4_Backward_Propagation_with_NumPy.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/binhvd/Data-Analytics-3-Labs/blob/main/4_Backward_Propagation_with_NumPy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dmN0rAJ-iBXQ"
      },
      "source": [
        "# Let's code a Neural Network in plain NumPy\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlRHCmOUiBXS"
      },
      "source": [
        "Using high-level frameworks like Keras, TensorFlow or PyTorch allows us to build very complex models quickly. However, it is worth taking the time to look inside and understand underlying concepts. This time we will try to make use of our knowledge and build a fully operational neural network using only NumPy. Finally, we will also use it to solve simple classification problems and compare its performance with the model built with Keras."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxJCEL7tiBXS"
      },
      "source": [
        "## Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VhooJD1PiBXS"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from IPython.display import Image"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "staowefqiBXT"
      },
      "source": [
        "## Network architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dNKDzYqsiBXT"
      },
      "source": [
        "![Network architecture](https://github.com/SkalskiP/ILearnDeepLearning.py/blob/master/01_mysteries_of_neural_networks/03_numpy_neural_net/supporting_visualizations/nn_architecture.png?raw=1)\n",
        "\n",
        "<b>Figure 1.</b> Example of dense neural network architecture"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxyyypdBiBXT"
      },
      "source": [
        "## First things first"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "G0KgHJl3iBXU"
      },
      "source": [
        "Before we start programming, let's stop for a moment and prepare a basic roadmap. Our goal is to create a program capable of creating a densely connected neural network with the specified architecture (number and size of layers and appropriate activation function). An example of such a network is presented in Figure 1. Above all, we must be able to our network and make predictions using it.\n",
        "\n",
        "![Roadmap](https://github.com/SkalskiP/ILearnDeepLearning.py/blob/master/01_mysteries_of_neural_networks/03_numpy_neural_net/supporting_visualizations/blueprint.gif?raw=1)\n",
        "\n",
        "<b>Figure 2.</b> Neural network blueprint\n",
        "\n",
        "Diagram above shows what operations will have to be performed during the training of our neural network. It also shows how many parameters we will have to update and read at different stages of a single iteration. Building the right data structure and skillfully managing its state is the most difficult part of our task."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZS82SFiHiBXU"
      },
      "source": [
        "## Initiation of neural network layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KYcYWLgciBXU"
      },
      "source": [
        "![Parameters sizes](https://github.com/SkalskiP/ILearnDeepLearning.py/blob/master/01_mysteries_of_neural_networks/03_numpy_neural_net/supporting_visualizations/params_sizes.png?raw=1)\n",
        "\n",
        "<b>Figure 3.</b> Dimensions of weight matrix W and bias vector b for layer l.\n",
        "\n",
        "Let's start with by initiating weight matrix W and bias vector b for each layer. In Figure 3 I have prepared a small cheatsheet, which will help us to asign the appropriate dimensions for these coefficients. Superscript [l] denotes the index of the current layer (counted from 1). I assumed that the information describing the NN architecture will be delivered to our program in the form of list. Each item in the list is a dictionary describing the basic parameters of a single network layer: input_dim - the size of the signal vector supplied as an input for the layer, output_dim - the size of the activation vector obtained at the output of the layer and activation - the activation function to be used inside the layer."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As a thumb rule, weight matrix has following dimensions :\n",
        "\n",
        "*   The number of rows must equal the number of neurons in the previous layer: **n<sup>[l-1]</sup>**.\n",
        "*   The number of columns must match the number of neurons in the next layer: **n<sup>[l]</sup>**.\n",
        "\n",
        "**Therefore**, weight matrix dimension = (n<sup>[l-1]</sup> X n<sup>[l]</sup>). If you take the transpose, it becomes (n<sup>[l]</sup> X n<sup>[l-1]</sup>).\n",
        "\n",
        "**However**, people sometimes stores W<sup>T</sup> directly, so they don't need to calculate it again as seen in the Figure 3."
      ],
      "metadata": {
        "id": "GmZ2EGcDsUzA"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mFSeDfYjiBXV"
      },
      "outputs": [],
      "source": [
        "NN_ARCHITECTURE = [\n",
        "    {\"input_dim\": 2, \"output_dim\": 25, \"activation\": \"relu\"},\n",
        "    {\"input_dim\": 25, \"output_dim\": 50, \"activation\": \"relu\"},\n",
        "    {\"input_dim\": 50, \"output_dim\": 50, \"activation\": \"relu\"},\n",
        "    {\"input_dim\": 50, \"output_dim\": 25, \"activation\": \"relu\"},\n",
        "    {\"input_dim\": 25, \"output_dim\": 1, \"activation\": \"sigmoid\"},\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P8fu2mEziBXV"
      },
      "source": [
        "## Initiation of parameter values for each layer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zaDplkNZiBXV"
      },
      "source": [
