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paulnovellopaulnovello
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docs: unified nitebooks and checked execution
1 parent e10f2f4 commit eb19601

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+6348
-5658
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‎docs/notebooks/tensorflow/demo_ash_tf.ipynb

+573-409
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‎docs/notebooks/tensorflow/demo_dknn_tf.ipynb

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‎docs/notebooks/tensorflow/demo_energy_tf.ipynb

+549-505
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‎docs/notebooks/tensorflow/demo_entropy_tf.ipynb

+549-502
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‎docs/notebooks/tensorflow/demo_gen_tf.ipynb

+557-520
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‎docs/notebooks/tensorflow/demo_gram_tf.ipynb

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‎docs/notebooks/tensorflow/demo_mahalanobis_tf.ipynb

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‎docs/notebooks/tensorflow/demo_mls_msp_tf.ipynb

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‎docs/notebooks/tensorflow/demo_odin_tf.ipynb

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‎docs/notebooks/tensorflow/demo_react_tf.ipynb

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‎docs/notebooks/tensorflow/demo_rmds_tf.ipynb

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@@ -28,13 +28,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"\n",
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"import os\n",
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"\n",
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"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
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"\n",
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"from IPython.display import clear_output\n",
@@ -101,7 +102,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -121,9 +122,6 @@
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"ds_train, _ = data_handler.split_by_class(ds_train, in_labels)\n",
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"ds_in, ds_out = data_handler.split_by_class(data_test, in_labels)\n",
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"\n",
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"feature_names = data_handler.get_ds_feature_keys(ds_in)\n",
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"print(feature_names) # we have to remove \"id\" from the dataset\n",
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"\n",
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"\n",
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"# 3- Prepare data (preprocess, shuffle, batch)\n",
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"def preprocess_fn(inputs):\n",
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -324,9 +322,6 @@
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"# 1b- Load out-of-distribution dataset: SVHN\n",
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"ds_out = data_handler.load_dataset(\"svhn_cropped\", load_kwargs={\"split\": \"test\"})\n",
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"\n",
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"feature_names = data_handler.get_ds_feature_keys(ds_in)\n",
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"print(feature_names) # we have to remove \"id\" from the dataset\n",
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"\n",
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"\n",
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"# 2- prepare data (preprocess, shuffle, batch)\n",
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"def preprocess_fn(inputs):\n",
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")\n",
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"ds_out = data_handler.prepare(\n",
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" ds_out, batch_size, preprocess_fn, columns=[\"image\", \"label\"]\n",
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")"
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")\n",
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"\n",
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"clear_output()"
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]
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},
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{

‎docs/notebooks/tensorflow/demo_scale_tf.ipynb

+12-49
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‎docs/notebooks/tensorflow/demo_she_tf.ipynb

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‎docs/notebooks/tensorflow/demo_vim_tf.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import os\n",
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"\n",
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"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
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"\n",
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"from IPython.display import clear_output\n",
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
@@ -127,9 +128,6 @@
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"ds_train, _ = data_handler.split_by_class(ds_train, in_labels)\n",
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"ds_in, ds_out = data_handler.split_by_class(data_test, in_labels)\n",
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"\n",
130-
"feature_names = data_handler.get_ds_feature_keys(ds_in)\n",
131-
"print(feature_names) # we have to remove \"id\" from the dataset\n",
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"\n",
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"\n",
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"# 3- Prepare data (preprocess, shuffle, batch)\n",
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"def preprocess_fn(inputs):\n",
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"# 1b- Load out-of-distribution dataset: SVHN\n",
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"ds_out = data_handler.load_dataset(\"svhn_cropped\", load_kwargs={\"split\": \"test\"})\n",
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"\n",
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"feature_names = data_handler.get_ds_feature_keys(ds_in)\n",
335-
"print(feature_names) # we have to remove \"id\" from the dataset\n",
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"\n",
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"\n",
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"# 2- prepare data (preprocess, shuffle, batch)\n",
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"def preprocess_fn(inputs):\n",
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")\n",
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"ds_out = data_handler.prepare(\n",
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" ds_out, batch_size, preprocess_fn, columns=[\"image\", \"label\"]\n",
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")"
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")\n",
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"\n",
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"clear_output()"
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]
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},
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{

‎docs/notebooks/torch/demo_dknn_torch.ipynb

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‎docs/notebooks/torch/demo_energy_torch.ipynb

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‎docs/notebooks/torch/demo_gen_torch.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n",
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"import os\n",
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"\n",
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"os.environ[\"TF_CPP_MIN_LOG_LEVEL\"] = \"2\"\n",
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"\n",
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"from IPython.display import clear_output\n",
@@ -119,7 +121,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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")\n",
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"ds_out = data_handler.prepare(\n",
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" ds_out, batch_size, preprocess_fn, columns=[\"input\", \"label\"]\n",
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")"
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")\n",
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"\n",
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"clear_output()"
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]
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},
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{

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