{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "407c9473",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "14cccaae-d3b6-4ae5-a28a-5fed4b998783",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def init_params():\n",
    "    W1 = np.random.rand(10,1024) - 0.5\n",
    "    b1 = np.random.rand(10,1) - 0.5\n",
    "    W2 = np.random.rand(61,10) - 0.5\n",
    "    b2 = np.random.rand(61,1) - 0.5\n",
    "    return W1, b1 , W2, b2\n",
    "def ReLU(Z):\n",
    "    return np.maximum(Z,0)\n",
    "def softmax(Z):\n",
    "    A = np.exp(Z) / sum(np.exp(Z))\n",
    "    return A\n",
    "def forward_prop(W1, b1, W2, b2, X):\n",
    "    Z1 = W1.dot(X) + b1\n",
    "    A1 = ReLU(Z1)\n",
    "    Z2 = W2.dot(A1) + b2\n",
    "    A2 = softmax(Z2)\n",
    "    return Z1, A1, Z2, A2\n",
    "def ReLU_deriv(Z):\n",
    "    return Z > 0\n",
    "def one_hot(Y):\n",
    "    one_hot_Y = np.zeros((Y.size, Y.max() + 1))\n",
    "    one_hot_Y[np.arange(Y.size), Y] = 1\n",
    "    one_hot_Y = one_hot_Y.T\n",
    "    return one_hot_Y\n",
    "def backward_prop(Z1, A1, Z2, A2, W1, W2, X, Y):\n",
    "    one_hot_Y = one_hot(Y)\n",
    "    dZ2 = A2 - one_hot_Y\n",
    "    dW2 = 1 / m * dZ2.dot(A1.T)\n",
    "    db2 = 1 / m * np.sum(dZ2)\n",
    "    dZ1 = W2.T.dot(dZ2) * ReLU_deriv(Z1)\n",
    "    dW1 = 1 / m * dZ1.dot(X.T)\n",
    "    db1 = 1 / m * np.sum(dZ1)\n",
    "    return dW1, db1, dW2, db2\n",
    "def update_params(W1, b1, W2, b2, dW1, db1, dW2, db2, alpha):\n",
    "    W1 = W1 - alpha * dW1\n",
    "    b1 = b1 - alpha * db1    \n",
    "    W2 = W2 - alpha * dW2  \n",
    "    b2 = b2 - alpha * db2    \n",
    "    return W1, b1, W2, b2\n",
    "def get_predictions(A2):\n",
    "    return np.argmax(A2, 0)\n",
    "def get_accuracy(predictions, Y):\n",
    "    #print(predictions, Y)\n",
    "    return np.sum(predictions == Y) / Y.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ec251927-46fc-413d-abb8-34fafb5a429d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "#import data_ready as dr\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "'''\n",
    "npz = np.load(\"weights.npz\")\n",
    "W1 = np.array(npz['arr_0'])\n",
    "b1 = np.array(npz['arr_1'])\n",
    "W2 = np.array(npz['arr_2'])\n",
    "b2 = np.array(npz['arr_3'])\n",
    "'''\n",
    "def encode_image(X,W1,b1,W2,b2):\n",
    "    current_image = X\n",
    "    _, _, _, A2  = forward_prop(W1,b1,W2,b2,current_image)\n",
    "    return A2\n",
    "    #print(A2)\n",
    "    #np.save('pred', A2)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff4758bc-7e5d-47ba-a3d4-aa4afde6165f",
   "metadata": {},
   "source": [
    "### Load in The Weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d323d295-5f29-4233-975c-1d5eab88a830",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "bt_npz = np.load(\"bt_weights.npz\")\n",
    "cr_npz = np.load(\"cr_weights.npz\")\n",
    "gt_npz = np.load(\"gt_weights.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "62160a9e-f166-47d1-88af-cc767385d09f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "W1_bt = np.array(bt_npz['arr_0'])\n",
    "b1_bt = np.array(bt_npz['arr_1'])\n",
    "W2_bt = np.array(bt_npz['arr_2'])\n",
    "b2_bt = np.array(bt_npz['arr_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "daa2d8e0-8f76-436d-9e3d-dfb711808e43",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "W1_cr = np.array(cr_npz['arr_0'])\n",
    "b1_cr = np.array(cr_npz['arr_1'])\n",
    "W2_cr = np.array(cr_npz['arr_2'])\n",
    "b2_cr = np.array(cr_npz['arr_3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17e3cd24-21b1-440c-8e38-f91597368771",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "W1_gt = np.array(gt_npz['arr_0'])\n",
    "b1_gt = np.array(gt_npz['arr_1'])\n",
    "W2_gt = np.array(gt_npz['arr_2'])\n",
    "b2_gt = np.array(gt_npz['arr_3'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ae55872-8e28-419d-85af-1ef185a254ce",
   "metadata": {},
   "source": [
    "### Load in the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0f230135-264d-4b89-b0a6-cab229ed0047",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "datac = pd.read_csv('cro_data_test.csv')\n",
    "datac = np.array(datac)\n",
    "\n",
    "m,n = datac.shape\n",
    "data_trainc = datac[1000:m].T\n",
    "\n",
    "Y_trainc = data_trainc[0].astype(int)\n",
    "X_trainc = data_trainc[1:n]\n",
    "\n",
