reknit examples

Author: t-kalinowski

vignettes/examples/index.Rmd

@@ -1,7 +1,7 @@
 ---
 title: Keras examples
 output: rmarkdown::html_vignette
-date: 'Last Modified: 2023-11-30; Last Rendered: 2024-07-16'
+date: 'Last Modified: 2023-11-30; Last Rendered: 2024-09-04'
 vignette: >
   %\VignetteIndexEntry{Keras examples}
   %\VignetteEngine{knitr::rmarkdown}

vignettes/examples/nlp/neural_machine_translation_with_transformer.Rmd

@@ -583,7 +583,7 @@ transformer |> fit(train_ds, epochs = epochs,
 ```
 
 ```
-## 1297/1297 - 43s - 33ms/step - accuracy: 0.7229 - loss: 1.9745 - val_accuracy: 0.7338 - val_loss: 1.7418
+## 1297/1297 - 48s - 37ms/step - accuracy: 0.7463 - loss: 1.8009 - val_accuracy: 0.7657 - val_loss: 1.5766
 ```
 
 

vignettes/examples/nlp/text_classification_from_scratch.Rmd

@@ -402,11 +402,11 @@ model |> fit(train_ds, validation_data = val_ds, epochs = epochs)
 
 ```
 ## Epoch 1/3
-## 625/625 - 5s - 8ms/step - accuracy: 0.6903 - loss: 0.5292 - val_accuracy: 0.8612 - val_loss: 0.3235
+## 625/625 - 6s - 10ms/step - accuracy: 0.6953 - loss: 0.5231 - val_accuracy: 0.8618 - val_loss: 0.3205
 ## Epoch 2/3
-## 625/625 - 1s - 2ms/step - accuracy: 0.9054 - loss: 0.2398 - val_accuracy: 0.8698 - val_loss: 0.3342
+## 625/625 - 1s - 2ms/step - accuracy: 0.9032 - loss: 0.2390 - val_accuracy: 0.8742 - val_loss: 0.3113
 ## Epoch 3/3
-## 625/625 - 1s - 2ms/step - accuracy: 0.9553 - loss: 0.1253 - val_accuracy: 0.8744 - val_loss: 0.3402
+## 625/625 - 2s - 2ms/step - accuracy: 0.9553 - loss: 0.1211 - val_accuracy: 0.8666 - val_loss: 0.3531
 ```
 
 ## Evaluate the model on the test set
@@ -417,15 +417,15 @@ model |> evaluate(test_ds)
 ```
 
 ```
-## 782/782 - 1s - 2ms/step - accuracy: 0.8630 - loss: 0.3716
+## 782/782 - 1s - 2ms/step - accuracy: 0.8520 - loss: 0.3960
 ```
 
 ```
 ## $accuracy
-## [1] 0.86296
+## [1] 0.85204
 ##
 ## $loss
-## [1] 0.3716201
+## [1] 0.3959631
 ```
 
 ## Make an end-to-end model
@@ -455,12 +455,12 @@ end_to_end_model |> evaluate(raw_test_ds)
 ```
 
 ```
-## 782/782 - 3s - 4ms/step - accuracy: 0.8630 - loss: 0.0000e+00
+## 782/782 - 3s - 4ms/step - accuracy: 0.8520 - loss: 0.0000e+00
 ```
 
