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CynthiaCR
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emotions_classifier

Image Classification
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 38,
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+ "metadata": {
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+ },
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+ "id": "YjdZ0vD5HQVO",
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+ "outputs": [
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+ {
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+ "output_type": "stream",
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+ "name": "stdout",
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+ "text": [
1245
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
1246
+ "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.29.2)\n",
1247
+ "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (2.12.0)\n",
1248
+ "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.0)\n",
1249
+ "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
1250
+ "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.14.1)\n",
1251
+ "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
1252
+ "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
1253
+ "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
1254
+ "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
1255
+ "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
1256
+ "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.13.3)\n",
1257
+ "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
1258
+ "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
1259
+ "Requirement already satisfied: dill<0.3.7,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.6)\n",
1260
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
1261
+ "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.2.0)\n",
1262
+ "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.14)\n",
1263
+ "Requirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.4.0)\n",
1264
+ "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.4)\n",
1265
+ "Requirement already satisfied: responses<0.19 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.18.0)\n",
1266
+ "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
1267
+ "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.0.12)\n",
1268
+ "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
1269
+ "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
1270
+ "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
1271
+ "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.3)\n",
1272
+ "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
1273
+ "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.5.0)\n",
1274
+ "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
1275
+ "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
1276
+ "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
1277
+ "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
1278
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
1279
+ "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n"
1280
+ ]
1281
+ }
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+ ],
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+ "source": [
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+ "pip install transformers datasets evaluate"
1285
+ ]
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+ },
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+ {
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+ "cell_type": "code",
1289
+ "source": [
1290
+ "from huggingface_hub import notebook_login\n",
1291
+ "\n",
1292
+ "notebook_login()"
1293
+ ],
1294
+ "metadata": {
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+ "colab": {
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+ "base_uri": "https://localhost:8080/",
