Best_model.ipynb

{
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# This notebook include the best model of our experiments"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# import package"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Requirement already satisfied: ipython-autotime in c:\\users\\natha\\anaconda3\\lib\\site-packages (0.2.0)\nRequirement already satisfied: ipython in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython-autotime) (7.22.0)\nRequirement already satisfied: setuptools>=18.5 in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (56.0.0)\nRequirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (3.0.18)\nRequirement already satisfied: pygments in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (2.8.1)\nRequirement already satisfied: traitlets>=4.2 in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (5.0.5)\nRequirement already satisfied: decorator in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (5.0.6)\nRequirement already satisfied: jedi>=0.16 in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (0.18.0)\nRequirement already satisfied: pickleshare in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (0.7.5)\nRequirement already satisfied: backcall in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (0.2.0)\nRequirement already satisfied: colorama in c:\\users\\natha\\anaconda3\\lib\\site-packages (from ipython->ipython-autotime) (0.4.4)\nRequirement already satisfied: parso<0.9.0,>=0.8.0 in c:\\users\\natha\\anaconda3\\lib\\site-packages (from jedi>=0.16->ipython->ipython-autotime) (0.8.2)\nRequirement already satisfied: wcwidth in c:\\users\\natha\\anaconda3\\lib\\site-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->ipython-autotime) (0.2.5)\nRequirement already satisfied: ipython-genutils in c:\\users\\natha\\anaconda3\\lib\\site-packages (from traitlets>=4.2->ipython->ipython-autotime) (0.2.0)\nThe autotime extension is already loaded. To reload it, use:\n  %reload_ext autotime\ntime: 2.05 s\n"
     ]
    }
   ],
   "source": [
    "import math, random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# install auto timing package\n",
    "!pip install ipython-autotime\n",
    "# enable autotime\n",
    "%load_ext autotime"
   ]
  },
  {
   "source": [
    "# Load dataset and normalization"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Loaded dataset and normalized\ntime: 125 ms\n"
     ]
    }
   ],
   "source": [
    "# Load in the training set\n",
    "X_train = np.load('Assignment1-Dataset/train_data.npy')\n",
    "y_train = np.load('Assignment1-Dataset/train_label.npy')\n",
    "# Reshape the label to a 1-d vector\n",
    "y_train = y_train.reshape(-1,)\n",
    "\n",
    "# # Load in the test set\n",
    "X_test = np.load('Assignment1-Dataset/test_data.npy')\n",
    "y_test = np.load('Assignment1-Dataset/test_label.npy')\n",
    "# Reshape the label to a 1-d vector\n",
    "y_test = y_test.reshape(-1,)\n",
    "\n",
    "# Normalize (optional)\n",
    "def normalize(X, X2):\n",
    "    X = (X - X.mean(axis=0)) / X.std(axis=0)\n",
    "    X2 = (X2 - X.mean(axis=0)) / X.std(axis=0) # Normalize the data with the mean and sd from train set\n",
    "    return X, X2\n",
    "\n",
    "X_train, X_test = normalize(X_train, X_test)\n",
    "\n",
    "print(\"Loaded dataset and normalized\")"
   ]
  },
  {
   "source": [
    "# Neural Network Code"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "# Creating a Dense Layer class\n",
    "class dense:\n",
    "    # Initiate the class with required input and output dimension, and regularization\n",
    "    def __init__(self, input_dim, output_dim, w_regularizer_l1=0, w_regularizer_l2=0, \n",
    "b_regularizer_l1=0, b_regularizer_l2=0):\n",
    "        \n",
    "        # Initiate the weights and bias\n",
    "        self.w = 0.01 * np.random.randn(input_dim, output_dim)\n",
