{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:08.874062Z",
     "start_time": "2025-11-04T22:33:08.871974Z"
    }
   },
   "source": "import numpy as np",
   "outputs": [],
   "execution_count": 97
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:08.918897Z",
     "start_time": "2025-11-04T22:33:08.917441Z"
    }
   },
   "cell_type": "code",
   "source": [
    "nn_architecture = [\n",
    "    {\"input_dim\": 2, \"output_dim\": 4, \"activation\": \"relu\"},\n",
    "    {\"input_dim\": 4, \"output_dim\": 6, \"activation\": \"relu\"},\n",
    "    {\"input_dim\": 6, \"output_dim\": 6, \"activation\": \"relu\"},\n",
    "    {\"input_dim\": 6, \"output_dim\": 4, \"activation\": \"relu\"},\n",
    "    {\"input_dim\": 4, \"output_dim\": 1, \"activation\": \"sigmoid\"},\n",
    "]"
   ],
   "id": "48cafaf4b64967bb",
   "outputs": [],
   "execution_count": 98
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:08.972468Z",
     "start_time": "2025-11-04T22:33:08.966895Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def init_layers(nn_architecture, seed = 99):\n",
    "    np.random.seed(seed)\n",
    "    number_of_layers = len(nn_architecture)\n",
    "    params_values = {}\n",
    "\n",
    "    for idx, layer in enumerate(nn_architecture):\n",
    "        layer_idx = idx + 1\n",
    "        layer_input_size = layer[\"input_dim\"]\n",
    "        layer_output_size = layer[\"output_dim\"]\n",
    "\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\n"
   ],
   "id": "d13137630b41b756",
   "outputs": [],
   "execution_count": 99
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.025064Z",
     "start_time": "2025-11-04T22:33:09.020911Z"
    }
   },
   "cell_type": "code",
   "source": [
    "params = init_layers(nn_architecture)\n",
    "# params"
   ],
   "id": "31f205147667dea6",
   "outputs": [],
   "execution_count": 100
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.075270Z",
     "start_time": "2025-11-04T22:33:09.073161Z"
    }
   },
   "cell_type": "code",
   "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;"
   ],
   "id": "c1b960e7dcf09d91",
   "outputs": [],
   "execution_count": 101
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.131020Z",
     "start_time": "2025-11-04T22:33:09.123827Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def single_layer_forward_propagation(A_prev, W_curr, b_curr, activation=\"relu\"):\n",
    "    Z_curr = np.dot(W_curr, A_prev) + b_curr\n",
    "\n",
    "    if activation == \"relu\":\n",
    "        activation_func = relu\n",
    "    elif activation == \"sigmoid\":\n",
    "        activation_func = sigmoid\n",
    "    else:\n",
    "        raise Exception('Non-supported activation function')\n",
    "\n",
    "    return activation_func(Z_curr), Z_curr"
   ],
   "id": "efae2e184daf2fce",
   "outputs": [],
   "execution_count": 102
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.180896Z",
     "start_time": "2025-11-04T22:33:09.175920Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def full_forward_propagation(X, params_values, nn_architecture):\n",
    "    memory = {}\n",
    "    A_curr = X\n",
    "\n",
    "    for idx, layer in enumerate(nn_architecture):\n",
    "        layer_idx = idx + 1\n",
    "        A_prev = A_curr\n",
    "\n",
    "        activ_function_curr = layer[\"activation\"]\n",
    "        W_curr = params_values[\"W\" + str(layer_idx)]\n",
    "        b_curr = params_values[\"b\" + str(layer_idx)]\n",
    "        A_curr, Z_curr = single_layer_forward_propagation(A_prev, W_curr, b_curr, activ_function_curr)\n",
    "\n",
    "        memory[\"A\" + str(idx)] = A_prev\n",
    "        memory[\"Z\" + str(layer_idx)] = Z_curr\n",
    "\n",
    "    return A_curr, memory"
   ],
   "id": "c3cd9e8f51dbe967",
   "outputs": [],
   "execution_count": 103
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.235246Z",
     "start_time": "2025-11-04T22:33:09.228689Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def get_cost_value(Y_hat, Y):\n",
    "    m = Y_hat.shape[1]\n",
    "    cost = -1 / m * (np.dot(Y, np.log(Y_hat).T) + np.dot(1 - Y, np.log(1 - Y_hat).T))\n",
    "    return np.squeeze(cost)\n",
    "\n",
    "# an auxiliary function that converts probability into class\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_\n",
    "\n",
    "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()"
   ],
   "id": "121416e7bbab57bb",
   "outputs": [],
   "execution_count": 104
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.290642Z",
