diff --git a/examples/nlp/img/multiple_choice_task_with_transfer_learning/multiple_choice_task_with_transfer_learning_32_0.png b/examples/nlp/img/multiple_choice_task_with_transfer_learning/multiple_choice_task_with_transfer_learning_32_0.png
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diff --git a/examples/nlp/img/multiple_choice_task_with_transfer_learning/multiple_choice_task_with_transfer_learning_33_0.png b/examples/nlp/img/multiple_choice_task_with_transfer_learning/multiple_choice_task_with_transfer_learning_33_0.png
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diff --git a/examples/nlp/ipynb/multiple_choice_task_with_transfer_learning.ipynb b/examples/nlp/ipynb/multiple_choice_task_with_transfer_learning.ipynb
index b3ad5e7b68..bd2c851f55 100644
--- a/examples/nlp/ipynb/multiple_choice_task_with_transfer_learning.ipynb
+++ b/examples/nlp/ipynb/multiple_choice_task_with_transfer_learning.ipynb
@@ -10,7 +10,7 @@
"\n",
"**Author:** Md Awsafur Rahman
\n",
"**Date created:** 2023/09/14
\n",
- "**Last modified:** 2023/09/14
\n",
+ "**Last modified:** 2024/01/10
\n",
"**Description:** Use pre-trained nlp models for multiplechoice task."
]
},
@@ -45,9 +45,10 @@
},
"outputs": [],
"source": [
- "import keras_nlp\n",
+ "\n",
"import keras\n",
- "import tensorflow as tf # For tf.data only.\n",
+ "import keras_nlp\n",
+ "import tensorflow as tf\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
@@ -856,9 +857,9 @@
" question = row.startphrase\n",
" pred_answer = f\"ending{pred_answers[i]}\"\n",
" true_answer = f\"ending{true_answers[i]}\"\n",
- " print(f\"\u2753 Sentence {i+1}:\\n{question}\\n\")\n",
- " print(f\"\u2705 True Ending: {true_answer}\\n >> {row[true_answer]}\\n\")\n",
- " print(f\"\ud83e\udd16 Predicted Ending: {pred_answer}\\n >> {row[pred_answer]}\\n\")\n",
+ " print(f\"\u2753 Sentence {i+1}:\\n{question}\\n\")\n",
+ " print(f\"\u2705 True Ending: {true_answer}\\n >> {row[true_answer]}\\n\")\n",
+ " print(f\"\ud83e\udd16 Predicted Ending: {pred_answer}\\n >> {row[pred_answer]}\\n\")\n",
" print(\"-\" * 90, \"\\n\")"
]
},
diff --git a/examples/nlp/md/multiple_choice_task_with_transfer_learning.md b/examples/nlp/md/multiple_choice_task_with_transfer_learning.md
index c682d6fade..d782ab4086 100644
--- a/examples/nlp/md/multiple_choice_task_with_transfer_learning.md
+++ b/examples/nlp/md/multiple_choice_task_with_transfer_learning.md
@@ -2,7 +2,7 @@
**Author:** Md Awsafur Rahman
**Date created:** 2023/09/14
-**Last modified:** 2023/09/14
+**Last modified:** 2024/01/10
**Description:** Use pre-trained nlp models for multiplechoice task.
@@ -23,9 +23,10 @@ unlike question answering. We will use SWAG dataset to demonstrate this example.
```python
-import keras_nlp
+
import keras
-import tensorflow as tf # For tf.data only.
+import keras_nlp
+import tensorflow as tf
import numpy as np
import pandas as pd
@@ -45,29 +46,55 @@ In this example we'll use **SWAG** dataset for multiplechoice task.
```
---2023-11-13 20:05:24-- https://github.com/rowanz/swagaf/archive/refs/heads/master.zip
-Resolving github.com (github.com)... 192.30.255.113
-Connecting to github.com (github.com)|192.30.255.113|:443... connected.
-HTTP request sent, awaiting response... 302 Found
+--2024-01-11 01:43:38-- https://github.com/rowanz/swagaf/archive/refs/heads/master.zip
+Resolving github.com (github.com)... 140.82.112.4
+Connecting to github.com (github.com)|140.82.112.4|:443...
+
+connected.
+
+HTTP request sent, awaiting response...
+
+302 Found
Location: https://codeload.github.com/rowanz/swagaf/zip/refs/heads/master [following]
---2023-11-13 20:05:25-- https://codeload.github.com/rowanz/swagaf/zip/refs/heads/master
-Resolving codeload.github.com (codeload.github.com)... 20.29.134.24
-Connecting to codeload.github.com (codeload.github.com)|20.29.134.24|:443... connected.
-HTTP request sent, awaiting response... 200 OK
+--2024-01-11 01:43:38-- https://codeload.github.com/rowanz/swagaf/zip/refs/heads/master
+Resolving codeload.github.com (codeload.github.com)... 140.82.113.9
+Connecting to codeload.github.com (codeload.github.com)|140.82.113.9|:443...
+
+connected.
+
+HTTP request sent, awaiting response...
+
+200 OK
Length: unspecified [application/zip]
Saving to: ‘swag.zip’
```
-
-```
-swag.zip [ <=> ] 19.94M 4.25MB/s in 4.7s
-```
-
+
+swag.zip [<=> ] 0 --.-KB/s
+
+
+swag.zip [ <=> ] 84.24K 229KB/s
+
+
+swag.zip [ <=> ] 1.46M 2.44MB/s
+
+
+swag.zip [ <=> ] 2.20M 1.48MB/s
+
+
+swag.zip [ <=> ] 8.59M 3.16MB/s
+
+
+swag.zip [ <=> ] 15.40M 4.96MB/s
+
+
+swag.zip [ <=> ] 17.54M 5.05MB/s
+swag.zip [ <=> ] 19.94M 5.71MB/s in 3.5s
```
-2023-11-13 20:05:30 (4.25 MB/s) - ‘swag.zip’ saved [20905751]
+2024-01-11 01:43:42 (5.06 MB/s) - ‘swag.zip’ saved [20905751]
```
@@ -223,8 +250,6 @@ for k, v in outs.items():
```
-CUDA backend failed to initialize: Found CUDA version 12010, but JAX was built against version 12020, which is newer. The copy of CUDA that is installed must be at least as new as the version against which JAX was built. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
-
token_ids : (4, 200)
padding_mask : (4, 200)
@@ -572,6 +597,205 @@ def build_model():
model = build_model()
```
+
+```
+Downloading from https://www.kaggle.com/api/v1/models/keras/deberta_v3/keras/deberta_v3_extra_small_en/2/download/config.json...
+
+```
+
+
+ 0%| | 0.00/539 [00:00
+```
+Downloading from https://www.kaggle.com/api/v1/models/keras/deberta_v3/keras/deberta_v3_extra_small_en/2/download/model.weights.h5...
+
+```
+
```
Epoch 1/5
- 183/183 ━━━━━━━━━━━━━━━━━━━━ 5087s 25s/step - accuracy: 0.2563 - loss: 1.3884 - val_accuracy: 0.5150 - val_loss: 1.3742 - learning_rate: 1.0000e-06
-Epoch 2/5
- 183/183 ━━━━━━━━━━━━━━━━━━━━ 4529s 25s/step - accuracy: 0.3825 - loss: 1.3364 - val_accuracy: 0.7125 - val_loss: 0.9071 - learning_rate: 2.9000e-06
-Epoch 3/5
- 183/183 ━━━━━━━━━━━━━━━━━━━━ 4524s 25s/step - accuracy: 0.6144 - loss: 1.0118 - val_accuracy: 0.7425 - val_loss: 0.8017 - learning_rate: 4.8000e-06
-Epoch 4/5
- 183/183 ━━━━━━━━━━━━━━━━━━━━ 4522s 25s/step - accuracy: 0.6744 - loss: 0.8460 - val_accuracy: 0.7625 - val_loss: 0.7323 - learning_rate: 4.7230e-06
-Epoch 5/5
- 183/183 ━━━━━━━━━━━━━━━━━━━━ 4517s 25s/step - accuracy: 0.7200 - loss: 0.7458 - val_accuracy: 0.7750 - val_loss: 0.7022 - learning_rate: 4.4984e-06
+
+WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
+I0000 00:00:1704605251.846942 12962 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
```
----
-## Inference
+
+ 1/183 [..............................] - ETA: 16:51:51 - loss: 1.3865 - accuracy: 0.3750
+
+```
+
+```
+
+ 2/183 [..............................] - ETA: 44:52 - loss: 1.3652 - accuracy: 0.3750
-```python
-# Make predictions using the trained model on last validation data
-predictions = model.predict(
- valid_ds,
- batch_size=CFG.batch_size, # max batch size = valid size
- verbose=1,
-)
+
+```
+
+```
+
+ 3/183 [..............................] - ETA: 44:29 - loss: 1.3851 - accuracy: 0.2500
-# Format predictions and true answers
-pred_answers = np.arange(4)[np.argsort(-predictions)][:, 0]
-true_answers = valid_df.label.values
+
+```
+
+```
+
+ 4/183 [..............................] - ETA: 44:12 - loss: 1.3875 - accuracy: 0.2188
-# Check 5 Predictions
-print("# Predictions\n")
-for i in range(0, 50, 10):
- row = valid_df.iloc[i]
- question = row.startphrase
- pred_answer = f"ending{pred_answers[i]}"
- true_answer = f"ending{true_answers[i]}"
- print(f"❓ Sentence {i+1}:\n{question}\n")
- print(f"✅ True Ending: {true_answer}\n >> {row[true_answer]}\n")
- print(f"🤖 Predicted Ending: {pred_answer}\n >> {row[pred_answer]}\n")
- print("-" * 90, "\n")
+
+```
+
+```
+
+ 5/183 [..............................] - ETA: 43:54 - loss: 1.3896 - accuracy: 0.2000
+
+
+```
+
+```
+
+ 6/183 [..............................] - ETA: 43:41 - loss: 1.3942 - accuracy: 0.1875
+
+
+```
+
+```
+
+ 7/183 [>.............................] - ETA: 43:25 - loss: 1.3928 - accuracy: 0.2143
+
+
+```
+
+```
+
+ 8/183 [>.............................] - ETA: 43:22 - loss: 1.3933 - accuracy: 0.2031
+
+
+```
+
+```
+
+ 9/183 [>.............................] - ETA: 42:54 - loss: 1.3934 - accuracy: 0.2083
+
+
+```
+
+```
+
+ 10/183 [>.............................] - ETA: 42:27 - loss: 1.3901 - accuracy: 0.2375
+
+
+```
+
+```
+
+ 11/183 [>.............................] - ETA: 42:04 - loss: 1.3898 - accuracy: 0.2500
+
+
+```
+
+```
+
+ 12/183 [>.............................] - ETA: 41:42 - loss: 1.3903 - accuracy: 0.2396
+
+
+```
+
+```
+
+ 13/183 [=>............................] - ETA: 41:21 - loss: 1.3900 - accuracy: 0.2500
+
+
+```
+
+```
+
+ 14/183 [=>............................] - ETA: 41:02 - loss: 1.3908 - accuracy: 0.2321
+
+
+```
+
+```
+
+ 15/183 [=>............................] - ETA: 40:45 - loss: 1.3919 - accuracy: 0.2167
+
+
```
+
+```
+
+ 16/183 [=>............................] - ETA: 40:26 - loss: 1.3927 - accuracy: 0.2109
+
+
+```
+
+```
+
+ 17/183 [=>............................] - ETA: 40:08 - loss: 1.3935 - accuracy: 0.2132
+
+
+```
+
+```
+
+ 18/183 [=>............................] - ETA: 39:51 - loss: 1.3920 - accuracy: 0.2153
+
+
+```
+
+```
+
+ 19/183 [==>...........................] - ETA: 39:34 - loss: 1.3905 - accuracy: 0.2171
+
+
+```
+
+```
+
+ 20/183 [==>...........................] - ETA: 39:16 - loss: 1.3900 - accuracy: 0.2188
+
+
+```
+
+```
+
+ 21/183 [==>...........................] - ETA: 39:01 - loss: 1.3908 - accuracy: 0.2202
+
+
+```
+
+```
+
+ 22/183 [==>...........................] - ETA: 38:44 - loss: 1.3915 - accuracy: 0.2159
+
+
+```
+
+```
+
+ 23/183 [==>...........................] - ETA: 38:28 - loss: 1.3892 - accuracy: 0.2391
+
+
+```
+
+```
+
+ 24/183 [==>...........................] - ETA: 38:12 - loss: 1.3891 - accuracy: 0.2396
+
+
+```
+
+```
+
+ 25/183 [===>..........................] - ETA: 37:56 - loss: 1.3893 - accuracy: 0.2350
+
+
+```
+
+```
+
+ 26/183 [===>..........................] - ETA: 37:40 - loss: 1.3893 - accuracy: 0.2404
+
+
+```
+
+```
+
+ 27/183 [===>..........................] - ETA: 37:25 - loss: 1.3886 - accuracy: 0.2454
+
+
+```
+
+```
+
+ 28/183 [===>..........................] - ETA: 37:09 - loss: 1.3880 - accuracy: 0.2455
+
+
+```
+
+```
+
+ 29/183 [===>..........................] - ETA: 36:55 - loss: 1.3874 - accuracy: 0.2457
+
+
+```
+
+```
+
+ 30/183 [===>..........................] - ETA: 36:39 - loss: 1.3877 - accuracy: 0.2458
+
+
+```
+
+```
+
+ 31/183 [====>.........................] - ETA: 36:23 - loss: 1.3870 - accuracy: 0.2540
+
+
+```
+
+```
+
+ 32/183 [====>.........................] - ETA: 36:08 - loss: 1.3870 - accuracy: 0.2578
+
+
+```
+
+```
+
+ 33/183 [====>.........................] - ETA: 35:53 - loss: 1.3870 - accuracy: 0.2538
+
+
+```
+
+```
+
+ 34/183 [====>.........................] - ETA: 35:38 - loss: 1.3882 - accuracy: 0.2500
+
+
+```
+
+```
+
+ 35/183 [====>.........................] - ETA: 35:23 - loss: 1.3876 - accuracy: 0.2536
+
+
+```
+
+```
+
+ 36/183 [====>.........................] - ETA: 35:07 - loss: 1.3878 - accuracy: 0.2500
+
+
+```
+
+```
+
+ 37/183 [=====>........................] - ETA: 34:53 - loss: 1.3871 - accuracy: 0.2568
+
+
+```
+
+```
+
+ 38/183 [=====>........................] - ETA: 34:38 - loss: 1.3875 - accuracy: 0.2566
+
+
+```
+
+```
+
+ 39/183 [=====>........................] - ETA: 34:22 - loss: 1.3875 - accuracy: 0.2564
+
+
+```
+
+```
+
+ 40/183 [=====>........................] - ETA: 34:07 - loss: 1.3875 - accuracy: 0.2531
+
+
+```
+
+```
+
+ 41/183 [=====>........................] - ETA: 33:52 - loss: 1.3874 - accuracy: 0.2530
+
+
+```
+
+```
+
+ 42/183 [=====>........................] - ETA: 33:38 - loss: 1.3886 - accuracy: 0.2500
+
+
+```
+
+```
+
+ 43/183 [======>.......................] - ETA: 33:23 - loss: 1.3876 - accuracy: 0.2529
+
+
+```
+
+```
+
+ 44/183 [======>.......................] - ETA: 33:08 - loss: 1.3876 - accuracy: 0.2557
+
+
+```
+
+```
+
+ 45/183 [======>.......................] - ETA: 32:54 - loss: 1.3875 - accuracy: 0.2583
+
+
+```
+
+```
+
+ 46/183 [======>.......................] - ETA: 32:39 - loss: 1.3875 - accuracy: 0.2582
+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
+ 50/183 [=======>......................] - ETA: 31:40 - loss: 1.3869 - accuracy: 0.2600
+
+
+```
+
+```
+
+ 51/183 [=======>......................] - ETA: 31:25 - loss: 1.3862 - accuracy: 0.2647
+
+
+```
+
+```
+
+ 52/183 [=======>......................] - ETA: 31:10 - loss: 1.3865 - accuracy: 0.2596
+
+
+```
+
+```
+
+ 53/183 [=======>......................] - ETA: 30:56 - loss: 1.3869 - accuracy: 0.2571
+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
+ 63/183 [=========>....................] - ETA: 28:31 - loss: 1.3855 - accuracy: 0.2679
+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
+ 69/183 [==========>...................] - ETA: 27:05 - loss: 1.3864 - accuracy: 0.2591
+
+
+```
+
+```
+
+ 70/183 [==========>...................] - ETA: 26:51 - loss: 1.3860 - accuracy: 0.2607
+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
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+
+
+```
+
+```
+
+ 89/183 [=============>................] - ETA: 22:17 - loss: 1.3852 - accuracy: 0.2711
+
+
+```
+
+```
+
+ 90/183 [=============>................] - ETA: 22:03 - loss: 1.3852 - accuracy: 0.2694
+
+
+```
+
+```
+
+ 91/183 [=============>................] - ETA: 21:49 - loss: 1.3851 - accuracy: 0.2720
+
+
+```
+
+```
+
+ 92/183 [==============>...............] - ETA: 21:35 - loss: 1.3851 - accuracy: 0.2717
+
+
+```
+
+```
+
+ 93/183 [==============>...............] - ETA: 21:20 - loss: 1.3853 - accuracy: 0.2702
+
+
+```
+
+```
+
+ 94/183 [==============>...............] - ETA: 21:06 - loss: 1.3856 - accuracy: 0.2699
+
+
+```
+
+```
+
+ 95/183 [==============>...............] - ETA: 20:52 - loss: 1.3855 - accuracy: 0.2711