    "        self.b = np.zeros((1, output_dim))\n",
    "        \n",
    "        # Initiate the regularizer lambda value\n",
    "        self.w_regularizer_l1 = w_regularizer_l1\n",
    "        self.w_regularizer_l2 = w_regularizer_l2\n",
    "        self.b_regularizer_l1 = b_regularizer_l1\n",
    "        self.b_regularizer_l2 = b_regularizer_l2\n",
    "    \n",
    "    # Forward propagation of the Dense layer\n",
    "    def forward(self, inputs):\n",
    "        self.inputs = inputs # Save the inputs when it is pased into the layer\n",
    "        self.output = inputs.dot(self.w) + self.b # Calculate the output of this later\n",
    "    \n",
    "    # Backward propagation of the Dense layer\n",
    "    def backward(self, dvalues):\n",
    "        # Get gradient on weights and bias\n",
    "        # Dot product of input transposed and the gradient from previous layer\n",
    "        self.d_w = np.dot(self.inputs.T, dvalues)\n",
    "        self.d_b = np.sum(dvalues, axis=0, keepdims=True) # Sum up the delta from previous layer\n",
    "        \n",
    "        # Add the regularization penalties calculated from the original weights if to the gradient of the weights\n",
    "        if self.w_regularizer_l1 > 0:\n",
    "            dL1 = np.ones_like(self.w)\n",
    "            # For negative weights, the derivative of it is -1\n",
    "            dL1[self.w < 0] = -1\n",
    "            self.d_w += self.w_regularizer_l1 * dL1\n",
    "        \n",
    "        if self.w_regularizer_l2 > 0:\n",
    "            self.d_w += 2 * self.w_regularizer_l1 * self.w\n",
    "\n",
    "        # Add the regularization penalties calculated from the original bias if to the gradient of the weights\n",
    "        if self.b_regularizer_l1 > 0:\n",
    "            dL1 = np.ones_like(self.b)\n",
    "            # For negative bias, the derivative of it is -1\n",
    "            dL1[self.b < 0] = -1\n",
    "            self.d_w += self.b_regularizer_l1 * dL1\n",
    "        \n",
    "        if self.b_regularizer_l2 > 0:\n",
    "            self.d_b += self.b_regularizer_l2 * self.b\n",
    "            \n",
    "        # Gradient wrt inputs to be backprogated to the next layer\n",
    "        self.dinputs = np.dot(dvalues, self.w.T)\n",
    "    \n",
    "# Create the ReLu activaton layer class\n",
    "class ReLu:\n",
    "    \n",
    "    # Forward propagation for ReLu\n",
    "    def forward(self, inputs):\n",
    "        self.inputs = inputs # Save the inputs passed into this later\n",
    "        self.output = np.maximum(0, inputs) # Apply the relu function on each inputs\n",
    "    \n",
    "    # Back propagation for Relu\n",
    "    def backward(self, dvalues):\n",
    "        # Use the copy of the gradient instead on changing them\n",
    "        self.dinputs = dvalues.copy()\n",
    "        self.dinputs[self.inputs <0] = 0 # Apply the derivative of ReLu to the gradient wrt to gradient from last layer\n",
    "\n",
    "# Create the tanh activaton layer class\n",
    "class tanh:\n",
    "    def forward(self,inputs):\n",
    "        self.inputs = inputs\n",
    "        self.output = np.tanh(self.inputs)\n",
    "\n",
    "    def backward(self, dvalues):\n",
    "        self.dinputs = dvalues.copy()\n",
    "        self.dinputs = 1.0-(self.dinputs**2)\n",
    "\n",
    "# Create the Softmax layer class\n",
    "class softmax:\n",
    "    # Forward propagation for Softmax\n",
    "    def forward(self, inputs):\n",
    "        self.inputs = inputs # Save the inputs passed into this layer\n",
    "        \n",
    "        # Apply the softmax function to the input\n",
    "        exp_values = np.exp(inputs - np.max(inputs, axis=1, keepdims=True))\n",
    "        prob = exp_values / np.sum(exp_values, axis=1, keepdims=True)\n",