     "start_time": "2025-11-04T22:33:09.283697Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def single_layer_backward_propagation(dA_curr, W_curr, b_curr, Z_curr, A_prev, activation=\"relu\"):\n",
    "    m = A_prev.shape[1]\n",
    "\n",
    "    if activation == \"relu\":\n",
    "        backward_activation_func = relu_backward\n",
    "    elif activation == \"sigmoid\":\n",
    "        backward_activation_func = sigmoid_backward\n",
    "    else:\n",
    "        raise Exception('Non-supported activation function')\n",
    "\n",
    "    dZ_curr = backward_activation_func(dA_curr, Z_curr)\n",
    "    dW_curr = np.dot(dZ_curr, A_prev.T) / m\n",
    "    db_curr = np.sum(dZ_curr, axis=1, keepdims=True) / m\n",
    "    dA_prev = np.dot(W_curr.T, dZ_curr)\n",
    "\n",
    "    return dA_prev, dW_curr, db_curr"
   ],
   "id": "92e4b87664f18a63",
   "outputs": [],
   "execution_count": 105
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.345156Z",
     "start_time": "2025-11-04T22:33:09.337966Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def full_backward_propagation(Y_hat, Y, memory, params_values, nn_architecture):\n",
    "    grads_values = {}\n",
    "    m = Y.shape[1]\n",
    "    Y = Y.reshape(Y_hat.shape)\n",
    "\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",
    "        layer_idx_curr = layer_idx_prev + 1\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",
    "        W_curr = params_values[\"W\" + str(layer_idx_curr)]\n",
    "        b_curr = params_values[\"b\" + str(layer_idx_curr)]\n",
    "\n",
    "        dA_prev, dW_curr, db_curr = single_layer_backward_propagation(\n",
    "            dA_curr, W_curr, b_curr, Z_curr, A_prev, activ_function_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"
   ],
   "id": "2c8e4eed1846f003",
   "outputs": [],
   "execution_count": 106
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.409004Z",
     "start_time": "2025-11-04T22:33:09.405329Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def update(params_values, grads_values, nn_architecture, learning_rate):\n",
    "    for layer_idx, layer in enumerate(nn_architecture):\n",
    "        # layer_idx=layer_idx+1\n",
    "        params_values[\"W\" + str(layer_idx)] -= learning_rate * grads_values[\"dW\" + str(layer_idx)]\n",
    "        params_values[\"b\" + str(layer_idx)] -= learning_rate * grads_values[\"db\" + str(layer_idx)]\n",
    "\n",
    "    return params_values;"
   ],
   "id": "16320b953a183511",
   "outputs": [],
   "execution_count": 107
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.455132Z",
     "start_time": "2025-11-04T22:33:09.452975Z"
    }
   },
   "cell_type": "code",
   "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 % 1000 == 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"
   ],
   "id": "fce33f70bba3898",
   "outputs": [],
   "execution_count": 108
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.500509Z",
     "start_time": "2025-11-04T22:33:09.498676Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "import tensorflow as tf\n",
    "\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"
   ],
   "id": "cccd73b5018799d4",
   "outputs": [],
   "execution_count": 109
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.547352Z",
     "start_time": "2025-11-04T22:33:09.545714Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# number of samples in the data set\n",
    "N_SAMPLES = 1000\n",
    "# ratio between training and test sets\n",
    "TEST_SIZE = 0.1"
   ],
   "id": "4f66ffa878f01c02",
   "outputs": [],
   "execution_count": 110
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.597021Z",
     "start_time": "2025-11-04T22:33:09.592659Z"
    }
   },
   "cell_type": "code",
   "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)"
   ],
   "id": "bebe0ed00a2d514",
   "outputs": [],
   "execution_count": 111
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.706999Z",
     "start_time": "2025-11-04T22:33:09.649338Z"
    }
   },
   "cell_type": "code",
   "source": [
    "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), nn_architecture, 100000, 0.001, verbose=True)\n",
    "# params_values\n"
   ],
   "id": "ce04892d496c5147",
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'W0'",
     "output_type": "error",
     "traceback": [
      "\u001B[31m---------------------------------------------------------------------------\u001B[39m",
