diff --git a/README.ipynb b/README.ipynb
index 4ca6affcd6b1cea0c8f5c5d18811aa104a3ffb88..add4bf8656a75d43b45eed3a7e12e5788f3b0759 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "26e42746",
+ "id": "d32ade6b",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-10-12T14:41:38.853343Z",
- "iopub.status.busy": "2022-10-12T14:41:38.852566Z",
- "iopub.status.idle": "2022-10-12T14:41:38.864329Z",
- "shell.execute_reply": "2022-10-12T14:41:38.863589Z"
+ "iopub.execute_input": "2022-10-12T22:58:07.403985Z",
+ "iopub.status.busy": "2022-10-12T22:58:07.403126Z",
+ "iopub.status.idle": "2022-10-12T22:58:07.414447Z",
+ "shell.execute_reply": "2022-10-12T22:58:07.413483Z"
},
"jupyter": {
"source_hidden": true
@@ -52,7 +52,7 @@
"For more information, you can contact us at : \n",
"[<img width=\"200px\" style=\"vertical-align:middle\" src=\"fidle/img/00-Mail_contact.svg\"></img>](#top)\n",
"\n",
- "Current Version : <!-- VERSION_BEGIN -->2.1b4<!-- VERSION_END -->\n",
+ "Current Version : <!-- VERSION_BEGIN -->2.1b5<!-- VERSION_END -->\n",
"\n",
"\n",
"## Course materials\n",
@@ -67,7 +67,7 @@
"## Jupyter notebooks\n",
"\n",
"<!-- TOC_BEGIN -->\n",
- "<!-- Automatically generated on : 12/10/22 16:41:37 -->\n",
+ "<!-- Automatically generated on : 13/10/22 00:58:06 -->\n",
"\n",
"### Linear and logistic regression\n",
"- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb) \n",
@@ -229,7 +229,7 @@
"from IPython.display import display,Markdown\n",
"display(Markdown(open('README.md', 'r').read()))\n",
"#\n",
- "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 12/10/22 16:41:37"
+ "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 13/10/22 00:58:06"
]
}
],
diff --git a/README.md b/README.md
index bb5a0c2f2e5223f23566bbac307a9c446aab3b6f..eb321978324b6d232bad94cde30ef7754d5b4b3d 100644
--- a/README.md
+++ b/README.md
@@ -31,7 +31,7 @@ For more information, see **https://fidle.cnrs.fr** :
For more information, you can contact us at :
[<img width="200px" style="vertical-align:middle" src="fidle/img/00-Mail_contact.svg"></img>](#top)
-Current Version : <!-- VERSION_BEGIN -->2.1b4<!-- VERSION_END -->
+Current Version : <!-- VERSION_BEGIN -->2.1b5<!-- VERSION_END -->
## Course materials
@@ -46,7 +46,7 @@ Have a look about **[How to get and install](https://fidle.cnrs.fr/installation)
## Jupyter notebooks
<!-- TOC_BEGIN -->
-<!-- Automatically generated on : 12/10/22 16:41:37 -->
+<!-- Automatically generated on : 13/10/22 00:58:06 -->
### Linear and logistic regression
- **[LINR1](LinearReg/01-Linear-Regression.ipynb)** - [Linear regression with direct resolution](LinearReg/01-Linear-Regression.ipynb)
diff --git a/VAE/02-VAE-with-MNIST.ipynb b/VAE/02-VAE-with-MNIST.ipynb
index 813d4082c76093e036efc2032389134ce9d5a257..242c937f9f9e1130aff7256249040bd548165db0 100644
--- a/VAE/02-VAE-with-MNIST.ipynb
+++ b/VAE/02-VAE-with-MNIST.ipynb
