diff --git a/AE/01-Prepare-MNIST-dataset.ipynb b/AE/01-Prepare-MNIST-dataset.ipynb
index 5d4a658bb18b1b4ecf6b35d295d04c306b73433d..15917f6a88677d08b0f164f30f210a851796a420 100644
--- a/AE/01-Prepare-MNIST-dataset.ipynb
+++ b/AE/01-Prepare-MNIST-dataset.ipynb
@@ -57,7 +57,7 @@
"### 1.2 - Parameters\n",
"`prepared_dataset` : Filename of the future prepared dataset (example : ./data/mnist-noisy.h5)\\\n",
"`scale` : Dataset scale. 1 mean 100% of the dataset - set 0.1 for tests\\\n",
- "`progress_verbosity`: Verbosity of progress bar: 0=no progress, 1: progress bar, 2: One line"
+ "`progress_verbosity`: Verbosity of progress bar: 0=silent, 1=progress bar, 2=One line"
]
},
{
@@ -68,7 +68,7 @@
"source": [
"prepared_dataset = './data/mnist-noisy.h5'\n",
"scale = 1\n",
- "progress_verbosity = 2"
+ "progress_verbosity = 1"
]
},
{
diff --git a/AE/02-AE-with-MNIST.ipynb b/AE/02-AE-with-MNIST.ipynb
index 8d9eed3b099b84f4bf8c28c19353417df5d8802b..894707f8fbdfa0362c7cd2c5c2ea1dd355db7205 100644
--- a/AE/02-AE-with-MNIST.ipynb
+++ b/AE/02-AE-with-MNIST.ipynb
@@ -61,11 +61,10 @@
"from modules.MNIST import MNIST\n",
"from modules.ImagesCallback import ImagesCallback\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/AE2'\n",
- "run_id, run_dir, datasets_dir = fidle.init('AE2', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('AE2')"
]
},
{
@@ -92,7 +91,7 @@
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = 123\n",
"\n",
- "scale = 1\n",
+ "scale = .1\n",
"\n",
"latent_dim = 10\n",
"\n",
@@ -116,7 +115,7 @@
"outputs": [],
"source": [
"fidle.override('prepared_dataset', 'dataset_seed', 'scale', 'latent_dim')\n",
- "fidle.override('train_prop', 'batch_size', 'epochs')"
+ "fidle.override('train_prop', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -410,11 +409,9 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -427,7 +424,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/AE/03-AE-with-MNIST-post.ipynb b/AE/03-AE-with-MNIST-post.ipynb
index 2be5ab0bf7c3b08a9ed0db05222b2fcc1a9a56ca..e75e4138199257b6622b9b8c9f568bce36b099c6 100644
--- a/AE/03-AE-with-MNIST-post.ipynb
+++ b/AE/03-AE-with-MNIST-post.ipynb
@@ -50,11 +50,10 @@
"\n",
"from modules.MNIST import MNIST\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/AE2'\n",
- "run_id, run_dir, datasets_dir = fidle.init('AE3', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('AE3')"
]
},
{
@@ -76,6 +75,7 @@
"outputs": [],
"source": [
"prepared_dataset = './data/mnist-noisy.h5'\n",
+ "saved_models = './run/AE2/models'\n",
"dataset_seed = 123\n",
"scale = 1\n",
"train_prop = .8"
@@ -139,7 +139,7 @@
"metadata": {},
"outputs": [],
"source": [
- "model = keras.models.load_model(f'{run_dir}/models/best_model.h5')"
+ "model = keras.models.load_model(f'{saved_models}/best_model.h5')"
]
},
{
@@ -155,7 +155,10 @@
"metadata": {},
"outputs": [],
"source": [
- "denoised_test = model.predict(noisy_test)\n",
+ "from tabnanny import verbose\n",
+ "\n",
+ "\n",
+ "denoised_test = model.predict(noisy_test,verbose=0)\n",
"\n",
"print('Denoised images (denoised_test) shape : ',denoised_test.shape)"
]
@@ -276,7 +279,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -290,7 +293,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.9"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/AE/04-ExtAE-with-MNIST.ipynb b/AE/04-ExtAE-with-MNIST.ipynb
index fe23ccc5bf64e63d635892e727f57fd36a2249ca..86c2c217b3e46617dd91c07e38d81d6f19414b22 100644
--- a/AE/04-ExtAE-with-MNIST.ipynb
+++ b/AE/04-ExtAE-with-MNIST.ipynb
@@ -64,11 +64,10 @@
"from modules.MNIST import MNIST\n",
"from modules.ImagesCallback import ImagesCallback\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/AE4'\n",
- "run_id, run_dir, datasets_dir = fidle.init('AE4', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('AE4')"
]
},
{
@@ -95,7 +94,7 @@