    "        \n",
    "        # Save the output values (predicted probabilities of each class) in this layer\n",
    "        self.output = prob\n",
    "    \n",
    "    # Back propagation for Softmax\n",
    "    def backward(self, dvalues):\n",
    "        # Create an empty array with shape (n_class,)\n",
    "        self.inputs = np.empty_like(dvalues)\n",
    "        \n",
    "        # Calculating the gradient of Softmax layer using its derivative\n",
    "        for idx, (single_output, single_dvalue) in enumerate(zip(self.output, dvalues)):\n",
    "            single_output = single_output.reshape(-1, 1)\n",
    "            jacob_matrix = np.diagflat(single_output) - np.dot(single_output, single_output.T)\n",
    "        self.dinputs[idx] = np.dot(jacob_matrix, single_dvalue)\n",
    "    \n",
    "# Create a general Loss class for different loss function\n",
    "class Loss:\n",
    "    \n",
    "    # Add l1, l2, loss to a lump sum regularization loss\n",
    "    def regularization_loss(self, layer):\n",
    "        regularization_loss = 0\n",
    "        # Add l1 loss of weights to regularization loss\n",
    "        if layer.w_regularizer_l1 > 0:\n",
    "            regularization_loss += layer.w_regularizer_l1 * np.sum(np.abs(layer.w))\n",
    "        # Add l2 loss of weights to regularization loss\n",
    "        if layer.w_regularizer_l2 > 0:\n",
    "            regularization_loss += layer.w_regularizer_l2 * np.sum(np.abs(layer.w * layer.w))\n",
    "        # Add l1 loss of biasa to regularization loss \n",
    "        if layer.b_regularizer_l1 > 0:\n",
    "            regularization_loss += layer.b_regularizer_l1 * np.sum(np.abs(layer.b))\n",
    "        # Add l2 loss of biasa to regularization loss\n",
    "        if layer.b_regularizer_l2 > 0:\n",
    "            regularization_loss += layer.b_regularizer_l1 * np.sum(np.abs(layer.b * layer.b))\n",
    "        \n",
    "        return regularization_loss\n",
    "    \n",
    "    # A vanilla way to calculate loss using the loss function without regularization\n",
    "    def calculate(self, output, y):\n",
    "        sample_losses = self.forward(output, y)\n",
    "        data_loss = np.mean(sample_losses)\n",
    "        return data_loss\n",
    "    \n",
    "# Create Crossentropy loss class with inherit the generl Loss class\n",
    "class Loss_CategoricalCrossentropy(Loss):\n",
    "    \n",
    "    # Forward propagation of CrossEntropy loss function\n",
    "    # which takes the output of the softmax layer as y_pred\n",
    "    def forward(self, y_pred, y):\n",
    "        sample = len(y_pred)\n",
    "        \n",
    "        y_pred_clipped = np.clip(y_pred, 1e-7, 1-1e-7) # clipped the prediction to avoid divisio by 0\n",
    "        \n",
    "        if len(y.shape) == 1:\n",
    "            # Produce the vector of the predicted probability of the correct label\n",
    "            correct_confidence = y_pred_clipped[range(sample), y] \n",
    "        if len(y.shape) == 2:\n",
    "            correct_confidence = np.sum(y_pred_clipped * y, axis=1)\n",
    "        \n",
    "        # Calculate the negative log likelihoods\n",
    "        negative_log_likelihoods = -np.log(correct_confidence)\n",
    "        return negative_log_likelihoods\n",
    "    \n",
    "    # Back propagation of CrossEntropy loss function\n",
    "    def backward(self, dvalues, y):\n",
    "        \n",
    "        # Get the number of predictions (length of y_pred)\n",
    "        sample = len(dvalues)\n",
    "        # Get the number of class in the prediction\n",
    "        labels = len(dvalues[0])\n",
    "        \n",
    "        # Create one-hot encoding if y is an index label\n",
    "        if len(y.shape) == 1:\n",
    "            y = np.eye(labels)[y]\n",
    "        \n",