@@ -426,29 +426,35 @@
"metadata": {},
"outputs": [],
"source": [
- "grid_size = 18\n",
- "grid_scale = 1\n",
+ "if latent_dim>2:\n",
"\n",
- "# ---- Draw a ppf grid\n",
+ " print('Sorry, This part can only work if the latent space is of dimension 2')\n",
"\n",
- "grid=[]\n",
- "for y in scipy.stats.norm.ppf(np.linspace(0.99, 0.01, grid_size),scale=grid_scale):\n",
- " for x in scipy.stats.norm.ppf(np.linspace(0.01, 0.99, grid_size),scale=grid_scale):\n",
- " grid.append( (x,y) )\n",
- "grid=np.array(grid)\n",
+ "else:\n",
+ " \n",
+ " grid_size = 18\n",
+ " grid_scale = 1\n",
"\n",
- "# ---- Draw latentspoints and grid\n",
+ " # ---- Draw a ppf grid\n",
"\n",
- "fig = plt.figure(figsize=(10, 8))\n",
- "plt.scatter(z[:, 0] , z[:, 1], c=y_show, cmap= 'tab10', alpha=0.5, s=20)\n",
- "plt.scatter(grid[:, 0] , grid[:, 1], c = 'black', s=60, linewidth=2, marker='+', alpha=1)\n",
- "fidle.scrawler.save_fig('08-Latent-grid')\n",
- "plt.show()\n",
+ " grid=[]\n",
+ " for y in scipy.stats.norm.ppf(np.linspace(0.99, 0.01, grid_size),scale=grid_scale):\n",
+ " for x in scipy.stats.norm.ppf(np.linspace(0.01, 0.99, grid_size),scale=grid_scale):\n",
+ " grid.append( (x,y) )\n",
+ " grid=np.array(grid)\n",
"\n",
- "# ---- Plot grid corresponding images\n",
+ " # ---- Draw latentspoints and grid\n",
"\n",
- "x_reconst = vae.decoder.predict([grid])\n",
- "fidle.scrawler.images(x_reconst, indices='all', columns=grid_size, x_size=0.5,y_size=0.5, y_padding=0,spines_alpha=0.1, save_as='09-Latent-morphing')\n",
+ " fig = plt.figure(figsize=(10, 8))\n",
+ " plt.scatter(z[:, 0] , z[:, 1], c=y_show, cmap= 'tab10', alpha=0.5, s=20)\n",
+ " plt.scatter(grid[:, 0] , grid[:, 1], c = 'black', s=60, linewidth=2, marker='+', alpha=1)\n",
+ " fidle.scrawler.save_fig('08-Latent-grid')\n",
+ " plt.show()\n",
+ "\n",
+ " # ---- Plot grid corresponding images\n",
+ "\n",
+ " x_reconst = vae.decoder.predict([grid])\n",
+ " fidle.scrawler.images(x_reconst, indices='all', columns=grid_size, x_size=0.5,y_size=0.5, y_padding=0,spines_alpha=0.1, save_as='09-Latent-morphing')\n",
"\n"
]
},
diff --git a/VAE/03-VAE-with-MNIST-post.ipynb b/VAE/03-VAE-with-MNIST-post.ipynb
index cca90efe40889c04ae0bc67ee939d84f54109c24..730d530035897ea33439a57b51749609d6c56ef5 100644
--- a/VAE/03-VAE-with-MNIST-post.ipynb
+++ b/VAE/03-VAE-with-MNIST-post.ipynb
@@ -222,23 +222,29 @@
"metadata": {},
"outputs": [],
"source": [
- "# ---- Softmax rescale\n",
- "#\n",
- "zs = np.exp(z)/np.sum(np.exp(z),axis=1,keepdims=True)\n",
- "# zc = zs * 1/np.max(zs)\n",
- "\n",
- "# ---- Create collection\n",
- "#\n",
- "c = Collection(zs, colors=y_show, labels=y_show)\n",
- "c.attrs.markers_colormap = {'colorscale':'Rainbow','cmin':0,'cmax':latent_dim}\n",
- "c.attrs.markers_size = 4\n",
- "c.attrs.markers_border_width = 0\n",
- "c.attrs.markers_opacity = 0.7\n",
- "\n",
- "s = Simplex.build(latent_dim)\n",
