example: tf.parse_single_example(example, features={\n",
    "            \"text\": tf.VarLenFeature(tf.int64),\n",
    "            \"rating\": tf.VarLenFeature(tf.int64)\n",
    "        }))\n",
    "        dataset = dataset.map(lambda example: (\n",
    "            pad_or_truncate(example['text'].values, max_length),\n",
    "            example['rating'].values\n",
    "        ))\n",
    "        dataset = dataset.batch(batch_size)\n",
    "        return dataset.make_one_shot_iterator().get_next()\n",
    "    return _input_fn\n",
    "\n",
    "def imdb_test_input_fn(text, max_length=100):\n",
    "    \"\"\"A simple input function for single examples\"\"\"\n",
    "    def _input_fn():\n",
    "        input_tensor = tf.constant(encode_text(text))\n",
    "        dataset = pad_or_truncate(input_tensor, max_length)\n",
    "        dataset = tf.data.Dataset.from_tensors(dataset)\n",
    "        dataset = dataset.batch(1)\n",
    "        return dataset\n",
    "    return _input_fn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building the model function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def imdb_rnn_model_fn(features, labels, mode):\n",
    "    text = features['text']\n",
    "    lengths = features['length']\n",
    "    \n",
    "    # This will encode our text as a one-hot vector of length 9272\n",
    "    text_one_hot = tf.one_hot(text, 9272)\n",
    "    \n",
    "    # Here we create the LSTM-based RNN that will produce outputs of 128 elements\n",
    "    rnn_cell = tf.nn.rnn_cell.LSTMCell(128, state_is_tuple=True)\n",
    "    \n",
    "    # The dynamic_rnn function runs the LSTM network for the appropriate number of steps,\n",
    "    # which is equal to the length of the IMDB movie review text\n",
    "    outputs, (c, h) = tf.nn.dynamic_rnn(rnn_cell,\n",
    "                                       text_one_hot,\n",
    "                                       sequence_length=lengths,\n",
    "                                       dtype=tf.float32)\n",
    "    \n",
    "    # The output, `h`, is the last valid RNN output. We put this through a dense layer of 11 units\n",
    "    # where each unit corresponds to one possible movie rating (0 to 10)\n",
    "    logits = tf.layers.dense(h, 11, activation=None)\n",
    "    \n",
    "    # If we're not doing a prediction, we'll set up the optimiser and loss function\n",
    "    loss, train_op = None, None\n",
    "    if mode != tf.estimator.ModeKeys.PREDICT:\n",
    "        loss = tf.losses.softmax_cross_entropy(labels, logits)\n",
    "        train_op = tf.train.AdamOptimizer(learning_rate=0.005).minimize(\n",
    "            loss, global_step=tf.train.get_or_create_global_step())\n",
    "    \n",
    "    # Set up the probabilities and class prediction tensors\n",
    "    probabilities = tf.nn.softmax(logits)\n",
    "    predicted_classes = tf.argmax(probabilities, axis=1)\n",
    "    \n",
    "    # If we're evaluating the model, set up out evaluation metrics\n",
    "    metrics = None\n",
    "    if mode == tf.estimator.ModeKeys.EVAL:\n",
    "        rating = tf.argmax(labels, axis=1)\n",
    "        accuracy = tf.metrics.accuracy(labels=rating,\n",
    "                                       predictions=predicted_classes,\n",
    "                                       name='acc_op')\n",
    "        accuracy_pos_neg = tf.metrics.accuracy(labels=rating > 5,\n",
    "                                       predictions=predicted_classes > 5,\n",
    "                                       name='acc_op_pn')\n",
    "        metrics = {\n",
    "            'accuracy': accuracy,\n",
    "            'accuracy_pos_neg': accuracy_pos_neg\n",
    "        }\n",
    "    \n",
    "    # If we're performing a prediction, make sure these are organised nicely in a dictionary\n",
    "    predictions = None\n",
    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "        predictions = {\n",
    "            'probabilities': probabilities,\n",
    "            'predictions': predicted_classes\n",
    "        }\n",
    "    \n",
    "    # Return our model, ready to go!\n",
    "    return tf.estimator.EstimatorSpec(\n",
    "        mode=mode,\n",
    "        predictions=predictions,\n",
    "        loss=loss,\n",
    "        train_op=train_op,\n",
    "        eval_metric_ops=metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating the estimator object\n",
    "Because this model can take some time to train on CPU-only systems, we've provided a pretrained example. The estimator will preload these parameters based on the path provided by `get_imdb_pretrained()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "imdb_model = tf.estimator.Estimator(imdb_rnn_model_fn, model_dir=get_imdb_pretrained())\n",
    "print()\n",
    "# Evaluate the model using IMDB test data. Use only the first 300 records for testing to speed things up\n",
    "# (but normally the entire validation set should be used!)\n",
    "print(\"EVALUATING...\")\n",
    "imdb_model.evaluate(imdb_input_fn(test=True, batch_size=100, first_n_records=300))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on a small sample of the test set, this model doesn't do so well detecting the exact rating (it gets it exactly right only about a quarter of the time), but it is ~80% accurate on the overall positive/negative sentiment. Have a go with your own examples:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = \"\"\"\n",
    "The movie was fairly average despite its big budget and hype.\n",
    "\"\"\"\n",
    "result = list(imdb_model.predict(imdb_test_input_fn(test_string)))\n",
    "ax = sns.barplot(y=result[0]['probabilities'], x=list(range(0,11)))\n",
    "ax.set(xlabel='Rating', ylabel='Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = \"\"\"\n",
    "One of the best movies I've ever seen in my life!\n",
    "\"\"\"\n",
    "result = list(imdb_model.predict(imdb_test_input_fn(test_string)))\n",
    "ax = sns.barplot(y=result[0]['probabilities'], x=list(range(0,11)))\n",
    "ax.set(xlabel='Rating', ylabel='Probability')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_string = \"\"\"\n",
    "Absolutely awful. I can't believe I spent my money on something so boring!\n",
    "\"\"\"\n",
    "result = list(imdb_model.predict(imdb_test_input_fn(test_string)))\n",
    "ax = sns.barplot(y=result[0]['probabilities'], x=list(range(0,11)))\n",
    "ax.set(xlabel='Rating', ylabel='Probability')\n",
    "plt.show()"
   ]
  }
 ],
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