      "\u001B[31mKeyError\u001B[39m                                  Traceback (most recent call last)",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[112]\u001B[39m\u001B[32m, line 1\u001B[39m\n\u001B[32m----> \u001B[39m\u001B[32m1\u001B[39m params_values = \u001B[43mtrain\u001B[49m\u001B[43m(\u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtranspose\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX_train\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnp\u001B[49m\u001B[43m.\u001B[49m\u001B[43mtranspose\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_train\u001B[49m\u001B[43m.\u001B[49m\u001B[43mreshape\u001B[49m\u001B[43m(\u001B[49m\u001B[43m(\u001B[49m\u001B[43my_train\u001B[49m\u001B[43m.\u001B[49m\u001B[43mshape\u001B[49m\u001B[43m[\u001B[49m\u001B[32;43m0\u001B[39;49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnn_architecture\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m100000\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[32;43m0.001\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mverbose\u001B[49m\u001B[43m=\u001B[49m\u001B[38;5;28;43;01mTrue\u001B[39;49;00m\u001B[43m)\u001B[49m\n\u001B[32m      2\u001B[39m \u001B[38;5;66;03m# params_values\u001B[39;00m\n",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[108]\u001B[39m\u001B[32m, line 23\u001B[39m, in \u001B[36mtrain\u001B[39m\u001B[34m(X, Y, nn_architecture, epochs, learning_rate, verbose, callback)\u001B[39m\n\u001B[32m     21\u001B[39m grads_values = full_backward_propagation(Y_hat, Y, cashe, params_values, nn_architecture)\n\u001B[32m     22\u001B[39m \u001B[38;5;66;03m# updating model state\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m23\u001B[39m params_values = \u001B[43mupdate\u001B[49m\u001B[43m(\u001B[49m\u001B[43mparams_values\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mgrads_values\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mnn_architecture\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mlearning_rate\u001B[49m\u001B[43m)\u001B[49m\n\u001B[32m     25\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m(i % \u001B[32m1000\u001B[39m == \u001B[32m0\u001B[39m):\n\u001B[32m     26\u001B[39m     \u001B[38;5;28;01mif\u001B[39;00m(verbose):\n",
      "\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[107]\u001B[39m\u001B[32m, line 4\u001B[39m, in \u001B[36mupdate\u001B[39m\u001B[34m(params_values, grads_values, nn_architecture, learning_rate)\u001B[39m\n\u001B[32m      1\u001B[39m \u001B[38;5;28;01mdef\u001B[39;00m\u001B[38;5;250m \u001B[39m\u001B[34mupdate\u001B[39m(params_values, grads_values, nn_architecture, learning_rate):\n\u001B[32m      2\u001B[39m     \u001B[38;5;28;01mfor\u001B[39;00m layer_idx, layer \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28menumerate\u001B[39m(nn_architecture):\n\u001B[32m      3\u001B[39m         \u001B[38;5;66;03m# layer_idx=layer_idx+1\u001B[39;00m\n\u001B[32m----> \u001B[39m\u001B[32m4\u001B[39m         \u001B[43mparams_values\u001B[49m\u001B[43m[\u001B[49m\u001B[33;43m\"\u001B[39;49m\u001B[33;43mW\u001B[39;49m\u001B[33;43m\"\u001B[39;49m\u001B[43m \u001B[49m\u001B[43m+\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;28;43mstr\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mlayer_idx\u001B[49m\u001B[43m)\u001B[49m\u001B[43m]\u001B[49m -= learning_rate * grads_values[\u001B[33m\"\u001B[39m\u001B[33mdW\u001B[39m\u001B[33m\"\u001B[39m + \u001B[38;5;28mstr\u001B[39m(layer_idx)]\n\u001B[32m      5\u001B[39m         params_values[\u001B[33m\"\u001B[39m\u001B[33mb\u001B[39m\u001B[33m\"\u001B[39m + \u001B[38;5;28mstr\u001B[39m(layer_idx)] -= learning_rate * grads_values[\u001B[33m\"\u001B[39m\u001B[33mdb\u001B[39m\u001B[33m\"\u001B[39m + \u001B[38;5;28mstr\u001B[39m(layer_idx)]\n\u001B[32m      7\u001B[39m     \u001B[38;5;28;01mreturn\u001B[39;00m params_values\n",
      "\u001B[31mKeyError\u001B[39m: 'W0'"
     ]
    }
   ],
   "execution_count": 112
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.744698885Z",
     "start_time": "2025-11-04T22:30:15.730115Z"
    }
   },
   "cell_type": "code",
   "source": [
    "Y_test_hat, _ = full_forward_propagation(np.transpose(X_test), params_values, nn_architecture)\n",
    "\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))\n"
   ],
   "id": "26e7a2a8848714d9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy: 0.46 - David\n"
     ]
    }
   ],
   "execution_count": 87
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.745013072Z",
     "start_time": "2025-11-04T22:30:15.777514Z"
    }
   },
   "cell_type": "code",
   "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\n",
    "# output directory (the folder must be created on the drive)\n",
    "OUTPUT_DIR = \"./binary_classification_vizualizations/\"\n",
    "os.makedirs(OUTPUT_DIR, exist_ok=True)\n",
    "### Definition of grid boundaries\n",
    "grid = np.mgrid[GRID_X_START:GRID_X_END:100j, GRID_Y_START:GRID_Y_END:100j]\n",