+
+
+```
+
+```
+
+ 96/183 [==============>...............] - ETA: 20:37 - loss: 1.3856 - accuracy: 0.2695
+
+
+```
+
+```
+
+ 97/183 [==============>...............] - ETA: 20:23 - loss: 1.3855 - accuracy: 0.2706
+
+
+```
+
+```
+
+ 98/183 [===============>..............] - ETA: 20:09 - loss: 1.3852 - accuracy: 0.2730
+
+
+```
+
+```
+
+ 99/183 [===============>..............] - ETA: 19:54 - loss: 1.3853 - accuracy: 0.2715
+
+
+```
+
+```
+
+100/183 [===============>..............] - ETA: 19:40 - loss: 1.3854 - accuracy: 0.2713
+
+
+```
+
+```
+
+101/183 [===============>..............] - ETA: 19:26 - loss: 1.3856 - accuracy: 0.2698
+
+
+```
+
+```
+
+102/183 [===============>..............] - ETA: 19:12 - loss: 1.3856 - accuracy: 0.2684
+
+
+```
+
+```
+
+103/183 [===============>..............] - ETA: 18:57 - loss: 1.3854 - accuracy: 0.2694
+
+
+```
+
+```
+
+104/183 [================>.............] - ETA: 18:43 - loss: 1.3852 - accuracy: 0.2716
+
+
+```
+
+```
+
+105/183 [================>.............] - ETA: 18:29 - loss: 1.3854 - accuracy: 0.2714
+
+
+```
+
+```
+
+106/183 [================>.............] - ETA: 18:15 - loss: 1.3851 - accuracy: 0.2748
+
+
+```
+
+```
+
+107/183 [================>.............] - ETA: 18:00 - loss: 1.3852 - accuracy: 0.2745
+
+
+```
+
+```
+
+108/183 [================>.............] - ETA: 17:46 - loss: 1.3853 - accuracy: 0.2720
+
+
+```
+
+```
+
+109/183 [================>.............] - ETA: 17:32 - loss: 1.3854 - accuracy: 0.2718
+
+
+```
+
+```
+
+110/183 [=================>............] - ETA: 17:18 - loss: 1.3855 - accuracy: 0.2716
+
+
+```
+
+```
+
+111/183 [=================>............] - ETA: 17:03 - loss: 1.3856 - accuracy: 0.2703
+
+
+```
+
+```
+
+112/183 [=================>............] - ETA: 16:49 - loss: 1.3859 - accuracy: 0.2701
+
+
+```
+
+```
+
+113/183 [=================>............] - ETA: 16:35 - loss: 1.3859 - accuracy: 0.2699
+
+
+```
+
+```
+
+114/183 [=================>............] - ETA: 16:21 - loss: 1.3859 - accuracy: 0.2697
+
+
+```
+
+```
+
+115/183 [=================>............] - ETA: 16:06 - loss: 1.3862 - accuracy: 0.2707
+
+
+```
+
+```
+
+116/183 [==================>...........] - ETA: 15:52 - loss: 1.3858 - accuracy: 0.2737
+
+
+```
+
+```
+
+117/183 [==================>...........] - ETA: 15:38 - loss: 1.3857 - accuracy: 0.2746
+
+
+```
+
+```
+
+118/183 [==================>...........] - ETA: 15:24 - loss: 1.3856 - accuracy: 0.2744
+
+
+```
+
+```
+
+119/183 [==================>...........] - ETA: 15:09 - loss: 1.3858 - accuracy: 0.2731
+
+
+```
+
+```
+
+120/183 [==================>...........] - ETA: 14:55 - loss: 1.3861 - accuracy: 0.2719
+
+
+```
+
+```
+
+121/183 [==================>...........] - ETA: 14:41 - loss: 1.3862 - accuracy: 0.2707
+
+
+```
+
+```
+
+122/183 [===================>..........] - ETA: 14:27 - loss: 1.3862 - accuracy: 0.2705
+
+
+```
+
+```
+
+123/183 [===================>..........] - ETA: 14:12 - loss: 1.3861 - accuracy: 0.2703
+
+
+```
+
+```
+
+124/183 [===================>..........] - ETA: 13:58 - loss: 1.3860 - accuracy: 0.2692
+
+
+```
+
+```
+
+125/183 [===================>..........] - ETA: 13:44 - loss: 1.3861 - accuracy: 0.2670
+
+
+```
+
+```
+
+126/183 [===================>..........] - ETA: 13:30 - loss: 1.3860 - accuracy: 0.2679
+
+
+```
+
+```
+
+127/183 [===================>..........] - ETA: 13:15 - loss: 1.3858 - accuracy: 0.2677
+
+
+```
+
+```
+
+128/183 [===================>..........] - ETA: 13:01 - loss: 1.3856 - accuracy: 0.2715
+
+
+```
+
+```
+
+129/183 [====================>.........] - ETA: 12:47 - loss: 1.3855 - accuracy: 0.2723
+
+
+```
+
+```
+
+130/183 [====================>.........] - ETA: 12:33 - loss: 1.3854 - accuracy: 0.2731
+
+
+```
+
+```
+
+131/183 [====================>.........] - ETA: 12:19 - loss: 1.3854 - accuracy: 0.2719
+
+
+```
+
+```
+
+132/183 [====================>.........] - ETA: 12:04 - loss: 1.3854 - accuracy: 0.2718
+
+
+```
+
+```
+
+133/183 [====================>.........] - ETA: 11:50 - loss: 1.3854 - accuracy: 0.2707
+
+
+```
+
+```
+
+134/183 [====================>.........] - ETA: 11:36 - loss: 1.3854 - accuracy: 0.2705
+
+
+```
+
+```
+
+135/183 [=====================>........] - ETA: 11:22 - loss: 1.3853 - accuracy: 0.2694
+
+
+```
+
+```
+
+136/183 [=====================>........] - ETA: 11:07 - loss: 1.3854 - accuracy: 0.2693
+
+
+```
+
+```
+
+137/183 [=====================>........] - ETA: 10:53 - loss: 1.3854 - accuracy: 0.2710
+
+
+```
+
+```
+
+138/183 [=====================>........] - ETA: 10:39 - loss: 1.3856 - accuracy: 0.2717
+
+
+```
+
+```
+
+139/183 [=====================>........] - ETA: 10:25 - loss: 1.3855 - accuracy: 0.2743
+
+
+```
+
+```
+
+140/183 [=====================>........] - ETA: 10:10 - loss: 1.3853 - accuracy: 0.2741
+
+
+```
+
+```
+
+141/183 [======================>.......] - ETA: 9:56 - loss: 1.3854 - accuracy: 0.2730
+
+
+```
+
+```
+
+142/183 [======================>.......] - ETA: 9:42 - loss: 1.3854 - accuracy: 0.2720
+
+
+```
+
+```
+
+143/183 [======================>.......] - ETA: 9:28 - loss: 1.3852 - accuracy: 0.2719
+
+
+```
+
+```
+
+144/183 [======================>.......] - ETA: 9:14 - loss: 1.3852 - accuracy: 0.2717
+
+
+```
+
+```
+
+145/183 [======================>.......] - ETA: 8:59 - loss: 1.3852 - accuracy: 0.2724
+
+
+```
+
+```
+
+146/183 [======================>.......] - ETA: 8:45 - loss: 1.3853 - accuracy: 0.2723
+
+
+```
+
+```
+
+147/183 [=======================>......] - ETA: 8:31 - loss: 1.3852 - accuracy: 0.2730
+
+
+```
+
+```
+
+148/183 [=======================>......] - ETA: 8:17 - loss: 1.3852 - accuracy: 0.2736
+
+
+```
+
+```
+
+149/183 [=======================>......] - ETA: 8:03 - loss: 1.3851 - accuracy: 0.2743
+
+
+```
+
+```
+
+150/183 [=======================>......] - ETA: 7:48 - loss: 1.3848 - accuracy: 0.2758
+
+
+```
+
+```
+
+151/183 [=======================>......] - ETA: 7:34 - loss: 1.3847 - accuracy: 0.2757
+
+
+```
+
+```
+
+152/183 [=======================>......] - ETA: 7:20 - loss: 1.3847 - accuracy: 0.2755
+
+
+```
+
+```
+
+153/183 [========================>.....] - ETA: 7:06 - loss: 1.3847 - accuracy: 0.2753
+
+
+```
+
+```
+
+154/183 [========================>.....] - ETA: 6:52 - loss: 1.3848 - accuracy: 0.2744
+
+
+```
+
+```
+
+155/183 [========================>.....] - ETA: 6:37 - loss: 1.3847 - accuracy: 0.2742
+
+
+```
+
+```
+
+156/183 [========================>.....] - ETA: 6:23 - loss: 1.3848 - accuracy: 0.2740
+
+
+```
+
+```
+
+157/183 [========================>.....] - ETA: 6:09 - loss: 1.3848 - accuracy: 0.2731
+
+
+```
+
+```
+
+158/183 [========================>.....] - ETA: 5:55 - loss: 1.3848 - accuracy: 0.2737
+
+
+```
+
+```
+
+159/183 [=========================>....] - ETA: 5:40 - loss: 1.3848 - accuracy: 0.2752
+
+
+```
+
+```
+
+160/183 [=========================>....] - ETA: 5:26 - loss: 1.3849 - accuracy: 0.2742
+
+
+```
+
+```
+
+161/183 [=========================>....] - ETA: 5:12 - loss: 1.3845 - accuracy: 0.2772
+
+
+```
+
+```
+
+162/183 [=========================>....] - ETA: 4:58 - loss: 1.3847 - accuracy: 0.2770
+
+
+```
+
+```
+
+163/183 [=========================>....] - ETA: 4:44 - loss: 1.3849 - accuracy: 0.2768
+
+
+```
+
+```
+
+164/183 [=========================>....] - ETA: 4:29 - loss: 1.3848 - accuracy: 0.2774
+
+
+```
+
+```
+
+165/183 [==========================>...] - ETA: 4:15 - loss: 1.3848 - accuracy: 0.2780
+
+
+```
+
+```
+
+166/183 [==========================>...] - ETA: 4:01 - loss: 1.3848 - accuracy: 0.2764
+
+
+```
+
+```
+
+167/183 [==========================>...] - ETA: 3:47 - loss: 1.3847 - accuracy: 0.2762
+
+
+```
+
+```
+
+168/183 [==========================>...] - ETA: 3:33 - loss: 1.3848 - accuracy: 0.2753
+
+
+```
+
+```
+
+169/183 [==========================>...] - ETA: 3:18 - loss: 1.3848 - accuracy: 0.2759
+
+
+```
+
+```
+
+170/183 [==========================>...] - ETA: 3:04 - loss: 1.3846 - accuracy: 0.2779
+
+
+```
+
+```
+
+171/183 [===========================>..] - ETA: 2:50 - loss: 1.3849 - accuracy: 0.2770
+
+
+```
+
+```
+
+172/183 [===========================>..] - ETA: 2:36 - loss: 1.3850 - accuracy: 0.2754
+
+
+```
+
+```
+
+173/183 [===========================>..] - ETA: 2:22 - loss: 1.3849 - accuracy: 0.2760
+
+
+```
+
+```
+
+174/183 [===========================>..] - ETA: 2:07 - loss: 1.3849 - accuracy: 0.2766
+
+
+```
+
+```
+
+175/183 [===========================>..] - ETA: 1:53 - loss: 1.3849 - accuracy: 0.2779
+
+
+```
+
+```
+
+176/183 [===========================>..] - ETA: 1:39 - loss: 1.3846 - accuracy: 0.2784
+
+
+```
+
+```
+
+177/183 [============================>.] - ETA: 1:25 - loss: 1.3846 - accuracy: 0.2797
+
+
+```
+
+```
+
+178/183 [============================>.] - ETA: 1:11 - loss: 1.3844 - accuracy: 0.2809
+
+
+```
+
+```
+
+179/183 [============================>.] - ETA: 56s - loss: 1.3844 - accuracy: 0.2807
+
+
+```
+
+```
+
+180/183 [============================>.] - ETA: 42s - loss: 1.3843 - accuracy: 0.2806
+
+
+```
+
+```
+
+181/183 [============================>.] - ETA: 28s - loss: 1.3843 - accuracy: 0.2818
+
+
+```
+
+```
+
+182/183 [============================>.] - ETA: 14s - loss: 1.3843 - accuracy: 0.2823
+
+
+```
+
+```
+
+183/183 [==============================] - ETA: 0s - loss: 1.3842 - accuracy: 0.2828
+
+
+```
+/usr/local/python/3.10.13/lib/python3.10/site-packages/keras_nlp/src/models/task.py:47: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
+ return id(getattr(self, attr)) not in self._functional_layer_ids
+/usr/local/python/3.10.13/lib/python3.10/site-packages/keras_nlp/src/models/task.py:47: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
+ return id(getattr(self, attr)) not in self._functional_layer_ids
+/usr/local/python/3.10.13/lib/python3.10/site-packages/keras_nlp/src/models/backbone.py:37: UserWarning: `Model.state_updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
+ return id(getattr(self, attr)) not in self._functional_layer_ids
+/usr/local/python/3.10.13/lib/python3.10/site-packages/keras_nlp/src/models/backbone.py:37: UserWarning: `layer.updates` will be removed in a future version. This property should not be used in TensorFlow 2.0, as `updates` are applied automatically.
+ return id(getattr(self, attr)) not in self._functional_layer_ids
+
+
+```
+
+183/183 [==============================] - 3111s 15s/step - loss: 1.3842 - accuracy: 0.2828 - val_loss: 1.3755 - val_accuracy: 0.5225 - lr: 1.0000e-06
+
+
+
+```
+Epoch 2/5
+
+```
+
+
+ 1/183 [..............................] - ETA: 46:52 - loss: 1.3583 - accuracy: 0.5000
+
+
+```
+
+```
+
+ 2/183 [..............................] - ETA: 43:21 - loss: 1.3603 - accuracy: 0.3750
+
+
+```
+
+```
+
+ 3/183 [..............................] - ETA: 43:12 - loss: 1.3608 - accuracy: 0.3750
+
+
+```
+
+```
+
+ 4/183 [..............................] - ETA: 42:59 - loss: 1.3601 - accuracy: 0.4062
+
+
+```
+
+```
+
+ 5/183 [..............................] - ETA: 42:33 - loss: 1.3611 - accuracy: 0.3750
+
+
+```
+
+```
+
+ 6/183 [..............................] - ETA: 42:14 - loss: 1.3652 - accuracy: 0.3333
+
+
+```
+
+```
+
+ 7/183 [>.............................] - ETA: 41:54 - loss: 1.3719 - accuracy: 0.2857
+
+
+```
+
+```
+
+ 8/183 [>.............................] - ETA: 41:38 - loss: 1.3726 - accuracy: 0.2969
+
+
+```
+
+```
+
+ 9/183 [>.............................] - ETA: 41:22 - loss: 1.3738 - accuracy: 0.2917
+
+
+```
+
+```
+
+ 10/183 [>.............................] - ETA: 41:05 - loss: 1.3772 - accuracy: 0.2875
+
+
+```
+
+```
+
+ 11/183 [>.............................] - ETA: 40:54 - loss: 1.3764 - accuracy: 0.2955
+
+
+```
+
+```
+
+ 12/183 [>.............................] - ETA: 40:37 - loss: 1.3753 - accuracy: 0.3021
+
+
+```
+
+```
+
+ 13/183 [=>............................] - ETA: 40:22 - loss: 1.3747 - accuracy: 0.3173
+
+
+```
+
+```
+
+ 14/183 [=>............................] - ETA: 40:06 - loss: 1.3742 - accuracy: 0.3214
+
+
+```
+
+```
+
+ 15/183 [=>............................] - ETA: 39:50 - loss: 1.3715 - accuracy: 0.3333
+
+
+```
+
+```
+
+ 16/183 [=>............................] - ETA: 39:34 - loss: 1.3716 - accuracy: 0.3359
+
+
+```
+
+```
+
+ 17/183 [=>............................] - ETA: 39:19 - loss: 1.3721 - accuracy: 0.3382
+
+
+```
+
+```
+
+ 18/183 [=>............................] - ETA: 39:03 - loss: 1.3735 - accuracy: 0.3194
+
+
+```
+
+```
+
+ 19/183 [==>...........................] - ETA: 38:48 - loss: 1.3746 - accuracy: 0.3158
+
+
+```
+
+```
+
+ 20/183 [==>...........................] - ETA: 38:33 - loss: 1.3740 - accuracy: 0.3250
+
+
+```
+
+```
+
+ 21/183 [==>...........................] - ETA: 38:18 - loss: 1.3744 - accuracy: 0.3274
+
+
+```
+
+```
+
+ 22/183 [==>...........................] - ETA: 38:04 - loss: 1.3718 - accuracy: 0.3352
+
+
+```
+
+```
+
+ 23/183 [==>...........................] - ETA: 37:49 - loss: 1.3711 - accuracy: 0.3424
+
+
+```
+
+```
+
+ 24/183 [==>...........................] - ETA: 37:34 - loss: 1.3712 - accuracy: 0.3333
+
+
+```
+
+```
+
+ 25/183 [===>..........................] - ETA: 37:20 - loss: 1.3700 - accuracy: 0.3400
+
+
+```
+
+```
+
+ 26/183 [===>..........................] - ETA: 37:05 - loss: 1.3709 - accuracy: 0.3365
+
+
+```
+
+```
+
+ 27/183 [===>..........................] - ETA: 36:51 - loss: 1.3708 - accuracy: 0.3380
+
+
+```
+
+```
+
+ 28/183 [===>..........................] - ETA: 36:36 - loss: 1.3709 - accuracy: 0.3348
+
+
+```
+
+```
+
+ 29/183 [===>..........................] - ETA: 36:22 - loss: 1.3708 - accuracy: 0.3362
+
+
+```
+
+```
+
+ 30/183 [===>..........................] - ETA: 36:07 - loss: 1.3706 - accuracy: 0.3375
+
+
+```
+
+```
+
+ 31/183 [====>.........................] - ETA: 35:53 - loss: 1.3708 - accuracy: 0.3387