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+ "height": 415,
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+ "outputId": "3fef96d3-6f52-4dca-89ae-055247f333d1"
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+ "execution_count": 39,
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+ "outputs": [
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+ "data": {
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+ "cell_type": "code",
1341
+ "source": [
1342
+ "from datasets import load_dataset\n",
1343
+ "\n",
1344
+ "emotions_df = load_dataset(\"FastJobs/Visual_Emotional_Analysis\", split=\"train[:800]\") "
1345
+ ],
1346
+ "metadata": {
1347
+ "colab": {
1348
+ "base_uri": "https://localhost:8080/"
1349
+ },
1350
+ "id": "jePsbV2DHlCk",
1351
+ "outputId": "72fc3cd5-37c9-4f6a-f5d4-34327cd4cf03"
1352
+ },
1353
+ "execution_count": 40,
1354
+ "outputs": [
1355
+ {
1356
+ "output_type": "stream",
1357
+ "name": "stderr",
1358
+ "text": [
1359
+ "WARNING:datasets.builder:Found cached dataset imagefolder (/root/.cache/huggingface/datasets/FastJobs___imagefolder/FastJobs--Visual_Emotional_Analysis-bbb0f5e70847fc91/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f)\n"
1360
+ ]
1361
+ }
1362
+ ]
1363
+ },
1364
+ {
1365
+ "cell_type": "code",
1366
+ "source": [
1367
+ "len(emotions_df)"
1368
+ ],
1369
+ "metadata": {
1370
+ "colab": {
1371
+ "base_uri": "https://localhost:8080/"
1372
+ },
1373
+ "id": "PB3_wpmzJFsh",
1374
+ "outputId": "ae86b6e2-51d6-4c93-eb0e-acd7e428db53"
1375
+ },
1376
+ "execution_count": 41,
1377
+ "outputs": [
1378
+ {
1379
+ "output_type": "execute_result",
1380
+ "data": {
1381
+ "text/plain": [
1382
+ "800"
1383
+ ]
1384
+ },
1385
+ "metadata": {},
1386
+ "execution_count": 41
1387
+ }
1388
+ ]
1389
+ },
1390
+ {
1391
+ "cell_type": "code",
1392
+ "source": [
1393
+ "emotions_df = emotions_df.train_test_split(test_size=0.2)"
1394
+ ],
1395
+ "metadata": {
1396
+ "id": "5Nw4bJfTHlLT"
1397
+ },
1398
+ "execution_count": 42,
1399
+ "outputs": []
1400
+ },
1401
+ {
1402
+ "cell_type": "code",
1403
+ "source": [
1404
+ "# size of the train dataset\n",
1405
+ "len(emotions_df['train'])"
1406
+ ],
1407
+ "metadata": {
1408
+ "colab": {
1409
+ "base_uri": "https://localhost:8080/"
1410
+ },
1411
+ "id": "ppzENe5zRkxQ",
1412
+ "outputId": "06730bbc-80e6-4cac-f525-bc78eeadf233"
1413
+ },
1414
+ "execution_count": 43,
1415
+ "outputs": [
1416
+ {
1417
+ "output_type": "execute_result",
1418
+ "data": {
1419
+ "text/plain": [
1420
+ "640"
1421
+ ]
1422
+ },
1423
+ "metadata": {},
1424
+ "execution_count": 43
1425
+ }
1426
+ ]
1427
+ },
1428
+ {
1429
+ "cell_type": "code",
1430
+ "source": [
1431
+ "# size of the test dataset\n",
1432
+ "len(emotions_df['test'])"
1433
+ ],
1434
+ "metadata": {
1435
+ "colab": {
1436
+ "base_uri": "https://localhost:8080/"
1437
+ },
1438
+ "id": "wXnineZgRnLw",
1439
+ "outputId": "544690f7-d579-4ef4-aa0a-2671b8ecbcb2"
1440
+ },
1441
+ "execution_count": 44,
1442
+ "outputs": [
1443
+ {
1444
+ "output_type": "execute_result",
1445
+ "data": {
1446
+ "text/plain": [
1447
+ "160"
1448
+ ]
1449
+ },
1450
+ "metadata": {},
1451
+ "execution_count": 44
1452
+ }
1453
+ ]
1454
+ },
1455
+ {
1456
+ "cell_type": "code",
1457
+ "source": [
1458
+ "# create 2 dictionary \n",
1459
+ "# dic1: maps the label name to an integer\n",
1460
+ "# dic2: maps the label id(integer) to a label name\n",
1461
+ "labels = emotions_df[\"train\"].features[\"label\"].names\n",
1462
+ "label2id, id2label = dict(), dict()\n",
1463
+ "for i, label in enumerate(labels):\n",
1464
+ " label2id[label] = str(i)\n",
1465
+ " id2label[str(i)] = label\n"
1466
+ ],
1467
+ "metadata": {
1468
+ "id": "DoqhAfVTR4Qs"
1469
+ },
1470
+ "execution_count": 45,
1471
+ "outputs": []
1472
+ },
1473
+ {
1474
+ "cell_type": "code",
1475
+ "source": [
1476
+ "label2id"
1477
+ ],
1478
+ "metadata": {
1479
+ "colab": {
1480
+ "base_uri": "https://localhost:8080/"
1481
+ },