 ```
 ## $accuracy
-## [1] 0.86296
+## [1] 0.85204
 ##
 ## $loss
 ## [1] 0

vignettes/examples/structured_data/imbalanced_classification.Rmd

@@ -201,19 +201,19 @@ cat("Target: "); str(y)
 
 ```
 ## Input: List of 13
-##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=63>
-##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=4>
-##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=124>
-##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=197>
+##  $ age     :<tf.Tensor: shape=(), dtype=int32, numpy=45>
+##  $ sex     :<tf.Tensor: shape=(), dtype=int32, numpy=1>
+##  $ cp      :<tf.Tensor: shape=(), dtype=int32, numpy=1>
+##  $ trestbps:<tf.Tensor: shape=(), dtype=int32, numpy=110>
+##  $ chol    :<tf.Tensor: shape=(), dtype=int32, numpy=264>
 ##  $ fbs     :<tf.Tensor: shape=(), dtype=int32, numpy=0>
 ##  $ restecg :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=136>
-##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=1>
-##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=0.0>
+##  $ thalach :<tf.Tensor: shape=(), dtype=int32, numpy=132>
+##  $ exang   :<tf.Tensor: shape=(), dtype=int32, numpy=0>
+##  $ oldpeak :<tf.Tensor: shape=(), dtype=float32, numpy=1.2>
 ##  $ slope   :<tf.Tensor: shape=(), dtype=int32, numpy=2>
 ##  $ ca      :<tf.Tensor: shape=(), dtype=int32, numpy=0>
-##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'normal'>
+##  $ thal    :<tf.Tensor: shape=(), dtype=string, numpy=b'reversible'>
 ## Target: <tf.Tensor: shape=(), dtype=int32, numpy=0>
 ```
 
@@ -371,7 +371,7 @@ preprocessed_x
 
 ```
 ## tf.Tensor(
-## [[0. 0. 0. ... 0. 1. 0.]
+## [[0. 0. 0. ... 0. 0. 0.]
 ##  [0. 0. 0. ... 0. 0. 0.]
 ##  [0. 0. 0. ... 0. 0. 0.]
 ##  ...
@@ -463,45 +463,45 @@ training_model |> fit(
 