        "Let's finally focus on the main task that we have to accomplish in this part - the initiation of layers parameters. Initial weights values cannot be equal because it leads to breaking symmetry problem. Basically, if all weights are the same, no matter what was the input X, all units in hidden layer will be the same too. In a way, we got stuck in the initial state without any hope for escape, no matter how long will we train our model and how deep our network is. The use of small values increases the efficiency of our algorithm during first iterations. Looking at the graph of the sigmoid function, shown in Figure 4, we can see that it reaches the highest derivative value for numbers close to zero, which has significant effect on the speed of learning of our NN. All in all parameter initiation using small random numbers is simple approach, but it guarantees good enough starting point for out algorithm. Prepared parameters values are stored in a python dictionary with a key that uniquely identifies to which layer they belong. The dictionary is returned at the end of the function, so we can use it in the next stages of our algorithm."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P80pyXb4iBXW"
      },
      "outputs": [],
      "source": [
        "def init_layers(nn_architecture, seed = 99):\n",
        "    # random seed initiation\n",
        "    np.random.seed(seed)\n",
        "    # number of layers in our neural network\n",
        "    number_of_layers = len(nn_architecture)\n",
        "    # parameters storage initiation\n",
        "    params_values = {}\n",
        "    \n",
        "    # iteration over network layers\n",
        "    for idx, layer in enumerate(nn_architecture):\n",
        "        # we number network layers from 1\n",
        "        layer_idx = idx + 1\n",
        "        \n",
        "        # extracting the number of units in layers\n",
        "        layer_input_size = layer[\"input_dim\"]\n",
        "        layer_output_size = layer[\"output_dim\"]\n",
        "        \n",
        "        # initiating the values of the W matrix\n",
        "        # and vector b for subsequent layers\n",
        "        params_values['W' + str(layer_idx)] = np.random.randn(\n",
        "            layer_output_size, layer_input_size) * 0.1\n",
        "        params_values['b' + str(layer_idx)] = np.random.randn(\n",
        "            layer_output_size, 1) * 0.1\n",
        "        \n",
        "    return params_values"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "params = init_layers(NN_ARCHITECTURE)\n",
        "params['W4'].shape == (25,50)"
      ],
      "metadata": {
        "id": "Tk19aOJ0n271",
        "outputId": "e760d409-c1b6-442d-b84c-e5110dfcf1c5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w8ceWEj9iBXW"
      },
      "source": [
        "## Activation functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8cIutluiBXW"
      },
      "source": [
        "Amongst all the functions that we will use today, there are a few very simple but powerful ones. Activation functions can be written in a single line of code, but they give the neural nets non-linearity and therefore the expressiveness that they need. \"Without them, our neural network would become a combination of linear functions, so it would be just a linear function itself.\" There are many activation functions, but in this project I decided to provide the possibility of using two of them - sigmoid and ReLU. We also have to prepare their derivatives in order to be able to go full circle and pass both forward and backward propagation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hKb0Ha5uiBXX"
      },
      "source": [
        "![Activations](https://github.com/SkalskiP/ILearnDeepLearning.py/blob/master/01_mysteries_of_neural_networks/03_numpy_neural_net/supporting_visualizations/activations.gif?raw=1)\n",
        "\n",
        "<b>Figure 4.</b> Activation functions used in the algorithm."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NM4G8tcCiBXX"
      },
      "outputs": [],
      "source": [
        "def sigmoid(Z):\n",
        "    return 1/(1+np.exp(-Z))\n",
        "\n",
        "def relu(Z):\n",
        "    return np.maximum(0,Z)\n",
        "\n",
        "def sigmoid_backward(dA, Z):\n",
        "    sig = sigmoid(Z)\n",
        "    return dA * sig * (1 - sig)\n",
        "\n",
        "def relu_backward(dA, Z):\n",
        "    dZ = np.array(dA, copy = True)\n",
        "    dZ[Z <= 0] = 0;\n",
        "    return dZ;"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "sigmoid(0) == 0.5 and relu(-10) == 0"
      ],
      "metadata": {
        "id": "069USD_zvE-h",
        "outputId": "48831f64-a32c-4c7e-9198-252bee6331cc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gQKEkXhSiBXX"
      },
      "source": [
        "## Single layer forward propagation step"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqjqIr_MiBXX"
      },
      "source": [
        "This part of the code is probably the most straightforward and easy to understand . Given input signal from previous layer, we compute affine transformation Z and then apply selected activation function. By using NumPy, we can leverage vectorization - performing matrix operations, for whole layer and whole batch of examples at once. This eliminates iteration and significantly speeds up our calculations. In addition to the calculated matrix A, our function also returns an intermediate value of Z. What for? The answer is shown in Figure 2. We will need Z during the backward step."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZnLaiLLSiBXX"
      },
      "source": [
        "$$\\boldsymbol{Z}^{[l]} = \\boldsymbol{W}^{[l]} \\cdot \\boldsymbol{A}^{[l-1]} + \\boldsymbol{b}^{[l]}$$"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tryiqZYKiBXY"
      },
      "source": [
        "$$\\boldsymbol{A}^{[l]} = g^{[l]}(\\boldsymbol{Z}^{[l]})$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KNeOra4miBXY"
      },
      "outputs": [],
      "source": [
        "def single_layer_forward_propagation(A_prev, W_curr, b_curr, activation=\"relu\"):\n",
        "    # calculation of the input value for the activation function\n",
        "    # TODO: Please calculate Z\n",
        "    Z_curr = ...\n",
        "    \n",
        "    # selection of activation function\n",
        "    if activation is \"relu\":\n",
        "        activation_func = relu\n",
        "    elif activation is \"sigmoid\":\n",
        "        activation_func = sigmoid\n",
        "    else:\n",
