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fchollet committed Aug 31, 2020
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2 changes: 1 addition & 1 deletion examples/keras_recipes/md/tfrecord.md
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**Last modified:** 2020/08/07<br>


<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/keras_recipesipynb/tfrecord.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/keras_recipestfrecord.py)
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/keras_recipes/ipynb/tfrecord.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/keras_recipes/tfrecord.py)


**Description:** Loading TFRecords for computer vision models.
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2 changes: 1 addition & 1 deletion examples/nlp/ipynb/pretrained_word_embeddings.ipynb
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"outputs": [],
"source": [
"voc = vectorizer.get_vocabulary()\n",
"word_index = dict(zip(voc, range(2, len(voc))))"
"word_index = dict(zip(voc, range(len(voc))))"
]
},
{
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50 changes: 26 additions & 24 deletions examples/nlp/md/pretrained_word_embeddings.md
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**Description:** Text classification on the Newsgroup20 dataset using pre-trained GloVe word embeddings.


<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlpipynb/pretrained_word_embeddings.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlppretrained_word_embeddings.py)
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/pretrained_word_embeddings.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlp/pretrained_word_embeddings.py)



Expand Down Expand Up @@ -257,7 +257,7 @@ Here's a dict mapping words to their indices:

```python
voc = vectorizer.get_vocabulary()
word_index = dict(zip(voc, range(2, len(voc))))
word_index = dict(zip(voc, range(len(voc))))
```

As you can see, we obtain the same encoding as above for our test sentence:
Expand All @@ -273,7 +273,7 @@ test = ["the", "cat", "sat", "on", "the", "mat"]

<div class="k-default-codeblock">
```
[4, 3699, 1688, 17, 4, 5945]
[2, 3697, 1686, 15, 2, 5943]
```
</div>
Expand Down Expand Up @@ -344,7 +344,7 @@ print("Converted %d words (%d misses)" % (hits, misses))