+
+
+```
+
+```
+
+ 32/183 [====>.........................] - ETA: 35:38 - loss: 1.3699 - accuracy: 0.3359
+
+
+```
+
+```
+
+ 33/183 [====>.........................] - ETA: 35:24 - loss: 1.3704 - accuracy: 0.3295
+
+
+```
+
+```
+
+ 34/183 [====>.........................] - ETA: 35:10 - loss: 1.3712 - accuracy: 0.3199
+
+
+```
+
+```
+
+ 35/183 [====>.........................] - ETA: 34:55 - loss: 1.3718 - accuracy: 0.3214
+
+
+```
+
+```
+
+ 36/183 [====>.........................] - ETA: 34:41 - loss: 1.3712 - accuracy: 0.3264
+
+
+```
+
+```
+
+ 37/183 [=====>........................] - ETA: 34:27 - loss: 1.3713 - accuracy: 0.3243
+
+
+```
+
+```
+
+ 38/183 [=====>........................] - ETA: 34:13 - loss: 1.3701 - accuracy: 0.3289
+
+
+```
+
+```
+
+ 39/183 [=====>........................] - ETA: 33:58 - loss: 1.3701 - accuracy: 0.3237
+
+
+```
+
+```
+
+ 40/183 [=====>........................] - ETA: 33:44 - loss: 1.3697 - accuracy: 0.3250
+
+
+```
+
+```
+
+ 41/183 [=====>........................] - ETA: 33:30 - loss: 1.3700 - accuracy: 0.3201
+
+
+```
+
+```
+
+ 42/183 [=====>........................] - ETA: 33:16 - loss: 1.3699 - accuracy: 0.3214
+
+
+```
+
+```
+
+ 43/183 [======>.......................] - ETA: 33:01 - loss: 1.3695 - accuracy: 0.3227
+
+
+```
+
+```
+
+ 44/183 [======>.......................] - ETA: 32:48 - loss: 1.3689 - accuracy: 0.3210
+
+
+```
+
+```
+
+ 45/183 [======>.......................] - ETA: 32:33 - loss: 1.3685 - accuracy: 0.3222
+
+
+```
+
+```
+
+ 46/183 [======>.......................] - ETA: 32:19 - loss: 1.3692 - accuracy: 0.3207
+
+
+```
+
+```
+
+ 47/183 [======>.......................] - ETA: 32:05 - loss: 1.3682 - accuracy: 0.3271
+
+
+```
+
+```
+
+ 48/183 [======>.......................] - ETA: 31:50 - loss: 1.3682 - accuracy: 0.3281
+
+
+```
+
+```
+
+ 49/183 [=======>......................] - ETA: 31:36 - loss: 1.3685 - accuracy: 0.3291
+
+
+```
+
+```
+
+ 50/183 [=======>......................] - ETA: 31:22 - loss: 1.3682 - accuracy: 0.3250
+
+
+```
+
+```
+
+ 51/183 [=======>......................] - ETA: 31:08 - loss: 1.3690 - accuracy: 0.3235
+
+
+```
+
+```
+
+ 52/183 [=======>......................] - ETA: 30:53 - loss: 1.3690 - accuracy: 0.3221
+
+
+```
+
+```
+
+ 53/183 [=======>......................] - ETA: 30:39 - loss: 1.3692 - accuracy: 0.3231
+
+
+```
+
+```
+
+ 54/183 [=======>......................] - ETA: 30:25 - loss: 1.3690 - accuracy: 0.3241
+
+
+```
+
+```
+
+ 55/183 [========>.....................] - ETA: 30:11 - loss: 1.3688 - accuracy: 0.3273
+
+
+```
+
+```
+
+ 56/183 [========>.....................] - ETA: 29:56 - loss: 1.3685 - accuracy: 0.3281
+
+
+```
+
+```
+
+ 57/183 [========>.....................] - ETA: 29:42 - loss: 1.3679 - accuracy: 0.3311
+
+
+```
+
+```
+
+ 58/183 [========>.....................] - ETA: 29:28 - loss: 1.3671 - accuracy: 0.3319
+
+
+```
+
+```
+
+ 59/183 [========>.....................] - ETA: 29:14 - loss: 1.3670 - accuracy: 0.3326
+
+
+```
+
+```
+
+ 60/183 [========>.....................] - ETA: 29:00 - loss: 1.3672 - accuracy: 0.3313
+
+
+```
+
+```
+
+ 61/183 [=========>....................] - ETA: 28:45 - loss: 1.3673 - accuracy: 0.3279
+
+
+```
+
+```
+
+ 62/183 [=========>....................] - ETA: 28:31 - loss: 1.3669 - accuracy: 0.3286
+
+
+```
+
+```
+
+ 63/183 [=========>....................] - ETA: 28:17 - loss: 1.3667 - accuracy: 0.3234
+
+
+```
+
+```
+
+ 64/183 [=========>....................] - ETA: 28:03 - loss: 1.3669 - accuracy: 0.3223
+
+
+```
+
+```
+
+ 65/183 [=========>....................] - ETA: 27:49 - loss: 1.3662 - accuracy: 0.3231
+
+
+```
+
+```
+
+ 66/183 [=========>....................] - ETA: 27:35 - loss: 1.3663 - accuracy: 0.3239
+
+
+```
+
+```
+
+ 67/183 [=========>....................] - ETA: 27:20 - loss: 1.3659 - accuracy: 0.3265
+
+
+```
+
+```
+
+ 68/183 [==========>...................] - ETA: 27:06 - loss: 1.3657 - accuracy: 0.3272
+
+
+```
+
+```
+
+ 69/183 [==========>...................] - ETA: 26:52 - loss: 1.3648 - accuracy: 0.3315
+
+
+```
+
+```
+
+ 70/183 [==========>...................] - ETA: 26:38 - loss: 1.3654 - accuracy: 0.3286
+
+
+```
+
+```
+
+ 71/183 [==========>...................] - ETA: 26:24 - loss: 1.3644 - accuracy: 0.3327
+
+
+```
+
+```
+
+ 72/183 [==========>...................] - ETA: 26:10 - loss: 1.3645 - accuracy: 0.3316
+
+
+```
+
+```
+
+ 73/183 [==========>...................] - ETA: 25:56 - loss: 1.3645 - accuracy: 0.3305
+
+
+```
+
+```
+
+ 74/183 [===========>..................] - ETA: 25:41 - loss: 1.3641 - accuracy: 0.3311
+
+
+```
+
+```
+
+ 75/183 [===========>..................] - ETA: 25:27 - loss: 1.3628 - accuracy: 0.3367
+
+
+```
+
+```
+
+ 76/183 [===========>..................] - ETA: 25:13 - loss: 1.3616 - accuracy: 0.3421
+
+
+```
+
+```
+
+ 77/183 [===========>..................] - ETA: 24:59 - loss: 1.3614 - accuracy: 0.3425
+
+
+```
+
+```
+
+ 78/183 [===========>..................] - ETA: 24:45 - loss: 1.3616 - accuracy: 0.3397
+
+
+```
+
+```
+
+ 79/183 [===========>..................] - ETA: 24:31 - loss: 1.3610 - accuracy: 0.3418
+
+
+```
+
+```
+
+ 80/183 [============>.................] - ETA: 24:17 - loss: 1.3608 - accuracy: 0.3438
+
+
+```
+
+```
+
+ 81/183 [============>.................] - ETA: 24:03 - loss: 1.3599 - accuracy: 0.3441
+
+
+```
+
+```
+
+ 82/183 [============>.................] - ETA: 23:49 - loss: 1.3591 - accuracy: 0.3460
+
+
+```
+
+```
+
+ 83/183 [============>.................] - ETA: 23:35 - loss: 1.3589 - accuracy: 0.3479
+
+
+```
+
+```
+
+ 84/183 [============>.................] - ETA: 23:21 - loss: 1.3583 - accuracy: 0.3482
+
+
+```
+
+```
+
+ 85/183 [============>.................] - ETA: 23:07 - loss: 1.3575 - accuracy: 0.3515
+
+
+```
+
+```
+
+ 86/183 [=============>................] - ETA: 22:53 - loss: 1.3558 - accuracy: 0.3561
+
+
+```
+
+```
+
+ 87/183 [=============>................] - ETA: 22:39 - loss: 1.3560 - accuracy: 0.3549
+
+
+```
+
+```
+
+ 88/183 [=============>................] - ETA: 22:25 - loss: 1.3535 - accuracy: 0.3594
+
+
+```
+
+```
+
+ 89/183 [=============>................] - ETA: 22:10 - loss: 1.3529 - accuracy: 0.3596
+
+
+```
+
+```
+
+ 90/183 [=============>................] - ETA: 21:56 - loss: 1.3529 - accuracy: 0.3597
+
+
+```
+
+```
+
+ 91/183 [=============>................] - ETA: 21:42 - loss: 1.3510 - accuracy: 0.3613
+
+
+```
+
+```
+
+ 92/183 [==============>...............] - ETA: 21:28 - loss: 1.3498 - accuracy: 0.3614
+
+
+```
+
+```
+
+ 93/183 [==============>...............] - ETA: 21:14 - loss: 1.3492 - accuracy: 0.3629
+
+
+```
+
+```
+
+ 94/183 [==============>...............] - ETA: 21:00 - loss: 1.3485 - accuracy: 0.3644
+
+
+```
+
+```
+
+ 95/183 [==============>...............] - ETA: 20:45 - loss: 1.3484 - accuracy: 0.3658
+
+
+```
+
+```
+
+ 96/183 [==============>...............] - ETA: 20:31 - loss: 1.3486 - accuracy: 0.3672
+
+
+```
+
+```
+
+ 97/183 [==============>...............] - ETA: 20:17 - loss: 1.3488 - accuracy: 0.3647
+
+
+```
+
+```
+
+ 98/183 [===============>..............] - ETA: 20:03 - loss: 1.3484 - accuracy: 0.3648
+
+
+```
+
+```
+
+ 99/183 [===============>..............] - ETA: 19:49 - loss: 1.3478 - accuracy: 0.3662
+
+
+```
+
+```
+
+100/183 [===============>..............] - ETA: 19:34 - loss: 1.3469 - accuracy: 0.3650
+
+
+```
+
+```
+
+101/183 [===============>..............] - ETA: 19:20 - loss: 1.3474 - accuracy: 0.3651
+
+
+```
+
+```
+
+102/183 [===============>..............] - ETA: 19:06 - loss: 1.3457 - accuracy: 0.3689
+
+
+```
+
+```
+
+103/183 [===============>..............] - ETA: 18:52 - loss: 1.3453 - accuracy: 0.3689
+
+
+```
+
+```
+
+104/183 [================>.............] - ETA: 18:38 - loss: 1.3444 - accuracy: 0.3714
+
+
+```
+
+```
+
+105/183 [================>.............] - ETA: 18:24 - loss: 1.3441 - accuracy: 0.3726
+
+
+```
+
+```
+
+106/183 [================>.............] - ETA: 18:10 - loss: 1.3431 - accuracy: 0.3750
+
+
+```
+
+```
+
+107/183 [================>.............] - ETA: 17:55 - loss: 1.3425 - accuracy: 0.3738
+
+
+```
+
+```
+
+108/183 [================>.............] - ETA: 17:41 - loss: 1.3421 - accuracy: 0.3738
+
+
+```
+
+```
+
+109/183 [================>.............] - ETA: 17:27 - loss: 1.3419 - accuracy: 0.3739
+
+
+```
+
+```
+
+110/183 [=================>............] - ETA: 17:13 - loss: 1.3412 - accuracy: 0.3750
+
+
+```
+
+```
+
+111/183 [=================>............] - ETA: 16:59 - loss: 1.3400 - accuracy: 0.3773
+
+
+```
+
+```
+
+112/183 [=================>............] - ETA: 16:45 - loss: 1.3393 - accuracy: 0.3795
+
+
+```
+
+```
+
+113/183 [=================>............] - ETA: 16:30 - loss: 1.3380 - accuracy: 0.3827
+
+
+```
+
+```
+
+114/183 [=================>............] - ETA: 16:16 - loss: 1.3366 - accuracy: 0.3860
+
+
+```
+
+```
+
+115/183 [=================>............] - ETA: 16:02 - loss: 1.3350 - accuracy: 0.3870
+
+
+```
+
+```
+
+116/183 [==================>...........] - ETA: 15:48 - loss: 1.3334 - accuracy: 0.3901
+
+
+```
+
+```
+
+117/183 [==================>...........] - ETA: 15:34 - loss: 1.3331 - accuracy: 0.3900
+
+
+```
+
+```
+
+118/183 [==================>...........] - ETA: 15:20 - loss: 1.3324 - accuracy: 0.3909
+
+
+```
+
+```
+
+119/183 [==================>...........] - ETA: 15:06 - loss: 1.3308 - accuracy: 0.3929
+
+
+```
+
+```
+
+120/183 [==================>...........] - ETA: 14:51 - loss: 1.3316 - accuracy: 0.3906
+
+
+```
+
+```
+
+121/183 [==================>...........] - ETA: 14:37 - loss: 1.3312 - accuracy: 0.3895
+
+
+```
+
+```
+
+122/183 [===================>..........] - ETA: 14:23 - loss: 1.3294 - accuracy: 0.3924
+
+
+```
+
+```
+
+123/183 [===================>..........] - ETA: 14:09 - loss: 1.3284 - accuracy: 0.3953
+
+
+```
+
+```
+
+124/183 [===================>..........] - ETA: 13:55 - loss: 1.3269 - accuracy: 0.3972
+
+
+```
+
+```
+
+125/183 [===================>..........] - ETA: 13:40 - loss: 1.3249 - accuracy: 0.4000
+
+
+```
+
+```
+
+126/183 [===================>..........] - ETA: 13:26 - loss: 1.3245 - accuracy: 0.3988
+
+
+```
+
+```
+
+127/183 [===================>..........] - ETA: 13:12 - loss: 1.3231 - accuracy: 0.4006
+
+
+```
+
+```
+
+128/183 [===================>..........] - ETA: 12:58 - loss: 1.3233 - accuracy: 0.3994
+
+
+```
+
+```
+
+129/183 [====================>.........] - ETA: 12:44 - loss: 1.3212 - accuracy: 0.4021
+
+
+```
+
+```
+
+130/183 [====================>.........] - ETA: 12:30 - loss: 1.3206 - accuracy: 0.4029
+
+
+```
+
+```
+
+131/183 [====================>.........] - ETA: 12:16 - loss: 1.3187 - accuracy: 0.4065
+
+
+```
+
+```
+
+132/183 [====================>.........] - ETA: 12:01 - loss: 1.3173 - accuracy: 0.4091
+
+
+```
+
+```
+
+133/183 [====================>.........] - ETA: 11:47 - loss: 1.3162 - accuracy: 0.4098
+
+
+```
+
+```
+
+134/183 [====================>.........] - ETA: 11:33 - loss: 1.3148 - accuracy: 0.4123
+
+
+```
+
+```
+
+135/183 [=====================>........] - ETA: 11:19 - loss: 1.3149 - accuracy: 0.4120
+
+
+```
+
+```
+
+136/183 [=====================>........] - ETA: 11:05 - loss: 1.3150 - accuracy: 0.4118
+
+
+```
+
+```
+
+137/183 [=====================>........] - ETA: 10:51 - loss: 1.3143 - accuracy: 0.4106
+
+
+```
+
+```
+
+138/183 [=====================>........] - ETA: 10:36 - loss: 1.3133 - accuracy: 0.4130
+
+
+```
+
+```
+
+139/183 [=====================>........] - ETA: 10:22 - loss: 1.3127 - accuracy: 0.4128
+
+
+```
+
+```
+
+140/183 [=====================>........] - ETA: 10:08 - loss: 1.3136 - accuracy: 0.4125
+
+
+```
+
+```
+
+141/183 [======================>.......] - ETA: 9:54 - loss: 1.3125 - accuracy: 0.4131
+
+
+```
+
+```
+
+142/183 [======================>.......] - ETA: 9:40 - loss: 1.3115 - accuracy: 0.4155
+
+
+```
+
+```
+
+143/183 [======================>.......] - ETA: 9:26 - loss: 1.3108 - accuracy: 0.4161
+
+
+```
+
+```
+
+144/183 [======================>.......] - ETA: 9:11 - loss: 1.3098 - accuracy: 0.4167
+
+
+```
+
+```
+
+145/183 [======================>.......] - ETA: 8:57 - loss: 1.3086 - accuracy: 0.4172
+
+
+```
+
+```
+
+146/183 [======================>.......] - ETA: 8:43 - loss: 1.3083 - accuracy: 0.4161
+
+
+```
+
+```
+
+147/183 [=======================>......] - ETA: 8:29 - loss: 1.3067 - accuracy: 0.4184
+
+
+```
+
+```
+
+148/183 [=======================>......] - ETA: 8:15 - loss: 1.3047 - accuracy: 0.4215
+
+
+```
+
+```
+
+149/183 [=======================>......] - ETA: 8:01 - loss: 1.3037 - accuracy: 0.4211
+
+
+```
+
+```
+
+150/183 [=======================>......] - ETA: 7:47 - loss: 1.3029 - accuracy: 0.4217
+
+
+```
+
+```
+
+151/183 [=======================>......] - ETA: 7:32 - loss: 1.3021 - accuracy: 0.4222
+
+
+```
+
+```
+
+152/183 [=======================>......] - ETA: 7:18 - loss: 1.3007 - accuracy: 0.4243
+
+
+```
+
+```
+
+153/183 [========================>.....] - ETA: 7:04 - loss: 1.3017 - accuracy: 0.4240
+
+
+```
+
+```
+
+154/183 [========================>.....] - ETA: 6:50 - loss: 1.3006 - accuracy: 0.4253
+
+
+```
+
+```
+
+155/183 [========================>.....] - ETA: 6:36 - loss: 1.2986 - accuracy: 0.4282
+
+
+```
+
+```
+
+156/183 [========================>.....] - ETA: 6:22 - loss: 1.2965 - accuracy: 0.4303
+
+
+```
+
+```
+
+157/183 [========================>.....] - ETA: 6:08 - loss: 1.2964 - accuracy: 0.4283
+
+
+```
+
+```
+
+158/183 [========================>.....] - ETA: 5:53 - loss: 1.2953 - accuracy: 0.4288
+
+
+```
+
+```
+
+159/183 [=========================>....] - ETA: 5:39 - loss: 1.2931 - accuracy: 0.4316
+
+
+```
+
+```
+
+160/183 [=========================>....] - ETA: 5:25 - loss: 1.2923 - accuracy: 0.4320
+
+
+```
+
+```
+