1482
+ "id": "aRTpaEITR5UN",
1483
+ "outputId": "06001bae-a14d-47e2-8c1b-721e4e94748e"
1484
+ },
1485
+ "execution_count": 46,
1486
+ "outputs": [
1487
+ {
1488
+ "output_type": "execute_result",
1489
+ "data": {
1490
+ "text/plain": [
1491
+ "{'anger': '0',\n",
1492
+ " 'contempt': '1',\n",
1493
+ " 'disgust': '2',\n",
1494
+ " 'fear': '3',\n",
1495
+ " 'happy': '4',\n",
1496
+ " 'neutral': '5',\n",
1497
+ " 'sad': '6',\n",
1498
+ " 'surprise': '7'}"
1499
+ ]
1500
+ },
1501
+ "metadata": {},
1502
+ "execution_count": 46
1503
+ }
1504
+ ]
1505
+ },
1506
+ {
1507
+ "cell_type": "code",
1508
+ "source": [
1509
+ "id2label"
1510
+ ],
1511
+ "metadata": {
1512
+ "colab": {
1513
+ "base_uri": "https://localhost:8080/"
1514
+ },
1515
+ "id": "z1gyQ1ZZR5XV",
1516
+ "outputId": "191228fa-1b9e-4ac3-f9e3-a5028d37ff57"
1517
+ },
1518
+ "execution_count": 47,
1519
+ "outputs": [
1520
+ {
1521
+ "output_type": "execute_result",
1522
+ "data": {
1523
+ "text/plain": [
1524
+ "{'0': 'anger',\n",
1525
+ " '1': 'contempt',\n",
1526
+ " '2': 'disgust',\n",
1527
+ " '3': 'fear',\n",
1528
+ " '4': 'happy',\n",
1529
+ " '5': 'neutral',\n",
1530
+ " '6': 'sad',\n",
1531
+ " '7': 'surprise'}"
1532
+ ]
1533
+ },
1534
+ "metadata": {},
1535
+ "execution_count": 47
1536
+ }
1537
+ ]
1538
+ },
1539
+ {
1540
+ "cell_type": "code",
1541
+ "source": [
1542
+ "from transformers import AutoImageProcessor\n",
1543
+ "\n",
1544
+ "checkpoint = \"google/vit-base-patch16-224-in21k\"\n",
1545
+ "image_processor = AutoImageProcessor.from_pretrained(checkpoint)"
1546
+ ],
1547
+ "metadata": {
1548
+ "id": "QmVyt4p1R5a6"
1549
+ },
1550
+ "execution_count": 48,
1551
+ "outputs": []
1552
+ },
1553
+ {
1554
+ "cell_type": "code",
1555
+ "source": [],
1556
+ "metadata": {
1557
+ "id": "y3wZZw6QR5eU"
1558
+ },
1559
+ "execution_count": 48,
1560
+ "outputs": []
1561
+ },
1562
+ {
1563
+ "cell_type": "code",
1564
+ "source": [],
1565
+ "metadata": {
1566
+ "id": "npIDtsLgTROr"
1567
+ },
1568
+ "execution_count": 48,
1569
+ "outputs": []
1570
+ },
1571
+ {
1572
+ "cell_type": "code",
1573
+ "source": [
1574
+ "import numpy as np\n",
1575
+ "import tensorflow as tf\n",
1576
+ "from PIL import Image\n",
1577
+ "\n",
1578
+ "# convert image to a tensor\n",
1579
+ "def convert_to_tf_tensor(image: Image):\n",
1580
+ " np_image = np.array(image)\n",
1581
+ " tf_image = tf.convert_to_tensor(np_image)\n",
1582
+ " # `expand_dims()` is used to add a batch dimension since\n",
1583
+ " # the TF augmentation layers operates on batched inputs.\n",
1584
+ " return tf.expand_dims(tf_image, 0)\n",
1585
+ "\n",
1586
+ "\n",
1587
+ "def preprocess_train(example_batch):\n",
1588
+ " \"\"\"Apply train_transforms across a batch.\"\"\"\n",
1589
+ " images = [\n",
1590
+ " train_data_augmentation(convert_to_tf_tensor(image.convert(\"RGB\"))) for image in example_batch[\"image\"]\n",
1591
+ " ]\n",
1592
+ " example_batch[\"pixel_values\"] = [tf.transpose(tf.squeeze(image)) for image in images]\n",
1593
+ " return example_batch\n",
1594
+ "\n",
1595
+ "\n",
1596
+ "def preprocess_val(example_batch):\n",
1597
+ " \"\"\"Apply val_transforms across a batch.\"\"\"\n",
1598
+ " images = [\n",
1599
+ " val_data_augmentation(convert_to_tf_tensor(image.convert(\"RGB\"))) for image in example_batch[\"image\"]\n",
1600
+ " ]\n",
1601
+ " example_batch[\"pixel_values\"] = [tf.transpose(tf.squeeze(image)) for image in images]\n",
1602
+ " return example_batch"
1603
+ ],
1604
+ "metadata": {
1605
+ "id": "kWOEaQ8FRHzA"
1606
+ },
1607
+ "execution_count": 49,
1608
+ "outputs": []
1609
+ },
1610
+ {
1611
+ "cell_type": "code",
1612
+ "source": [],
1613
+ "metadata": {
1614
+ "id": "ma5JiYWITRRl"
1615
+ },
1616
+ "execution_count": 49,
1617
+ "outputs": []
1618
+ },
1619
+ {
1620
+ "cell_type": "code",
1621
+ "source": [
1622
+ "# apply transform to the training and testing dataset\n",
1623
+ "\n",
1624
+ "emotions_df[\"train\"].set_transform(preprocess_train)\n",