    "grid_2d = grid.reshape(2, -1).T\n",
    "XX, YY = grid"
   ],
   "id": "b070f03d55981894",
   "outputs": [],
   "execution_count": 88
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.756230428Z",
     "start_time": "2025-11-04T22:30:15.825657Z"
    }
   },
   "cell_type": "code",
   "source": [
    "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()"
   ],
   "id": "553e08ddc23ab78c",
   "outputs": [],
   "execution_count": 89
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.756818533Z",
     "start_time": "2025-11-04T22:32:16.103852Z"
    }
   },
   "cell_type": "code",
   "source": "# make_plot(X, y, \"Dataset\")",
   "id": "36c83562b7404392",
   "outputs": [],
   "execution_count": 96
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.757036155Z",
     "start_time": "2025-11-04T22:31:17.522478Z"
    }
   },
   "cell_type": "code",
   "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",
    "    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)\n",
    "\n",
    "\n",
    "# Training\n",
    "params_values = train(np.transpose(X_train), np.transpose(y_train.reshape((y_train.shape[0], 1))), nn_architecture,\n",
    "                      100000, 0.001, False, callback_numpy_plot)\n",
    "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",
    "\n",
    "make_plot(X_test, y_test, \"NumPy Model\", file_name=None, XX=XX, YY=YY, preds=prediction_probs_numpy)"
   ],
   "id": "b6a4d6a1a1fb289",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 94
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.757406779Z",
     "start_time": "2025-11-04T22:30:18.009348Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "dc0b9266ae37298a",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.757826120Z",
     "start_time": "2025-11-04T22:30:18.058506Z"
    }
   },
   "cell_type": "code",
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(25, input_dim=2,activation='relu'))\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(25, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])\n",
    "\n",
    "# Training\n",
    "history = model.fit(X_train, y_train, epochs=200, verbose=0)"
   ],
   "id": "f05ff40ed26e45c2",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oskar/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "execution_count": 91
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.758008336Z",
     "start_time": "2025-11-04T22:30:23.750365Z"
    }
   },
   "cell_type": "code",
   "source": [
    "Y_test_prob = model.predict(X_test)\n",
    "Y_test_hat = (Y_test_prob > 0.5).astype(int).ravel()\n",
    "acc_test = accuracy_score(y_test, Y_test_hat)\n",
    "print(\"Test set accuracy: {:.2f} - Goliath\".format(acc_test))"
   ],
   "id": "ef52bee9c93081d3",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m4/4\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 7ms/step \n",
      "Test set accuracy: 0.99 - Goliath\n"
     ]
    }
   ],
   "execution_count": 92
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-04T22:33:09.758218682Z",
     "start_time": "2025-11-04T22:30:23.812884Z"
    }
   },
   "cell_type": "code",
   "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",
    "    prediction_probs = model.predict(grid_2d, batch_size=32, verbose=0)\n",
    "    prediction_probs = prediction_probs.reshape(-1)\n",
    "    make_plot(X_test, y_test, plot_title, file_name=file_path, XX=XX, YY=YY, preds=prediction_probs)\n",
    "\n",
    "\n",
    "# Adding callback functions that they will run in every epoch\n",
    "testmodelcb = keras.callbacks.LambdaCallback(on_epoch_end=callback_keras_plot)\n",
    "\n",
    "# Building a model\n",
    "model = Sequential()\n",
    "model.add(Dense(25, input_dim=2, activation='relu'))\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(50, activation='relu'))\n",
    "model.add(Dense(25, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer=\"sgd\", metrics=['accuracy'])\n",
    "\n",
    "# Training\n",
    "history = model.fit(X_train, y_train, epochs=200, verbose=0, callbacks=[testmodelcb])\n",
    "prediction_probs = model.predict(grid_2d, batch_size=32, verbose=0)\n",
    "prediction_probs = prediction_probs.reshape(-1)\n",
    "make_plot(X_test, y_test, \"Keras Model\", file_name=None, XX=XX, YY=YY, preds=prediction_probs)\n"
   ],
   "id": "6feab7da06e7a828",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/oskar/projects/nn-from-scratch/.venv/lib/python3.13/site-packages/keras/src/layers/core/dense.py:95: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1200 with 1 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 93
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}