    "        # Calculate the gradient wrt to input values using the derivative of the crossentropy loss\n",
    "        self.dinputs = -y / dvalues\n",
    "        \n",
    "        # Normalize the gradient according to the batch size\n",
    "        self.dinputs = self.dinputs / sample\n",
    "\n",
    "# Create the output layer as a combination of Softmax and CrossEntroly Loss\n",
    "class softmax_crossentropy_loss():\n",
    "    \n",
    "    # Initiate the layer with the chosen activation and loss function\n",
    "    def __init__(self):\n",
    "        self.activation = softmax()\n",
    "        self.loss = Loss_CategoricalCrossentropy()\n",
    "    \n",
    "    # Forward propogation for this layer\n",
    "    def forward(self, inputs, y, test=False):\n",
    "            \n",
    "            # Get predictsion by pass forwad in the Softmax class\n",
    "            # with the input it received from the previous layer\n",
    "            self.activation.forward(inputs)\n",
    "            \n",
    "            # Save the output values (prediction) in this layer\n",
    "            self.output = self.activation.output\n",
    "            \n",
    "            # If we are doing testing, no more follow up action\n",
    "            if test == True:\n",
    "                pass\n",
    "            # During training, use the chosen loss function to get the loss\n",
    "            elif test == False:\n",
    "                return self.loss.calculate(self.output, y)\n",
    "    \n",
    "    # Back propagation for this layer\n",
    "    def backward(self, dvalues, y):\n",
    "        sample = len(dvalues)\n",
    "        \n",
    "        # In case the predicted values are vector\n",
    "        # Get the index with highest probability\n",
    "        if len(y) == 2:\n",
    "            y = np.argmax(y, axis=1)\n",
    "        \n",
    "        # Calculate the gradient of from the cross entropy loss\n",
    "        # dinputs and dvalues here are the output (prediction) from the output layer (softmax)\n",
    "        self.dinputs = dvalues.copy()\n",
    "        self.dinputs[range(sample), y] -= 1 # Calculate the gradient of loss and softmax using chain rule\n",
    "        self.dinputs = self.dinputs / sample # Normalize the gradient\n",
    "\n",
    "# Create the Optimizer (Stochastic gradient descent) class\n",
    "class Optimizer_SGD:\n",
    "    \n",
    "    # Initiate the class with the learning rate, decay rate and momentum\n",
    "    def __init__(self, lr=0.1, decay=0., momentum=0.):\n",
    "        self.lr = lr\n",
    "        self.current_lr = lr\n",
    "        self.decay = decay\n",
    "        self.iterations = 0\n",
    "        self.momentum = momentum\n",
    "    \n",
    "    # Precall the decay function\n",
    "    # Since interation initiated at 0, the learning rate is still the preset one\n",
    "    def pre_update(self):\n",
    "        # Continue update the learning rate with the decay rate after each epochs\n",
    "        if self.decay:\n",
    "            self.current_lr = self.lr * (1 / (1 + self.decay * self.iterations))\n",
    "    \n",
    "    # Update the weights and bias with the gradient from back propagation\n",
    "    def update_params(self, layer):\n",
    "        \n",
    "        # If there is a momentum, we apply momentun to the updates\n",
    "        if self.momentum:\n",
    "            if not hasattr(layer, 'weight_momentums'):\n",
    "                \n",
    "                # Momentum at start is 0\n",
    "                layer.weight_momentums = np.zeros_like(layer.w)\n",
    "                layer.bias_momentum = np.zeros_like(layer.b)\n",
    "            \n",
    "            # The negative update is increased by the momentum\n",
    "            w_updates = self.momentum * layer.weight_momentums - self.current_lr * layer.d_w\n",