<div class="k-default-codeblock">
```
Converted 17997 words (2001 misses)
Converted 17999 words (2001 misses)
```
</div>
Expand Down Expand Up @@ -453,45 +453,47 @@ model.fit(x_train, y_train, batch_size=128, epochs=20, validation_data=(x_val, y
<div class="k-default-codeblock">
```
Epoch 1/20
125/125 [==============================] - 8s 59ms/step - loss: 3.0124 - acc: 0.0669 - val_loss: 2.9522 - val_acc: 0.0725
125/125 [==============================] - 8s 57ms/step - loss: 2.8766 - acc: 0.0945 - val_loss: 2.0770 - val_acc: 0.2956
Epoch 2/20
125/125 [==============================] - 7s 58ms/step - loss: 2.9439 - acc: 0.0888 - val_loss: 2.9447 - val_acc: 0.0818
125/125 [==============================] - 7s 58ms/step - loss: 2.0792 - acc: 0.2887 - val_loss: 1.6626 - val_acc: 0.4076
Epoch 3/20
125/125 [==============================] - 8s 60ms/step - loss: 2.8670 - acc: 0.1072 - val_loss: 2.8869 - val_acc: 0.1015
125/125 [==============================] - 7s 60ms/step - loss: 1.5632 - acc: 0.4527 - val_loss: 1.3000 - val_acc: 0.5609
Epoch 4/20
125/125 [==============================] - 8s 61ms/step - loss: 2.7579 - acc: 0.1387 - val_loss: 2.7014 - val_acc: 0.1370
125/125 [==============================] - 8s 60ms/step - loss: 1.2945 - acc: 0.5612 - val_loss: 1.2282 - val_acc: 0.5944
Epoch 5/20
125/125 [==============================] - 8s 61ms/step - loss: 2.6353 - acc: 0.1703 - val_loss: 2.5607 - val_acc: 0.1870
125/125 [==============================] - 8s 61ms/step - loss: 1.1137 - acc: 0.6209 - val_loss: 1.0695 - val_acc: 0.6409
Epoch 6/20
125/125 [==============================] - 8s 63ms/step - loss: 2.4557 - acc: 0.2215 - val_loss: 2.4721 - val_acc: 0.2081
125/125 [==============================] - 8s 61ms/step - loss: 0.9556 - acc: 0.6718 - val_loss: 1.1743 - val_acc: 0.6124
Epoch 7/20
125/125 [==============================] - 8s 64ms/step - loss: 2.3065 - acc: 0.2597 - val_loss: 2.3387 - val_acc: 0.2478
125/125 [==============================] - 8s 61ms/step - loss: 0.8235 - acc: 0.7172 - val_loss: 1.0126 - val_acc: 0.6602
Epoch 8/20
125/125 [==============================] - 8s 65ms/step - loss: 2.1402 - acc: 0.3081 - val_loss: 2.5133 - val_acc: 0.2226
125/125 [==============================] - 8s 65ms/step - loss: 0.7268 - acc: 0.7475 - val_loss: 1.0608 - val_acc: 0.6632
Epoch 9/20
125/125 [==============================] - 8s 64ms/step - loss: 1.9627 - acc: 0.3493 - val_loss: 2.2369 - val_acc: 0.2806
125/125 [==============================] - 8s 63ms/step - loss: 0.6441 - acc: 0.7759 - val_loss: 1.0606 - val_acc: 0.6664
Epoch 10/20
125/125 [==============================] - 8s 66ms/step - loss: 1.8266 - acc: 0.3889 - val_loss: 2.2180 - val_acc: 0.3103
125/125 [==============================] - 8s 63ms/step - loss: 0.5409 - acc: 0.8120 - val_loss: 1.0380 - val_acc: 0.6884
Epoch 11/20
125/125 [==============================] - 8s 67ms/step - loss: 1.6598 - acc: 0.4411 - val_loss: 2.2910 - val_acc: 0.2988
125/125 [==============================] - 8s 65ms/step - loss: 0.4846 - acc: 0.8273 - val_loss: 1.1073 - val_acc: 0.6729
Epoch 12/20
125/125 [==============================] - 8s 68ms/step - loss: 1.5119 - acc: 0.4961 - val_loss: 2.3962 - val_acc: 0.3083
125/125 [==============================] - 8s 62ms/step - loss: 0.4173 - acc: 0.8553 - val_loss: 1.1256 - val_acc: 0.6864
Epoch 13/20
125/125 [==============================] - 8s 68ms/step - loss: 1.3293 - acc: 0.5510 - val_loss: 2.6619 - val_acc: 0.2941
125/125 [==============================] - 8s 63ms/step - loss: 0.3419 - acc: 0.8808 - val_loss: 1.1576 - val_acc: 0.6979
Epoch 14/20
125/125 [==============================] - 9s 70ms/step - loss: 1.2461 - acc: 0.5798 - val_loss: 2.5586 - val_acc: 0.2968
125/125 [==============================] - 8s 68ms/step - loss: 0.2869 - acc: 0.9053 - val_loss: 1.1381 - val_acc: 0.6974
Epoch 15/20
125/125 [==============================] - 10s 76ms/step - loss: 1.0760 - acc: 0.6296 - val_loss: 2.4376 - val_acc: 0.3443
125/125 [==============================] - 8s 67ms/step - loss: 0.2617 - acc: 0.9118 - val_loss: 1.3850 - val_acc: 0.6747
Epoch 16/20
125/125 [==============================] - 10s 78ms/step - loss: 0.9680 - acc: 0.6772 - val_loss: 2.9347 - val_acc: 0.3233
125/125 [==============================] - 8s 67ms/step - loss: 0.2543 - acc: 0.9152 - val_loss: 1.3119 - val_acc: 0.6972
Epoch 17/20
125/125 [==============================] - 10s 79ms/step - loss: 0.8464 - acc: 0.7164 - val_loss: 2.4003 - val_acc: 0.3903
125/125 [==============================] - 8s 66ms/step - loss: 0.2109 - acc: 0.9267 - val_loss: 1.3145 - val_acc: 0.6954
Epoch 18/20
125/125 [==============================] - 9s 74ms/step - loss: 0.7512 - acc: 0.7500 - val_loss: 2.8509 - val_acc: 0.3223
125/125 [==============================] - 8s 64ms/step - loss: 0.1939 - acc: 0.9364 - val_loss: 1.4054 - val_acc: 0.7009
Epoch 19/20
125/125 [==============================] - 8s 68ms/step - loss: 0.6477 - acc: 0.7852 - val_loss: 3.6992 - val_acc: 0.3081
125/125 [==============================] - 8s 67ms/step - loss: 0.1873 - acc: 0.9379 - val_loss: 1.7441 - val_acc: 0.6667
Epoch 20/20
71/125 [================>.............] - ETA: 3s - loss: 0.5732 - acc: 0.8188
125/125 [==============================] - 9s 70ms/step - loss: 0.1762 - acc: 0.9420 - val_loss: 1.5269 - val_acc: 0.6927
<tensorflow.python.keras.callbacks.History at 0x157134890>
```
</div>
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20 changes: 10 additions & 10 deletions examples/nlp/md/text_classification_from_scratch.md
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**Description:** Text sentiment classification starting from raw text files.