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = None\n",
"\n",
- "scale = 1\n",
+ "scale = .1\n",
"\n",
"latent_dim = 10\n",
"\n",
@@ -119,7 +118,7 @@
"outputs": [],
"source": [
"fidle.override('prepared_dataset', 'dataset_seed', 'scale', 'latent_dim')\n",
- "fidle.override('train_prop', 'batch_size', 'epochs')"
+ "fidle.override('train_prop', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -141,7 +140,7 @@
" train_prop = train_prop,\n",
" seed = dataset_seed,\n",
" shuffle = True,\n",
- " filename=prepared_dataset )"
+ " filename = prepared_dataset )"
]
},
{
@@ -429,7 +428,7 @@
"metadata": {},
"outputs": [],
"source": [
- "denoised_test, classcat_test = model.predict(noisy_test)\n",
+ "denoised_test, classcat_test = model.predict(noisy_test, verbose=0)\n",
"\n",
"print('Denoised images (denoised_test) shape : ',denoised_test.shape)\n",
"print('Predicted classes (classcat_test) shape : ',classcat_test.shape)"
@@ -507,7 +506,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -521,7 +520,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.8.5"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/AE/05-ExtAE-with-MNIST.ipynb b/AE/05-ExtAE-with-MNIST.ipynb
index 4dc1e5e865a2914a97ffeca66a9ad95a8750c016..75309a3a30e0f79b4eab0d35003b4d4a2e80c709 100644
--- a/AE/05-ExtAE-with-MNIST.ipynb
+++ b/AE/05-ExtAE-with-MNIST.ipynb
@@ -64,11 +64,10 @@
"from modules.MNIST import MNIST\n",
"from modules.ImagesCallback import ImagesCallback\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/AE5'\n",
- "run_id, run_dir, datasets_dir = fidle.init('AE5', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('AE5')"
]
},
{
@@ -95,7 +94,7 @@
"prepared_dataset = './data/mnist-noisy.h5'\n",
"dataset_seed = None\n",
"\n",
- "scale = 1\n",
+ "scale = .1\n",
"\n",
"latent_dim = 10\n",
"\n",
@@ -119,7 +118,7 @@
"outputs": [],
"source": [
"fidle.override('prepared_dataset', 'dataset_seed', 'scale', 'latent_dim')\n",
- "fidle.override('train_prop', 'batch_size', 'epochs')"
+ "fidle.override('train_prop', 'batch_size', 'epochs', 'fit_verbosity')"
]
},
{
@@ -136,11 +135,12 @@
"metadata": {},
"outputs": [],
"source": [
- "clean_train,clean_test, noisy_train,noisy_test, class_train,class_test = MNIST.reload_prepared_dataset(scale = scale, \n",
+ "clean_train,clean_test, noisy_train,noisy_test, class_train,class_test = MNIST.reload_prepared_dataset(\n",
+ " scale = scale, \n",
" train_prop = train_prop,\n",
" seed = dataset_seed,\n",
" shuffle = True,\n",
- " filename=prepared_dataset )"
+ " filename = prepared_dataset )"
]
},
{
@@ -460,7 +460,7 @@
"metadata": {},
"outputs": [],
"source": [
- "denoised_test, classcat_test = model.predict(noisy_test)\n",
+ "denoised_test, classcat_test = model.predict(noisy_test, verbose=0)\n",
"\n",
"print('Denoised images (denoised_test) shape : ',denoised_test.shape)\n",
"print('Predicted classes (classcat_test) shape : ',classcat_test.shape)"
@@ -538,7 +538,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -552,7 +552,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.9"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb b/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
index e58707ed49d3ff77e7ca0637859f4215d7ceb2b6..e629e8fa94fac7df364b5db055697ae7adb35b33 100644
--- a/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
+++ b/DCGAN/01-DCGAN-Draw-me-a-sheep.ipynb
@@ -60,11 +60,10 @@
"from modules.models import DCGAN\n",
"from modules.callbacks import ImagesCallback\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
+ "# Init Fidle environment\n",
"\n",
- "run_dir = './run/SHEEP1.001' # Output directory\n",
- "run_id, run_dir, datasets_dir = fidle.init('SHEEP1', run_dir)"
+ "run_id, run_dir, datasets_dir = fidle.init('SHEEP1')"
]
},
{
@@ -90,7 +89,7 @@
"epochs = 10\n",
"batch_size = 32\n",
"num_img = 12\n",
- "fit_verbosity = 1"