+161/183 [=========================>....] - ETA: 5:11 - loss: 1.2916 - accuracy: 0.4332
+
+
+```
+
+```
+
+162/183 [=========================>....] - ETA: 4:57 - loss: 1.2905 - accuracy: 0.4344
+
+
+```
+
+```
+
+163/183 [=========================>....] - ETA: 4:43 - loss: 1.2901 - accuracy: 0.4348
+
+
+```
+
+```
+
+164/183 [=========================>....] - ETA: 4:28 - loss: 1.2891 - accuracy: 0.4360
+
+
+```
+
+```
+
+165/183 [==========================>...] - ETA: 4:14 - loss: 1.2881 - accuracy: 0.4356
+
+
+```
+
+```
+
+166/183 [==========================>...] - ETA: 4:00 - loss: 1.2860 - accuracy: 0.4375
+
+
+```
+
+```
+
+167/183 [==========================>...] - ETA: 3:46 - loss: 1.2835 - accuracy: 0.4386
+
+
+```
+
+```
+
+168/183 [==========================>...] - ETA: 3:32 - loss: 1.2838 - accuracy: 0.4375
+
+
+```
+
+```
+
+169/183 [==========================>...] - ETA: 3:18 - loss: 1.2829 - accuracy: 0.4379
+
+
+```
+
+```
+
+170/183 [==========================>...] - ETA: 3:03 - loss: 1.2830 - accuracy: 0.4390
+
+
+```
+
+```
+
+171/183 [===========================>..] - ETA: 2:49 - loss: 1.2800 - accuracy: 0.4415
+
+
+```
+
+```
+
+172/183 [===========================>..] - ETA: 2:35 - loss: 1.2784 - accuracy: 0.4419
+
+
+```
+
+```
+
+173/183 [===========================>..] - ETA: 2:21 - loss: 1.2765 - accuracy: 0.4429
+
+
+```
+
+```
+
+174/183 [===========================>..] - ETA: 2:07 - loss: 1.2746 - accuracy: 0.4447
+
+
+```
+
+```
+
+175/183 [===========================>..] - ETA: 1:53 - loss: 1.2730 - accuracy: 0.4457
+
+
+```
+
+```
+
+176/183 [===========================>..] - ETA: 1:39 - loss: 1.2705 - accuracy: 0.4489
+
+
+```
+
+```
+
+177/183 [============================>.] - ETA: 1:24 - loss: 1.2696 - accuracy: 0.4492
+
+
+```
+
+```
+
+178/183 [============================>.] - ETA: 1:10 - loss: 1.2686 - accuracy: 0.4501
+
+
+```
+
+```
+
+179/183 [============================>.] - ETA: 56s - loss: 1.2679 - accuracy: 0.4511
+
+
+```
+
+```
+
+180/183 [============================>.] - ETA: 42s - loss: 1.2671 - accuracy: 0.4521
+
+
+```
+
+```
+
+181/183 [============================>.] - ETA: 28s - loss: 1.2666 - accuracy: 0.4523
+
+
+```
+
+```
+
+182/183 [============================>.] - ETA: 14s - loss: 1.2642 - accuracy: 0.4547
+
+
+```
+
+```
+
+183/183 [==============================] - ETA: 0s - loss: 1.2631 - accuracy: 0.4556
+
+
+```
+
+```
+
+183/183 [==============================] - 2748s 15s/step - loss: 1.2631 - accuracy: 0.4556 - val_loss: 0.9210 - val_accuracy: 0.7075 - lr: 2.9000e-06
+
+
+
+```
+Epoch 3/5
+
+```
+
+
+ 1/183 [..............................] - ETA: 46:11 - loss: 1.1680 - accuracy: 0.3750
+
+
+```
+
+```
+
+ 2/183 [..............................] - ETA: 43:28 - loss: 1.0826 - accuracy: 0.5625
+
+
+```
+
+```
+
+ 3/183 [..............................] - ETA: 43:21 - loss: 1.1881 - accuracy: 0.5417
+
+
+```
+
+```
+
+ 4/183 [..............................] - ETA: 43:06 - loss: 1.1313 - accuracy: 0.5625
+
+
+```
+
+```
+
+ 5/183 [..............................] - ETA: 42:50 - loss: 1.1148 - accuracy: 0.6000
+
+
+```
+
+```
+
+ 6/183 [..............................] - ETA: 42:32 - loss: 1.0660 - accuracy: 0.6458
+
+
+```
+
+```
+
+ 7/183 [>.............................] - ETA: 42:14 - loss: 1.0620 - accuracy: 0.6429
+
+
+```
+
+```
+
+ 8/183 [>.............................] - ETA: 41:56 - loss: 1.0516 - accuracy: 0.6562
+
+
+```
+
+```
+
+ 9/183 [>.............................] - ETA: 41:40 - loss: 1.0623 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 10/183 [>.............................] - ETA: 41:28 - loss: 1.0573 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 11/183 [>.............................] - ETA: 41:12 - loss: 1.0500 - accuracy: 0.6364
+
+
+```
+
+```
+
+ 12/183 [>.............................] - ETA: 40:55 - loss: 1.0126 - accuracy: 0.6667
+
+
+```
+
+```
+
+ 13/183 [=>............................] - ETA: 40:39 - loss: 1.0203 - accuracy: 0.6538
+
+
+```
+
+```
+
+ 14/183 [=>............................] - ETA: 40:22 - loss: 1.0258 - accuracy: 0.6518
+
+
+```
+
+```
+
+ 15/183 [=>............................] - ETA: 40:06 - loss: 1.0312 - accuracy: 0.6583
+
+
+```
+
+```
+
+ 16/183 [=>............................] - ETA: 39:50 - loss: 1.0269 - accuracy: 0.6562
+
+
+```
+
+```
+
+ 17/183 [=>............................] - ETA: 39:33 - loss: 1.0329 - accuracy: 0.6618
+
+
+```
+
+```
+
+ 18/183 [=>............................] - ETA: 39:18 - loss: 1.0382 - accuracy: 0.6667
+
+
+```
+
+```
+
+ 19/183 [==>...........................] - ETA: 39:03 - loss: 1.0372 - accuracy: 0.6579
+
+
+```
+
+```
+
+ 20/183 [==>...........................] - ETA: 38:48 - loss: 1.0363 - accuracy: 0.6500
+
+
+```
+
+```
+
+ 21/183 [==>...........................] - ETA: 38:33 - loss: 1.0430 - accuracy: 0.6369
+
+
+```
+
+```
+
+ 22/183 [==>...........................] - ETA: 38:18 - loss: 1.0392 - accuracy: 0.6420
+
+
+```
+
+```
+
+ 23/183 [==>...........................] - ETA: 38:03 - loss: 1.0438 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 24/183 [==>...........................] - ETA: 37:49 - loss: 1.0405 - accuracy: 0.6302
+
+
+```
+
+```
+
+ 25/183 [===>..........................] - ETA: 37:34 - loss: 1.0383 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 26/183 [===>..........................] - ETA: 37:20 - loss: 1.0392 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 27/183 [===>..........................] - ETA: 37:05 - loss: 1.0422 - accuracy: 0.6204
+
+
+```
+
+```
+
+ 28/183 [===>..........................] - ETA: 36:50 - loss: 1.0441 - accuracy: 0.6205
+
+
+```
+
+```
+
+ 29/183 [===>..........................] - ETA: 36:35 - loss: 1.0482 - accuracy: 0.6164
+
+
+```
+
+```
+
+ 30/183 [===>..........................] - ETA: 36:21 - loss: 1.0541 - accuracy: 0.6042
+
+
+```
+
+```
+
+ 31/183 [====>.........................] - ETA: 36:06 - loss: 1.0507 - accuracy: 0.6089
+
+
+```
+
+```
+
+ 32/183 [====>.........................] - ETA: 35:52 - loss: 1.0431 - accuracy: 0.6211
+
+
+```
+
+```
+
+ 33/183 [====>.........................] - ETA: 35:37 - loss: 1.0418 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 34/183 [====>.........................] - ETA: 35:22 - loss: 1.0382 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 35/183 [====>.........................] - ETA: 35:08 - loss: 1.0369 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 36/183 [====>.........................] - ETA: 34:53 - loss: 1.0396 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 37/183 [=====>........................] - ETA: 34:38 - loss: 1.0396 - accuracy: 0.6216
+
+
+```
+
+```
+
+ 38/183 [=====>........................] - ETA: 34:24 - loss: 1.0478 - accuracy: 0.6118
+
+
+```
+
+```
+
+ 39/183 [=====>........................] - ETA: 34:09 - loss: 1.0412 - accuracy: 0.6154
+
+
+```
+
+```
+
+ 40/183 [=====>........................] - ETA: 33:55 - loss: 1.0397 - accuracy: 0.6156
+
+
+```
+
+```
+
+ 41/183 [=====>........................] - ETA: 33:41 - loss: 1.0378 - accuracy: 0.6159
+
+
+```
+
+```
+
+ 42/183 [=====>........................] - ETA: 33:26 - loss: 1.0331 - accuracy: 0.6190
+
+
+```
+
+```
+
+ 43/183 [======>.......................] - ETA: 33:13 - loss: 1.0287 - accuracy: 0.6192
+
+
+```
+
+```
+
+ 44/183 [======>.......................] - ETA: 32:58 - loss: 1.0366 - accuracy: 0.6136
+
+
+```
+
+```
+
+ 45/183 [======>.......................] - ETA: 32:43 - loss: 1.0337 - accuracy: 0.6167
+
+
+```
+
+```
+
+ 46/183 [======>.......................] - ETA: 32:29 - loss: 1.0309 - accuracy: 0.6196
+
+
+```
+
+```
+
+ 47/183 [======>.......................] - ETA: 32:15 - loss: 1.0355 - accuracy: 0.6144
+
+
+```
+
+```
+
+ 48/183 [======>.......................] - ETA: 32:00 - loss: 1.0365 - accuracy: 0.6146
+
+
+```
+
+```
+
+ 49/183 [=======>......................] - ETA: 31:46 - loss: 1.0344 - accuracy: 0.6173
+
+
+```
+
+```
+
+ 50/183 [=======>......................] - ETA: 31:32 - loss: 1.0364 - accuracy: 0.6175
+
+
+```
+
+```
+
+ 51/183 [=======>......................] - ETA: 31:17 - loss: 1.0385 - accuracy: 0.6152
+
+
+```
+
+```
+
+ 52/183 [=======>......................] - ETA: 31:03 - loss: 1.0415 - accuracy: 0.6106
+
+
+```
+
+```
+
+ 53/183 [=======>......................] - ETA: 30:50 - loss: 1.0360 - accuracy: 0.6156
+
+
+```
+
+```
+
+ 54/183 [=======>......................] - ETA: 30:36 - loss: 1.0329 - accuracy: 0.6181
+
+
+```
+
+```
+
+ 55/183 [========>.....................] - ETA: 30:21 - loss: 1.0340 - accuracy: 0.6136
+
+
+```
+
+```
+
+ 56/183 [========>.....................] - ETA: 30:07 - loss: 1.0357 - accuracy: 0.6094
+
+
+```
+
+```
+
+ 57/183 [========>.....................] - ETA: 29:53 - loss: 1.0389 - accuracy: 0.6096
+
+
+```
+
+```
+
+ 58/183 [========>.....................] - ETA: 29:38 - loss: 1.0403 - accuracy: 0.6099
+
+
+```
+
+```
+
+ 59/183 [========>.....................] - ETA: 29:24 - loss: 1.0437 - accuracy: 0.6038
+
+
+```
+
+```
+
+ 60/183 [========>.....................] - ETA: 29:09 - loss: 1.0459 - accuracy: 0.6042
+
+
+```
+
+```
+
+ 61/183 [=========>....................] - ETA: 28:55 - loss: 1.0509 - accuracy: 0.6025
+
+
+```
+
+```
+
+ 62/183 [=========>....................] - ETA: 28:41 - loss: 1.0511 - accuracy: 0.5988
+
+
+```
+
+```
+
+ 63/183 [=========>....................] - ETA: 28:27 - loss: 1.0479 - accuracy: 0.5992
+
+
+```
+
+```
+
+ 64/183 [=========>....................] - ETA: 28:12 - loss: 1.0548 - accuracy: 0.5977
+
+
+```
+
+```
+
+ 65/183 [=========>....................] - ETA: 27:58 - loss: 1.0570 - accuracy: 0.5962
+
+
+```
+
+```
+
+ 66/183 [=========>....................] - ETA: 27:44 - loss: 1.0555 - accuracy: 0.5966
+
+
+```
+
+```
+
+ 67/183 [=========>....................] - ETA: 27:29 - loss: 1.0566 - accuracy: 0.5951
+
+
+```
+
+```
+
+ 68/183 [==========>...................] - ETA: 27:15 - loss: 1.0571 - accuracy: 0.5938
+
+
+```
+
+```
+
+ 69/183 [==========>...................] - ETA: 27:01 - loss: 1.0523 - accuracy: 0.5978
+
+
+```
+
+```
+
+ 70/183 [==========>...................] - ETA: 26:47 - loss: 1.0483 - accuracy: 0.6000
+
+
+```
+
+```
+
+ 71/183 [==========>...................] - ETA: 26:32 - loss: 1.0489 - accuracy: 0.5968
+
+
+```
+
+```
+
+ 72/183 [==========>...................] - ETA: 26:18 - loss: 1.0491 - accuracy: 0.5955
+
+
+```
+
+```
+
+ 73/183 [==========>...................] - ETA: 26:04 - loss: 1.0486 - accuracy: 0.5959
+
+
+```
+
+```
+
+ 74/183 [===========>..................] - ETA: 25:49 - loss: 1.0532 - accuracy: 0.5912
+
+
+```
+
+```
+
+ 75/183 [===========>..................] - ETA: 25:35 - loss: 1.0535 - accuracy: 0.5917
+
+
+```
+
+```
+
+ 76/183 [===========>..................] - ETA: 25:21 - loss: 1.0568 - accuracy: 0.5888
+
+
+```
+
+```
+
+ 77/183 [===========>..................] - ETA: 25:06 - loss: 1.0600 - accuracy: 0.5844
+
+
+```
+
+```
+
+ 78/183 [===========>..................] - ETA: 24:52 - loss: 1.0557 - accuracy: 0.5865
+
+
+```
+
+```
+
+ 79/183 [===========>..................] - ETA: 24:38 - loss: 1.0538 - accuracy: 0.5870
+
+
+```
+
+```
+
+ 80/183 [============>.................] - ETA: 24:23 - loss: 1.0543 - accuracy: 0.5844
+
+
+```
+
+```
+
+ 81/183 [============>.................] - ETA: 24:09 - loss: 1.0528 - accuracy: 0.5864
+
+
+```
+
+```
+
+ 82/183 [============>.................] - ETA: 23:55 - loss: 1.0547 - accuracy: 0.5838
+
+
+```
+
+```
+
+ 83/183 [============>.................] - ETA: 23:41 - loss: 1.0558 - accuracy: 0.5798
+
+
+```
+
+```
+
+ 84/183 [============>.................] - ETA: 23:26 - loss: 1.0564 - accuracy: 0.5804
+
+
+```
+
+```
+
+ 85/183 [============>.................] - ETA: 23:12 - loss: 1.0548 - accuracy: 0.5794
+
+
+```
+
+```
+
+ 86/183 [=============>................] - ETA: 22:58 - loss: 1.0542 - accuracy: 0.5770
+
+
+```
+
+```
+
+ 87/183 [=============>................] - ETA: 22:44 - loss: 1.0569 - accuracy: 0.5761
+
+
+```
+
+```
+
+ 88/183 [=============>................] - ETA: 22:29 - loss: 1.0551 - accuracy: 0.5781
+
+
+```
+
+```
+
+ 89/183 [=============>................] - ETA: 22:15 - loss: 1.0525 - accuracy: 0.5815
+
+
+```
+
+```
+
+ 90/183 [=============>................] - ETA: 22:01 - loss: 1.0552 - accuracy: 0.5778
+
+
+```
+
+```
+
+ 91/183 [=============>................] - ETA: 21:47 - loss: 1.0529 - accuracy: 0.5797
+
+
+```
+
+```
+
+ 92/183 [==============>...............] - ETA: 21:32 - loss: 1.0501 - accuracy: 0.5802
+
+
+```
+
+```
+
+ 93/183 [==============>...............] - ETA: 21:18 - loss: 1.0496 - accuracy: 0.5793
+
+
+```
+
+```
+
+ 94/183 [==============>...............] - ETA: 21:04 - loss: 1.0512 - accuracy: 0.5798
+
+
+```
+
+```
+
+ 95/183 [==============>...............] - ETA: 20:49 - loss: 1.0518 - accuracy: 0.5803
+
+
+```
+
+```
+
+ 96/183 [==============>...............] - ETA: 20:35 - loss: 1.0520 - accuracy: 0.5807
+
+
+```
+
+```
+
+ 97/183 [==============>...............] - ETA: 20:21 - loss: 1.0550 - accuracy: 0.5812
+
+
+```
+
+```
+
+ 98/183 [===============>..............] - ETA: 20:07 - loss: 1.0576 - accuracy: 0.5791
+
+
+```
+
+```
+
+ 99/183 [===============>..............] - ETA: 19:53 - loss: 1.0571 - accuracy: 0.5770
+
+
+```
+
+```
+
+100/183 [===============>..............] - ETA: 19:38 - loss: 1.0547 - accuracy: 0.5775
+
+
+```
+
+```
+
+101/183 [===============>..............] - ETA: 19:24 - loss: 1.0547 - accuracy: 0.5767
+
+
+```
+
+```
+
+102/183 [===============>..............] - ETA: 19:10 - loss: 1.0536 - accuracy: 0.5748
+
+
+```
+
+```
+
+103/183 [===============>..............] - ETA: 18:56 - loss: 1.0514 - accuracy: 0.5752
+
+
+```
+
+```
+
+104/183 [================>.............] - ETA: 18:41 - loss: 1.0495 - accuracy: 0.5769
+
+
+```
+
+```
+