1625
+ "emotions_df[\"test\"].set_transform(preprocess_val)"
1626
+ ],
1627
+ "metadata": {
1628
+ "id": "UpWd7hc7RH3I"
1629
+ },
1630
+ "execution_count": 50,
1631
+ "outputs": []
1632
+ },
1633
+ {
1634
+ "cell_type": "code",
1635
+ "source": [
1636
+ "#create a batch of examples using DefaultDataCollator\n",
1637
+ "from transformers import DefaultDataCollator\n",
1638
+ "\n",
1639
+ "data_collator = DefaultDataCollator(return_tensors=\"tf\")"
1640
+ ],
1641
+ "metadata": {
1642
+ "id": "0zQOcaYBTRVA"
1643
+ },
1644
+ "execution_count": 51,
1645
+ "outputs": []
1646
+ },
1647
+ {
1648
+ "cell_type": "code",
1649
+ "source": [],
1650
+ "metadata": {
1651
+ "id": "zPYyzlsNTRfg"
1652
+ },
1653
+ "execution_count": 51,
1654
+ "outputs": []
1655
+ },
1656
+ {
1657
+ "cell_type": "code",
1658
+ "source": [
1659
+ "# evaluate accuracy\n",
1660
+ "import evaluate\n",
1661
+ "\n",
1662
+ "accuracy = evaluate.load(\"accuracy\")"
1663
+ ],
1664
+ "metadata": {
1665
+ "id": "fVaQvixOVVsz"
1666
+ },
1667
+ "execution_count": 52,
1668
+ "outputs": []
1669
+ },
1670
+ {
1671
+ "cell_type": "code",
1672
+ "source": [
1673
+ "import numpy as np\n",
1674
+ "\n",
1675
+ "def compute_metrics(eval_pred):\n",
1676
+ " predictions, labels = eval_pred\n",
1677
+ " predictions = np.argmax(predictions, axis=1)\n",
1678
+ " return accuracy.compute(predictions=predictions, references=labels)"
1679
+ ],
1680
+ "metadata": {
1681
+ "id": "nEj80b7FTseY"
1682
+ },
1683
+ "execution_count": 53,
1684
+ "outputs": []
1685
+ },
1686
+ {
1687
+ "cell_type": "code",
1688
+ "source": [],
1689
+ "metadata": {
1690
+ "id": "vn9G6JZ0Tsh4"
1691
+ },
1692
+ "execution_count": 53,
1693
+ "outputs": []
1694
+ },
1695
+ {
1696
+ "cell_type": "markdown",
1697
+ "source": [
1698
+ "TRAIN"
1699
+ ],
1700
+ "metadata": {
1701
+ "id": "LvDvoIFzUEk-"
1702
+ }
1703
+ },
1704
+ {
1705
+ "cell_type": "code",
1706
+ "source": [
1707
+ "from transformers import create_optimizer\n",
1708
+ "\n",
1709
+ "batch_size = 16\n",
1710
+ "num_epochs = 20\n",
1711
+ "num_train_steps = len(emotions_df[\"train\"]) * num_epochs\n",
1712
+ "learning_rate = 3e-4\n",
1713
+ "weight_decay_rate = 0.01\n",
1714
+ "\n",
1715
+ "optimizer, lr_schedule = create_optimizer(\n",
1716
+ " init_lr=learning_rate,\n",
1717
+ " num_train_steps=num_train_steps,\n",
1718
+ " weight_decay_rate=weight_decay_rate,\n",
1719
+ " num_warmup_steps=0,\n",
1720
+ ")"
1721
+ ],
1722
+ "metadata": {
1723
+ "id": "xGYIJKUHTskw"
1724
+ },
1725
+ "execution_count": 54,
1726
+ "outputs": []
1727
+ },
1728
+ {
1729
+ "cell_type": "code",
1730
+ "source": [],
1731
+ "metadata": {
1732
+ "id": "M3ZPsb9LTsoL"
1733
+ },
1734
+ "execution_count": 54,
1735
+ "outputs": []
1736
+ },
1737
+ {
1738
+ "cell_type": "code",
1739
+ "source": [
1740
+ "from transformers import TFAutoModelForImageClassification\n",
1741
+ "\n",
1742
+ "model = TFAutoModelForImageClassification.from_pretrained(\n",
1743
+ " checkpoint,\n",
1744
+ " id2label=id2label,\n",
1745
+ " label2id=label2id,\n",
1746
+ ")"
1747
+ ],
1748
+ "metadata": {
1749
+ "colab": {
1750
+ "base_uri": "https://localhost:8080/"
1751
+ },
1752
+ "id": "KABXS5tkfFM8",
1753
+ "outputId": "4e5cbbc5-4334-4e5d-f96e-84bed9153db3"
1754
+ },
1755
+ "execution_count": 55,
1756
+ "outputs": [
1757
+ {
1758
+ "output_type": "stream",
1759
+ "name": "stderr",
1760
+ "text": [
1761
+ "Some layers from the model checkpoint at google/vit-base-patch16-224-in21k were not used when initializing TFViTForImageClassification: ['vit/pooler/dense/kernel:0', 'vit/pooler/dense/bias:0']\n",
1762
+ "- This IS expected if you are initializing TFViTForImageClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
1763
+ "- This IS NOT expected if you are initializing TFViTForImageClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
1764