+ "fit_verbosity = 2"
]
},
{
@@ -384,11 +383,8 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -402,7 +398,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb b/DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
index 71316e52bcf11296be5d3a11d09904526e7ce1ed..c136ac8efdc58e1e883b2f42537e192f730e268a 100644
--- a/DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
+++ b/DCGAN/02-WGANGP-Draw-me-a-sheep.ipynb
@@ -60,11 +60,10 @@
"from modules.models import WGANGP\n",
"from modules.callbacks import ImagesCallback\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/SHEEP2.001' # Output directory\n",
- "run_id, run_dir, datasets_dir = fidle.init('SHEEP2', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('SHEEP2')"
]
},
{
@@ -91,7 +90,7 @@
"n_critic = 2\n",
"batch_size = 64\n",
"num_img = 12\n",
- "fit_verbosity = 1"
+ "fit_verbosity = 2"
]
},
{
diff --git a/GTSRB/01-Preparation-of-data.ipynb b/GTSRB/01-Preparation-of-data.ipynb
index e01d6a17fb08b5abbfd2da34f47e2e3fee03863d..d3924885f83e4434b619af96ac56b8a4a9b01d92 100644
--- a/GTSRB/01-Preparation-of-data.ipynb
+++ b/GTSRB/01-Preparation-of-data.ipynb
@@ -71,6 +71,7 @@
"\n",
"You can choose to perform tests or generate the whole enhanced dataset by setting the following parameters: \n",
"`scale` : 1 mean 100% of the dataset - set 0.1 for tests \n",
+ "`progress_verbosity`: Verbosity of progress bar: 0=silent, 1=progress bar, 2=One line \n",
"`output_dir` : where to write enhanced dataset, could be :\n",
" - `./data`, for tests purpose\n",
" - `<datasets_dir>/GTSRB/enhanced` to add clusters in your datasets dir. \n",
@@ -94,7 +95,7 @@
"# scale = 1\n",
"# output_dir = f'{datasets_dir}/GTSRB/enhanced'\n",
"\n",
- "# ---- Verbosity - 0 = silent, 1 = one line, 2 = full progress bar\n",
+ "# ---- Verbosity\n",
"#\n",
"progress_verbosity = 2"
]
diff --git a/README.ipynb b/README.ipynb
index ed7a93685f15be2cdbe366c0b7b27e2c25377baf..e3280c1de45c36ef9fe6379eef8f882901ff35ee 100644
--- a/README.ipynb
+++ b/README.ipynb
@@ -3,13 +3,13 @@
{
"cell_type": "code",
"execution_count": 1,
- "id": "a6392f98",
+ "id": "ca61843b",
"metadata": {
"execution": {
- "iopub.execute_input": "2022-10-10T11:07:39.161206Z",
- "iopub.status.busy": "2022-10-10T11:07:39.160393Z",
- "iopub.status.idle": "2022-10-10T11:07:39.172452Z",
- "shell.execute_reply": "2022-10-10T11:07:39.171611Z"
+ "iopub.execute_input": "2022-10-10T20:30:25.278804Z",
+ "iopub.status.busy": "2022-10-10T20:30:25.278247Z",
+ "iopub.status.idle": "2022-10-10T20:30:25.289806Z",
+ "shell.execute_reply": "2022-10-10T20:30:25.289004Z"
},
"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 -->2.02b3<!-- VERSION_END -->\n",
+ "Current Version : <!-- VERSION_BEGIN -->2.02b5<!-- VERSION_END -->\n",
"\n",
"\n",
"## Course materials\n",
@@ -226,7 +226,7 @@
"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 : 10/10/22 13:07:38"
+ "# This README is visible under Jupiter Lab ;-)# Automatically generated on : 10/10/22 22:30:24"
]
}
],
diff --git a/README.md b/README.md
index ef4f47e4f1e16402cff716fa2655a014a6015d11..63e52096e6a311e3875d19d253e2dbc480405b10 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 -->2.02b3<!-- VERSION_END -->
+Current Version : <!-- VERSION_BEGIN -->2.02b5<!-- VERSION_END -->
## Course materials
diff --git a/VAE/01-VAE-with-MNIST.ipynb b/VAE/01-VAE-with-MNIST.ipynb
index 4ca25fc7a33b458bbd3231a297c1ae134b396940..8b48f7ad35106ff1b1139fcd57dd2de028afddde 100644
--- a/VAE/01-VAE-with-MNIST.ipynb
+++ b/VAE/01-VAE-with-MNIST.ipynb
@@ -13,7 +13,7 @@
"\n",
"## Objectives :\n",
" - Understanding and implementing a **variational autoencoder** neurals network (VAE)\n",
- " - Understanding **Keras functional API**\n",
+ " - Understanding **Keras functional API**, using two custom layers\n",
"\n",