    "            \n",
    "            # Update the momentum from the update after each epoch\n",
    "            layer.weight_momentums = w_updates\n",
    "            \n",
    "            # Similar handling for bias\n",
    "            b_updates = self.momentum * layer.bias_momentum - self.current_lr * layer.d_b\n",
    "            layer.bias_momentum = b_updates\n",
    "        \n",
    "        else:\n",
    "            # Regular updates by multiplying the gradient with the learning rate\n",
    "            w_updates = -self.current_lr * layer.d_w\n",
    "            b_updates = -self.current_lr * layer.d_b\n",
    "        \n",
    "        # Update the weights and bias\n",
    "        layer.w += w_updates\n",
    "        layer.b += b_updates\n",
    "        \n",
    "    def update_BN_param(self, layer):\n",
    "        \n",
    "        # If there is a momentum, we apply momentun to the updates\n",
    "        if self.momentum:\n",
    "            if not hasattr(layer, 'gamma_momentums'):\n",
    "                \n",
    "                # Momentum at start is 0\n",
    "                layer.gamma_momentums = np.zeros_like(layer.gamma)\n",
    "                layer.beta_momentum = np.zeros_like(layer.beta)\n",
    "            \n",
    "            # The negative update is increased by the momentum\n",
    "            gamma_updates = self.momentum * layer.gamma_momentums - self.current_lr * layer.d_gamma\n",
    "            \n",
    "            # Update the momentum from the update after each epoch\n",
    "            layer.gamma_momentums = gamma_updates\n",
    "            \n",
    "            # Similar handling for bias\n",
    "            beta_updates = self.momentum * layer.beta_momentum - self.current_lr * layer.d_beta\n",
    "            layer.beta_momentum = beta_updates\n",
    "        \n",
    "        else:\n",
    "            # Regular updates by multiplying the gradient with the learning rate\n",
    "            gamma_updates = -self.current_lr * layer.d_gamma\n",
    "            beta_updates =  -self.current_lr * layer.d_beta\n",
    "        \n",
    "        # Update the weights and bias\n",
    "        layer.gamma += gamma_updates\n",
    "        layer.beta += beta_updates\n",
    "    \n",
    "    # Keep track of the number of epoches\n",
    "    def post_update(self):\n",
    "        self.iterations += 1\n",
    "\n",
    "# Create the Dropout layer\n",
    "class Dropout:\n",
    "    # Initiate layer with drop out rate (drop out here is the ratio of units to be disabled)\n",
    "    def __init__(self, rate):\n",
    "        self.rate = 1-rate\n",
    "    \n",
    "    # Forward propagation for dropout layer\n",
    "    def forward(self, inputs):\n",
    "        # Save the inputs in this layer\n",
    "        self.inputs = inputs\n",
    "        \n",
    "        # Create the mask using the bernoulli distribution with rate as the probability\n",
    "        # Dividing the self.rate keeps the total values of the input the same\n",
    "        self.binary_mask = np.random.binomial(1, self.rate, size=inputs.shape) / self.rate\n",
    "        \n",
    "        # Disable the randomly selected neurons\n",
    "        self.output = inputs * self.binary_mask\n",
    "        \n",
    "    # Back propagation for Drop out layer\n",
    "    def backward(self, dvalues):\n",
    "        self.dinputs = dvalues * self.binary_mask\n",
    "\n",
    "# Create the Batch Normalization layer\n",
    "class Batch_norm:\n",
    "    \n",
    "    # Initiate the learnable parameters gamma and beta with default values\n",
    "    def __init__(self,gamma=0.99, beta=0.):\n",
    "        self.g = gamma\n",
    "        self.b = beta\n",
    "    \n",
    "    # Forward propagation\n",
    "    def forward(self, inputs):\n",