+ "Some layers of TFViTForImageClassification were not initialized from the model checkpoint at google/vit-base-patch16-224-in21k and are newly initialized: ['classifier']\n",
1765
+ "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
1766
+ ]
1767
+ }
1768
+ ]
1769
+ },
1770
+ {
1771
+ "cell_type": "code",
1772
+ "source": [],
1773
+ "metadata": {
1774
+ "id": "lZMOoitIUexL"
1775
+ },
1776
+ "execution_count": 55,
1777
+ "outputs": []
1778
+ },
1779
+ {
1780
+ "cell_type": "code",
1781
+ "source": [
1782
+ "# avoiding overfitting \n",
1783
+ "\n",
1784
+ "from tensorflow import keras\n",
1785
+ "from tensorflow.keras import layers\n",
1786
+ "\n",
1787
+ "size = (image_processor.size[\"height\"], image_processor.size[\"width\"])\n",
1788
+ "\n",
1789
+ "# Transformations for the training set\n",
1790
+ "# data augmentation to make the model more robust and to avoid overfitting\n",
1791
+ "train_data_augmentation = keras.Sequential(\n",
1792
+ " [\n",
1793
+ " layers.RandomCrop(size[0], size[1]),\n",
1794
+ " layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n",
1795
+ " layers.RandomFlip(\"horizontal\"),\n",
1796
+ " layers.RandomRotation(factor=0.02),\n",
1797
+ " layers.RandomZoom(height_factor=0.2, width_factor=0.2),\n",
1798
+ " ],\n",
1799
+ " name=\"train_data_augmentation\",\n",
1800
+ ")\n",
1801
+ "\n",
1802
+ "# Transformations for the validation set\n",
1803
+ "val_data_augmentation = keras.Sequential(\n",
1804
+ " [\n",
1805
+ " layers.CenterCrop(size[0], size[1]),\n",
1806
+ " layers.Rescaling(scale=1.0 / 127.5, offset=-1),\n",
1807
+ " ],\n",
1808
+ " name=\"val_data_augmentation\",\n",
1809
+ ")"
1810
+ ],
1811
+ "metadata": {
1812
+ "id": "EtXaPaGCVE7e"
1813
+ },
1814
+ "execution_count": 56,
1815
+ "outputs": []
1816
+ },
1817
+ {
1818
+ "cell_type": "code",
1819
+ "source": [],
1820
+ "metadata": {
1821
+ "id": "4eKP4h7rVFNr"
1822
+ },
1823
+ "execution_count": 56,
1824
+ "outputs": []
1825
+ },
1826
+ {
1827
+ "cell_type": "code",
1828
+ "source": [
1829
+ "# converting our train dataset to tensor dataset (tf.data.Dataset)\n",
1830
+ "tf_train_dataset = emotions_df[\"train\"].to_tf_dataset(\n",
1831
+ " columns=\"pixel_values\", label_cols=\"label\", shuffle=True, batch_size=batch_size, collate_fn=data_collator\n",
1832
+ ")\n",
1833
+ "\n",
1834
+ "# converting our test dataset to tensor dataset (tf.data.Dataset)\n",
1835
+ "tf_eval_dataset = emotions_df[\"test\"].to_tf_dataset(\n",
1836
+ " columns=\"pixel_values\", label_cols=\"label\", shuffle=True, batch_size=batch_size, collate_fn=data_collator\n",
1837
+ ")"
1838
+ ],
1839
+ "metadata": {
1840
+ "id": "3j70Yv9dRH6t"
1841
+ },
1842
+ "execution_count": 57,
1843
+ "outputs": []
1844
+ },
1845
+ {
1846
+ "cell_type": "code",
1847
+ "source": [
1848
+ "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
1849
+ "\n",
1850
+ "loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)\n",
1851
+ "model.compile(optimizer=optimizer, loss=loss)"
1852
+ ],
1853
+ "metadata": {
1854
+ "id": "iiDCBtU5UjqM"
1855
+ },
1856
+ "execution_count": 58,
1857
+ "outputs": []
1858
+ },
1859
+ {
1860
+ "cell_type": "code",
1861
+ "source": [
1862
+ "from transformers.keras_callbacks import KerasMetricCallback, PushToHubCallback\n",
1863
+ "\n",
1864
+ "metric_callback = KerasMetricCallback(metric_fn=compute_metrics, eval_dataset=tf_eval_dataset)\n",
1865
+ "push_to_hub_callback = PushToHubCallback(\n",
1866
+ " output_dir=\"emotions_classifier\",\n",
1867
+ " tokenizer=image_processor,\n",
1868
+ " save_strategy=\"no\",\n",
1869
+ ")\n",
1870
+ "callbacks = [metric_callback, push_to_hub_callback]"
1871
+ ],
1872
+ "metadata": {
1873
+ "colab": {
1874
+ "base_uri": "https://localhost:8080/"
1875
+ },
1876
+ "id": "kt3kPnOkUjuG",
1877
+ "outputId": "ceb5b9e9-768b-46ea-8df1-45022649b6f4"
1878
+ },
1879
+ "execution_count": 59,
1880
+ "outputs": [
1881
+ {
1882
+ "output_type": "stream",
1883
+ "name": "stderr",
1884
+ "text": [
1885