- "s.attrs.width = 1000\n",
- "s.attrs.height = 1000\n",
- "s.plot(c)"
+ "if latent_dim<4:\n",
+ "\n",
+ " print('Sorry, This part can only work if the latent space is greater than 3')\n",
+ "\n",
+ "else:\n",
+ "\n",
+ " # ---- Softmax rescale\n",
+ " #\n",
+ " zs = np.exp(z)/np.sum(np.exp(z),axis=1,keepdims=True)\n",
+ " # zc = zs * 1/np.max(zs)\n",
+ "\n",
+ " # ---- Create collection\n",
+ " #\n",
+ " c = Collection(zs, colors=y_show, labels=y_show)\n",
+ " c.attrs.markers_colormap = {'colorscale':'Rainbow','cmin':0,'cmax':latent_dim}\n",
+ " c.attrs.markers_size = 4\n",
+ " c.attrs.markers_border_width = 0\n",
+ " c.attrs.markers_opacity = 0.7\n",
+ "\n",
+ " s = Simplex.build(latent_dim)\n",
+ " s.attrs.width = 1000\n",
+ " s.attrs.height = 1000\n",
+ " s.plot(c)"
]
},
{
diff --git a/fidle/about.yml b/fidle/about.yml
index 00946f5929ce8cec053c50578474884ae239859c..0780ad5cb536428d5fb20039a0406e91c6fe95c1 100644
--- a/fidle/about.yml
+++ b/fidle/about.yml
@@ -13,7 +13,7 @@
#
# This file describes the notebooks used by the Fidle training.
-version: 2.1b4
+version: 2.1b5
content: notebooks
name: Notebooks Fidle
description: All notebooks used by the Fidle training
diff --git a/fidle/ci/default.yml b/fidle/ci/default.yml
index 2f23ed0d8311f8fab43be46f42806a18e577853f..eb09f39865ad0b4392c4d21f685ad9d57b31c938 100644
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -1,6 +1,6 @@
campain:
version: '1.0'
- description: Automatically generated ci profile (12/10/22 16:41:37)
+ description: Automatically generated ci profile (13/10/22 00:58:06)
directory: ./campains/default
existing_notebook: 'remove # remove|skip'
report_template: 'fidle # fidle|default'
diff --git a/fidle/ci/cpu-native.yml b/fidle/ci/default_settings.yml
similarity index 98%
rename from fidle/ci/cpu-native.yml
rename to fidle/ci/default_settings.yml
index 2841536853a8b6df2084ef38c09f655abf5ef8c0..69df1d9e20bb1d027ad3a921b12e2b16a0c17de4 100644
--- a/fidle/ci/cpu-native.yml
+++ b/fidle/ci/default_settings.yml
@@ -1,7 +1,7 @@
campain:
version: 1.0
- description: Full validation on CPU (fast)
- directory: ./campains/cpu_small
+ description: Complete validation of notebooks with default settings
+ directory: ./campains/default_settings
existing_notebook: skip
report_template: fidle
timeout: 6000
@@ -199,7 +199,7 @@ SYNOP2:
sequence_len: default
batch_size: default
epochs: default
- fit_verbosity: default
+ fit_verbosity: 2
SYNOP3:
notebook: SYNOP/SYNOP3-12h-predictions.ipynb
diff --git a/fidle/ci/scale1_settings.yml b/fidle/ci/scale1_settings.yml
new file mode 100644
index 0000000000000000000000000000000000000000..f7d275b6e6398a2de5f3c27d9ee15957817129ca
--- /dev/null
+++ b/fidle/ci/scale1_settings.yml
@@ -0,0 +1,464 @@
+campain:
+ version: 1.0
+ description: Full validation of notebooks with scale parameters set to 1
+ directory: ./campains/cpu_small