        "        raise Exception('Non-supported activation function')\n",
        "        \n",
        "    # return of calculated activation A and the intermediate Z matrix\n",
        "    return activation_func(Z_curr), Z_curr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "A,_ = single_layer_forward_propagation([[1], [2]], [[1, 2],[3, 4],[5, 6]], [[7], [8], [9]])\n",
        "(A == [[12], [19], [26]]).all()"
      ],
      "metadata": {
        "id": "-rlPZPgAvnlQ",
        "outputId": "11cb4a23-c4eb-4eca-84a8-30f8c3f0a7c8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gZaPSmYCiBXY"
      },
      "source": [
        "![Matrix sizes 2](https://github.com/SkalskiP/ILearnDeepLearning.py/blob/master/01_mysteries_of_neural_networks/03_numpy_neural_net/supporting_visualizations/matrix_sizes_2.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dSeXXwtqiBXY"
      },
      "source": [
        "## Full forward propagation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mH_52xlSiBXY"
      },
      "source": [
        "With the single_layer_forward_propagation function completed, we can easily build a whole step forward. This is a slightly more complex function, whose role is not only to perform predictions but also to organize the collection of intermediate values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wspjdwSGiBXZ"
      },
      "outputs": [],
      "source": [
        "def full_forward_propagation(X, params_values, nn_architecture):\n",
        "    # creating a temporary memory to store the information needed for a backward step\n",
        "    memory = {}\n",
        "    # X vector is the activation for layer 0 \n",
        "    A_curr = X\n",
        "    \n",
        "    # iteration over network layers\n",
        "    for idx, layer in enumerate(nn_architecture):\n",
        "        # we number network layers from 1\n",
        "        layer_idx = idx + 1\n",
        "        # transfer the activation from the previous iteration\n",
        "        A_prev = A_curr\n",
        "        \n",
        "        # extraction of the activation function for the current layer\n",
        "        activ_function_curr = layer[\"activation\"]\n",
        "        # extraction of W for the current layer\n",
        "        W_curr = params_values[\"W\" + str(layer_idx)]\n",
        "        # extraction of b for the current layer\n",
        "        b_curr = params_values[\"b\" + str(layer_idx)]\n",
        "        \n",
        "        # TODO: Calculate activation for the current layer\n",
        "        A_curr, Z_curr = ...\n",
        "        \n",
        "        # saving calculated values in the memory\n",
        "        memory[\"A\" + str(idx)] = A_prev\n",
        "        memory[\"Z\" + str(layer_idx)] = Z_curr\n",
        "       \n",
        "    # return of prediction vector and a dictionary containing intermediate values\n",
        "    return A_curr, memory"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "nn_architecture = [\n",
        "    {\"input_dim\": 2, \"output_dim\": 3, \"activation\": \"relu\"},\n",
        "]\n",
        "X = [[1], [2]]\n",
        "params_values = {\n",
        "    'W1': [[1, 2],[3, 4],[5, 6]],\n",
        "    'b1': [[7], [8], [9]]\n",
        "}\n",
        "A,m = full_forward_propagation(X, params_values, nn_architecture)\n",
        "(A == [[12], [19], [26]]).all()"
      ],
      "metadata": {
        "id": "oSB_-dLXAI4z",
        "outputId": "73767497-0273-42e6-d0ce-39f9f57c6d1c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'A0': [[1], [2]], 'Z1': array([[12],\n",
              "        [19],\n",
              "        [26]])}"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TH2pUhFoiBXa"
      },
      "source": [
        "## Single layer backward propagation step"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yutCxMzjiBXa"
      },
      "source": [
        "Sadly, backward propagation is regarded by many inexperienced deep learning enthusiasts as algorithm that is intimidating and difficult to understand. The combination of differential calculus and linear algebra very often deters people who do not have a solid mathematical training. \n",
        "\n",
        "Often people confuse backward propaganda with gradient descent, but in fact these are two separate matters. The purpose of the first one is to calculate the gradient effectively, whereas the second one is to use the calculated gradient to optimize. In NN, we calculate the gradient of the cost function (discussed earlier) in respect to parameters, but backpropagation can be used to calculate derivatives of any function. The essence of this algorithm is the recursive use of a chain rule known from differential calculus - calculate a derivative of functions created by assembling other functions, whose derivatives we already know. This process - for one network layer - is described by the following formulas. Unfortunately, due to the fact that this article focuses mainly on practical implementation, I'll omit the derivation. Looking at the formulas, it becomes obvious why we decided to remember the values of the A and Z matrices for intermediate layers in a forward step.\n",
        "\n",
        "$$\\boldsymbol{dW}^{[l]} = \\frac{\\partial L }{\\partial \\boldsymbol{W}^{[l]}} = \\frac{1}{m} \\boldsymbol{dZ}^{[l]} \\boldsymbol{A}^{[l-1] T}$$\n",
        "\n",
        "$$\\boldsymbol{db}^{[l]} = \\frac{\\partial L }{\\partial \\boldsymbol{b}^{[l]}} = \\frac{1}{m} \\sum_{i = 1}^{m} \\boldsymbol{dZ}^{[l](i)}$$\n",
        "\n",
        "$$\\boldsymbol{dA}^{[l-1]} = \\frac{\\partial L }{\\partial \\boldsymbol{A}^{[l-1]}} = \\boldsymbol{W}^{[l] T} \\boldsymbol{dZ}^{[l]}$$\n",
        "\n",
        "$$\\boldsymbol{dZ}^{[l]} = \\boldsymbol{dA}^{[l]} * g'(\\boldsymbol{Z}^{[l]})$$\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CyEMWRc1iBXa"
      },
      "outputs": [],
      "source": [
        "def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation=\"relu\"):\n",
        "    # number of examples\n",
        "    m = A_prev.shape[1]\n",
        "    \n",
        "    # selection of activation function\n",