+105/183 [================>.............] - ETA: 18:27 - loss: 1.0462 - accuracy: 0.5774
+
+
+```
+
+```
+
+106/183 [================>.............] - ETA: 18:13 - loss: 1.0451 - accuracy: 0.5790
+
+
+```
+
+```
+
+107/183 [================>.............] - ETA: 17:59 - loss: 1.0434 - accuracy: 0.5818
+
+
+```
+
+```
+
+108/183 [================>.............] - ETA: 17:44 - loss: 1.0424 - accuracy: 0.5833
+
+
+```
+
+```
+
+109/183 [================>.............] - ETA: 17:30 - loss: 1.0421 - accuracy: 0.5826
+
+
+```
+
+```
+
+110/183 [=================>............] - ETA: 17:16 - loss: 1.0409 - accuracy: 0.5830
+
+
+```
+
+```
+
+111/183 [=================>............] - ETA: 17:02 - loss: 1.0415 - accuracy: 0.5833
+
+
+```
+
+```
+
+112/183 [=================>............] - ETA: 16:47 - loss: 1.0403 - accuracy: 0.5837
+
+
+```
+
+```
+
+113/183 [=================>............] - ETA: 16:33 - loss: 1.0377 - accuracy: 0.5852
+
+
+```
+
+```
+
+114/183 [=================>............] - ETA: 16:19 - loss: 1.0367 - accuracy: 0.5866
+
+
+```
+
+```
+
+115/183 [=================>............] - ETA: 16:05 - loss: 1.0357 - accuracy: 0.5880
+
+
+```
+
+```
+
+116/183 [==================>...........] - ETA: 15:50 - loss: 1.0347 - accuracy: 0.5884
+
+
+```
+
+```
+
+117/183 [==================>...........] - ETA: 15:36 - loss: 1.0329 - accuracy: 0.5897
+
+
+```
+
+```
+
+118/183 [==================>...........] - ETA: 15:22 - loss: 1.0314 - accuracy: 0.5900
+
+
+```
+
+```
+
+119/183 [==================>...........] - ETA: 15:08 - loss: 1.0309 - accuracy: 0.5903
+
+
+```
+
+```
+
+120/183 [==================>...........] - ETA: 14:54 - loss: 1.0273 - accuracy: 0.5927
+
+
+```
+
+```
+
+121/183 [==================>...........] - ETA: 14:39 - loss: 1.0271 - accuracy: 0.5919
+
+
+```
+
+```
+
+122/183 [===================>..........] - ETA: 14:25 - loss: 1.0277 - accuracy: 0.5922
+
+
+```
+
+```
+
+123/183 [===================>..........] - ETA: 14:11 - loss: 1.0252 - accuracy: 0.5925
+
+
+```
+
+```
+
+124/183 [===================>..........] - ETA: 13:57 - loss: 1.0260 - accuracy: 0.5927
+
+
+```
+
+```
+
+125/183 [===================>..........] - ETA: 13:42 - loss: 1.0269 - accuracy: 0.5930
+
+
+```
+
+```
+
+126/183 [===================>..........] - ETA: 13:28 - loss: 1.0241 - accuracy: 0.5942
+
+
+```
+
+```
+
+127/183 [===================>..........] - ETA: 13:14 - loss: 1.0223 - accuracy: 0.5965
+
+
+```
+
+```
+
+128/183 [===================>..........] - ETA: 13:00 - loss: 1.0215 - accuracy: 0.5947
+
+
+```
+
+```
+
+129/183 [====================>.........] - ETA: 12:46 - loss: 1.0225 - accuracy: 0.5940
+
+
+```
+
+```
+
+130/183 [====================>.........] - ETA: 12:31 - loss: 1.0200 - accuracy: 0.5962
+
+
+```
+
+```
+
+131/183 [====================>.........] - ETA: 12:17 - loss: 1.0190 - accuracy: 0.5964
+
+
+```
+
+```
+
+132/183 [====================>.........] - ETA: 12:03 - loss: 1.0178 - accuracy: 0.5975
+
+
+```
+
+```
+
+133/183 [====================>.........] - ETA: 11:49 - loss: 1.0159 - accuracy: 0.5987
+
+
+```
+
+```
+
+134/183 [====================>.........] - ETA: 11:35 - loss: 1.0160 - accuracy: 0.5989
+
+
+```
+
+```
+
+135/183 [=====================>........] - ETA: 11:20 - loss: 1.0147 - accuracy: 0.5991
+
+
+```
+
+```
+
+136/183 [=====================>........] - ETA: 11:06 - loss: 1.0153 - accuracy: 0.5993
+
+
+```
+
+```
+
+137/183 [=====================>........] - ETA: 10:52 - loss: 1.0184 - accuracy: 0.5976
+
+
+```
+
+```
+
+138/183 [=====================>........] - ETA: 10:38 - loss: 1.0174 - accuracy: 0.5978
+
+
+```
+
+```
+
+139/183 [=====================>........] - ETA: 10:24 - loss: 1.0192 - accuracy: 0.5980
+
+
+```
+
+```
+
+140/183 [=====================>........] - ETA: 10:09 - loss: 1.0221 - accuracy: 0.5955
+
+
+```
+
+```
+
+141/183 [======================>.......] - ETA: 9:55 - loss: 1.0244 - accuracy: 0.5931
+
+
+```
+
+```
+
+142/183 [======================>.......] - ETA: 9:41 - loss: 1.0228 - accuracy: 0.5951
+
+
+```
+
+```
+
+143/183 [======================>.......] - ETA: 9:27 - loss: 1.0211 - accuracy: 0.5970
+
+
+```
+
+```
+
+144/183 [======================>.......] - ETA: 9:13 - loss: 1.0235 - accuracy: 0.5946
+
+
+```
+
+```
+
+145/183 [======================>.......] - ETA: 8:59 - loss: 1.0241 - accuracy: 0.5940
+
+
+```
+
+```
+
+146/183 [======================>.......] - ETA: 8:44 - loss: 1.0228 - accuracy: 0.5950
+
+
+```
+
+```
+
+147/183 [=======================>......] - ETA: 8:30 - loss: 1.0229 - accuracy: 0.5961
+
+
+```
+
+```
+
+148/183 [=======================>......] - ETA: 8:16 - loss: 1.0204 - accuracy: 0.5980
+
+
+```
+
+```
+
+149/183 [=======================>......] - ETA: 8:02 - loss: 1.0193 - accuracy: 0.5990
+
+
+```
+
+```
+
+150/183 [=======================>......] - ETA: 7:48 - loss: 1.0173 - accuracy: 0.6000
+
+
+```
+
+```
+
+151/183 [=======================>......] - ETA: 7:33 - loss: 1.0190 - accuracy: 0.5993
+
+
+```
+
+```
+
+152/183 [=======================>......] - ETA: 7:19 - loss: 1.0233 - accuracy: 0.5979
+
+
+```
+
+```
+
+153/183 [========================>.....] - ETA: 7:05 - loss: 1.0230 - accuracy: 0.5972
+
+
+```
+
+```
+
+154/183 [========================>.....] - ETA: 6:51 - loss: 1.0230 - accuracy: 0.5974
+
+
+```
+
+```
+
+155/183 [========================>.....] - ETA: 6:37 - loss: 1.0223 - accuracy: 0.5992
+
+
+```
+
+```
+
+156/183 [========================>.....] - ETA: 6:22 - loss: 1.0204 - accuracy: 0.5994
+
+
+```
+
+```
+
+157/183 [========================>.....] - ETA: 6:08 - loss: 1.0204 - accuracy: 0.5987
+
+
+```
+
+```
+
+158/183 [========================>.....] - ETA: 5:54 - loss: 1.0201 - accuracy: 0.5965
+
+
+```
+
+```
+
+159/183 [=========================>....] - ETA: 5:40 - loss: 1.0199 - accuracy: 0.5975
+
+
+```
+
+```
+
+160/183 [=========================>....] - ETA: 5:26 - loss: 1.0168 - accuracy: 0.5992
+
+
+```
+
+```
+
+161/183 [=========================>....] - ETA: 5:12 - loss: 1.0155 - accuracy: 0.6009
+
+
+```
+
+```
+
+162/183 [=========================>....] - ETA: 4:57 - loss: 1.0131 - accuracy: 0.6019
+
+
+```
+
+```
+
+163/183 [=========================>....] - ETA: 4:43 - loss: 1.0144 - accuracy: 0.6020
+
+
+```
+
+```
+
+164/183 [=========================>....] - ETA: 4:29 - loss: 1.0145 - accuracy: 0.6021
+
+
+```
+
+```
+
+165/183 [==========================>...] - ETA: 4:15 - loss: 1.0138 - accuracy: 0.6030
+
+
+```
+
+```
+
+166/183 [==========================>...] - ETA: 4:01 - loss: 1.0127 - accuracy: 0.6032
+
+
+```
+
+```
+
+167/183 [==========================>...] - ETA: 3:46 - loss: 1.0127 - accuracy: 0.6018
+
+
+```
+
+```
+
+168/183 [==========================>...] - ETA: 3:32 - loss: 1.0102 - accuracy: 0.6027
+
+
+```
+
+```
+
+169/183 [==========================>...] - ETA: 3:18 - loss: 1.0119 - accuracy: 0.6013
+
+
+```
+
+```
+
+170/183 [==========================>...] - ETA: 3:04 - loss: 1.0113 - accuracy: 0.6007
+
+
+```
+
+```
+
+171/183 [===========================>..] - ETA: 2:50 - loss: 1.0134 - accuracy: 0.6001
+
+
+```
+
+```
+
+172/183 [===========================>..] - ETA: 2:36 - loss: 1.0117 - accuracy: 0.6010
+
+
+```
+
+```
+
+173/183 [===========================>..] - ETA: 2:21 - loss: 1.0100 - accuracy: 0.6012
+
+
+```
+
+```
+
+174/183 [===========================>..] - ETA: 2:07 - loss: 1.0084 - accuracy: 0.6020
+
+
+```
+
+```
+
+175/183 [===========================>..] - ETA: 1:53 - loss: 1.0060 - accuracy: 0.6043
+
+
+```
+
+```
+
+176/183 [===========================>..] - ETA: 1:39 - loss: 1.0041 - accuracy: 0.6044
+
+
+```
+
+```
+
+177/183 [============================>.] - ETA: 1:25 - loss: 1.0025 - accuracy: 0.6059
+
+
+```
+
+```
+
+178/183 [============================>.] - ETA: 1:10 - loss: 1.0034 - accuracy: 0.6053
+
+
+```
+
+```
+
+179/183 [============================>.] - ETA: 56s - loss: 1.0040 - accuracy: 0.6054
+
+
+```
+
+```
+
+180/183 [============================>.] - ETA: 42s - loss: 1.0036 - accuracy: 0.6049
+
+
+```
+
+```
+
+181/183 [============================>.] - ETA: 28s - loss: 1.0028 - accuracy: 0.6050
+
+
+```
+
+```
+
+182/183 [============================>.] - ETA: 14s - loss: 1.0028 - accuracy: 0.6051
+
+
+```
+
+```
+
+183/183 [==============================] - ETA: 0s - loss: 1.0013 - accuracy: 0.6052
+
+
+```
+
+```
+
+183/183 [==============================] - 2755s 15s/step - loss: 1.0013 - accuracy: 0.6052 - val_loss: 0.7730 - val_accuracy: 0.7475 - lr: 4.8000e-06
+
+
+
+```
+Epoch 4/5
+
+```
+
+
+ 1/183 [..............................] - ETA: 45:34 - loss: 0.7885 - accuracy: 0.7500
+
+
+```
+
+```
+
+ 2/183 [..............................] - ETA: 43:17 - loss: 0.8742 - accuracy: 0.6875
+
+
+```
+
+```
+
+ 3/183 [..............................] - ETA: 43:02 - loss: 0.7851 - accuracy: 0.7083
+
+
+```
+
+```
+
+ 4/183 [..............................] - ETA: 42:51 - loss: 0.9176 - accuracy: 0.6562
+
+
+```
+
+```
+
+ 5/183 [..............................] - ETA: 42:39 - loss: 0.8677 - accuracy: 0.7000
+
+
+```
+
+```
+
+ 6/183 [..............................] - ETA: 42:25 - loss: 0.8238 - accuracy: 0.7292
+
+
+```
+
+```
+
+ 7/183 [>.............................] - ETA: 42:09 - loss: 0.8209 - accuracy: 0.7143
+
+
+```
+
+```
+
+ 8/183 [>.............................] - ETA: 41:54 - loss: 0.8494 - accuracy: 0.6875
+
+
+```
+
+```
+
+ 9/183 [>.............................] - ETA: 41:40 - loss: 0.8458 - accuracy: 0.6944
+
+
+```
+
+```
+
+ 10/183 [>.............................] - ETA: 41:25 - loss: 0.8841 - accuracy: 0.6625
+
+
+```
+
+```
+
+ 11/183 [>.............................] - ETA: 41:10 - loss: 0.8833 - accuracy: 0.6705
+
+
+```
+
+```
+
+ 12/183 [>.............................] - ETA: 40:54 - loss: 0.8520 - accuracy: 0.6979
+
+
+```
+
+```
+
+ 13/183 [=>............................] - ETA: 40:38 - loss: 0.8262 - accuracy: 0.7115
+
+
+```
+
+```
+
+ 14/183 [=>............................] - ETA: 40:23 - loss: 0.8389 - accuracy: 0.7054
+
+
+```
+
+```
+
+ 15/183 [=>............................] - ETA: 40:08 - loss: 0.8262 - accuracy: 0.7250
+
+
+```
+
+```
+
+ 16/183 [=>............................] - ETA: 39:53 - loss: 0.8368 - accuracy: 0.7188
+
+
+```
+
+```
+
+ 17/183 [=>............................] - ETA: 39:39 - loss: 0.8293 - accuracy: 0.7279
+
+
+```
+
+```
+
+ 18/183 [=>............................] - ETA: 39:25 - loss: 0.8353 - accuracy: 0.7222
+
+
+```
+
+```
+
+ 19/183 [==>...........................] - ETA: 39:09 - loss: 0.8331 - accuracy: 0.7237
+
+
+```
+
+```
+
+ 20/183 [==>...........................] - ETA: 38:54 - loss: 0.8452 - accuracy: 0.7063
+
+
+```
+
+```
+
+ 21/183 [==>...........................] - ETA: 38:38 - loss: 0.8396 - accuracy: 0.7083
+
+
+```
+
+```
+
+ 22/183 [==>...........................] - ETA: 38:23 - loss: 0.8370 - accuracy: 0.7102
+
+
+```
+
+```
+
+ 23/183 [==>...........................] - ETA: 38:08 - loss: 0.8350 - accuracy: 0.7120
+
+
+```
+
+```
+
+ 24/183 [==>...........................] - ETA: 37:53 - loss: 0.8551 - accuracy: 0.7031
+
+
+```
+
+```
+
+ 25/183 [===>..........................] - ETA: 37:38 - loss: 0.8385 - accuracy: 0.7150
+
+
+```
+
+```
+
+ 26/183 [===>..........................] - ETA: 37:23 - loss: 0.8379 - accuracy: 0.7163
+
+
+```
+
+```
+
+ 27/183 [===>..........................] - ETA: 37:09 - loss: 0.8418 - accuracy: 0.7130
+
+
+```
+
+```
+
+ 28/183 [===>..........................] - ETA: 36:54 - loss: 0.8280 - accuracy: 0.7232
+
+
+```
+
+```
+
+ 29/183 [===>..........................] - ETA: 36:39 - loss: 0.8371 - accuracy: 0.7069
+
+
+```
+
+```
+
+ 30/183 [===>..........................] - ETA: 36:24 - loss: 0.8461 - accuracy: 0.7042
+
+
+```
+
+```
+
+ 31/183 [====>.........................] - ETA: 36:10 - loss: 0.8476 - accuracy: 0.7056
+
+
+```
+
+```
+
+ 32/183 [====>.........................] - ETA: 35:55 - loss: 0.8437 - accuracy: 0.7031
+
+
+```
+
+```
+
+ 33/183 [====>.........................] - ETA: 35:40 - loss: 0.8428 - accuracy: 0.7045
+
+
+```
+
+```
+
+ 34/183 [====>.........................] - ETA: 35:25 - loss: 0.8396 - accuracy: 0.7096
+
+
+```
+
+```
+
+ 35/183 [====>.........................] - ETA: 35:11 - loss: 0.8330 - accuracy: 0.7107
+
+
+```
+
+```
+
+ 36/183 [====>.........................] - ETA: 34:56 - loss: 0.8283 - accuracy: 0.7118
+
+
+```
+
+```
+
+ 37/183 [=====>........................] - ETA: 34:41 - loss: 0.8235 - accuracy: 0.7162
+
+
+```
+
+```
+
+ 38/183 [=====>........................] - ETA: 34:27 - loss: 0.8247 - accuracy: 0.7171
+
+
+```
+
+```
+
+ 39/183 [=====>........................] - ETA: 34:12 - loss: 0.8305 - accuracy: 0.7147
+
+
+```
+
+```
+
+ 40/183 [=====>........................] - ETA: 33:58 - loss: 0.8233 - accuracy: 0.7219
+
+
+```
+
+```
+
+ 41/183 [=====>........................] - ETA: 33:43 - loss: 0.8217 - accuracy: 0.7195
+
+
+```
+
+```
+
+ 42/183 [=====>........................] - ETA: 33:28 - loss: 0.8180 - accuracy: 0.7202
+
+
+```
+
+```
+
+ 43/183 [======>.......................] - ETA: 33:15 - loss: 0.8268 - accuracy: 0.7209
+
+
+```
+
+```
+
+ 44/183 [======>.......................] - ETA: 33:00 - loss: 0.8309 - accuracy: 0.7131
+
+
+```
+
+```
+
+ 45/183 [======>.......................] - ETA: 32:46 - loss: 0.8377 - accuracy: 0.7111
+
+
+```
+
+```
+
+ 46/183 [======>.......................] - ETA: 32:31 - loss: 0.8367 - accuracy: 0.7120
+
+
+```
+
+```
+
+ 47/183 [======>.......................] - ETA: 32:17 - loss: 0.8331 - accuracy: 0.7154
+
+
+```
+
+```
+
+ 48/183 [======>.......................] - ETA: 32:02 - loss: 0.8366 - accuracy: 0.7109
+
+
+```
+
+```
+
+ 49/183 [=======>......................] - ETA: 31:48 - loss: 0.8383 - accuracy: 0.7117
+
+
+```
+
+```
+
