fix

1 files changed, 131 insertions, 0 deletions

diff --git a/plots.ipynb b/plots.ipynb
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+++ b/plots.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# load csv with colmns epoch, train_loss, test_loss, cer, wer\n",
+    "# test_loss, cer, wer should not be plotted if they are 0.0\n",
+    "# plot train_loss and test_loss in one plot\n",
+    "# plot cer and wer in one plot\n",
+    " \n",
+    "# save plots as png\n",
+    "\n",
+    "csv_path = \"metrics.csv\"\n",
+    "\n",
+    "# load csv\n",
+    "df = pd.read_csv(csv_path)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot train_loss and test_loss\n",
+    "# do not use colors, distinguis by line style. use solid for train_loss and dashed for test_loss\n",
+    "plt.plot(df['epoch'], df['train_loss'], label='train_loss', linestyle='solid', color='black')\n",
+    "\n",
+    "# create zip with epoch and test_loss for all epochs\n",
+    "# filter out all test_loss with value 0.0\n",
+    "# plot test_loss\n",
+    "epoch_loss = zip(df['epoch'], df['test_loss'])\n",
+    "epoch_loss = list(filter(lambda x: x[1] != 0.0, epoch_loss))\n",
+    "plt.plot([x[0] for x in epoch_loss], [x[1] for x in epoch_loss], label='test_loss', linestyle='dashed', color='black')\n",
+    "\n",
+    "# add markers for test_loss\n",
+    "for x, y in epoch_loss:\n",
+    "    plt.plot(x, y, marker='o', markersize=3, color='black')\n",
+    "\n",
+    "plt.xlabel('epoch')\n",
+    "plt.ylabel('loss')\n",
+    "plt.legend()\n",
+    "\n",
+    "# add ticks every 5 epochs\n",
+    "plt.xticks(range(0, 70, 5))\n",
+    "\n",
+    "# set y limits to 0\n",
+    "plt.ylim(bottom=0)\n",
+    "# reduce margins\n",
+    "plt.tight_layout()\n",
+    "# increase resolution\n",
+    "plt.savefig('train_test_loss.png', dpi=300)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "epoch_cer = zip(df['epoch'], df['cer'])\n",
+    "epoch_cer = list(filter(lambda x: x[1] != 0.0, epoch_cer))\n",
+    "plt.plot([x[0] for x in epoch_cer], [x[1] for x in epoch_cer], label='cer', linestyle='solid', color='black')\n",
+    "\n",
+    "# add markers for cer\n",
+    "for x, y in epoch_cer:\n",
+    "    plt.plot(x, y, marker='o', markersize=3, color='black')\n",
+    "    \n",
+    "epoch_wer = zip(df['epoch'], df['wer'])\n",
+    "epoch_wer = list(filter(lambda x: x[1] != 0.0, epoch_wer))\n",
+    "plt.plot([x[0] for x in epoch_wer], [x[1] for x in epoch_wer], label='wer', linestyle='dashed', color='black')\n",
+    "\n",
+    "# add markers for wer\n",
+    "for x, y in epoch_wer:\n",
+    "    plt.plot(x, y, marker='o', markersize=3, color='black')\n",
+    "    \n",
+    "# set y limits to 0 and 1\n",
+    "plt.ylim(bottom=0, top=1)\n",
+    "plt.xlabel('epoch')\n",
+    "plt.ylabel('error rate')\n",
+    "plt.legend()\n",
+    "# reduce margins\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# add ticks every 5 epochs\n",
+    "plt.xticks(range(0, 70, 5))\n",
+    "\n",
+    "# add ticks every 0.1   \n",
+    "plt.yticks([x/10 for x in range(0, 11, 1)])\n",
+    "\n",
+    "# increase resolution\n",
+    "plt.savefig('cer_wer.png', dpi=300)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "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": "3.10.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}