    "\n",
    "        # Save the output from previous layer as inputs\n",
    "        self.inputs = inputs\n",
    "\n",
    "        # Get the mean, variance of this batch\n",
    "        self.x_mean = inputs.mean(axis=0)\n",
    "        self.x_var = inputs.var(axis=0)\n",
    "\n",
    "        # Normalize the batch using the equation from the paper\n",
    "        self.inv_var = 1/ (self.x_var + 1e-7)\n",
    "        output = (inputs - self.x_mean) * (self.inv_var) ** 0.5\n",
    "\n",
    "        # Save the output of this layer and to pass it to the next layer\n",
    "        self.output = output\n",
    "    \n",
    "    # Backward propagation\n",
    "    def backward(self, dvalues):\n",
    "        \n",
    "        # Get the number of samples and the dimension of the data\n",
    "        N, d = dvalues.shape\n",
    "\n",
    "        # Initiate the learnable parameters\n",
    "        self.gamma = np.full((N, d), self.g)\n",
    "        self.beta = np.full((N, d), self.b)\n",
    "        \n",
    "        # Assign the normalized values as x_til for simpler code\n",
    "        x_til = self.output\n",
    "\n",
    "        # intermediate partial derivatives\n",
    "        dx_til = dvalues * self.gamma\n",
    "\n",
    "        # final partial derivatives\n",
    "        dinputs = (1. / N) * self.inv_var  * (N*dx_til - np.sum(dx_til, axis=0)\n",
    "            - x_til*np.sum(dx_til*x_til, axis=0))\n",
    "        dbeta = np.sum(dvalues, axis=0)\n",
    "        dgamma = np.sum(x_til*dvalues, axis=0)\n",
    "        \n",
    "        # return the gradient and pass them to the next layer if suitable\n",
    "        self.dinputs = dinputs\n",
    "        self.d_gamma = dgamma\n",
    "        self.d_beta = dbeta"
   ]
  },
  {
   "source": [
    "# Helper Function"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "# Apply Mini-batch\n",
    "def create_batches(inputs, targets, batchsize=64):\n",
    "\n",
    "    assert inputs.shape[0] == targets.shape[0] # Ensure the input and label has same length\n",
    "    indices = np.arange(inputs.shape[0]) # Create a list of indices with length of the input samples\n",
    "    np.random.shuffle(indices) # Random sample the indices\n",
    "\n",
    "    # Yield the batches for training\n",
    "    for start_idx in range(0, inputs.shape[0] - batchsize + 1, batchsize):\n",
    "        excerpt = indices[start_idx:start_idx + batchsize]\n",
    "        yield inputs[excerpt], targets[excerpt]\n",
    "\n",
    "def confusion_matrix(pred, actual):\n",
    "    K = len(np.unique(actual))\n",
    "\n",
    "    result = np.zeros((K,K))\n",
    "\n",
    "    # Rows are truth Columns are predictions\n",
    "    for i in range(len(actual)):\n",
    "        result[actual[i]][pred[i]] += 1\n",
    "\n",
    "    precision = []\n",
    "    recall = []\n",
    "\n",
    "    for i in range(K):\n",
    "        precision.append(result[i,i] / np.sum(result[i,:]))\n",
    "        recall.append(result[i,i] / result[i,:].T.sum())\n",
    "    \n",
    "    precision = float(np.sum(precision) / len(precision))\n",
    "    recall = float(np.sum(recall) / len(recall))\n",
    "\n",
    "    f1 = 2 * (precision * recall) / (precision + recall)\n",
    "\n",
    "    return precision, recall, f1"
   ]
  },
  {
   "source": [
    "# Best Model Architecture:\n",
    "# 2-FC layer + BN "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "def experiment(epochs, batch_size, learning_rate, dropout_rate, all_acc, all_loss, all_test_acc, all_test_loss, y_train, y_test):\n",
    "    # Model architecture\n",
    "    dense1 = dense(128,128, w_regularizer_l2 = 0,b_regularizer_l2 = 0)\n",
    "    batch_norm1 = Batch_norm()\n",
    "    activation1 = ReLu()\n",
    "    dense2 = dense(128,10, w_regularizer_l2 = 0,b_regularizer_l2 = 0)\n",