<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlpipynb/text_classification_from_scratch.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlptext_classification_from_scratch.py)
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/nlp/ipynb/text_classification_from_scratch.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/nlp/text_classification_from_scratch.py)



Expand Down Expand Up @@ -42,7 +42,7 @@ Let's download the data and inspect its structure.
```
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 80.2M 100 80.2M 0 0 51.5M 0 0:00:01 0:00:01 --:--:-- 51.5M
100 80.2M 100 80.2M 0 0 45.3M 0 0:00:01 0:00:01 --:--:-- 45.3M
```
</div>
Expand Down Expand Up @@ -336,13 +336,13 @@ model.fit(train_ds, validation_data=val_ds, epochs=epochs)
<div class="k-default-codeblock">
```
Epoch 1/3
625/625 [==============================] - 33s 52ms/step - loss: 0.6070 - accuracy: 0.6100 - val_loss: 0.3287 - val_accuracy: 0.8582
625/625 [==============================] - 32s 51ms/step - loss: 0.6288 - accuracy: 0.5835 - val_loss: 0.3283 - val_accuracy: 0.8610
Epoch 2/3
625/625 [==============================] - 34s 54ms/step - loss: 0.2665 - accuracy: 0.8930 - val_loss: 0.3320 - val_accuracy: 0.8676
625/625 [==============================] - 31s 50ms/step - loss: 0.2808 - accuracy: 0.8859 - val_loss: 0.3005 - val_accuracy: 0.8796
Epoch 3/3
625/625 [==============================] - 34s 55ms/step - loss: 0.1360 - accuracy: 0.9505 - val_loss: 0.3847 - val_accuracy: 0.8672
625/625 [==============================] - 31s 50ms/step - loss: 0.1450 - accuracy: 0.9467 - val_loss: 0.3795 - val_accuracy: 0.8726
<tensorflow.python.keras.callbacks.History at 0x14bd13c50>
<tensorflow.python.keras.callbacks.History at 0x137444c90>
```
</div>
Expand All @@ -356,9 +356,9 @@ model.evaluate(test_ds)

<div class="k-default-codeblock">
```
782/782 [==============================] - 8s 10ms/step - loss: 0.3904 - accuracy: 0.8614
782/782 [==============================] - 7s 9ms/step - loss: 0.3999 - accuracy: 0.8650
[0.3903675377368927, 0.8613600134849548]
[0.39986345171928406, 0.8649600148200989]
```
</div>
Expand Down Expand Up @@ -389,9 +389,9 @@ end_to_end_model.evaluate(raw_test_ds)

<div class="k-default-codeblock">
```
782/782 [==============================] - 13s 16ms/step - loss: 0.3903 - accuracy: 0.8608
782/782 [==============================] - 11s 13ms/step - loss: 0.4062 - accuracy: 0.8630
[0.39036768674850464, 0.8613600134849548]
[0.3998638987541199, 0.8649600148200989]
```
</div>
2 changes: 1 addition & 1 deletion examples/nlp/pretrained_word_embeddings.py
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Expand Up @@ -151,7 +151,7 @@
"""

voc = vectorizer.get_vocabulary()
word_index = dict(zip(voc, range(2, len(voc))))
word_index = dict(zip(voc, range(len(voc))))

"""
As you can see, we obtain the same encoding as above for our test sentence:
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2 changes: 1 addition & 1 deletion examples/vision/md/super_resolution_sub_pixel.md
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**Description:** Implementing Super-Resolution using Efficient sub-pixel model on BSDS500.