+ existing_notebook: skip
+ report_template: fidle
+ timeout: 6000
+ environment_vars:
+ FIDLE_SAVE_FIGS: true
+ TF_CPP_MIN_LOG_LEVEL: 2
+
+
+#
+# ------------ LinearReg
+#
+LINR1:
+ notebook: LinearReg/01-Linear-Regression.ipynb
+GRAD1:
+ notebook: LinearReg/02-Gradient-descent.ipynb
+POLR1:
+ notebook: LinearReg/03-Polynomial-Regression.ipynb
+LOGR1:
+ notebook: LinearReg/04-Logistic-Regression.ipynb
+
+#
+# ------------ IRIS
+#
+PER57:
+ notebook: IRIS/01-Simple-Perceptron.ipynb
+
+#
+# ------------ BHPD
+#
+BHPD1:
+ notebook: BHPD/01-DNN-Regression.ipynb
+ overrides:
+ fit_verbosity: 2
+BHPD2:
+ notebook: BHPD/02-DNN-Regression-Premium.ipynb
+ overrides:
+ fit_verbosity: 2
+
+#
+# ------------ MNIST
+#
+MNIST1:
+ notebook: MNIST/01-DNN-MNIST.ipynb
+ overrides:
+ fit_verbosity: 2
+
+MNIST2:
+ notebook: MNIST/02-CNN-MNIST.ipynb
+ overrides:
+ fit_verbosity: 2
+
+#
+# ------------ GTSRB
+#
+GTSRB1:
+ notebook: GTSRB/01-Preparation-of-data.ipynb
+ overrides:
+ scale: .05
+ output_dir: ./data
+ progress_verbosity: 2
+
+GTSRB2:
+ notebook: GTSRB/02-First-convolutions.ipynb
+ after: GTSRB1
+ overrides:
+ enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
+ dataset_name: set-24x24-L
+ batch_size: 64
+ epochs: 5
+ scale: 1
+ fit_verbosity: 2
+
+GTSRB3:
+ notebook: GTSRB/03-Tracking-and-visualizing.ipynb
+ after: GTSRB1
+ overrides:
+ enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
+ dataset_name: set-24x24-L
+ batch_size: 64
+ epochs: 5
+ scale: 1
+ fit_verbosity: 2
+
+GTSRB4:
+ notebook: GTSRB/04-Data-augmentation.ipynb
+ after: GTSRB1
+ overrides:
+ enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
+ dataset_name: set-24x24-L
+ batch_size: 64
+ epochs: 5
+ scale: 1
+ fit_verbosity: 2
+
+GTSRB5_1:
+ notebook: GTSRB/05-Full-convolutions.ipynb
+ after: GTSRB1
+ overrides:
+ run_dir: ./run/GTSRB5
+ enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
+ datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
+ models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
+ batch_size: 64
+ epochs: 16
+ scale: 1
+ with_datagen: False
+ fit_verbosity: 2
+
+GTSRB5_2:
+ notebook: GTSRB/05-Full-convolutions.ipynb
+ after: GTSRB1
+ overrides:
+ run_dir: ./run/GTSRB5
+ enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
+ datasets: "['set-24x24-L', 'set-24x24-RGB', 'set-48x48-L', 'set-48x48-RGB', 'set-24x24-L-LHE', 'set-24x24-RGB-HE', 'set-48x48-L-LHE', 'set-48x48-RGB-HE']"
+ models: "{'v1':'get_model_v1', 'v2':'get_model_v2', 'v3':'get_model_v3'}"
+ batch_size: 64
+ epochs: 16
+ scale: 1
+ with_datagen: False
+ fit_verbosity: 2
+
+GTSRB5_3:
+ notebook: GTSRB/05-Full-convolutions.ipynb
+ after: GTSRB1
+ overrides:
+ run_dir: ./run/GTSRB5
+ enhanced_dir: '{datasets_dir}/GTSRB/enhanced'
+ datasets: "['set-48x48-L', 'set-48x48-RGB']"
+ models: "{'v2':'get_model_v2', 'v3':'get_model_v3'}"
+ batch_size: 64
