chore: delete unused notebooks

1 files changed, 0 insertions, 169 deletions

diff --git a/notebooks/05-sanity-check-multihead-attention.ipynb b/notebooks/05-sanity-check-multihead-attention.ipynb
deleted file mode 100644
index 952636b..0000000
--- a/notebooks/05-sanity-check-multihead-attention.ipynb
+++ /dev/null
@@ -1,169 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "\n",
-    "import cv2\n",
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torch import nn\n",
-    "from importlib.util import find_spec\n",
-    "if find_spec(\"text_recognizer\") is None:\n",
-    "    import sys\n",
-    "    sys.path.append('..')\n",
-    "\n",
-    "from text_recognizer.networks.transformer.attention import MultiHeadAttention"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "temp_mha = MultiHeadAttention(hidden_dim=512, num_heads=8)\n",
-    "def print_out(Q, K, V):\n",
-    "    temp_out, temp_attn = temp_mha.scaled_dot_product_attention(Q, K, V)\n",
-    "    print('Attention weights are:', temp_attn.squeeze())\n",
-    "    print('Output is:', temp_out.squeeze())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_K = torch.tensor(\n",
-    "    [[10, 0, 0],\n",
-    "     [ 0,10, 0],\n",
-    "     [ 0, 0,10],\n",
-    "     [ 0, 0,10]]\n",
-    ").float()[None,None]\n",
-    "\n",
-    "test_V = torch.tensor(\n",
-    "    [[   1,0,0],\n",
-    "     [  10,0,0],\n",
-    "     [ 100,5,0],\n",
-    "     [1000,6,0]]\n",
-    ").float()[None,None]\n",
-    "\n",
-    "test_Q = torch.tensor(\n",
-    "    [[0, 10, 0]]\n",
-    ").float()[None,None]\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attention weights are: tensor([8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26])\n",
-      "Output is: tensor([1.0000e+01, 9.2766e-25, 0.0000e+00])\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_out(test_Q, test_K, test_V)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Attends to the second element, as it should!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attention weights are: tensor([4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01])\n",
-      "Output is: tensor([550.0000,   5.5000,   0.0000])\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_Q = torch.tensor([[0, 0, 10]]).float()[None,None]\n",
-    "print_out(test_Q, test_K, test_V)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Focuses equally on the third and fourth key."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Attention weights are: tensor([[4.2166e-26, 4.2166e-26, 5.0000e-01, 5.0000e-01],\n",
-      "        [8.4333e-26, 1.0000e+00, 8.4333e-26, 8.4333e-26],\n",
-      "        [5.0000e-01, 5.0000e-01, 4.2166e-26, 4.2166e-26]])\n",
-      "Output is: tensor([[5.5000e+02, 5.5000e+00, 0.0000e+00],\n",
-      "        [1.0000e+01, 9.2766e-25, 0.0000e+00],\n",
-      "        [5.5000e+00, 4.6383e-25, 0.0000e+00]])\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_Q = torch.tensor(\n",
-    "    [[0, 0, 10], [0, 10, 0], [10, 10, 0]]\n",
-    ").float()[None,None]\n",
-    "print_out(test_Q, test_K, test_V)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "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": "3.9.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}