+ 50/183 [=======>......................] - ETA: 31:34 - loss: 0.8408 - accuracy: 0.7075
+
+
+```
+
+```
+
+ 51/183 [=======>......................] - ETA: 31:19 - loss: 0.8529 - accuracy: 0.7010
+
+
+```
+
+```
+
+ 52/183 [=======>......................] - ETA: 31:05 - loss: 0.8545 - accuracy: 0.7019
+
+
+```
+
+```
+
+ 53/183 [=======>......................] - ETA: 30:50 - loss: 0.8502 - accuracy: 0.7075
+
+
+```
+
+```
+
+ 54/183 [=======>......................] - ETA: 30:36 - loss: 0.8479 - accuracy: 0.7083
+
+
+```
+
+```
+
+ 55/183 [========>.....................] - ETA: 30:21 - loss: 0.8517 - accuracy: 0.7068
+
+
+```
+
+```
+
+ 56/183 [========>.....................] - ETA: 30:07 - loss: 0.8541 - accuracy: 0.7009
+
+
+```
+
+```
+
+ 57/183 [========>.....................] - ETA: 29:53 - loss: 0.8560 - accuracy: 0.6996
+
+
+```
+
+```
+
+ 58/183 [========>.....................] - ETA: 29:38 - loss: 0.8572 - accuracy: 0.6983
+
+
+```
+
+```
+
+ 59/183 [========>.....................] - ETA: 29:24 - loss: 0.8588 - accuracy: 0.6928
+
+
+```
+
+```
+
+ 60/183 [========>.....................] - ETA: 29:10 - loss: 0.8648 - accuracy: 0.6896
+
+
+```
+
+```
+
+ 61/183 [=========>....................] - ETA: 28:55 - loss: 0.8662 - accuracy: 0.6885
+
+
+```
+
+```
+
+ 62/183 [=========>....................] - ETA: 28:41 - loss: 0.8701 - accuracy: 0.6855
+
+
+```
+
+```
+
+ 63/183 [=========>....................] - ETA: 28:27 - loss: 0.8699 - accuracy: 0.6845
+
+
+```
+
+```
+
+ 64/183 [=========>....................] - ETA: 28:13 - loss: 0.8668 - accuracy: 0.6875
+
+
+```
+
+```
+
+ 65/183 [=========>....................] - ETA: 27:58 - loss: 0.8760 - accuracy: 0.6846
+
+
+```
+
+```
+
+ 66/183 [=========>....................] - ETA: 27:44 - loss: 0.8789 - accuracy: 0.6799
+
+
+```
+
+```
+
+ 67/183 [=========>....................] - ETA: 27:30 - loss: 0.8779 - accuracy: 0.6791
+
+
+```
+
+```
+
+ 68/183 [==========>...................] - ETA: 27:15 - loss: 0.8763 - accuracy: 0.6783
+
+
+```
+
+```
+
+ 69/183 [==========>...................] - ETA: 27:01 - loss: 0.8751 - accuracy: 0.6775
+
+
+```
+
+```
+
+ 70/183 [==========>...................] - ETA: 26:47 - loss: 0.8755 - accuracy: 0.6750
+
+
+```
+
+```
+
+ 71/183 [==========>...................] - ETA: 26:32 - loss: 0.8697 - accuracy: 0.6778
+
+
+```
+
+```
+
+ 72/183 [==========>...................] - ETA: 26:18 - loss: 0.8729 - accuracy: 0.6736
+
+
+```
+
+```
+
+ 73/183 [==========>...................] - ETA: 26:04 - loss: 0.8713 - accuracy: 0.6747
+
+
+```
+
+```
+
+ 74/183 [===========>..................] - ETA: 25:50 - loss: 0.8721 - accuracy: 0.6740
+
+
+```
+
+```
+
+ 75/183 [===========>..................] - ETA: 25:35 - loss: 0.8787 - accuracy: 0.6700
+
+
+```
+
+```
+
+ 76/183 [===========>..................] - ETA: 25:21 - loss: 0.8769 - accuracy: 0.6694
+
+
+```
+
+```
+
+ 77/183 [===========>..................] - ETA: 25:07 - loss: 0.8743 - accuracy: 0.6705
+
+
+```
+
+```
+
+ 78/183 [===========>..................] - ETA: 24:53 - loss: 0.8794 - accuracy: 0.6683
+
+
+```
+
+```
+
+ 79/183 [===========>..................] - ETA: 24:39 - loss: 0.8800 - accuracy: 0.6677
+
+
+```
+
+```
+
+ 80/183 [============>.................] - ETA: 24:24 - loss: 0.8773 - accuracy: 0.6687
+
+
+```
+
+```
+
+ 81/183 [============>.................] - ETA: 24:10 - loss: 0.8769 - accuracy: 0.6682
+
+
+```
+
+```
+
+ 82/183 [============>.................] - ETA: 23:56 - loss: 0.8788 - accuracy: 0.6677
+
+
+```
+
+```
+
+ 83/183 [============>.................] - ETA: 23:42 - loss: 0.8773 - accuracy: 0.6672
+
+
+```
+
+```
+
+ 84/183 [============>.................] - ETA: 23:27 - loss: 0.8800 - accuracy: 0.6652
+
+
+```
+
+```
+
+ 85/183 [============>.................] - ETA: 23:13 - loss: 0.8819 - accuracy: 0.6632
+
+
+```
+
+```
+
+ 86/183 [=============>................] - ETA: 22:59 - loss: 0.8789 - accuracy: 0.6657
+
+
+```
+
+```
+
+ 87/183 [=============>................] - ETA: 22:45 - loss: 0.8813 - accuracy: 0.6667
+
+
+```
+
+```
+
+ 88/183 [=============>................] - ETA: 22:31 - loss: 0.8872 - accuracy: 0.6619
+
+
+```
+
+```
+
+ 89/183 [=============>................] - ETA: 22:16 - loss: 0.8847 - accuracy: 0.6629
+
+
+```
+
+```
+
+ 90/183 [=============>................] - ETA: 22:02 - loss: 0.8823 - accuracy: 0.6639
+
+
+```
+
+```
+
+ 91/183 [=============>................] - ETA: 21:48 - loss: 0.8859 - accuracy: 0.6621
+
+
+```
+
+```
+
+ 92/183 [==============>...............] - ETA: 21:34 - loss: 0.8902 - accuracy: 0.6617
+
+
+```
+
+```
+
+ 93/183 [==============>...............] - ETA: 21:20 - loss: 0.8896 - accuracy: 0.6626
+
+
+```
+
+```
+
+ 94/183 [==============>...............] - ETA: 21:05 - loss: 0.8932 - accuracy: 0.6622
+
+
+```
+
+```
+
+ 95/183 [==============>...............] - ETA: 20:51 - loss: 0.8946 - accuracy: 0.6618
+
+
+```
+
+```
+
+ 96/183 [==============>...............] - ETA: 20:37 - loss: 0.8960 - accuracy: 0.6628
+
+
+```
+
+```
+
+ 97/183 [==============>...............] - ETA: 20:23 - loss: 0.8934 - accuracy: 0.6649
+
+
+```
+
+```
+
+ 98/183 [===============>..............] - ETA: 20:08 - loss: 0.8942 - accuracy: 0.6658
+
+
+```
+
+```
+
+ 99/183 [===============>..............] - ETA: 19:54 - loss: 0.9002 - accuracy: 0.6629
+
+
+```
+
+```
+
+100/183 [===============>..............] - ETA: 19:40 - loss: 0.8975 - accuracy: 0.6637
+
+
+```
+
+```
+
+101/183 [===============>..............] - ETA: 19:26 - loss: 0.8965 - accuracy: 0.6646
+
+
+```
+
+```
+
+102/183 [===============>..............] - ETA: 19:11 - loss: 0.8963 - accuracy: 0.6642
+
+
+```
+
+```
+
+103/183 [===============>..............] - ETA: 18:57 - loss: 0.8950 - accuracy: 0.6650
+
+
+```
+
+```
+
+104/183 [================>.............] - ETA: 18:43 - loss: 0.8935 - accuracy: 0.6659
+
+
+```
+
+```
+
+105/183 [================>.............] - ETA: 18:29 - loss: 0.8912 - accuracy: 0.6667
+
+
+```
+
+```
+
+106/183 [================>.............] - ETA: 18:14 - loss: 0.8940 - accuracy: 0.6663
+
+
+```
+
+```
+
+107/183 [================>.............] - ETA: 18:00 - loss: 0.8913 - accuracy: 0.6671
+
+
+```
+
+```
+
+108/183 [================>.............] - ETA: 17:46 - loss: 0.8920 - accuracy: 0.6667
+
+
+```
+
+```
+
+109/183 [================>.............] - ETA: 17:32 - loss: 0.8915 - accuracy: 0.6674
+
+
+```
+
+```
+
+110/183 [=================>............] - ETA: 17:18 - loss: 0.8893 - accuracy: 0.6682
+
+
+```
+
+```
+
+111/183 [=================>............] - ETA: 17:03 - loss: 0.8909 - accuracy: 0.6667
+
+
+```
+
+```
+
+112/183 [=================>............] - ETA: 16:49 - loss: 0.8894 - accuracy: 0.6674
+
+
+```
+
+```
+
+113/183 [=================>............] - ETA: 16:35 - loss: 0.8909 - accuracy: 0.6659
+
+
+```
+
+```
+
+114/183 [=================>............] - ETA: 16:21 - loss: 0.8908 - accuracy: 0.6667
+
+
+```
+
+```
+
+115/183 [=================>............] - ETA: 16:07 - loss: 0.8943 - accuracy: 0.6652
+
+
+```
+
+```
+
+116/183 [==================>...........] - ETA: 15:53 - loss: 0.8956 - accuracy: 0.6659
+
+
+```
+
+```
+
+117/183 [==================>...........] - ETA: 15:40 - loss: 0.8949 - accuracy: 0.6656
+
+
+```
+
+```
+
+118/183 [==================>...........] - ETA: 15:26 - loss: 0.8907 - accuracy: 0.6684
+
+
+```
+
+```
+
+119/183 [==================>...........] - ETA: 15:11 - loss: 0.8892 - accuracy: 0.6691
+
+
+```
+
+```
+
+120/183 [==================>...........] - ETA: 14:57 - loss: 0.8889 - accuracy: 0.6677
+
+
+```
+
+```
+
+121/183 [==================>...........] - ETA: 14:43 - loss: 0.8853 - accuracy: 0.6694
+
+
+```
+
+```
+
+122/183 [===================>..........] - ETA: 14:29 - loss: 0.8860 - accuracy: 0.6691
+
+
+```
+
+```
+
+123/183 [===================>..........] - ETA: 14:14 - loss: 0.8832 - accuracy: 0.6707
+
+
+```
+
+```
+
+124/183 [===================>..........] - ETA: 14:00 - loss: 0.8812 - accuracy: 0.6724
+
+
+```
+
+```
+
+125/183 [===================>..........] - ETA: 13:46 - loss: 0.8806 - accuracy: 0.6720
+
+
+```
+
+```
+
+126/183 [===================>..........] - ETA: 13:32 - loss: 0.8807 - accuracy: 0.6696
+
+
+```
+
+```
+
+127/183 [===================>..........] - ETA: 13:17 - loss: 0.8778 - accuracy: 0.6713
+
+
+```
+
+```
+
+128/183 [===================>..........] - ETA: 13:03 - loss: 0.8790 - accuracy: 0.6719
+
+
+```
+
+```
+
+129/183 [====================>.........] - ETA: 12:49 - loss: 0.8775 - accuracy: 0.6725
+
+
+```
+
+```
+
+130/183 [====================>.........] - ETA: 12:35 - loss: 0.8775 - accuracy: 0.6731
+
+
+```
+
+```
+
+131/183 [====================>.........] - ETA: 12:21 - loss: 0.8785 - accuracy: 0.6718
+
+
+```
+
+```
+
+132/183 [====================>.........] - ETA: 12:07 - loss: 0.8759 - accuracy: 0.6733
+
+
+```
+
+```
+
+133/183 [====================>.........] - ETA: 11:52 - loss: 0.8739 - accuracy: 0.6739
+
+
+```
+
+```
+
+134/183 [====================>.........] - ETA: 11:38 - loss: 0.8749 - accuracy: 0.6735
+
+
+```
+
+```
+
+135/183 [=====================>........] - ETA: 11:24 - loss: 0.8742 - accuracy: 0.6741
+
+
+```
+
+```
+
+136/183 [=====================>........] - ETA: 11:10 - loss: 0.8738 - accuracy: 0.6728
+
+
+```
+
+```
+
+137/183 [=====================>........] - ETA: 10:56 - loss: 0.8752 - accuracy: 0.6715
+
+
+```
+
+```
+
+138/183 [=====================>........] - ETA: 10:41 - loss: 0.8792 - accuracy: 0.6694
+
+
+```
+
+```
+
+139/183 [=====================>........] - ETA: 10:27 - loss: 0.8795 - accuracy: 0.6691
+
+
+```
+
+```
+
+140/183 [=====================>........] - ETA: 10:13 - loss: 0.8808 - accuracy: 0.6670
+
+
+```
+
+```
+
+141/183 [======================>.......] - ETA: 9:59 - loss: 0.8836 - accuracy: 0.6667
+
+
+```
+
+```
+
+142/183 [======================>.......] - ETA: 9:44 - loss: 0.8848 - accuracy: 0.6664
+
+
+```
+
+```
+
+143/183 [======================>.......] - ETA: 9:30 - loss: 0.8833 - accuracy: 0.6661
+
+
+```
+
+```
+
+144/183 [======================>.......] - ETA: 9:16 - loss: 0.8850 - accuracy: 0.6641
+
+
+```
+
+```
+
+145/183 [======================>.......] - ETA: 9:02 - loss: 0.8841 - accuracy: 0.6647
+
+
+```
+
+```
+
+146/183 [======================>.......] - ETA: 8:47 - loss: 0.8825 - accuracy: 0.6661
+
+
+```
+
+```
+
+147/183 [=======================>......] - ETA: 8:33 - loss: 0.8814 - accuracy: 0.6675
+
+
+```
+
+```
+
+148/183 [=======================>......] - ETA: 8:19 - loss: 0.8801 - accuracy: 0.6698
+
+
+```
+
+```
+
+149/183 [=======================>......] - ETA: 8:05 - loss: 0.8782 - accuracy: 0.6703
+
+
+```
+
+```
+
+150/183 [=======================>......] - ETA: 7:51 - loss: 0.8789 - accuracy: 0.6700
+
+
+```
+
+```
+
+151/183 [=======================>......] - ETA: 7:36 - loss: 0.8776 - accuracy: 0.6705
+
+
+```
+
+```
+
+152/183 [=======================>......] - ETA: 7:22 - loss: 0.8797 - accuracy: 0.6694
+
+
+```
+
+```
+
+153/183 [========================>.....] - ETA: 7:08 - loss: 0.8804 - accuracy: 0.6691
+
+
+```
+
+```
+
+154/183 [========================>.....] - ETA: 6:53 - loss: 0.8793 - accuracy: 0.6688
+
+
+```
+
+```
+
+155/183 [========================>.....] - ETA: 6:39 - loss: 0.8793 - accuracy: 0.6685
+
+
+```
+
+```
+
+156/183 [========================>.....] - ETA: 6:25 - loss: 0.8776 - accuracy: 0.6691
+
+
+```
+
+```
+
+157/183 [========================>.....] - ETA: 6:11 - loss: 0.8755 - accuracy: 0.6704
+
+
+```
+
+```
+
+158/183 [========================>.....] - ETA: 5:57 - loss: 0.8768 - accuracy: 0.6701
+
+
+```
+
+```
+
+159/183 [=========================>....] - ETA: 5:42 - loss: 0.8771 - accuracy: 0.6698
+
+
+```
+
+```
+
+160/183 [=========================>....] - ETA: 5:28 - loss: 0.8761 - accuracy: 0.6703
+
+
+```
+
+```
+
+161/183 [=========================>....] - ETA: 5:14 - loss: 0.8734 - accuracy: 0.6716
+
+
+```
+
+```
+
+162/183 [=========================>....] - ETA: 4:59 - loss: 0.8730 - accuracy: 0.6713
+
+
+```
+
+```
+
+163/183 [=========================>....] - ETA: 4:45 - loss: 0.8722 - accuracy: 0.6710
+
+
+```
+
+```
+
+164/183 [=========================>....] - ETA: 4:31 - loss: 0.8728 - accuracy: 0.6707
+
+
+```
+
+```
+
+165/183 [==========================>...] - ETA: 4:17 - loss: 0.8743 - accuracy: 0.6697
+
+
+```
+
+```
+
+166/183 [==========================>...] - ETA: 4:02 - loss: 0.8745 - accuracy: 0.6702
+
+
+```
+
+```
+
+167/183 [==========================>...] - ETA: 3:48 - loss: 0.8753 - accuracy: 0.6684
+
+
+```
+
+```
+
+168/183 [==========================>...] - ETA: 3:34 - loss: 0.8738 - accuracy: 0.6696
+
+
+```
+
+```
+
+169/183 [==========================>...] - ETA: 3:20 - loss: 0.8724 - accuracy: 0.6694
+
+
+```
+
+```
+
+170/183 [==========================>...] - ETA: 3:05 - loss: 0.8757 - accuracy: 0.6676
+
+
+```
+
+```
+
+171/183 [===========================>..] - ETA: 2:51 - loss: 0.8752 - accuracy: 0.6674
+
+
+```
+
+```
+
+172/183 [===========================>..] - ETA: 2:37 - loss: 0.8768 - accuracy: 0.6672
+
+
+```
+
+```
+
+173/183 [===========================>..] - ETA: 2:22 - loss: 0.8750 - accuracy: 0.6676
+
+
+```
+
+```
+
+174/183 [===========================>..] - ETA: 2:08 - loss: 0.8753 - accuracy: 0.6681
+
+
+```
+
+```
+
+175/183 [===========================>..] - ETA: 1:54 - loss: 0.8749 - accuracy: 0.6679
+
+
+```
+
+```
+
+176/183 [===========================>..] - ETA: 1:40 - loss: 0.8739 - accuracy: 0.6683
+
+
+```
+
+```
+
+177/183 [============================>.] - ETA: 1:25 - loss: 0.8720 - accuracy: 0.6688
+
+
+```
+
+```
+
+178/183 [============================>.] - ETA: 1:11 - loss: 0.8719 - accuracy: 0.6692
+
+
+```
+
+```
+
+179/183 [============================>.] - ETA: 57s - loss: 0.8739 - accuracy: 0.6669
+
+
+```
+
+```
+