        "    if activation is \"relu\":\n",
        "        backward_activation_func = relu_backward\n",
        "    elif activation is \"sigmoid\":\n",
        "        backward_activation_func = sigmoid_backward\n",
        "    else:\n",
        "        raise Exception('Non-supported activation function')\n",
        "    \n",
        "    # calculation of the activation function derivative\n",
        "    dZ_curr = backward_activation_func(dA_curr, Z_curr)\n",
        "    \n",
        "    # TODO: Calculate derivative of the matrix W\n",
        "    dW_curr = ...\n",
        "\n",
        "    # derivative of the vector b\n",
        "    db_curr = np.sum(dZ_curr, axis=1, keepdims=True) / m\n",
        "    \n",
        "    # TODO: Calculate derivative of the matrix A_prev\n",
        "    dA_prev = ...\n",
        "\n",
        "    return dA_prev, dW_curr, db_curr"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "dA_curr = np.array([[1], [2], [3]])\n",
        "W_curr = np.array([[1, 2],[3, 4],[5, 6]])\n",
        "b_curr = np.array([[7], [8], [9]])\n",
        "Z_curr = np.array([[12], [-19], [26]])\n",
        "A_prev = np.array([[1], [2]])\n",
        "dA_prev, dW_curr, db_curr = single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev)\n",
        "(dA_prev == [[16],[20]]).all() and (dW_curr == [[1., 2.], [0., 0.], [3., 6.]]).all() and (db_curr == [[1.], [0.], [3.]]).all()"
      ],
      "metadata": {
        "id": "KCxd6yA8I3BI",
        "outputId": "62284ab6-b7a4-49cd-d0cf-20b38c5ed2f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_RCn1_teiBXa"
      },
      "source": [
        "## Full backward propagation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nq10ax-8iBXa"
      },
      "source": [
        "Just like in the case of forward propagation, I decided to split the calculations into two separate functions. The first one - shown in Snippet 7 - focuses on a single layer and boils down to rewriting above formulas in NumPy. The second one, representing full backward propagation, deals primarily with key juggling to read and update values in three dictionaries. We start by calculating a derivative of the cost function with respect to the prediction vector - result of forward propagation. This is quite trivial as it only consists of rewriting the following formula. Then iterate through the layers of the network starting from the end and calculate the derivatives with respect to all parameters according to the diagram shown in Figure 6. Ultimately, function returns a python dictionary containing the gradient we are looking for.\n",
        "\n",
        "$$\\frac{\\partial L }{\\partial \\boldsymbol{\\hat{Y}}} = -(\\frac{\\boldsymbol{Y}}{\\boldsymbol{\\hat{Y}}}- \\frac{1-\\boldsymbol{Y}}{1-\\boldsymbol{\\hat{Y}}})$$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HyNMhm1CiBXa"
      },
      "outputs": [],
      "source": [
        "def full_backward_propagation(Y_hat, Y, memory, params_values, nn_architecture):\n",
        "    grads_values = {}\n",
        "    \n",
        "    # number of examples\n",
        "    m = Y.shape[1]\n",
        "    # a hack ensuring the same shape of the prediction vector and labels vector\n",
        "    Y = Y.reshape(Y_hat.shape)\n",
        "    \n",
        "    # initiation of gradient descent algorithm\n",
        "    dA_prev = - (np.divide(Y, Y_hat) - np.divide(1 - Y, 1 - Y_hat));\n",
        "    \n",
        "    for layer_idx_prev, layer in reversed(list(enumerate(nn_architecture))):\n",
        "        # we number network layers from 1\n",
        "        layer_idx_curr = layer_idx_prev + 1\n",
        "        # extraction of the activation function for the current layer\n",
        "        activ_function_curr = layer[\"activation\"]\n",
        "        \n",
        "        dA_curr = dA_prev\n",
        "        \n",
        "        A_prev = memory[\"A\" + str(layer_idx_prev)]\n",
        "        Z_curr = memory[\"Z\" + str(layer_idx_curr)]\n",
        "        \n",
        "        W_curr = params_values[\"W\" + str(layer_idx_curr)]\n",
        "        b_curr = params_values[\"b\" + str(layer_idx_curr)]\n",
        "        \n",
        "        # TODO: Do a single backward propagation\n",
        "        dA_prev, dW_curr, db_curr = ...\n",
        "        \n",
        "        grads_values[\"dW\" + str(layer_idx_curr)] = dW_curr\n",
        "        grads_values[\"db\" + str(layer_idx_curr)] = db_curr\n",
        "    \n",
        "    return grads_values"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "nn_architecture = [\n",
        "    {\"input_dim\": 2, \"output_dim\": 3, \"activation\": \"relu\"},\n",
        "]\n",
        "\n",
        "Y = np.array([[.1], [.2], [.3]])\n",
        "Y_hat = np.array([[.5], [.6], [.7]])\n",
        "\n",
        "memory = {\n",
        "    'A0': np.array([[1], [2]]), \n",
        "    'Z1': np.array([[12], [-19], [26]])\n",
        "}\n",
        "\n",
        "params_values = {\n",
        "    'W1': np.array([[1, 2],[3, 4],[5, 6]]),\n",
        "    'b1': np.array([[7], [8], [9]])\n",
        "}\n",
        "\n",
        "grads_values = full_backward_propagation(Y_hat, Y, memory, params_values, nn_architecture)\n",
        "(np.round(grads_values['dW1'], 2) == [[1.6, 3.2], [0., 0.], [1.90, 3.81]]).all() and (np.round(grads_values['db1'], 2) == [[1.6], [0], [1.90]]).all()"
      ],
      "metadata": {
        "id": "dTPRhnFAPBWa",
        "outputId": "fdb1847a-d599-4b68-dd5d-dca8257544f0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DsAjtLkNiBXb"
      },
      "source": [
        "## Updating parameter values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MuTbImstiBXb"
      },
      "source": [
        "The goal of this method is to update network parameters using gradient optimisation. In this way, we try to bring our target function closer to a minimum. To accomplish this task, we will use two dictionaries provided as function arguments: params_values, which stores the current values of parameters, and grads_values, which stores cost function derivatives calculated with respect to these parameters. Now you only need to apply the following equations for each layer. This is a very simple optimization algorithm, but I decided to use it because it is a great starting point for more advanced optimizers, which will probably be the subject of one of my next articles."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qFHgk_bkiBXb"