+ "/content/emotions_classifier is already a clone of https://huggingface.co/CynthiaCR/emotions_classifier. Make sure you pull the latest changes with `repo.git_pull()`.\n",
1886
+ "WARNING:huggingface_hub.repository:/content/emotions_classifier is already a clone of https://huggingface.co/CynthiaCR/emotions_classifier. Make sure you pull the latest changes with `repo.git_pull()`.\n"
1887
+ ]
1888
+ }
1889
+ ]
1890
+ },
1891
+ {
1892
+ "cell_type": "code",
1893
+ "source": [
1894
+ "model.fit(tf_train_dataset, validation_data=tf_eval_dataset, epochs=num_epochs, callbacks=callbacks)"
1895
+ ],
1896
+ "metadata": {
1897
+ "colab": {
1898
+ "base_uri": "https://localhost:8080/",
1899
+ "height": 920,
1900
+ "referenced_widgets": [
1901
+ "a965aa730e164b249ff040457c944d94",
1902
+ "cdefc2d658bc4bd3ba8b3b4d0affd263",
1903
+ "f41f320a42c54f90996b54af83689aff",
1904
+ "833a251db60140a598b9b4d28583271b",
1905
+ "b157a9c50b834c24acc1445c803c670c",
1906
+ "64deb634ee7249ad8ed9bd93c1979459",
1907
+ "b41049a008604c1ab7bdb4646092628d",
1908
+ "6d971fa517aa4c1295522279f29f0258",
1909
+ "4f316fbfcf3644cd84a3f146265c9ba2",
1910
+ "06067b563aa54cc386e4954a9702042f",
1911
+ "e2dca77a089e43caa918174707165b56"
1912
+ ]
1913
+ },
1914
+ "id": "k1m8BvqxVt-t",
1915
+ "outputId": "2594dd93-d133-4ae0-c4c9-50460962540e"
1916
+ },
1917
+ "execution_count": 60,
1918
+ "outputs": [
1919
+ {
1920
+ "output_type": "stream",
1921
+ "name": "stdout",
1922
+ "text": [
1923
+ "Epoch 1/20\n",
1924
+ "40/40 [==============================] - 69s 1s/step - loss: 2.0363 - val_loss: 2.0960 - accuracy: 0.1000\n",
1925
+ "Epoch 2/20\n",
1926
+ "40/40 [==============================] - 47s 1s/step - loss: 2.0822 - val_loss: 2.1254 - accuracy: 0.0813\n",
1927
+ "Epoch 3/20\n",
1928
+ "40/40 [==============================] - 47s 1s/step - loss: 1.9916 - val_loss: 1.9392 - accuracy: 0.2062\n",
1929
+ "Epoch 4/20\n",
1930
+ "40/40 [==============================] - 47s 1s/step - loss: 1.9223 - val_loss: 1.8385 - accuracy: 0.1688\n",
1931
+ "Epoch 5/20\n",
1932
+ "40/40 [==============================] - 48s 1s/step - loss: 1.8213 - val_loss: 1.7294 - accuracy: 0.2313\n",
1933
+ "Epoch 6/20\n",
1934
+ "40/40 [==============================] - 48s 1s/step - loss: 1.6940 - val_loss: 1.6953 - accuracy: 0.2625\n",
1935
+ "Epoch 7/20\n",
1936
+ "40/40 [==============================] - 48s 1s/step - loss: 1.7153 - val_loss: 1.6009 - accuracy: 0.3187\n",
1937
+ "Epoch 8/20\n",
1938
+ "40/40 [==============================] - 48s 1s/step - loss: 1.5788 - val_loss: 1.6385 - accuracy: 0.2750\n",
1939
+ "Epoch 9/20\n",
1940
+ "40/40 [==============================] - 48s 1s/step - loss: 1.5359 - val_loss: 1.5635 - accuracy: 0.3438\n",
1941
+ "Epoch 10/20\n",
1942
+ "40/40 [==============================] - 48s 1s/step - loss: 1.4768 - val_loss: 1.6180 - accuracy: 0.3250\n",
1943
+ "Epoch 11/20\n",
1944
+ "40/40 [==============================] - 50s 1s/step - loss: 1.4746 - val_loss: 1.6063 - accuracy: 0.3125\n",
1945
+ "Epoch 12/20\n",
1946
+ "40/40 [==============================] - 48s 1s/step - loss: 1.5163 - val_loss: 1.5641 - accuracy: 0.3625\n",
1947
+ "Epoch 13/20\n",
1948
+ "40/40 [==============================] - 48s 1s/step - loss: 1.4692 - val_loss: 1.5722 - accuracy: 0.3063\n",
1949
+ "Epoch 14/20\n",
1950
+ "40/40 [==============================] - 50s 1s/step - loss: 1.4468 - val_loss: 1.7363 - accuracy: 0.3500\n",
1951
+ "Epoch 15/20\n",
1952
+ "40/40 [==============================] - 48s 1s/step - loss: 1.7116 - val_loss: 1.7531 - accuracy: 0.2687\n",
1953
+ "Epoch 16/20\n",
1954
+ "40/40 [==============================] - 48s 1s/step - loss: 1.5334 - val_loss: 1.5908 - accuracy: 0.2562\n",
1955
+ "Epoch 17/20\n",
1956
+ "40/40 [==============================] - 48s 1s/step - loss: 1.4988 - val_loss: 1.5169 - accuracy: 0.3312\n",
1957
+ "Epoch 18/20\n",
1958
+ "40/40 [==============================] - 48s 1s/step - loss: 1.4605 - val_loss: 1.5041 - accuracy: 0.2812\n",