    "current_image_c = X_trainc[:,1,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1f2a757a-4c29-4271-a895-03415765b105",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "datag = pd.read_csv('gtsrb_data_test.csv')\n",
    "datag = np.array(datag)\n",
    "\n",
    "m,n = datag.shape\n",
    "data_traing = datag[1000:m].T\n",
    "\n",
    "Y_traing = data_traing[0].astype(int)\n",
    "X_traing = data_traing[1:n]\n",
    "\n",
    "current_image_g = X_traing[:,1,None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7026b28c-ef29-4706-9e9b-ed2417ab06eb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "datab = pd.read_csv('bel_data_test.csv')\n",
    "datab = np.array(datab)\n",
    "\n",
    "m,n = datab.shape\n",
    "data_trainb = datab[1000:m].T\n",
    "\n",
    "Y_trainb = data_trainb[0].astype(int)\n",
    "X_trainb = data_trainb[1:n]\n",
    "\n",
    "current_image_b = X_trainc[:,1,None]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0552a80-ab8f-4ac0-ba4b-bc3ab26a1944",
   "metadata": {},
   "source": [
    "# Encoding 1 Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3daa565f-64f0-4294-9c36-bb87d1904ad0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "c1 = encode_image(current_image_c,W1_cr,b1_cr,W2_cr,b2_cr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "cd9d9223-d626-43e9-86d7-ef9bd24e0bc7",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "b1 = encode_image(current_image_b,W1_bt,b1_bt,W2_bt,b2_bt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "22443477-852f-4978-ae65-6883ed9d1e6b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "g1 = encode_image(current_image_g,W1_gt,b1_gt,W2_gt,b2_gt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b33a151f-d6b1-4386-8fa8-7e88da8951c8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "np.save('data/Single_Encoding/pred_c', c1)\n",
    "np.save('data/Single_Encoding/pred_b', b1)\n",
    "np.save('data/Single_Encoding/pred_g', g1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0df95db-6f10-4f6e-a043-0a82b6e3763b",
   "metadata": {},
   "source": [
    "# Encoding 900 Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "002bd5b5-aead-413b-8051-64326ebef595",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for x in range(0,900):\n",
    "    current_image_g = X_traing[:,x,None]\n",
    "    g1 = encode_image(current_image_g,W1_gt,b1_gt,W2_gt,b2_gt)\n",
    "    np.save('data/9_G/pred_g' + str(x), g1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6513e7f4-ef60-4d96-8576-540d4ef91c7b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for x in range(0,900):\n",
    "    current_image_b = X_trainb[:,x,None]\n",
    "    b1 = encode_image(current_image_b,W1_bt,b1_bt,W2_bt,b2_bt)\n",
    "    np.save('data/9_B/pred_b' + str(x), b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fc9144a0-b415-463d-bcc7-5ebaed0467ce",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for x in range(0,900):\n",
    "    current_image_c = X_trainc[:,x,None]\n",
    "    c1 = encode_image(current_image_c,W1_cr,b1_cr,W2_cr,b2_cr)\n",
    "    np.save('data/9_C/pred_c' + str(x), c1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fbd5222-e2b9-4c1d-82a0-6294136a2a71",
   "metadata": {},
   "source": [
    "Encoding 1800 Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "00eba2e4-b5a1-4905-a20f-42d71eebaeff",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for x in range(0,1800):\n",
    "    current_image_g = X_traing[:,x,None]\n",
    "    g1 = encode_image(current_image_g,W1_gt,b1_gt,W2_gt,b2_gt)\n",
    "    np.save('data/18_G/pred_g' + str(x), g1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ee1bd756-09c1-401f-8301-95357ae52446",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for x in range(0,1800):\n",
    "    current_image_b = X_trainb[:,x,None]\n",
    "    b1 = encode_image(current_image_b,W1_bt,b1_bt,W2_bt,b2_bt)\n",
    "    np.save('data/18_B/pred_b' + str(x), b1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "65dfac08-626e-47ac-9d35-15b794998f8d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for x in range(0,1800):\n",
    "    current_image_c = X_trainc[:,x,None]\n",
    "    c1 = encode_image(current_image_c,W1_cr,b1_cr,W2_cr,b2_cr)\n",
    "    np.save('data/18_C/pred_c' + str(x), c1)"
   ]
  }
 ],
 "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": "3.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}