      },
      "outputs": [],
      "source": [
        "def update(params_values, grads_values, nn_architecture, learning_rate):\n",
        "\n",
        "    # iteration over network layers\n",
        "    for layer_idx, layer in enumerate(nn_architecture, 1):\n",
        "        # TODO: Calculate new W and b\n",
        "        params_values[\"W\" + str(layer_idx)] -= ...\n",
        "        params_values[\"b\" + str(layer_idx)] -= ...\n",
        "\n",
        "    return params_values;"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "params_values = {\n",
        "    'W1': np.array([[1., 2.],[3., 4.],[5., 6.]]),\n",
        "    'b1': np.array([[7.], [8.], [9.]])\n",
        "}\n",
        "grads_values = {\n",
        "    'dW1': np.array([[10, 20],[30, 40],[50, 60]]),\n",
        "    'db1': np.array([[70], [80], [90]])\n",
        "}\n",
        "nn_architecture = [\n",
        "    {\"input_dim\": 2, \"output_dim\": 3, \"activation\": \"relu\"},\n",
        "]\n",
        "\n",
        "learning_rate = 0.1\n",
        "params_values = update(params_values, grads_values, nn_architecture, learning_rate)\n",
        "np.sum(params_values['W1']) == 0 and np.sum(params_values['b1']) == 0"
      ],
      "metadata": {
        "id": "9eds6RMDUpxR",
        "outputId": "e0074f97-8c14-44aa-88bf-5bf5d09e59ba",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IeYIqwO0iBXZ"
      },
      "source": [
        "## Calculating cost"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uHW2g2ItiBXZ"
      },
      "source": [
        "In order to monitor our progress and make sure that we are moving in desired direction, we should routinely calculate the value of the loss function. \"Generally speaking, the loss function is designed to show how far we are from the 'ideal' solution.\" It is selected according to the problem we plan to solve, and frameworks such as Keras have many options to choose from. Because I am planning to test our NN for the classification of points between two classes, I decided to use binary crossentropy, which is defined by the following formulas. In order to give us more information on how our neural network is coping with the task, I have also decided to implement a function that will calculate accuracy for us.\n",
        "\n",
        "![Cost Function](https://github.com/SkalskiP/ILearnDeepLearning.py/blob/master/01_mysteries_of_neural_networks/03_numpy_neural_net/supporting_visualizations/cost_function.gif?raw=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eArGn80OiBXZ"
      },
      "outputs": [],
      "source": [
        "def get_cost_value(Y_hat, Y):\n",
        "    # number of examples\n",
        "    m = Y_hat.shape[1]\n",
        "\n",
        "    # TODO: calculate the cost according to the formula\n",
        "    cost = ...\n",
        "    return np.squeeze(cost)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "round(get_cost_value(np.array([[.1, .2]]), np.array([[.5, .3]]))[()], 4) == 0.9215"
      ],
      "metadata": {
        "id": "OV2iLFKWBIJp",
        "outputId": "5175fa59-abc1-4940-bad4-4e20f718e900",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M8Ty2PH7iBXZ"
      },
      "source": [
        "## Calculating accuracy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZlbeIrviBXZ"
      },
      "outputs": [],
      "source": [
        "# an auxiliary function that converts probability into class\n",
        "# > 0.5 -> 1 else -> 0\n",
        "def convert_prob_into_class(probs):\n",
        "    probs_ = np.copy(probs)\n",
        "    probs_[probs_ > 0.5] = 1\n",
        "    probs_[probs_ <= 0.5] = 0\n",
        "    return probs_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "(convert_prob_into_class([[.4, .7], [.6, .1]]) == [[0, 1], [1, 0]]).all()"
      ],
      "metadata": {
        "id": "hbeWLPRzGaYX",
        "outputId": "ffad0044-e761-4c5d-cfa1-5f91c8a3e5cd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kkKYgOWriBXZ"
      },
      "outputs": [],
      "source": [
        "def get_accuracy_value(Y_hat, Y):\n",
        "    Y_hat_ = convert_prob_into_class(Y_hat)\n",
        "    return (Y_hat_ == Y).all(axis=0).mean()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_accuracy_value([[.4, .7], [.6, .1]], [[0, 0], [1, 0]])"
      ],
      "metadata": {
        "id": "ClF4XWvIFuyJ",
        "outputId": "1edfebb2-5b7b-47de-d85e-87006fe6ace3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZvXFpjnciBXb"
      },
      "source": [
        "## Putting things together"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "prfRcT1liBXb"
      },
      "outputs": [],
      "source": [
        "def train(X, Y, nn_architecture, epochs, learning_rate, verbose=False, callback=None):\n",
        "    # initiation of neural net parameters\n",
        "    params_values = init_layers(nn_architecture, 2)\n",
        "    # initiation of lists storing the history \n",
        "    # of metrics calculated during the learning process \n",
        "    cost_history = []\n",
        "    accuracy_history = []\n",
        "    \n",
        "    # performing calculations for subsequent iterations\n",
        "    for i in range(epochs):\n",
        "        # step forward\n",
        "        Y_hat, cashe = full_forward_propagation(X, params_values, nn_architecture)\n",
        "        \n",
        "        # calculating metrics and saving them in history\n",
        "        cost = get_cost_value(Y_hat, Y)\n",
        "        cost_history.append(cost)\n",
        "        accuracy = get_accuracy_value(Y_hat, Y)\n",
        "        accuracy_history.append(accuracy)\n",
        "        \n",
        "        # step backward - calculating gradient\n",
        "        grads_values = full_backward_propagation(Y_hat, Y, cashe, params_values, nn_architecture)\n",