1959
+ "Epoch 19/20\n",
1960
+ "40/40 [==============================] - 48s 1s/step - loss: 1.3545 - val_loss: 1.4824 - accuracy: 0.3187\n",
1961
+ "Epoch 20/20\n",
1962
+ "40/40 [==============================] - 48s 1s/step - loss: 1.3846 - val_loss: 1.6122 - accuracy: 0.2687\n"
1963
+ ]
1964
+ },
1965
+ {
1966
+ "output_type": "display_data",
1967
+ "data": {
1968
+ "text/plain": [
1969
+ "Upload file tf_model.h5: 0%| | 1.00/328M [00:00<?, ?B/s]"
1970
+ ],
1971
+ "application/vnd.jupyter.widget-view+json": {
1972
+ "version_major": 2,
1973
+ "version_minor": 0,
1974
+ "model_id": "a965aa730e164b249ff040457c944d94"
1975
+ }
1976
+ },
1977
+ "metadata": {}
1978
+ },
1979
+ {
1980
+ "output_type": "stream",
1981
+ "name": "stderr",
1982
+ "text": [
1983
+ "To https://huggingface.co/CynthiaCR/emotions_classifier\n",
1984
+ " 6794f2e..9552b39 main -> main\n",
1985
+ "\n",
1986
+ "WARNING:huggingface_hub.repository:To https://huggingface.co/CynthiaCR/emotions_classifier\n",
1987
+ " 6794f2e..9552b39 main -> main\n",
1988
+ "\n"
1989
+ ]
1990
+ },
1991
+ {
1992
+ "output_type": "execute_result",
1993
+ "data": {
1994
+ "text/plain": [
1995
+ "<keras.callbacks.History at 0x7f131a6aae60>"
1996
+ ]
1997
+ },
1998
+ "metadata": {},
1999
+ "execution_count": 60
2000
+ }
2001
+ ]
2002
+ },
2003
+ {
2004
+ "cell_type": "markdown",
2005
+ "source": [
2006
+ "Prediction"
2007
+ ],
2008
+ "metadata": {
2009
+ "id": "29ZPkramjcir"
2010
+ }
2011
+ },
2012
+ {
2013
+ "cell_type": "code",
2014
+ "source": [],
2015
+ "metadata": {
2016
+ "id": "C_9-dH4cVuEB"
2017
+ },
2018
+ "execution_count": 60,
2019
+ "outputs": []
2020
+ },
2021
+ {
2022
+ "cell_type": "code",
2023
+ "source": [
2024
+ "ds = load_dataset(\"FastJobs/Visual_Emotional_Analysis\", split=\"train[:10]\")\n",
2025
+ "ds"
2026
+ ],
2027
+ "metadata": {
2028
+ "id": "JUFTj8TYiBwS",
2029
+ "colab": {
2030
+ "base_uri": "https://localhost:8080/"
2031
+ },
2032
+ "outputId": "c22db2f0-0443-4aa9-9346-739967bb43b8"
2033
+ },
2034
+ "execution_count": 61,
2035
+ "outputs": [
2036
+ {
2037
+ "output_type": "stream",
2038
+ "name": "stderr",
2039
+ "text": [
2040
+ "WARNING:datasets.builder:Found cached dataset imagefolder (/root/.cache/huggingface/datasets/FastJobs___imagefolder/FastJobs--Visual_Emotional_Analysis-bbb0f5e70847fc91/0.0.0/37fbb85cc714a338bea574ac6c7d0b5be5aff46c1862c1989b20e0771199e93f)\n"
2041
+ ]
2042
+ },
2043
+ {
2044
+ "output_type": "execute_result",
2045
+ "data": {
2046
+ "text/plain": [
2047
+ "Dataset({\n",
2048
+ " features: ['image', 'label'],\n",
2049
+ " num_rows: 10\n",
2050
+ "})"
2051
+ ]
2052
+ },
2053
+ "metadata": {},
2054
+ "execution_count": 61
2055
+ }
2056
+ ]
2057
+ },
2058
+ {
2059
+ "cell_type": "code",
2060
+ "source": [
2061
+ "image = ds[\"image\"][0]"
2062
+ ],
2063
+ "metadata": {
2064
+ "id": "WBXiMJ48qBKl"
2065
+ },
2066
+ "execution_count": 62,
2067
+ "outputs": []
2068
+ },
2069
+ {
2070
+ "cell_type": "code",
2071
+ "source": [
2072
+ "from transformers import pipeline\n",
2073
+ "\n",
2074
+ "classifier = pipeline(\"image-classification\", model=\"CynthiaCR/emotions_classifier\")\n",
2075
+ "classifier(image)"
2076
+ ],
2077
+ "metadata": {
2078
+ "colab": {
2079
+ "base_uri": "https://localhost:8080/",
2080
+ "height": 232,
2081
+ "referenced_widgets": [
2082
+ "75599d75d500474db4e9ca8801af0ac6",
2083
+ "5d8c864d082047b2a3b180985ae0b698",
2084
+ "502dd70810054869bb01aae69ab2431d",
2085
+ "53ff5a46800543d7a2e6ed532e0085eb",
2086
+ "8a3110bdb7844528a867a3f2d0daf028",
2087
+ "dbceb3e2a8b144bd8a899e7778b9fb67",
2088
+ "dc6982a803d34c2b9366323f44cf0ef7",
2089
+ "e4fa00ecdf7e4878bd07e3e81d22f7b0",
2090
+ "708979e23d1f4833b31b5c8f8c208cb4",
2091
+ "d0432428e4524f318bff7ccf2f91c6b4",
2092
+ "25a5031e71144bf4bec62694c16f39e2"
2093
+ ]
2094
+ },
2095
+ "id": "QkaFEJg2Ujxl",
2096
+ "outputId": "5ed6475b-9d54-48b8-b175-a16e39341a1d"