    "    loss_activation = softmax_crossentropy_loss()\n",
    "    optimizer = Optimizer_SGD(learning_rate, momentum = False)\n",
    "    # Create list to save the accuracies and losses during training\n",
    "    acc_train = []\n",
    "    loss_train = []\n",
    "    test_acc = []\n",
    "    test_loss = []\n",
    "    print(\"Training model...\")\n",
    "    print(\"-\"*30)\n",
    "    early_stop = 0\n",
    "    prev_loss = 10\n",
    "    for epoch in range(epochs):\n",
    "        # Mini-batch SGD\n",
    "        for batch in create_batches(X_train, y_train, batch_size):\n",
    "            x_batch, y_batch = batch\n",
    "            # Block 1 with (Dense, BN)\n",
    "            dense1.forward(x_batch)\n",
    "            batch_norm1.forward(dense1.output)\n",
    "            activation1.forward(batch_norm1.output)\n",
    "            # Block 2\n",
    "            dense2.forward(activation1.output)\n",
    "            data_loss = loss_activation.forward(dense2.output, y_batch)\n",
    "            # Add up the regularization loss\n",
    "            regularization_loss = loss_activation.loss.regularization_loss(dense1) + loss_activation.loss.regularization_loss(dense2)\n",
    "            # Calculate the total loss of this epoch training for visualization later\n",
    "            loss = data_loss + regularization_loss\n",
    "            # Backpropagation\n",
    "            loss_activation.backward(loss_activation.output, y_batch)\n",
    "            dense2.backward(loss_activation.dinputs)\n",
    "            # dropout1.backward(dense2.dinputs)\n",
    "            activation1.backward(dense2.dinputs)\n",
    "            batch_norm1.backward(activation1.dinputs)\n",
    "            dense1.backward(batch_norm1.dinputs)\n",
    "            # Update optimizer paramters\n",
    "            optimizer.pre_update()\n",
    "            # Update the weights and bias in the dense layers\n",
    "            optimizer.update_params(dense1)\n",
    "            optimizer.update_params(dense2)\n",
    "            # Update the learnable gamma and beta in batch normalization\n",
    "            optimizer.update_BN_param(batch_norm1)\n",
    "            # Iteration + 1 for SGD with momentum\n",
    "            optimizer.post_update()\n",
    "        # Make a forward pass using the training set and test set\n",
    "        # Block 1 with (Dense, Relu)\n",
    "        dense1.forward(X_train)\n",
    "        batch_norm1.forward(dense1.output)\n",
    "        activation1.forward(batch_norm1.output)\n",
    "        #Block 2 with (Dense)\n",
    "        dense2.forward(activation1.output)\n",
    "        train_val_loss = loss_activation.forward(dense2.output, y_train)\n",
    "        prediction = np.argmax(loss_activation.output, axis=1)\n",
    "        if len(y_train) == 2:\n",
    "            y_train = np.argmax(y_train, axis=1)\n",
    "        train_accuracy = np.mean(y_train==prediction)\n",
    "        # Save the accuracy and loss for visualization\n",
    "        acc_train.append(train_accuracy)\n",
    "        loss_train.append(train_val_loss)\n",
    "        # Evaluate on the test set\n",
    "        dense1.forward(X_test)\n",
    "        batch_norm1.forward(dense1.output)\n",
    "        activation1.forward(batch_norm1.output)\n",
    "        #Block 2 with (Dense)\n",
    "        dense2.forward(activation1.output)\n",
    "        testing_loss = loss_activation.forward(dense2.output, y_test)\n",
    "        prediction = np.argmax(loss_activation.output, axis=1)\n",
    "        if len(y_test) == 2:\n",
    "            y_test = np.argmax(y_test, axis=1)\n",
    "        testing_accuracy = np.mean(y_test==prediction)\n",
    "        # TP/ (TP + FP)\n",
    "        # p, r, f = confusion_matrix(y_test, prediction)\n",