        "        # updating model state\n",
        "        params_values = update(params_values, grads_values, nn_architecture, learning_rate)\n",
        "        \n",
        "        if(i % 50 == 0):\n",
        "            if(verbose):\n",
        "                print(\"Iteration: {:05} - cost: {:.5f} - accuracy: {:.5f}\".format(i, cost, accuracy))\n",
        "            if(callback is not None):\n",
        "                callback(i, params_values)\n",
        "            \n",
        "    return params_values"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing\n",
        "X = np.array([[1], [2]])\n",
        "Y = np.array([[.1], [.2], [.3]])\n",
        "nn_architecture = [\n",
        "    {\"input_dim\": 2, \"output_dim\": 3, \"activation\": \"sigmoid\"},\n",
        "]\n",
        "epochs = 1\n",
        "learning_rate = 0.01\n",
        "\n",
        "params_values = train(X, Y, nn_architecture, epochs, learning_rate)\n",
        "(np.round(params_values['W1'], 3) == [[-0.046, -0.014], [-0.217,  0.158], [-0.180, -0.086]]).all() and (np.round(params_values['b1'], 3) == [[ 0.046], [-0.128], [-0.107]]).all()"
      ],
      "metadata": {
        "id": "MrbyRTb2VueC",
        "outputId": "c4816259-655e-4c8b-bf4b-aba7b883ce06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0skqvAChiBXb"
      },
      "source": [
        "## David vs Goliath"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_7qedVJiBXb"
      },
      "source": [
        "### Imports"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OGm9U9P7iBXb"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from sklearn.datasets import make_moons\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib import cm\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "sns.set_style(\"whitegrid\")\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.utils import np_utils\n",
        "from keras import regularizers\n",
        "\n",
        "from sklearn.metrics import accuracy_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PRomMa1liBXc"
      },
      "source": [
        "### Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8Aero0joiBXc"
      },
      "outputs": [],
      "source": [
        "# number of samples in the data set\n",
        "N_SAMPLES = 1000\n",
        "# ratio between training and test sets\n",
        "TEST_SIZE = 0.1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EyHDCL6iBXc"
      },
      "source": [
        "### Create artificial dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "feRCoI8wiBXc"
      },
      "outputs": [],
      "source": [
        "X, y = make_moons(n_samples = N_SAMPLES, noise=0.2, random_state=100)\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gcq0cDENiBXc",
        "outputId": "75e87ecf-8d9f-4ffc-8fb1-55899fafb3c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 164
        }
      ],
      "source": [
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ksX0tna4iBXd",
        "outputId": "bfdfeb47-431e-43ac-9652-f46c8c40e0d8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1000,)"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ],
      "source": [
        "y.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VWvqIoiJiBXd"
      },
      "source": [
        "### Auxiliary plotting function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jFAyUqAZiBXd"
      },
      "outputs": [],
      "source": [
        "# the function making up the graph of a dataset\n",
        "def make_plot(X, y, plot_name, file_name=None, XX=None, YY=None, preds=None, dark=False):\n",
        "    if (dark):\n",
        "        plt.style.use('dark_background')\n",
        "    else:\n",
        "        sns.set_style(\"whitegrid\")\n",
        "    plt.figure(figsize=(16,12))\n",
        "    axes = plt.gca()\n",
        "    axes.set(xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
        "    plt.title(plot_name, fontsize=30)\n",
        "    plt.subplots_adjust(left=0.20)\n",
        "    plt.subplots_adjust(right=0.80)\n",
        "    if(XX is not None and YY is not None and preds is not None):\n",
        "        plt.contourf(XX, YY, preds.reshape(XX.shape), 25, alpha = 1, cmap=cm.Spectral)\n",
        "        plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap=\"Greys\", vmin=0, vmax=.6)\n",
        "    plt.scatter(X[:, 0], X[:, 1], c=y.ravel(), s=40, cmap=plt.cm.Spectral, edgecolors='black')\n",
        "    if(file_name):\n",
        "        plt.savefig(file_name)\n",
        "        plt.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zrk3IM9TiBXd"
      },
      "source": [
        "### Visualization of the data set"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWbB64I5iBXd",
        "outputId": "99bf52b7-9934-4ca1-dc06-c2feb055398d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "make_plot(X, y, \"Dataset\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sxRrqEdFiBXd"
      },
      "source": [
        "### Let's put our model to the test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zt0Gh3PRiBXd",
        "outputId": "2e60be43-3191-42fb-fd95-67e893a64659",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration: 00000 - cost: 0.69365 - accuracy: 0.50444\n",
            "Iteration: 00050 - cost: 0.69349 - accuracy: 0.50444\n",
            "Iteration: 00100 - cost: 0.69334 - accuracy: 0.50444\n",
            "Iteration: 00150 - cost: 0.69319 - accuracy: 0.50444\n",
            "Iteration: 00200 - cost: 0.69307 - accuracy: 0.50444\n",
            "Iteration: 00250 - cost: 0.69295 - accuracy: 0.50444\n",
            "Iteration: 00300 - cost: 0.69284 - accuracy: 0.50444\n",
            "Iteration: 00350 - cost: 0.69272 - accuracy: 0.50444\n",
            "Iteration: 00400 - cost: 0.69260 - accuracy: 0.50444\n",
            "Iteration: 00450 - cost: 0.69249 - accuracy: 0.50444\n",
            "Iteration: 00500 - cost: 0.69238 - accuracy: 0.50444\n",
            "Iteration: 00550 - cost: 0.69228 - accuracy: 0.50444\n",
            "Iteration: 00600 - cost: 0.69217 - accuracy: 0.50444\n",