2097
+ },
2098
+ "execution_count": 63,
2099
+ "outputs": [
2100
+ {
2101
+ "output_type": "display_data",
2102
+ "data": {
2103
+ "text/plain": [
2104
+ "Downloading tf_model.h5: 0%| | 0.00/344M [00:00<?, ?B/s]"
2105
+ ],
2106
+ "application/vnd.jupyter.widget-view+json": {
2107
+ "version_major": 2,
2108
+ "version_minor": 0,
2109
+ "model_id": "75599d75d500474db4e9ca8801af0ac6"
2110
+ }
2111
+ },
2112
+ "metadata": {}
2113
+ },
2114
+ {
2115
+ "output_type": "stream",
2116
+ "name": "stderr",
2117
+ "text": [
2118
+ "All model checkpoint layers were used when initializing TFViTForImageClassification.\n",
2119
+ "\n",
2120
+ "All the layers of TFViTForImageClassification were initialized from the model checkpoint at CynthiaCR/emotions_classifier.\n",
2121
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n"
2122
+ ]
2123
+ },
2124
+ {
2125
+ "output_type": "execute_result",
2126
+ "data": {
2127
+ "text/plain": [
2128
+ "[{'score': 0.32123512029647827, 'label': 'fear'},\n",
2129
+ " {'score': 0.31210750341415405, 'label': 'sad'},\n",
2130
+ " {'score': 0.1644315868616104, 'label': 'anger'},\n",
2131
+ " {'score': 0.10217338800430298, 'label': 'disgust'},\n",
2132
+ " {'score': 0.04358164221048355, 'label': 'contempt'}]"
2133
+ ]
2134
+ },
2135
+ "metadata": {},
2136
+ "execution_count": 63
2137
+ }
2138
+ ]
2139
+ },
2140
+ {
2141
+ "cell_type": "code",
2142
+ "source": [
2143
+ "from transformers import AutoImageProcessor\n",
2144
+ "\n",
2145
+ "image_processor = AutoImageProcessor.from_pretrained(\"CynthiaCR/emotions_classifier\")\n",
2146
+ "inputs = image_processor(image, return_tensors=\"tf\")"
2147
+ ],
2148
+ "metadata": {
2149
+ "id": "0vbEKCwX0ybE"
2150
+ },
2151
+ "execution_count": 64,
2152
+ "outputs": []
2153
+ },
2154
+ {
2155
+ "cell_type": "code",
2156
+ "source": [],
2157
+ "metadata": {
2158
+ "id": "sqhjJ_9jkdgl"
2159
+ },
2160
+ "execution_count": 64,
2161
+ "outputs": []
2162
+ },
2163
+ {
2164
+ "cell_type": "code",
2165
+ "source": [
2166
+ "from transformers import TFAutoModelForImageClassification\n",
2167
+ "\n",
2168
+ "model = TFAutoModelForImageClassification.from_pretrained(\"CynthiaCR/emotions_classifier\")\n",
2169
+ "logits = model(**inputs).logits"
2170
+ ],
2171
+ "metadata": {
2172
+ "id": "iCMCASWf0yew",
2173
+ "colab": {
2174
+ "base_uri": "https://localhost:8080/"
2175
+ },
2176
+ "outputId": "6f38799c-8478-4b6f-fba3-0beaa95ed8f5"
2177
+ },
2178
+ "execution_count": 65,
2179
+ "outputs": [
2180
+ {
2181
+ "output_type": "stream",
2182
+ "name": "stderr",
2183
+ "text": [
2184
+ "All model checkpoint layers were used when initializing TFViTForImageClassification.\n",
2185
+ "\n",
2186
+ "All the layers of TFViTForImageClassification were initialized from the model checkpoint at CynthiaCR/emotions_classifier.\n",
2187
+ "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFViTForImageClassification for predictions without further training.\n"
2188
+ ]
2189
+ }
2190
+ ]
2191
+ },
2192
+ {
2193
+ "cell_type": "code",
2194
+ "source": [],
2195
+ "metadata": {
2196
+ "id": "7iSRY48Gkdti"
2197
+ },
2198
+ "execution_count": 65,
2199
+ "outputs": []
2200
+ },
2201
+ {
2202
+ "cell_type": "code",
2203
+ "source": [
2204
+ "predicted_class_id = int(tf.math.argmax(logits, axis=-1)[0])\n",
2205
+ "model.config.id2label[predicted_class_id]"
2206
+ ],
2207
+ "metadata": {
2208
+ "id": "pJUFDX_e0yh9",
2209
+ "colab": {
2210
+ "base_uri": "https://localhost:8080/",
2211
+ "height": 36
2212
+ },
2213
+ "outputId": "c09c9df5-41d8-418e-a44c-0cab27fe28e1"
2214
+ },
2215
+ "execution_count": 66,
2216
+ "outputs": [
2217
+ {
2218
+ "output_type": "execute_result",
2219
+ "data": {
2220
+ "text/plain": [
2221
+ "'fear'"
2222
+ ],
2223
+ "application/vnd.google.colaboratory.intrinsic+json": {
2224
+ "type": "string"
2225
+ }
2226
+ },
2227
+ "metadata": {},
2228
+ "execution_count": 66
2229
+ }
2230
+ ]
2231
+ }
2232
+ ]
2233
+ }