    "        p, r, f = confusion_matrix(prediction, y_test)\n",
    "        test_acc.append(testing_accuracy)\n",
    "        test_loss.append(testing_loss)\n",
    "        if testing_loss < prev_loss:\n",
    "            prev_loss = testing_loss # update the lowest loss\n",
    "            early_stop *= 0\n",
    "            # print(early_stop)\n",
    "        elif testing_loss > prev_loss:\n",
    "            early_stop += 1\n",
    "        if epoch % 20 == 0:\n",
    "            print(f\"Epochs {epoch+1}/{epochs} => acc:{train_accuracy:.4f} |loss: {train_val_loss:.4f} |test_acc:{testing_accuracy:.4f} |test_loss: {testing_loss:.4f}|f1-score: {f:.4f}\") #precision: {p:.4f}, |recall: {r:.4f}\n",
    "        if early_stop == 20:\n",
    "            print(\"-\"*30)\n",
    "            print(\"Early stopping\")\n",
    "            print(\"-\"*30)\n",
    "            print(f\"Epochs {epoch+1}/{epochs} => acc:{train_accuracy:.4f} |loss: {train_val_loss:.4f} |test_acc:{testing_accuracy:.4f} |test_loss: {testing_loss:.4f}|f1-score: {f:.4f}\") #precision: {p:.4f}, |recall: {r:.4f}\n",
    "            break\n",
    "    all_acc.append(acc_train)\n",
    "    all_loss.append(loss_train)\n",
    "    all_test_acc.append(test_acc)\n",
    "    all_test_loss.append(test_loss)\n",
    "    # print(f”Epochs {epoch+1}/{epochs} => acc:{train_accuracy:.4f} |loss: {train_val_loss:.4f} |test_acc:{testing_accuracy:.4f} |test_loss: {testing_loss:.4f}|f1-score: {f:.4f}“) #precision: {p:.4f}, |recall: {r:.4f}\n",
    "    # print(‘Done Training’)\n",
    "    print('='* 50)\n",
    "    return all_acc, all_loss, all_test_acc, all_test_loss"
   ]
  },
  {
   "source": [
    "# Evaluation"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Training model...\n",
      "------------------------------\n",
      "Epochs 1/100 => acc:0.4225 |loss: 1.6789 |test_acc:0.3571 |test_loss: 1.8523|f1-score: 0.3571\n",
      "Epochs 21/100 => acc:0.6143 |loss: 1.1554 |test_acc:0.4311 |test_loss: 1.7724|f1-score: 0.4311\n",
      "------------------------------\n",
      "Early stopping\n",
      "------------------------------\n",
      "Epochs 31/100 => acc:0.6364 |loss: 1.0877 |test_acc:0.4344 |test_loss: 1.8105|f1-score: 0.4344\n",
      "==================================================\n"
     ]
    },
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "time: 20.9 s\n"
     ]
    }
   ],
   "source": [
    "#Set some of the paramaters for the training process\n",
    "epochs = 100\n",
    "batch_size = 128\n",
    "learning_rate = [0.01]\n",
    "dropout_rate = 0\n",
    "\n",
    "all_acc = []\n",
    "all_loss = []\n",
    "all_test_acc = []\n",
    "all_test_loss = []\n",
    "\n",
    "for i in learning_rate:\n",
    "    experiment(epochs, batch_size, i, dropout_rate, all_acc, all_loss, all_test_acc, all_test_loss, y_train, y_test)\n",
    "\n",
    "\n",
    "fig, axs = plt.subplots(2,2, figsize=(15,10))\n",
    "\n",
    "for label, i in enumerate(all_acc):\n",
    "    axs[0,0].plot(i, label=learning_rate[label])\n",
    "    axs[0,0].set_title(\"Accuracy\")\n",
    "    axs[0,0].set_xlabel(\"epoch\")\n",
    "\n",
    "for label, i in enumerate(all_loss):\n",
    "    axs[0,1].plot(i, label=learning_rate[label])\n",
    "    axs[0,1].set_title(\"Loss\")\n",
    "    axs[0,1].set_xlabel(\"epoch\")\n",
    "\n",
    "for label, i in enumerate(all_test_acc):\n",
    "    axs[1,0].plot(i, label=learning_rate[label])\n",
    "    axs[1,0].set_title(\"Test Accuracy\")\n",
    "    axs[1,0].set_xlabel(\"epoch\")\n",
    "\n",
    "for label, i in enumerate(all_test_loss):\n",
    "    axs[1,1].plot(i, label=learning_rate[label])\n",
    "    axs[1,1].set_title(\"Test Loss\")\n",
    "    axs[1,1].set_xlabel(\"epoch\")\n",
    "\n",
    "\n",
    "fig.suptitle('Best model')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}