            "Iteration: 00650 - cost: 0.69206 - accuracy: 0.50444\n",
            "Iteration: 00700 - cost: 0.69194 - accuracy: 0.50444\n",
            "Iteration: 00750 - cost: 0.69182 - accuracy: 0.50444\n",
            "Iteration: 00800 - cost: 0.69170 - accuracy: 0.50444\n",
            "Iteration: 00850 - cost: 0.69156 - accuracy: 0.50444\n",
            "Iteration: 00900 - cost: 0.69142 - accuracy: 0.50444\n",
            "Iteration: 00950 - cost: 0.69126 - accuracy: 0.50444\n",
            "Iteration: 01000 - cost: 0.69109 - accuracy: 0.50444\n",
            "Iteration: 01050 - cost: 0.69090 - accuracy: 0.50444\n",
            "Iteration: 01100 - cost: 0.69070 - accuracy: 0.50444\n",
            "Iteration: 01150 - cost: 0.69049 - accuracy: 0.50444\n",
            "Iteration: 01200 - cost: 0.69025 - accuracy: 0.50444\n",
            "Iteration: 01250 - cost: 0.69000 - accuracy: 0.50889\n",
            "Iteration: 01300 - cost: 0.68972 - accuracy: 0.52889\n",
            "Iteration: 01350 - cost: 0.68941 - accuracy: 0.59778\n",
            "Iteration: 01400 - cost: 0.68907 - accuracy: 0.66667\n",
            "Iteration: 01450 - cost: 0.68869 - accuracy: 0.72222\n",
            "Iteration: 01500 - cost: 0.68827 - accuracy: 0.76111\n",
            "Iteration: 01550 - cost: 0.68780 - accuracy: 0.79111\n",
            "Iteration: 01600 - cost: 0.68726 - accuracy: 0.81333\n",
            "Iteration: 01650 - cost: 0.68666 - accuracy: 0.82778\n",
            "Iteration: 01700 - cost: 0.68596 - accuracy: 0.83778\n",
            "Iteration: 01750 - cost: 0.68512 - accuracy: 0.84000\n",
            "Iteration: 01800 - cost: 0.68416 - accuracy: 0.84222\n",
            "Iteration: 01850 - cost: 0.68308 - accuracy: 0.84444\n",
            "Iteration: 01900 - cost: 0.68185 - accuracy: 0.84333\n",
            "Iteration: 01950 - cost: 0.68042 - accuracy: 0.84222\n",
            "Iteration: 02000 - cost: 0.67875 - accuracy: 0.84222\n",
            "Iteration: 02050 - cost: 0.67680 - accuracy: 0.84444\n",
            "Iteration: 02100 - cost: 0.67453 - accuracy: 0.84444\n",
            "Iteration: 02150 - cost: 0.67183 - accuracy: 0.84667\n",
            "Iteration: 02200 - cost: 0.66859 - accuracy: 0.84556\n",
            "Iteration: 02250 - cost: 0.66471 - accuracy: 0.84222\n",
            "Iteration: 02300 - cost: 0.66004 - accuracy: 0.84333\n",
            "Iteration: 02350 - cost: 0.65437 - accuracy: 0.84222\n",
            "Iteration: 02400 - cost: 0.64757 - accuracy: 0.84444\n",
            "Iteration: 02450 - cost: 0.63942 - accuracy: 0.84778\n",
            "Iteration: 02500 - cost: 0.62966 - accuracy: 0.84444\n",
            "Iteration: 02550 - cost: 0.61796 - accuracy: 0.84111\n",
            "Iteration: 02600 - cost: 0.60398 - accuracy: 0.84111\n",
            "Iteration: 02650 - cost: 0.58764 - accuracy: 0.84111\n",
            "Iteration: 02700 - cost: 0.56876 - accuracy: 0.84222\n",
            "Iteration: 02750 - cost: 0.54730 - accuracy: 0.84222\n",
            "Iteration: 02800 - cost: 0.52368 - accuracy: 0.85000\n",
            "Iteration: 02850 - cost: 0.49867 - accuracy: 0.85333\n",
            "Iteration: 02900 - cost: 0.47325 - accuracy: 0.85556\n",
            "Iteration: 02950 - cost: 0.44840 - accuracy: 0.85556\n",
            "Iteration: 03000 - cost: 0.42476 - accuracy: 0.85889\n",
            "Iteration: 03050 - cost: 0.40263 - accuracy: 0.86333\n",
            "Iteration: 03100 - cost: 0.38221 - accuracy: 0.86222\n",
            "Iteration: 03150 - cost: 0.36367 - accuracy: 0.86778\n",
            "Iteration: 03200 - cost: 0.34730 - accuracy: 0.87111\n",
            "Iteration: 03250 - cost: 0.33327 - accuracy: 0.87444\n",
            "Iteration: 03300 - cost: 0.32149 - accuracy: 0.87778\n",
            "Iteration: 03350 - cost: 0.31175 - accuracy: 0.87889\n",
            "Iteration: 03400 - cost: 0.30379 - accuracy: 0.88000\n",
            "Iteration: 03450 - cost: 0.29733 - accuracy: 0.88111\n",
            "Iteration: 03500 - cost: 0.29209 - accuracy: 0.88111\n",
            "Iteration: 03550 - cost: 0.28783 - accuracy: 0.88111\n",
            "Iteration: 03600 - cost: 0.28431 - accuracy: 0.88222\n",
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            "Iteration: 09750 - cost: 0.07500 - accuracy: 0.96889\n",
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            "Iteration: 09900 - cost: 0.07456 - accuracy: 0.96889\n",
            "Iteration: 09950 - cost: 0.07442 - accuracy: 0.96889\n"
          ]
        }
      ],
      "source": [
        "# Training\n",
        "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), NN_ARCHITECTURE, 10000, 0.01, True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dGxuIzgWiBXe"
      },
      "outputs": [],
      "source": [
        "# TODO: Make prediction on X_test\n",
        "Y_test_hat, _ = ..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0CiTtgAmiBXe",
        "outputId": "c9afeb12-5b83-4143-e3fc-05d80cdbb66a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test set accuracy: 0.98 - David\n"
          ]
        }
      ],
      "source": [
        "# Accuracy achieved on the test set\n",
        "acc_test = get_accuracy_value(Y_test_hat, np.transpose(y_test.reshape((y_test.shape[0], 1))))\n",
        "print(\"Test set accuracy: {:.2f} - David\".format(acc_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CojpbKmFiBXe"
      },
      "source": [
        "### Keras model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cx_OxB4yiBXe"
      },
      "outputs": [],
      "source": [
        "def build_keras:\n",
        "  # Building a model\n",
        "  model = Sequential()\n",
        "\n",
        "  # TODO: Create similar network with Keras\n",
        "  ...\n",
        "\n",
        "  model.compile(loss='binary_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])\n",
        "  return model\n",
        "\n",
        "\n",
        "model = build_keras()\n",
        "\n",
        "# TODO: Train the model with 200 epochs\n",
        "history = ..."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# TODO: Make prediction on X_test\n",
        "Y_test_hat = ..."