"The calculation needs being important, it is preferable to use a very simple dataset such as MNIST to start with. \n",
"...MNIST with a small scale if you haven't a GPU ;-)\n",
@@ -54,11 +54,10 @@
"from modules.datagen import MNIST\n",
"\n",
"import sys\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/VAE1.001'\n",
- "run_id, run_dir, datasets_dir = fidle.init('VAE1', run_dir)\n"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('VAE1')\n"
]
},
{
@@ -68,8 +67,7 @@
"## Step 2 - Parameters\n",
"`scale` : With scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU !\\\n",
"`latent_dim` : 2 dimensions is small, but usefull to draw !\\\n",
- "`fit_verbosity` : verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n",
- "\n",
+ "`fit_verbosity`: Verbosity of training progress bar: 0=silent, 1=progress bar, 2=One line \n",
"\n",
"`loss_weights` : Our **loss function** is the weighted sum of two loss:\n",
" - `r_loss` which measures the loss during reconstruction. \n",
@@ -88,7 +86,7 @@
"latent_dim = 2\n",
"loss_weights = [1,.001]\n",
"\n",
- "scale = 1\n",
+ "scale = 0.2\n",
"seed = 123\n",
"\n",
"batch_size = 64\n",
@@ -264,11 +262,12 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.chrono_start()\n",
+ "chrono=fidle.Chrono()\n",
+ "chrono.start()\n",
"\n",
"history = vae.fit(x_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list, verbose=fit_verbosity)\n",
"\n",
- "pwk.chrono_show()"
+ "chrono.show()"
]
},
{
@@ -383,11 +382,8 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -401,7 +397,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.10"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/02-VAE-with-MNIST.ipynb b/VAE/02-VAE-with-MNIST.ipynb
index f1e43cc0d939bbca16bab9481fb9abb513351d0a..9242044b2e5ee5b792d4fa604a60c0aaf938a877 100644
--- a/VAE/02-VAE-with-MNIST.ipynb
+++ b/VAE/02-VAE-with-MNIST.ipynb
@@ -13,7 +13,7 @@
"\n",
"## Objectives :\n",
" - Understanding and implementing a **variational autoencoder** neurals network (VAE)\n",
- " - Understanding a still more **advanced programming model**\n",
+ " - Understanding a still more **advanced programming model**, using a **custom model**\n",
"\n",
"The calculation needs being important, it is preferable to use a very simple dataset such as MNIST to start with. \n",
"...MNIST with a small scale if you haven't a GPU ;-)\n",
@@ -58,11 +58,10 @@
"from modules.callbacks import ImagesCallback, BestModelCallback\n",
"from modules.datagen import MNIST\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/VAE2.001'\n",
- "run_id, run_dir, datasets_dir = fidle.init('VAE2', run_dir)\n",
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('VAE2')\n",
"\n",
"VAE.about()"
]
@@ -72,10 +71,9 @@
"metadata": {},
"source": [
"## Step 2 - Parameters\n",
- "`scale` : With scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU ! \n",
+ "`scale` : with scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU ! \n",
"`latent_dim` : 2 dimensions is small, but usefull to draw ! \n",
- "`fit_verbosity` : verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch\n",
- "\n",
+ "`fit_verbosity`: Verbosity of training progress bar: 0=silent, 1=progress bar, 2=One line \n",
"\n",
"`loss_weights` : Our **loss function** is the weighted sum of two loss:\n",
" - `r_loss` which measures the loss during reconstruction. \n",
@@ -94,11 +92,11 @@
"latent_dim = 2\n",
"loss_weights = [1,.001] # [1, .001] give good results\n",
"\n",
- "scale = 1\n",
+ "scale = .2\n",
"seed = 123\n",
"\n",
"batch_size = 64\n",
- "epochs = 10\n",
+ "epochs = 5\n",
"fit_verbosity = 1"
]
},
@@ -260,11 +258,12 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.chrono_start()\n",
+ "chrono=fidle.Chrono()\n",
+ "chrono.start()\n",
"\n",
"history = vae.fit(x_data, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list, verbose=fit_verbosity)\n",
"\n",