+180/183 [============================>.] - ETA: 42s - loss: 0.8729 - accuracy: 0.6674
+
+
+```
+
+```
+
+181/183 [============================>.] - ETA: 28s - loss: 0.8722 - accuracy: 0.6678
+
+
+```
+
+```
+
+182/183 [============================>.] - ETA: 14s - loss: 0.8715 - accuracy: 0.6683
+
+
+```
+
+```
+
+183/183 [==============================] - ETA: 0s - loss: 0.8728 - accuracy: 0.6680
+
+
+```
+
+```
+
+183/183 [==============================] - 2778s 15s/step - loss: 0.8728 - accuracy: 0.6680 - val_loss: 0.7296 - val_accuracy: 0.7525 - lr: 4.7230e-06
+
+
+
+```
+Epoch 5/5
+183/183 [==============================] - 2764s 15s/step - loss: 0.7714 - accuracy: 0.7158 - val_loss: 0.7098 - val_accuracy: 0.7500 - lr: 4.4984e-06
+
+```
+
+
+ 1/183 [..............................] - ETA: 45:36 - loss: 0.7472 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 2/183 [..............................] - ETA: 44:02 - loss: 0.7747 - accuracy: 0.6875
+
+
+```
+
+```
+
+ 3/183 [..............................] - ETA: 43:51 - loss: 0.7801 - accuracy: 0.6667
+
+
+```
+
+```
+
+ 4/183 [..............................] - ETA: 43:41 - loss: 0.7945 - accuracy: 0.6250
+
+
+```
+
+```
+
+ 5/183 [..............................] - ETA: 43:26 - loss: 0.8398 - accuracy: 0.6500
+
+
+```
+
+```
+
+ 6/183 [..............................] - ETA: 43:07 - loss: 0.7811 - accuracy: 0.6875
+
+
+```
+
+```
+
+ 7/183 [>.............................] - ETA: 42:47 - loss: 0.8120 - accuracy: 0.6964
+
+
+```
+
+```
+
+ 8/183 [>.............................] - ETA: 42:30 - loss: 0.7819 - accuracy: 0.6875
+
+
+```
+
+```
+
+ 9/183 [>.............................] - ETA: 42:09 - loss: 0.7870 - accuracy: 0.6944
+
+
+```
+
+```
+
+ 10/183 [>.............................] - ETA: 41:50 - loss: 0.7538 - accuracy: 0.7125
+
+
+```
+
+```
+
+ 11/183 [>.............................] - ETA: 41:33 - loss: 0.7518 - accuracy: 0.7159
+
+
+```
+
+```
+
+ 12/183 [>.............................] - ETA: 41:17 - loss: 0.7440 - accuracy: 0.7188
+
+
+```
+
+```
+
+ 13/183 [=>............................] - ETA: 41:00 - loss: 0.7263 - accuracy: 0.7404
+
+
+```
+
+```
+
+ 14/183 [=>............................] - ETA: 40:44 - loss: 0.7001 - accuracy: 0.7589
+
+
+```
+
+```
+
+ 15/183 [=>............................] - ETA: 40:28 - loss: 0.7132 - accuracy: 0.7583
+
+
+```
+
+```
+
+ 16/183 [=>............................] - ETA: 40:11 - loss: 0.7123 - accuracy: 0.7500
+
+
+```
+
+```
+
+ 17/183 [=>............................] - ETA: 39:55 - loss: 0.7370 - accuracy: 0.7426
+
+
+```
+
+```
+
+ 18/183 [=>............................] - ETA: 39:38 - loss: 0.7365 - accuracy: 0.7431
+
+
+```
+
+```
+
+ 19/183 [==>...........................] - ETA: 39:22 - loss: 0.7469 - accuracy: 0.7303
+
+
+```
+
+```
+
+ 20/183 [==>...........................] - ETA: 39:07 - loss: 0.7500 - accuracy: 0.7375
+
+
+```
+
+```
+
+ 21/183 [==>...........................] - ETA: 38:52 - loss: 0.7459 - accuracy: 0.7440
+
+
+```
+
+```
+
+ 22/183 [==>...........................] - ETA: 38:37 - loss: 0.7396 - accuracy: 0.7557
+
+
+```
+
+```
+
+ 23/183 [==>...........................] - ETA: 38:21 - loss: 0.7387 - accuracy: 0.7500
+
+
+```
+
+```
+
+ 24/183 [==>...........................] - ETA: 38:06 - loss: 0.7266 - accuracy: 0.7552
+
+
+```
+
+```
+
+ 25/183 [===>..........................] - ETA: 37:51 - loss: 0.7416 - accuracy: 0.7400
+
+
+```
+
+```
+
+ 26/183 [===>..........................] - ETA: 37:37 - loss: 0.7411 - accuracy: 0.7404
+
+
+```
+
+```
+
+ 27/183 [===>..........................] - ETA: 37:23 - loss: 0.7337 - accuracy: 0.7454
+
+
+```
+
+```
+
+ 28/183 [===>..........................] - ETA: 37:09 - loss: 0.7336 - accuracy: 0.7411
+
+
+```
+
+```
+
+ 29/183 [===>..........................] - ETA: 36:54 - loss: 0.7294 - accuracy: 0.7457
+
+
+```
+
+```
+
+ 30/183 [===>..........................] - ETA: 36:40 - loss: 0.7293 - accuracy: 0.7417
+
+
+```
+
+```
+
+ 31/183 [====>.........................] - ETA: 36:26 - loss: 0.7399 - accuracy: 0.7379
+
+
+```
+
+```
+
+ 32/183 [====>.........................] - ETA: 36:11 - loss: 0.7355 - accuracy: 0.7422
+
+
+```
+
+```
+
+ 33/183 [====>.........................] - ETA: 35:57 - loss: 0.7283 - accuracy: 0.7462
+
+
+```
+
+```
+
+ 34/183 [====>.........................] - ETA: 35:43 - loss: 0.7330 - accuracy: 0.7426
+
+
+```
+
+```
+
+ 35/183 [====>.........................] - ETA: 35:28 - loss: 0.7390 - accuracy: 0.7321
+
+
+```
+
+```
+
+ 36/183 [====>.........................] - ETA: 35:14 - loss: 0.7381 - accuracy: 0.7292
+
+
+```
+
+```
+
+ 37/183 [=====>........................] - ETA: 34:59 - loss: 0.7374 - accuracy: 0.7264
+
+
+```
+
+```
+
+ 38/183 [=====>........................] - ETA: 34:45 - loss: 0.7359 - accuracy: 0.7303
+
+
+```
+
+```
+
+ 39/183 [=====>........................] - ETA: 34:31 - loss: 0.7345 - accuracy: 0.7340
+
+
+```
+
+```
+
+ 40/183 [=====>........................] - ETA: 34:16 - loss: 0.7355 - accuracy: 0.7344
+
+
+```
+
+```
+
+ 41/183 [=====>........................] - ETA: 34:01 - loss: 0.7277 - accuracy: 0.7378
+
+
+```
+
+```
+
+ 42/183 [=====>........................] - ETA: 33:48 - loss: 0.7305 - accuracy: 0.7381
+
+
+```
+
+```
+
+ 43/183 [======>.......................] - ETA: 33:33 - loss: 0.7320 - accuracy: 0.7355
+
+
+```
+
+```
+
+ 44/183 [======>.......................] - ETA: 33:18 - loss: 0.7283 - accuracy: 0.7386
+
+
+```
+
+```
+
+ 45/183 [======>.......................] - ETA: 33:03 - loss: 0.7341 - accuracy: 0.7333
+
+
+```
+
+```
+
+ 46/183 [======>.......................] - ETA: 32:48 - loss: 0.7445 - accuracy: 0.7255
+
+
+```
+
+```
+
+ 47/183 [======>.......................] - ETA: 32:33 - loss: 0.7413 - accuracy: 0.7287
+
+
+```
+
+```
+
+ 48/183 [======>.......................] - ETA: 32:18 - loss: 0.7428 - accuracy: 0.7292
+
+
+```
+
+```
+
+ 49/183 [=======>......................] - ETA: 32:03 - loss: 0.7457 - accuracy: 0.7270
+
+
+```
+
+```
+
+ 50/183 [=======>......................] - ETA: 31:49 - loss: 0.7504 - accuracy: 0.7250
+
+
+```
+
+```
+
+ 51/183 [=======>......................] - ETA: 31:34 - loss: 0.7524 - accuracy: 0.7181
+
+
+```
+
+```
+
+ 52/183 [=======>......................] - ETA: 31:19 - loss: 0.7636 - accuracy: 0.7163
+
+
+```
+
+```
+
+ 53/183 [=======>......................] - ETA: 31:04 - loss: 0.7691 - accuracy: 0.7123
+
+
+```
+
+```
+
+ 54/183 [=======>......................] - ETA: 30:50 - loss: 0.7681 - accuracy: 0.7130
+
+
+```
+
+```
+
+ 55/183 [========>.....................] - ETA: 30:35 - loss: 0.7717 - accuracy: 0.7091
+
+
+```
+
+```
+
+ 56/183 [========>.....................] - ETA: 30:20 - loss: 0.7725 - accuracy: 0.7098
+
+
+```
+
+```
+
+ 57/183 [========>.....................] - ETA: 30:05 - loss: 0.7781 - accuracy: 0.7105
+
+
+```
+
+```
+
+ 58/183 [========>.....................] - ETA: 29:51 - loss: 0.7811 - accuracy: 0.7091
+
+
+```
+
+```
+
+ 59/183 [========>.....................] - ETA: 29:36 - loss: 0.7856 - accuracy: 0.7097
+
+
+```
+
+```
+
+ 60/183 [========>.....................] - ETA: 29:22 - loss: 0.7853 - accuracy: 0.7083
+
+
+```
+
+```
+
+ 61/183 [=========>....................] - ETA: 29:07 - loss: 0.7866 - accuracy: 0.7111
+
+
+```
+
+```
+
+ 62/183 [=========>....................] - ETA: 28:53 - loss: 0.7861 - accuracy: 0.7117
+
+
+```
+
+```
+
+ 63/183 [=========>....................] - ETA: 28:38 - loss: 0.7909 - accuracy: 0.7083
+
+
+```
+
+```
+
+ 64/183 [=========>....................] - ETA: 28:24 - loss: 0.7890 - accuracy: 0.7070
+
+
+```
+
+```
+
+ 65/183 [=========>....................] - ETA: 28:09 - loss: 0.7919 - accuracy: 0.7058
+
+
+```
+
+```
+
+ 66/183 [=========>....................] - ETA: 27:54 - loss: 0.7981 - accuracy: 0.7027
+
+
+```
+
+```
+
+ 67/183 [=========>....................] - ETA: 27:40 - loss: 0.7995 - accuracy: 0.7015
+
+
+```
+
+```
+
+ 68/183 [==========>...................] - ETA: 27:25 - loss: 0.7972 - accuracy: 0.7022
+
+
+```
+
+```
+
+ 69/183 [==========>...................] - ETA: 27:11 - loss: 0.7957 - accuracy: 0.7029
+
+
+```
+
+```
+
+ 70/183 [==========>...................] - ETA: 26:57 - loss: 0.7951 - accuracy: 0.7036
+
+
+```
+
+```
+
+ 71/183 [==========>...................] - ETA: 26:43 - loss: 0.7939 - accuracy: 0.7042
+
+
+```
+
+```
+
+ 72/183 [==========>...................] - ETA: 26:28 - loss: 0.7897 - accuracy: 0.7049
+
+
+```
+
+```
+
+ 73/183 [==========>...................] - ETA: 26:13 - loss: 0.7879 - accuracy: 0.7055
+
+
+```
+
+```
+
+ 74/183 [===========>..................] - ETA: 25:59 - loss: 0.7864 - accuracy: 0.7044
+
+
+```
+
+```
+
+ 75/183 [===========>..................] - ETA: 25:44 - loss: 0.7859 - accuracy: 0.7050
+
+
+```
+
+```
+
+ 76/183 [===========>..................] - ETA: 25:30 - loss: 0.7883 - accuracy: 0.7056
+
+
+```
+
+```
+
+ 77/183 [===========>..................] - ETA: 25:16 - loss: 0.7881 - accuracy: 0.7062
+
+
+```
+
+```
+
+ 78/183 [===========>..................] - ETA: 25:01 - loss: 0.7931 - accuracy: 0.7035
+
+
+```
+
+```
+
+ 79/183 [===========>..................] - ETA: 24:47 - loss: 0.7971 - accuracy: 0.7009
+
+
+```
+
+```
+
+ 80/183 [============>.................] - ETA: 24:32 - loss: 0.7980 - accuracy: 0.7000
+
+
+```
+
+```
+
+ 81/183 [============>.................] - ETA: 24:18 - loss: 0.7948 - accuracy: 0.7022
+
+
+```
+
+```
+
+ 82/183 [============>.................] - ETA: 24:03 - loss: 0.7942 - accuracy: 0.7027
+
+
+```
+
+```
+
+ 83/183 [============>.................] - ETA: 23:49 - loss: 0.7900 - accuracy: 0.7048
+
+
+```
+
+```
+
+ 84/183 [============>.................] - ETA: 23:35 - loss: 0.7926 - accuracy: 0.7024
+
+
+```
+
+```
+
+ 85/183 [============>.................] - ETA: 23:20 - loss: 0.7966 - accuracy: 0.7000
+
+
+```
+
+```
+
+ 86/183 [=============>................] - ETA: 23:06 - loss: 0.7997 - accuracy: 0.6977
+
+
+```
+
+```
+
+ 87/183 [=============>................] - ETA: 22:52 - loss: 0.7973 - accuracy: 0.6997
+
+
+```
+
+```
+
+ 88/183 [=============>................] - ETA: 22:38 - loss: 0.8052 - accuracy: 0.6960
+
+
+```
+
+```
+
+ 89/183 [=============>................] - ETA: 22:23 - loss: 0.8064 - accuracy: 0.6952
+
+
+```
+
+```
+
+ 90/183 [=============>................] - ETA: 22:09 - loss: 0.8023 - accuracy: 0.6972
+
+
+```
+
+```
+
+ 91/183 [=============>................] - ETA: 21:54 - loss: 0.8045 - accuracy: 0.6978
+
+
+```
+
+```
+
+ 92/183 [==============>...............] - ETA: 21:40 - loss: 0.8056 - accuracy: 0.6984
+
+
+```
+
+```
+
+ 93/183 [==============>...............] - ETA: 21:26 - loss: 0.8058 - accuracy: 0.6976
+
+
+```
+
+```
+
+ 94/183 [==============>...............] - ETA: 21:11 - loss: 0.8039 - accuracy: 0.6981
+
+
+```
+
+```
+
+ 95/183 [==============>...............] - ETA: 20:57 - loss: 0.8073 - accuracy: 0.6987
+
+
+```
+
+```
+
+ 96/183 [==============>...............] - ETA: 20:42 - loss: 0.8063 - accuracy: 0.6992
+
+
+```
+
+```
+
+ 97/183 [==============>...............] - ETA: 20:28 - loss: 0.8076 - accuracy: 0.6972
+
+
+```
+
+```
+
+ 98/183 [===============>..............] - ETA: 20:14 - loss: 0.8061 - accuracy: 0.6977
+
+
+```
+
+```
+
+ 99/183 [===============>..............] - ETA: 19:59 - loss: 0.8094 - accuracy: 0.6944
+
+
+```
+
+```
+
+100/183 [===============>..............] - ETA: 19:45 - loss: 0.8154 - accuracy: 0.6925
+
+
+```
+
+```
+
+101/183 [===============>..............] - ETA: 19:31 - loss: 0.8130 - accuracy: 0.6943
+
+
+```
+
+```
+
+102/183 [===============>..............] - ETA: 19:16 - loss: 0.8113 - accuracy: 0.6949
+
+
+```
+
+```
+
+103/183 [===============>..............] - ETA: 19:02 - loss: 0.8098 - accuracy: 0.6966
+
+
+```
+
+```
+
+104/183 [================>.............] - ETA: 18:48 - loss: 0.8088 - accuracy: 0.6971
+
+
+```
+
+```
+
+105/183 [================>.............] - ETA: 18:33 - loss: 0.8074 - accuracy: 0.6976
+
+
+```
+
+```
+
+106/183 [================>.............] - ETA: 18:19 - loss: 0.8061 - accuracy: 0.6981
+
+
+```
+
+```
+
+107/183 [================>.............] - ETA: 18:05 - loss: 0.8043 - accuracy: 0.6986
+
+
+```
+
+```
+
+108/183 [================>.............] - ETA: 17:50 - loss: 0.8021 - accuracy: 0.7002
+
+
+```
+
+```
+
+109/183 [================>.............] - ETA: 17:36 - loss: 0.8002 - accuracy: 0.7018
+
+
+```
+
+```
+
+110/183 [=================>............] - ETA: 17:22 - loss: 0.7979 - accuracy: 0.7045
+
+
+```
+
+```
+
+111/183 [=================>............] - ETA: 17:07 - loss: 0.7951 - accuracy: 0.7050
+
+
+```
+
+```
+
+112/183 [=================>............] - ETA: 16:53 - loss: 0.7978 - accuracy: 0.7009
+
+
+```
+
+```
+
+113/183 [=================>............] - ETA: 16:39 - loss: 0.7977 - accuracy: 0.7013
+
+
+```
+
+```
+
+114/183 [=================>............] - ETA: 16:24 - loss: 0.8027 - accuracy: 0.6974
+
+
+```
+
+```
+
+115/183 [=================>............] - ETA: 16:10 - loss: 0.7983 - accuracy: 0.7000
+
+
+```
+
+```
+
+116/183 [==================>...........] - ETA: 15:56 - loss: 0.8009 - accuracy: 0.6983
+
+
+```
+
+```
+
+117/183 [==================>...........] - ETA: 15:41 - loss: 0.8030 - accuracy: 0.6976
+
+
+```
+
+```
+
+118/183 [==================>...........] - ETA: 15:27 - loss: 0.8015 - accuracy: 0.6981
+
+
+```
+
+```
+
+119/183 [==================>...........] - ETA: 15:13 - loss: 0.7986 - accuracy: 0.6996
+
+
+```
+
+```
+
+120/183 [==================>...........] - ETA: 14:59 - loss: 0.7966 - accuracy: 0.7010
+
+
+```
+
+```
+
+121/183 [==================>...........] - ETA: 14:44 - loss: 0.7946 - accuracy: 0.7025
+
+
+```
+
+```
+
+122/183 [===================>..........] - ETA: 14:30 - loss: 0.7923 - accuracy: 0.7049
+
+
+```
+
+```
+