      ],
      "metadata": {
        "id": "8NQKWOy7twJe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AuIk8BL-iBXe",
        "outputId": "fb816a60-ffa0-41e9-98d6-27f63ab73a10",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 5ms/step\n",
            "Test set accuracy: 0.98 - Goliath\n"
          ]
        }
      ],
      "source": [
        "acc_test = accuracy_score(y_test, convert_prob_into_class(Y_test_hat))\n",
        "print(\"Test set accuracy: {:.2f} - Goliath\".format(acc_test))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MT3yuho8iBXe"
      },
      "source": [
        "## Visualisation of the learning process"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2zWlAFUiBXe"
      },
      "source": [
        "### Settings"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WcQ6OPZ1iBXe",
        "outputId": "dd0dcae4-960b-4f13-ffe8-806de2b47b0d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: -c: line 0: syntax error near unexpected token `dirname'\n",
            "/bin/bash: -c: line 0: `mkdir(dirname)'\n"
          ]
        }
      ],
      "source": [
        "# boundary of the graph\n",
        "GRID_X_START = -1.5\n",
        "GRID_X_END = 2.5\n",
        "GRID_Y_START = -1.0\n",
        "GRID_Y_END = 2"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# output directory (the folder must be created on the drive)\n",
        "dirname = \"vizualization\"\n",
        "os.mkdir(dirname)\n",
        "OUTPUT_DIR = f\"./{dirname}/\""
      ],
      "metadata": {
        "id": "3medMHUjbVj8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "soeMipo_iBXf"
      },
      "source": [
        "### Definition of grid boundaries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jrSFXhKpiBXf"
      },
      "outputs": [],
      "source": [
        "grid = np.mgrid[GRID_X_START:GRID_X_END:100j,GRID_X_START:GRID_Y_END:100j]\n",
        "grid_2d = grid.reshape(2, -1).T\n",
        "XX, YY = grid"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fWFZ1L6jiBXf"
      },
      "source": [
        "### Keras Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7D-vk0riiBXf"
      },
      "outputs": [],
      "source": [
        "def callback_keras_plot(epoch, logs):\n",
        "    plot_title = \"Keras Model - It: {:05}\".format(epoch)\n",
        "    file_name = \"keras_model_{:05}.png\".format(epoch)\n",
        "    file_path = os.path.join(OUTPUT_DIR, file_name)\n",
        "    \n",
        "    prediction_probs = model.predict(grid_2d, batch_size=32, verbose=0)\n",
        "    make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u7UphL7XiBXf"
      },
      "outputs": [],
      "source": [
        "# Adding callback functions that they will run in every epoch\n",
        "testmodelcb = keras.callbacks.LambdaCallback(on_epoch_end=callback_keras_plot)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XD6Vz65wiBXf"
      },
      "outputs": [],
      "source": [
        "# Training\n",
        "model = build_keras()\n",
        "history = model.fit(X_train, y_train, epochs=200, verbose=0, callbacks=[testmodelcb])"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# merge png to a gif\n",
        "import imageio, glob\n",
        "\n",
        "with imageio.get_writer('keras.gif', mode='I') as writer:\n",
        "    for path in sorted(glob.glob(f\"{OUTPUT_DIR}/keras_*.png\")):\n",
        "        image = imageio.imread(path)\n",
        "        writer.append_data(image)\n",
        "        os.remove(path)"
      ],
      "metadata": {
        "id": "uuejMeu2licE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cfEMREV7iBXf"
      },
      "outputs": [],
      "source": [
        "rediction_probs = model.predict_proba(grid_2d, batch_size=32, verbose=0)\n",
        "make_plot(X_test, y_test, \"Keras Model\", file_name=None, XX=XX, YY=YY, preds=prediction_probs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lQGUKkiKiBXg"
      },
      "source": [
        "### NumPy Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5EbvUnsRiBXg"
      },
      "outputs": [],
      "source": [
        "def callback_numpy_plot(index, params):\n",
        "    plot_title = \"NumPy Model - It: {:05}\".format(index)\n",
        "    file_name = \"numpy_model_{:05}.png\".format(index//50)\n",
        "    file_path = os.path.join(OUTPUT_DIR, file_name)\n",
        "    \n",
        "    prediction_probs, _ = full_forward_propagation(np.transpose(grid_2d), params, NN_ARCHITECTURE)\n",
        "    prediction_probs = prediction_probs.reshape(prediction_probs.shape[1], 1)\n",
        "    make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs, dark=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P-6NDGBSiBXg",
        "outputId": "e3b863ba-58ab-4f93-ad1b-932a44f00a00",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:15: UserWarning: No contour levels were found within the data range.\n",
            "  from ipykernel import kernelapp as app\n"
          ]
        }
      ],
      "source": [
        "# Training\n",
        "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), NN_ARCHITECTURE, 10000, 0.01, False, callback_numpy_plot)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# merge png to a gif\n",
        "import imageio, glob\n",
        "\n",
        "with imageio.get_writer('numpy.gif', mode='I') as writer:\n",
        "    for path in sorted(glob.glob(f\"{OUTPUT_DIR}/numpy_*.png\")):\n",
        "        image = imageio.imread(path)\n",
        "        writer.append_data(image)\n",
        "        os.remove(path)"
      ],
      "metadata": {
        "id": "URZf8l1YdofZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iNu2MHN8iBXg",
        "outputId": "da800028-658b-4a66-9266-f8ae7ff2c94a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x864 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "prediction_probs_numpy, _ = full_forward_propagation(np.transpose(grid_2d), params_values, NN_ARCHITECTURE)\n",
        "prediction_probs_numpy = prediction_probs_numpy.reshape(prediction_probs_numpy.shape[1], 1)\n",
        "make_plot(X_test, y_test, \"NumPy Model\", file_name=None, XX=XX, YY=YY, preds=prediction_probs_numpy)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IEZu7M2SiBXg"
      },
      "source": [
        "## Reference\n",
        "https://towardsdatascience.com/lets-code-a-neural-network-in-plain-numpy-ae7e74410795"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.7"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}