+ epochs: 16
+ scale: 1
+ with_datagen: True
+ fit_verbosity: 2
+
+GTSRB6:
+ notebook: GTSRB/06-Notebook-as-a-batch.ipynb
+
+GTSRB7:
+ notebook: GTSRB/07-Show-report.ipynb
+ after: GTSRB5
+ overrides:
+ report_dir: ./run/GTSRB5
+
+#
+# ------------ IMDB
+#
+IMDB1:
+ notebook: IMDB/01-One-hot-encoding.ipynb
+ overrides:
+ vocab_size: 8000
+ hide_most_frequently: 0
+ batch_size: 512
+ epochs: 10
+ fit_verbosity: 2
+
+IMDB2:
+ notebook: IMDB/02-Keras-embedding.ipynb
+ overrides:
+ vocab_size: 8000
+ hide_most_frequently: 0
+ review_len: 256
+ dense_vector_size: 32
+ batch_size: 512
+ epochs: 30
+ output_dir: default
+ fit_verbosity: 2
+
+IMDB3:
+ notebook: IMDB/03-Prediction.ipynb
+ after: IMDB2
+ overrides:
+ vocab_size: 8000
+ review_len: 256
+ saved_models: default
+ dictionaries_dir: default
+
+IMDB4:
+ notebook: IMDB/04-Show-vectors.ipynb
+ after: IMDB2
+ overrides:
+ vocab_size: 8000
+ review_len: 256
+ saved_models: default
+ dictionaries_dir: default
+
+IMDB5:
+ notebook: IMDB/05-LSTM-Keras.ipynb
+ overrides:
+ vocab_size: 8000
+ hide_most_frequently: 0
+ review_len: 256
+ dense_vector_size: 32
+ batch_size: 512
+ epochs: 10
+ scale: 1
+ fit_verbosity: 2
+
+#
+# ------------ SYNOP
+#
+LADYB1:
+ notebook: SYNOP/LADYB1-Ladybug.ipynb
+ overrides:
+ scale: 1
+ train_prop: 0.8
+ sequence_len: 20
+ predict_len: 5
+ batch_size: 32
+ epochs: 10
+
+SYNOP1:
+ notebook: SYNOP/SYNOP1-Preparation-of-data.ipynb
+ overrides:
+ output_dir: default
+
+SYNOP2:
+ notebook: SYNOP/SYNOP2-First-predictions.ipynb
+ after: SYNOP1
+ overrides:
+ scale: 1
+ train_prop: 0.8
+ sequence_len: 16
+ batch_size: 32
+ epochs: 10
+ fit_verbosity: 2
+
+SYNOP3:
+ notebook: SYNOP/SYNOP3-12h-predictions.ipynb
+ after: SYNOP2
+ overrides:
+ iterations: 4
+ scale: 1
+ train_prop: 0.8
+ sequence_len: 16
+
+#
+# ------------ Transformers
+#
+# TRANS1:
+# notebook: Transformers/01-Distilbert.ipynb
+#
+# TRANS2:
+# notebook: Transformers/02-distilbert_colab.ipynb
+
+#
+# ------------ AE
+#
+AE1:
+ notebook: AE/01-Prepare-MNIST-dataset.ipynb
+ overrides:
+ prepared_dataset: default
+ scale: 1
+ progress_verbosity: 2
+
+AE2:
+ notebook: AE/02-AE-with-MNIST.ipynb
+ after: AE1
+ overrides:
+ prepared_dataset: default
+ dataset_seed: 123
+ scale: 1
+ latent_dim: 10
+ train_prop: 0.8
+ batch_size: 128
+ epochs: 30
+ fit_verbosity: 2
+
+AE3:
+ notebook: AE/03-AE-with-MNIST-post.ipynb
+ after: AE2
+ overrides:
+ prepared_dataset: default
+ dataset_seed: 123
+ scale: 1
+ train_prop: 0.8
+
+AE4:
+ notebook: AE/04-ExtAE-with-MNIST.ipynb
+ after: AE1
+ overrides:
+ prepared_dataset: default
+ dataset_seed: default
+ scale: 1
+ latent_dim: 10
+ train_prop: 0.8
+ batch_size: 128
+ epochs: 20
+ fit_verbosity: 2
+
+AE5:
+ notebook: AE/05-ExtAE-with-MNIST.ipynb
+ after: AE1
+ overrides:
+ prepared_dataset: default
+ dataset_seed: default
+ scale: 1
+ latent_dim: 10
+ train_prop: 0.8