<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/visionipynb/super_resolution_sub_pixel.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/visionsuper_resolution_sub_pixel.py)
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/examples/vision/ipynb/super_resolution_sub_pixel.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/examples/vision/super_resolution_sub_pixel.py)



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31 changes: 16 additions & 15 deletions guides/md/distributed_training.md
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Expand Up @@ -6,7 +6,7 @@
**Description:** Guide to multi-GPU & distributed training for Keras models.


<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guidesipynb/distributed_training.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guidesdistributed_training.py)
<img class="k-inline-icon" src="https://colab.research.google.com/img/colab_favicon.ico"/> [**View in Colab**](https://colab.research.google.com/github/keras-team/keras-io/blob/master/guides/ipynb/distributed_training.ipynb) <span class="k-dot">•</span><img class="k-inline-icon" src="https://github.com/favicon.ico"/> [**GitHub source**](https://github.com/keras-team/keras-io/blob/master/guides/distributed_training.py)



Expand Down Expand Up @@ -181,21 +181,16 @@ model.evaluate(test_dataset)
<div class="k-default-codeblock">
```
WARNING: Logging before flag parsing goes to stderr.
W0814 09:55:55.262259 4750663104 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
W0829 16:54:57.025418 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
Number of devices: 1
W0814 09:55:55.961071 4750663104 deprecation.py:323] From /usr/local/lib/python3.7/site-packages/tensorflow/python/data/ops/multi_device_iterator_ops.py:601: get_next_as_optional (from tensorflow.python.data.ops.iterator_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.Iterator.get_next_as_optional()` instead.
Epoch 1/2
1563/1563 [==============================] - 3s 2ms/step - loss: 0.2290 - sparse_categorical_accuracy: 0.9323 - val_loss: 0.1623 - val_sparse_categorical_accuracy: 0.9498
1563/1563 [==============================] - 3s 2ms/step - loss: 0.3767 - sparse_categorical_accuracy: 0.8889 - val_loss: 0.1257 - val_sparse_categorical_accuracy: 0.9623
Epoch 2/2
1563/1563 [==============================] - 2s 1ms/step - loss: 0.0946 - sparse_categorical_accuracy: 0.9709 - val_loss: 0.1025 - val_sparse_categorical_accuracy: 0.9683
313/313 [==============================] - 0s 802us/step - loss: 0.1029 - sparse_categorical_accuracy: 0.9677
1563/1563 [==============================] - 2s 2ms/step - loss: 0.1053 - sparse_categorical_accuracy: 0.9678 - val_loss: 0.0944 - val_sparse_categorical_accuracy: 0.9710
313/313 [==============================] - 0s 779us/step - loss: 0.0900 - sparse_categorical_accuracy: 0.9723
[0.1029156744480133, 0.9677000045776367]
[0.08995261788368225, 0.9722999930381775]
```
</div>
Expand Down Expand Up @@ -266,15 +261,21 @@ run_training(epochs=1)

<div class="k-default-codeblock">
```
W0814 09:56:01.662528 4750663104 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
W0829 16:55:03.609519 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
Creating a new model
1563/1563 - 3s - loss: 0.2276 - sparse_categorical_accuracy: 0.9320 - val_loss: 0.1319 - val_sparse_categorical_accuracy: 0.9579
W0814 09:56:04.756444 4750663104 cross_device_ops.py:1202] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
W0829 16:55:03.708506 4592479680 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.
1563/1563 - 4s - loss: 0.2242 - sparse_categorical_accuracy: 0.9321 - val_loss: 0.1243 - val_sparse_categorical_accuracy: 0.9647
W0829 16:55:07.981292 4592479680 cross_device_ops.py:1115] There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.
Restoring from ./ckpt/ckpt-1
1563/1563 - 3s - loss: 0.0976 - sparse_categorical_accuracy: 0.9698 - val_loss: 0.1005 - val_sparse_categorical_accuracy: 0.9690
W0829 16:55:08.245935 4592479680 callbacks.py:1270] Automatic model reloading for interrupted job was removed from the `ModelCheckpoint` callback in multi-worker mode, please use the `keras.callbacks.experimental.BackupAndRestore` callback instead. See this tutorial for details: https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#backupandrestore_callback.
1563/1563 - 4s - loss: 0.0948 - sparse_categorical_accuracy: 0.9709 - val_loss: 0.1006 - val_sparse_categorical_accuracy: 0.9699
```
</div>
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