 ```
 ## Epoch 1/20
-## 8/8 - 2s - 270ms/step - accuracy: 0.4357 - loss: 0.7353 - val_accuracy: 0.5000 - val_loss: 0.7068
+## 8/8 - 2s - 280ms/step - accuracy: 0.4689 - loss: 0.7471 - val_accuracy: 0.5167 - val_loss: 0.7019
 ## Epoch 2/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.5851 - loss: 0.6719 - val_accuracy: 0.6000 - val_loss: 0.6625
+## 8/8 - 1s - 140ms/step - accuracy: 0.5602 - loss: 0.6785 - val_accuracy: 0.6333 - val_loss: 0.6491
 ## Epoch 3/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.6100 - loss: 0.6419 - val_accuracy: 0.7000 - val_loss: 0.6241
+## 8/8 - 0s - 46ms/step - accuracy: 0.6307 - loss: 0.6478 - val_accuracy: 0.7000 - val_loss: 0.6053
 ## Epoch 4/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.6639 - loss: 0.5998 - val_accuracy: 0.7000 - val_loss: 0.5919
+## 8/8 - 0s - 12ms/step - accuracy: 0.6432 - loss: 0.6246 - val_accuracy: 0.7667 - val_loss: 0.5692
 ## Epoch 5/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.7593 - loss: 0.5628 - val_accuracy: 0.7000 - val_loss: 0.5648
+## 8/8 - 0s - 13ms/step - accuracy: 0.7178 - loss: 0.5813 - val_accuracy: 0.7667 - val_loss: 0.5359
 ## Epoch 6/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.7635 - loss: 0.5405 - val_accuracy: 0.7000 - val_loss: 0.5414
+## 8/8 - 0s - 13ms/step - accuracy: 0.7344 - loss: 0.5371 - val_accuracy: 0.7833 - val_loss: 0.5067
 ## Epoch 7/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.7759 - loss: 0.4975 - val_accuracy: 0.7167 - val_loss: 0.5204
+## 8/8 - 0s - 13ms/step - accuracy: 0.7884 - loss: 0.5158 - val_accuracy: 0.8333 - val_loss: 0.4810
 ## Epoch 8/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.7842 - loss: 0.4926 - val_accuracy: 0.7167 - val_loss: 0.5008
+## 8/8 - 0s - 13ms/step - accuracy: 0.7759 - loss: 0.5011 - val_accuracy: 0.8500 - val_loss: 0.4569
 ## Epoch 9/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8133 - loss: 0.4600 - val_accuracy: 0.7167 - val_loss: 0.4849
+## 8/8 - 0s - 13ms/step - accuracy: 0.7676 - loss: 0.4865 - val_accuracy: 0.8500 - val_loss: 0.4354
 ## Epoch 10/20
-## 8/8 - 0s - 14ms/step - accuracy: 0.8008 - loss: 0.4498 - val_accuracy: 0.7167 - val_loss: 0.4730
+## 8/8 - 0s - 13ms/step - accuracy: 0.7925 - loss: 0.4601 - val_accuracy: 0.8333 - val_loss: 0.4161
 ## Epoch 11/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8299 - loss: 0.4408 - val_accuracy: 0.7333 - val_loss: 0.4608
+## 8/8 - 0s - 13ms/step - accuracy: 0.7967 - loss: 0.4617 - val_accuracy: 0.8667 - val_loss: 0.3976
 ## Epoch 12/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8008 - loss: 0.4297 - val_accuracy: 0.7667 - val_loss: 0.4508
+## 8/8 - 0s - 13ms/step - accuracy: 0.7967 - loss: 0.4316 - val_accuracy: 0.8667 - val_loss: 0.3796
 ## Epoch 13/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8299 - loss: 0.3921 - val_accuracy: 0.7833 - val_loss: 0.4404
+## 8/8 - 0s - 13ms/step - accuracy: 0.8506 - loss: 0.4058 - val_accuracy: 0.8833 - val_loss: 0.3643
 ## Epoch 14/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8506 - loss: 0.3890 - val_accuracy: 0.8000 - val_loss: 0.4324
+## 8/8 - 0s - 13ms/step - accuracy: 0.8174 - loss: 0.4197 - val_accuracy: 0.8833 - val_loss: 0.3510
 ## Epoch 15/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8382 - loss: 0.3783 - val_accuracy: 0.8333 - val_loss: 0.4243
+## 8/8 - 0s - 14ms/step - accuracy: 0.8299 - loss: 0.3888 - val_accuracy: 0.8833 - val_loss: 0.3405
 ## Epoch 16/20
-## 8/8 - 0s - 14ms/step - accuracy: 0.8465 - loss: 0.3651 - val_accuracy: 0.8333 - val_loss: 0.4160
+## 8/8 - 0s - 13ms/step - accuracy: 0.8257 - loss: 0.3820 - val_accuracy: 0.8833 - val_loss: 0.3294
 ## Epoch 17/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8340 - loss: 0.3545 - val_accuracy: 0.8333 - val_loss: 0.4076
+## 8/8 - 0s - 13ms/step - accuracy: 0.8299 - loss: 0.3746 - val_accuracy: 0.8833 - val_loss: 0.3223
 ## Epoch 18/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8589 - loss: 0.3493 - val_accuracy: 0.8500 - val_loss: 0.4017
+## 8/8 - 0s - 13ms/step - accuracy: 0.8506 - loss: 0.3487 - val_accuracy: 0.8833 - val_loss: 0.3153
 ## Epoch 19/20
-## 8/8 - 0s - 14ms/step - accuracy: 0.8506 - loss: 0.3227 - val_accuracy: 0.8500 - val_loss: 0.3967
+## 8/8 - 0s - 14ms/step - accuracy: 0.8465 - loss: 0.3558 - val_accuracy: 0.8667 - val_loss: 0.3093
 ## Epoch 20/20
-## 8/8 - 0s - 13ms/step - accuracy: 0.8299 - loss: 0.3377 - val_accuracy: 0.8500 - val_loss: 0.3936
+## 8/8 - 0s - 14ms/step - accuracy: 0.8672 - loss: 0.3570 - val_accuracy: 0.8667 - val_loss: 0.3036
 ```
 
 We quickly get to 80% validation accuracy.
@@ -534,7 +534,7 @@ predictions <- inference_model |> predict(input_dict)
 ```
 
 ```
-## 1/1 - 0s - 273ms/step
+## 1/1 - 0s - 394ms/step
 ```
 
 ``` r
@@ -545,6 +545,6 @@ glue::glue(r"---(
 ```
 
 ```
-## This particular patient had a 42% probability
+## This particular patient had a 44.8% probability
 ## of having a heart disease, as evaluated by our model.
 ```