+123/183 [===================>..........] - ETA: 14:16 - loss: 0.7925 - accuracy: 0.7043
+
+
+```
+
+```
+
+124/183 [===================>..........] - ETA: 14:01 - loss: 0.7932 - accuracy: 0.7046
+
+
+```
+
+```
+
+125/183 [===================>..........] - ETA: 13:47 - loss: 0.7914 - accuracy: 0.7070
+
+
+```
+
+```
+
+126/183 [===================>..........] - ETA: 13:33 - loss: 0.7918 - accuracy: 0.7054
+
+
+```
+
+```
+
+127/183 [===================>..........] - ETA: 13:19 - loss: 0.7911 - accuracy: 0.7057
+
+
+```
+
+```
+
+128/183 [===================>..........] - ETA: 13:04 - loss: 0.7901 - accuracy: 0.7061
+
+
+```
+
+```
+
+129/183 [====================>.........] - ETA: 12:50 - loss: 0.7908 - accuracy: 0.7054
+
+
+```
+
+```
+
+130/183 [====================>.........] - ETA: 12:36 - loss: 0.7887 - accuracy: 0.7067
+
+
+```
+
+```
+
+131/183 [====================>.........] - ETA: 12:21 - loss: 0.7877 - accuracy: 0.7080
+
+
+```
+
+```
+
+132/183 [====================>.........] - ETA: 12:07 - loss: 0.7871 - accuracy: 0.7083
+
+
+```
+
+```
+
+133/183 [====================>.........] - ETA: 11:53 - loss: 0.7852 - accuracy: 0.7096
+
+
+```
+
+```
+
+134/183 [====================>.........] - ETA: 11:39 - loss: 0.7828 - accuracy: 0.7118
+
+
+```
+
+```
+
+135/183 [=====================>........] - ETA: 11:24 - loss: 0.7804 - accuracy: 0.7139
+
+
+```
+
+```
+
+136/183 [=====================>........] - ETA: 11:10 - loss: 0.7790 - accuracy: 0.7151
+
+
+```
+
+```
+
+137/183 [=====================>........] - ETA: 10:56 - loss: 0.7804 - accuracy: 0.7144
+
+
+```
+
+```
+
+138/183 [=====================>........] - ETA: 10:41 - loss: 0.7795 - accuracy: 0.7156
+
+
+```
+
+```
+
+139/183 [=====================>........] - ETA: 10:27 - loss: 0.7800 - accuracy: 0.7149
+
+
+```
+
+```
+
+140/183 [=====================>........] - ETA: 10:13 - loss: 0.7802 - accuracy: 0.7143
+
+
+```
+
+```
+
+141/183 [======================>.......] - ETA: 9:59 - loss: 0.7813 - accuracy: 0.7137
+
+
+```
+
+```
+
+142/183 [======================>.......] - ETA: 9:44 - loss: 0.7843 - accuracy: 0.7130
+
+
+```
+
+```
+
+143/183 [======================>.......] - ETA: 9:30 - loss: 0.7841 - accuracy: 0.7133
+
+
+```
+
+```
+
+144/183 [======================>.......] - ETA: 9:16 - loss: 0.7848 - accuracy: 0.7135
+
+
+```
+
+```
+
+145/183 [======================>.......] - ETA: 9:01 - loss: 0.7835 - accuracy: 0.7138
+
+
+```
+
+```
+
+146/183 [======================>.......] - ETA: 8:47 - loss: 0.7817 - accuracy: 0.7149
+
+
+```
+
+```
+
+147/183 [=======================>......] - ETA: 8:33 - loss: 0.7827 - accuracy: 0.7143
+
+
+```
+
+```
+
+148/183 [=======================>......] - ETA: 8:19 - loss: 0.7813 - accuracy: 0.7145
+
+
+```
+
+```
+
+149/183 [=======================>......] - ETA: 8:04 - loss: 0.7820 - accuracy: 0.7148
+
+
+```
+
+```
+
+150/183 [=======================>......] - ETA: 7:50 - loss: 0.7804 - accuracy: 0.7158
+
+
+```
+
+```
+
+151/183 [=======================>......] - ETA: 7:36 - loss: 0.7809 - accuracy: 0.7169
+
+
+```
+
+```
+
+152/183 [=======================>......] - ETA: 7:22 - loss: 0.7802 - accuracy: 0.7171
+
+
+```
+
+```
+
+153/183 [========================>.....] - ETA: 7:07 - loss: 0.7851 - accuracy: 0.7141
+
+
+```
+
+```
+
+154/183 [========================>.....] - ETA: 6:53 - loss: 0.7865 - accuracy: 0.7135
+
+
+```
+
+```
+
+155/183 [========================>.....] - ETA: 6:39 - loss: 0.7845 - accuracy: 0.7145
+
+
+```
+
+```
+
+156/183 [========================>.....] - ETA: 6:24 - loss: 0.7830 - accuracy: 0.7155
+
+
+```
+
+```
+
+157/183 [========================>.....] - ETA: 6:10 - loss: 0.7830 - accuracy: 0.7150
+
+
+```
+
+```
+
+158/183 [========================>.....] - ETA: 5:56 - loss: 0.7843 - accuracy: 0.7152
+
+
+```
+
+```
+
+159/183 [=========================>....] - ETA: 5:42 - loss: 0.7841 - accuracy: 0.7146
+
+
+```
+
+```
+
+160/183 [=========================>....] - ETA: 5:27 - loss: 0.7842 - accuracy: 0.7148
+
+
+```
+
+```
+
+161/183 [=========================>....] - ETA: 5:13 - loss: 0.7826 - accuracy: 0.7151
+
+
+```
+
+```
+
+162/183 [=========================>....] - ETA: 4:59 - loss: 0.7822 - accuracy: 0.7160
+
+
+```
+
+```
+
+163/183 [=========================>....] - ETA: 4:45 - loss: 0.7811 - accuracy: 0.7163
+
+
+```
+
+```
+
+164/183 [=========================>....] - ETA: 4:30 - loss: 0.7808 - accuracy: 0.7157
+
+
+```
+
+```
+
+165/183 [==========================>...] - ETA: 4:16 - loss: 0.7798 - accuracy: 0.7167
+
+
+```
+
+```
+
+166/183 [==========================>...] - ETA: 4:02 - loss: 0.7796 - accuracy: 0.7169
+
+
+```
+
+```
+
+167/183 [==========================>...] - ETA: 3:48 - loss: 0.7798 - accuracy: 0.7171
+
+
+```
+
+```
+
+168/183 [==========================>...] - ETA: 3:33 - loss: 0.7794 - accuracy: 0.7173
+
+
+```
+
+```
+
+169/183 [==========================>...] - ETA: 3:19 - loss: 0.7796 - accuracy: 0.7167
+
+
+```
+
+```
+
+170/183 [==========================>...] - ETA: 3:05 - loss: 0.7789 - accuracy: 0.7162
+
+
+```
+
+```
+
+171/183 [===========================>..] - ETA: 2:51 - loss: 0.7807 - accuracy: 0.7142
+
+
+```
+
+```
+
+172/183 [===========================>..] - ETA: 2:36 - loss: 0.7788 - accuracy: 0.7151
+
+
+```
+
+```
+
+173/183 [===========================>..] - ETA: 2:22 - loss: 0.7807 - accuracy: 0.7139
+
+
+```
+
+```
+
+174/183 [===========================>..] - ETA: 2:08 - loss: 0.7793 - accuracy: 0.7148
+
+
+```
+
+```
+
+175/183 [===========================>..] - ETA: 1:54 - loss: 0.7791 - accuracy: 0.7157
+
+
+```
+
+```
+
+176/183 [===========================>..] - ETA: 1:39 - loss: 0.7799 - accuracy: 0.7138
+
+
+```
+
+```
+
+177/183 [============================>.] - ETA: 1:25 - loss: 0.7768 - accuracy: 0.7154
+
+
+```
+
+```
+
+178/183 [============================>.] - ETA: 1:11 - loss: 0.7748 - accuracy: 0.7156
+
+
+```
+
+```
+
+179/183 [============================>.] - ETA: 57s - loss: 0.7741 - accuracy: 0.7158
+
+
+```
+
+```
+
+180/183 [============================>.] - ETA: 42s - loss: 0.7740 - accuracy: 0.7153
+
+
+```
+
+```
+
+181/183 [============================>.] - ETA: 28s - loss: 0.7727 - accuracy: 0.7162
+
+
+```
+
+```
+
+182/183 [============================>.] - ETA: 14s - loss: 0.7719 - accuracy: 0.7163
+
+
+```
+
+```
+
+183/183 [==============================] - ETA: 0s - loss: 0.7714 - accuracy: 0.7158
+
+
+```
+
+```
+
+183/183 [==============================] - 2764s 15s/step - loss: 0.7714 - accuracy: 0.7158 - val_loss: 0.7098 - val_accuracy: 0.7500 - lr: 4.4984e-06
+
+
+---
+## Inference
+
+
+```python
+# Make predictions using the trained model on last validation data
+predictions = model.predict(
+ valid_ds,
+ batch_size=CFG.batch_size, # max batch size = valid size
+ verbose=1,
+)
+
+# Format predictions and true answers
+pred_answers = np.arange(4)[np.argsort(-predictions)][:, 0]
+true_answers = valid_df.label.values
+
+# Check 5 Predictions
+print("# Predictions\n")
+for i in range(0, 50, 10):
+ row = valid_df.iloc[i]
+ question = row.startphrase
+ pred_answer = f"ending{pred_answers[i]}"
+ true_answer = f"ending{true_answers[i]}"
+ print(f"❓ Sentence {i+1}:\n{question}\n")
+ print(f"✅ True Ending: {true_answer}\n >> {row[true_answer]}\n")
+ print(f"🤖 Predicted Ending: {pred_answer}\n >> {row[pred_answer]}\n")
+ print("-" * 90, "\n")
+```
+
+
+ 1/50 [37m━━━━━━━━━━━━━━━━━━━━ 27:32 34s/step
+
+
+```
+
+```
+
+ 2/50 [37m━━━━━━━━━━━━━━━━━━━━ 2:17 3s/step
+
+
+```
+
+```
+
+ 3/50 ━[37m━━━━━━━━━━━━━━━━━━━ 2:14 3s/step
+
+
+```
+
+```
+
+ 4/50 ━[37m━━━━━━━━━━━━━━━━━━━ 2:12 3s/step
+
+
+```
+
+```
+
+ 5/50 ━━[37m━━━━━━━━━━━━━━━━━━ 2:10 3s/step
+
+
+```
+
+```
+
+ 6/50 ━━[37m━━━━━━━━━━━━━━━━━━ 2:06 3s/step
+
+
+```
+
+```
+
+ 7/50 ━━[37m━━━━━━━━━━━━━━━━━━ 2:04 3s/step
+
+
+```
+
+```
+
+ 8/50 ━━━[37m━━━━━━━━━━━━━━━━━ 2:01 3s/step
+
+
+```
+
+```
+
+ 9/50 ━━━[37m━━━━━━━━━━━━━━━━━ 1:58 3s/step
+
+
+```
+
+```
+
+ 10/50 ━━━━[37m━━━━━━━━━━━━━━━━ 1:55 3s/step
+
+
+```
+
+```
+
+ 11/50 ━━━━[37m━━━━━━━━━━━━━━━━ 1:52 3s/step
+
+
+```
+
+```
+
+ 12/50 ━━━━[37m━━━━━━━━━━━━━━━━ 1:49 3s/step
+
+
+```
+
+```
+
+ 13/50 ━━━━━[37m━━━━━━━━━━━━━━━ 1:46 3s/step
+
+
+```
+
+```
+
+ 14/50 ━━━━━[37m━━━━━━━━━━━━━━━ 1:43 3s/step
+
+
+```
+
+```
+
+ 15/50 ━━━━━━[37m━━━━━━━━━━━━━━ 1:41 3s/step
+
+
+```
+
+```
+
+ 16/50 ━━━━━━[37m━━━━━━━━━━━━━━ 1:38 3s/step
+
+
+```
+
+```
+
+ 17/50 ━━━━━━[37m━━━━━━━━━━━━━━ 1:35 3s/step
+
+
+```
+
+```
+
+ 18/50 ━━━━━━━[37m━━━━━━━━━━━━━ 1:32 3s/step
+
+
+```
+
+```
+
+ 19/50 ━━━━━━━[37m━━━━━━━━━━━━━ 1:29 3s/step
+
+
+```
+
+```
+
+ 20/50 ━━━━━━━━[37m━━━━━━━━━━━━ 1:26 3s/step
+
+
+```
+
+```
+
+ 21/50 ━━━━━━━━[37m━━━━━━━━━━━━ 1:23 3s/step
+
+
+```
+
+```
+
+ 22/50 ━━━━━━━━[37m━━━━━━━━━━━━ 1:20 3s/step
+
+
+```
+
+```
+
+ 23/50 ━━━━━━━━━[37m━━━━━━━━━━━ 1:17 3s/step
+
+
+```
+
+```
+
+ 24/50 ━━━━━━━━━[37m━━━━━━━━━━━ 1:15 3s/step
+
+
+```
+
+```
+
+ 25/50 ━━━━━━━━━━[37m━━━━━━━━━━ 1:12 3s/step
+
+
+```
+
+```
+
+ 26/50 ━━━━━━━━━━[37m━━━━━━━━━━ 1:09 3s/step
+
+
+```
+
+```
+
+ 27/50 ━━━━━━━━━━[37m━━━━━━━━━━ 1:06 3s/step
+
+
+```
+
+```
+
+ 28/50 ━━━━━━━━━━━[37m━━━━━━━━━ 1:03 3s/step
+
+
+```
+
+```
+
+ 29/50 ━━━━━━━━━━━[37m━━━━━━━━━ 1:00 3s/step
+
+
+```
+
+```
+
+ 30/50 ━━━━━━━━━━━━[37m━━━━━━━━ 57s 3s/step
+
+
+```
+
+```
+
+ 31/50 ━━━━━━━━━━━━[37m━━━━━━━━ 54s 3s/step
+
+
+```
+
+```
+
+ 32/50 ━━━━━━━━━━━━[37m━━━━━━━━ 51s 3s/step
+
+
+```
+
+```
+
+ 33/50 ━━━━━━━━━━━━━[37m━━━━━━━ 49s 3s/step
+
+
+```
+
+```
+
+ 34/50 ━━━━━━━━━━━━━[37m━━━━━━━ 46s 3s/step
+
+
+```
+
+```
+
+ 35/50 ━━━━━━━━━━━━━━[37m━━━━━━ 43s 3s/step
+
+
+```
+
+```
+
+ 36/50 ━━━━━━━━━━━━━━[37m━━━━━━ 40s 3s/step
+
+
+```
+
+```
+
+ 37/50 ━━━━━━━━━━━━━━[37m━━━━━━ 37s 3s/step
+
+
+```
+
+```
+
+ 38/50 ━━━━━━━━━━━━━━━[37m━━━━━ 34s 3s/step
+
+
+```
+
+```
+
+ 39/50 ━━━━━━━━━━━━━━━[37m━━━━━ 31s 3s/step
+
+
+```
+
+```
+
+ 40/50 ━━━━━━━━━━━━━━━━[37m━━━━ 28s 3s/step
+
+
+```
+
+```
+
+ 41/50 ━━━━━━━━━━━━━━━━[37m━━━━ 25s 3s/step
+
+
+```
+
+```
+
+ 42/50 ━━━━━━━━━━━━━━━━[37m━━━━ 23s 3s/step
+
+
+```
+
+```
+
+ 43/50 ━━━━━━━━━━━━━━━━━[37m━━━ 20s 3s/step
+
+
+```
+
+```
+
+ 44/50 ━━━━━━━━━━━━━━━━━[37m━━━ 17s 3s/step
+
+
+```
+
+```
+
+ 45/50 ━━━━━━━━━━━━━━━━━━[37m━━ 14s 3s/step
+
+
+```
+
+```
+
+ 46/50 ━━━━━━━━━━━━━━━━━━[37m━━ 11s 3s/step
+
+
+```
+
+```
+
+ 47/50 ━━━━━━━━━━━━━━━━━━[37m━━ 8s 3s/step
+
+
+```
+
+```
+
+ 48/50 ━━━━━━━━━━━━━━━━━━━[37m━ 5s 3s/step
+
+
+```
+
+```
+
+ 49/50 ━━━━━━━━━━━━━━━━━━━[37m━ 2s 3s/step
+
+
+```
+
+```
+
+ 50/50 ━━━━━━━━━━━━━━━━━━━━ 0s 3s/step
+
+
+```
+
+```
+
+ 50/50 ━━━━━━━━━━━━━━━━━━━━ 175s 3s/step
+
```
- 50/50 ━━━━━━━━━━━━━━━━━━━━ 274s 5s/step
# Predictions
```
```
-❓ Sentence 1:
+❓ Sentence 1:
The man shows the teens how to move the oars. The teens
```
```
-✅ True Ending: ending3
+✅ True Ending: ending3
>> follow the instructions of the man and row the oars.
```
```
-🤖 Predicted Ending: ending3
+🤖 Predicted Ending: ending3
>> follow the instructions of the man and row the oars.
```
@@ -769,21 +7799,21 @@ The man shows the teens how to move the oars. The teens
```
-❓ Sentence 11:
+❓ Sentence 11:
A lake reflects the mountains and the sky. Someone
```
```
-✅ True Ending: ending2
+✅ True Ending: ending2
>> runs along a desert highway.
```
```
-🤖 Predicted Ending: ending1
+🤖 Predicted Ending: ending1
>> remains by the door.
```
@@ -796,21 +7826,21 @@ A lake reflects the mountains and the sky. Someone
```
-❓ Sentence 21:
+❓ Sentence 21:
On screen, she smiles as someone holds up a present. He watches somberly as on screen, his mother
```
```
-✅ True Ending: ending1
+✅ True Ending: ending1
>> picks him up and plays with him in the garden.
```
```
-🤖 Predicted Ending: ending0
+🤖 Predicted Ending: ending0
>> comes out of her apartment, glowers at her laptop.
```
@@ -823,21 +7853,21 @@ On screen, she smiles as someone holds up a present. He watches somberly as on s
```
-❓ Sentence 31:
+❓ Sentence 31:
A woman in a black shirt is sitting on a bench. A man
```
```
-✅ True Ending: ending2
+✅ True Ending: ending2
>> sits behind a desk.
```
```
-🤖 Predicted Ending: ending0
+🤖 Predicted Ending: ending0
>> is dancing on a stage.
```
@@ -850,21 +7880,21 @@ A woman in a black shirt is sitting on a bench. A man
```
-❓ Sentence 41:
+❓ Sentence 41:
People are standing on sand wearing red shirts. They
```
```
-✅ True Ending: ending3
+✅ True Ending: ending3
>> are playing a game of soccer in the sand.
```
```
-🤖 Predicted Ending: ending3
+🤖 Predicted Ending: ending3
>> are playing a game of soccer in the sand.
```
diff --git a/examples/nlp/multiple_choice_task_with_transfer_learning.py b/examples/nlp/multiple_choice_task_with_transfer_learning.py
index dc3f254867..c83fafc375 100644
--- a/examples/nlp/multiple_choice_task_with_transfer_learning.py
+++ b/examples/nlp/multiple_choice_task_with_transfer_learning.py
@@ -2,7 +2,7 @@
Title: MultipleChoice Task with Transfer Learning
Author: Md Awsafur Rahman
Date created: 2023/09/14
-Last modified: 2023/09/14
+Last modified: 2024/01/10
Description: Use pre-trained nlp models for multiplechoice task.
Accelerator: GPU
"""
@@ -20,12 +20,10 @@
## Setup
"""
-"""shell
-"""
-import keras_nlp
import keras
-import tensorflow as tf # For tf.data only.
+import keras_nlp
+import tensorflow as tf
import numpy as np
import pandas as pd
@@ -542,9 +540,9 @@ def build_model():
question = row.startphrase
pred_answer = f"ending{pred_answers[i]}"
true_answer = f"ending{true_answers[i]}"
- print(f"❓ Sentence {i+1}:\n{question}\n")
- print(f"✅ True Ending: {true_answer}\n >> {row[true_answer]}\n")
- print(f"🤖 Predicted Ending: {pred_answer}\n >> {row[pred_answer]}\n")
+ print(f"❓ Sentence {i+1}:\n{question}\n")
+ print(f"✅ True Ending: {true_answer}\n >> {row[true_answer]}\n")
+ print(f"🤖 Predicted Ending: {pred_answer}\n >> {row[pred_answer]}\n")
print("-" * 90, "\n")
"""