+ batch_size: 128
+ epochs: 30
+ fit_verbosity: 2
+
+#
+# ------------ VAE
+#
+VAE1:
+ notebook: VAE/01-VAE-with-MNIST.ipynb
+ overrides:
+ latent_dim: 2
+ loss_weights: default
+ scale: 1
+ seed: default
+ batch_size: 64
+ epochs: 10
+ fit_verbosity: 2
+
+VAE2:
+ notebook: VAE/02-VAE-with-MNIST.ipynb
+ overrides:
+ latent_dim: 6
+ loss_weights: default
+ scale: 1
+ seed: default
+ batch_size: 64
+ epochs: 10
+ fit_verbosity: 2
+
+VAE3:
+ notebook: VAE/03-VAE-with-MNIST-post.ipynb
+ after: VAE2
+ overrides:
+ scale: 1
+ seed: default
+ models_dir: default
+
+VAE5:
+ notebook: VAE/05-About-CelebA.ipynb
+ overrides:
+ progress_verbosity: 2
+
+VAE6.1:
+ notebook: VAE/06-Prepare-CelebA-datasets.ipynb
+ overrides:
+ scale: 0.02
+ seed: default
+ cluster_size: 10000
+ image_size: (128,128)
+ output_dir: ./data
+ exit_if_exist: False
+ progress_verbosity: 2
+
+VAE6.2:
+ notebook: VAE/06-Prepare-CelebA-datasets.ipynb
+ overrides:
+ scale: 0.02
+ seed: default
+ cluster_size: 10000
+ image_size: (192,160)
+ output_dir: ./data
+ exit_if_exist: False
+ progress_verbosity: 2
+
+VAE7:
+ notebook: VAE/07-Check-CelebA.ipynb
+ after: VAE6.1
+ overrides:
+ image_size: (192,160)
+ enhanced_dir: ./data
+ progress_verbosity: 2
+
+VAE8:
+ notebook: VAE/08-VAE-with-CelebA-128x128.ipynb
+ after: VAE6.1
+ overrides:
+ scale: 1
+ image_size: (128,128)
+ enhanced_dir: '{datasets_dir}/celeba/enhanced'
+ latent_dim: 300
+ loss_weights: default
+ batch_size: 64
+ epochs: 15
+ fit_verbosity: 2
+
+VAE9:
+ notebook: VAE/09-VAE-with-CelebA-192x160.ipynb
+ after: VAE6.2
+ overrides:
+ scale: 1
+ image_size: (192,160)
+ enhanced_dir: '{datasets_dir}/celeba/enhanced'
+ latent_dim: 300
+ loss_weights: default
+ batch_size: 64
+ epochs: 10
+ fit_verbosity: 2
+
+VAE10.1:
+ notebook: VAE/10-VAE-with-CelebA-post.ipynb
+ after: VAE8
+ overrides:
+ image_size: (128,128)
+ enhanced_dir: '{datasets_dir}/celeba/enhanced'
+ models_dir: ./run/VAE8
+
+VAE10.2:
+ notebook: VAE/10-VAE-with-CelebA-post.ipynb
+ after: VAE9
+ overrides:
+ image_size: (192,160)
+ enhanced_dir: '{datasets_dir}/celeba/enhanced'
+ models_dir: ./run/VAE9
+
+#
+# ------------ DCGAN
+#
+SHEEP1:
+ notebook: DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
+ overrides:
+ scale: 1
+ latent_dim: 128
+ epochs: 8
+ batch_size: 32
+ num_img: 12
+ fit_verbosity: 2
+
+SHEEP2:
+ notebook: DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
+ overrides:
+ scale: 1
+ latent_dim: 128
+ epochs: 4
+ batch_size: 64
+ num_img: 12
+ fit_verbosity: 2
+
+#
+# ------------ DRL
+#
+# DRL1:
+# notebook: DRL/FIDLE_DQNfromScratch.ipynb
+
+# DRL2:
+# notebook: DRL/FIDLE_rl_baselines_zoo.ipynb
+
+#
+# ------------ Misc
+#
+ACTF1:
+ notebook: Misc/Activation-Functions.ipynb
+
+NP1:
+ notebook: Misc/Numpy.ipynb
+
+SCRATCH1:
+ notebook: Misc/Scratchbook.ipynb
+
+TSB1:
+ notebook: Misc/Using-Tensorboard.ipynb