From dc28cbe2b4ed77be92ee8b2b69a20689c3bf02a4 Mon Sep 17 00:00:00 2001
From: aktersnurra <gustaf.rydholm@gmail.com>
Date: Sun, 8 Nov 2020 14:54:44 +0100
Subject: new updates

---
 .../05-sanity-check-multihead-attention.ipynb      | 169 +++++++++++++++++++++
 1 file changed, 169 insertions(+)
 create mode 100644 src/notebooks/05-sanity-check-multihead-attention.ipynb

(limited to 'src/notebooks/05-sanity-check-multihead-attention.ipynb')

diff --git a/src/notebooks/05-sanity-check-multihead-attention.ipynb b/src/notebooks/05-sanity-check-multihead-attention.ipynb
new file mode 100644
index 0000000..54f0432
--- /dev/null
+++ b/src/notebooks/05-sanity-check-multihead-attention.ipynb
@@ -0,0 +1,169 @@
+{
+ "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",
+   "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": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
-- 
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