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Fixes for AMX bfloat16 transformer and numba vs numpy samples #1335

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Feb 6, 2023
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Fixes for AMX bfloat16 transformer and numba vs numpy samples #1335

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4 changes: 2 additions & 2 deletions ...es-and-Functionality/IntelPython_Numpy_Numba_dpex_kNN/IntelPython_Numpy_Numba_dpex_kNN.py
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Expand Up @@ -217,7 +217,7 @@ def knn_numba(X_train, y_train, X_test, k):
predictions = knn_numba(X_train.values, y_train.values, X_test.values, 3)
true_values = y_test.to_numpy()
accuracy = np.mean(predictions == true_values)
print('Numba accuracy:' + accuracy)
print('Numba accuracy:', accuracy)


# ## Numba_dpex k-NN
Expand Down Expand Up @@ -335,7 +335,7 @@ def knn_numba_dpex(train, train_labels, test, k, predictions, votes_to_classes_l

true_values = y_test.to_numpy()
accuracy = np.mean(predictions_numba == true_values)
print('Numba_dpex accuracy:' + accuracy)
print('Numba_dpex accuracy:', accuracy)


# In[ ]:
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2 changes: 1 addition & 1 deletion ...AMX_bfloat16_MixedPrecision/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision.ipynb
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Expand Up @@ -20,7 +20,7 @@
"id": "1bdd8c5b-209e-4a7d-9d98-2b552c987039",
"metadata": {},
"source": [
"# Tensorflow Transformer with AMX bfoat16 Mixed Precision Learning\n",
"# Tensorflow Transformer with AMX bfloat16 Mixed Precision Learning\n",
"\n",
"In this example we will be learning Transformer block for text classification using **IMBD dataset**. And then we will modify the code to use mixed precision learning with **bfloat16**. The example based on the [Text classification with Transformer Keras code example](https://keras.io/examples/nlp/text_classification_with_transformer/).\n",
"\n",
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2 changes: 1 addition & 1 deletion ...Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/README.md
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@@ -1,4 +1,4 @@
# `TensorFlow (TF) Transformer with Intel® Advanced Matrix Extensions (Intel® AMX) bfoat16 Mixed Precision Learning`
# `TensorFlow (TF) Transformer with Intel® Advanced Matrix Extensions (Intel® AMX) bfloat16 Mixed Precision Learning`

This sample code demonstrates optimizing a TensorFlow model with Intel® Advanced Matrix Extensions (Intel® AMX) using bfloat16 (Brain Floating Point) on 4th Gen Intel® Xeon® Scalable Processors (Sapphire Rapids).

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2 changes: 1 addition & 1 deletion ...res-and-Functionality/IntelTensorFlow_Transformer_AMX_bfloat16_MixedPrecision/sample.json
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@@ -1,6 +1,6 @@
{
"guid": "60A68888-6099-414E-999B-EDC7310A01EA",
"name": "TensorFlow Transformer with Advanced Matrix Extensions bfoat16 Mixed Precision Learning",
"name": "TensorFlow Transformer with Advanced Matrix Extensions bfloat16 Mixed Precision Learning",
"categories": ["Toolkit/oneAPI AI And Analytics/AI Getting Started Samples"],
"description": "This sample code demonstrates optimizing a TensorFlow model with Intel® Advanced Matrix Extensions (Intel® AMX) using bfloat16 (Brain Floating Point) on Sapphire Rapids",
"builder": ["cli"],
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