- "pwk.chrono_show()"
+ "chrono.show()"
]
},
{
@@ -368,7 +367,7 @@
"source": [
"# ---- Select few images\n",
"\n",
- "x_show = pwk.pick_dataset(x_data, n=10)\n",
+ "x_show = fidle.utils.pick_dataset(x_data, n=10)\n",
"\n",
"# ---- Get latent points and reconstructed images\n",
"\n",
@@ -395,11 +394,11 @@
"metadata": {},
"outputs": [],
"source": [
- "n_show = 20000\n",
+ "n_show = int(20000*scale)\n",
"\n",
"# ---- Select images\n",
"\n",
- "x_show, y_show = pwk.pick_dataset(x_data,y_data, n=n_show)\n",
+ "x_show, y_show = fidle.utils.pick_dataset(x_data,y_data, n=n_show)\n",
"\n",
"# ---- Get latent points\n",
"\n",
@@ -472,11 +471,8 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3 (ipykernel)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -490,7 +486,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.7.10"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/03-VAE-with-MNIST-post.ipynb b/VAE/03-VAE-with-MNIST-post.ipynb
index f07f842a3b8818f72d5bc7dc0a34f30cc0f40a30..fd37532618c91cfcff2acdccf339c4844bef06f6 100644
--- a/VAE/03-VAE-with-MNIST-post.ipynb
+++ b/VAE/03-VAE-with-MNIST-post.ipynb
@@ -145,7 +145,7 @@
"source": [
"# ---- Select few images\n",
"\n",
- "x_show = pwk.pick_dataset(x_data, n=10)\n",
+ "x_show = fidle.utils.pick_dataset(x_data, n=10)\n",
"\n",
"# ---- Get latent points and reconstructed images\n",
"\n",
@@ -176,7 +176,7 @@
"\n",
"# ---- Select images\n",
"\n",
- "x_show, y_show = pwk.pick_dataset(x_data,y_data, n=n_show)\n",
+ "x_show, y_show = fidle.utils.pick_dataset(x_data,y_data, n=n_show)\n",
"\n",
"# ---- Get latent points\n",
"\n",
@@ -249,11 +249,9 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -266,7 +264,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/05-About-CelebA.ipynb b/VAE/05-About-CelebA.ipynb
index 9d3e42c89dcfb88fcd72d903bff9d45dc0a84e0e..a475a9587191065fed0fc0b31cf2cf241364b8c7 100644
--- a/VAE/05-About-CelebA.ipynb
+++ b/VAE/05-About-CelebA.ipynb
@@ -42,18 +42,17 @@
"\n",
"from importlib import reload\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir='./run/VAE5'\n",
- "run_id, run_dir, datasets_dir = fidle.init('VAE5', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('VAE5')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
- "`progress_verbosity` : Progress bar verbosity, 0 = silent, 1 = progress bar, 2 = one line"
+ "`progress_verbosity`: Verbosity of progress bar: 0=silent, 1=progress bar, 2=One line"
]
},
{
@@ -129,7 +128,8 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.chrono_start()\n",
+ "chrono = fidle.Chrono()\n",
+ "chrono.start()\n",
"\n",
"nb_images=5000\n",
"filenames = [ f'{dataset_img}/{i}' for i in dataset_desc.image_id[:nb_images] ]\n",
@@ -141,12 +141,12 @@
"x_data=np.array(x)\n",
"x=None\n",
" \n",
- "duration=pwk.chrono_stop()\n",
+ "duration=chrono.get_delay()\n",
"print(f'\\nDuration : {duration:.2f} s')\n",
"print(f'Shape is : {x_data.shape}')\n",
"print(f'Numpy type : {x_data.dtype}')\n",
"\n",
- "pwk.display_md('<br>**Note :** Estimation for **200.000** normalized images : ')\n",
+ "fidle.utils.display_md('<br>**Note :** Estimation for **200.000** normalized images : ')\n",
"x_data=x_data/255\n",
"k=200000/nb_images\n",
"print(f'Charging time : {k*duration:.2f} s or {fidle.utils.hdelay(k*duration)}')\n",
@@ -239,11 +239,9 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -256,7 +254,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/06-Prepare-CelebA-datasets.ipynb b/VAE/06-Prepare-CelebA-datasets.ipynb
index 3c267bcf7ea033f6b5795376a19b728ed77afa5c..43cc6cf409015051f69c7faf021d36688435dff3 100644
--- a/VAE/06-Prepare-CelebA-datasets.ipynb
+++ b/VAE/06-Prepare-CelebA-datasets.ipynb
@@ -48,13 +48,10 @@
"import csv\n",
"import math, random\n",
"\n",
- "from importlib import reload\n",
- "\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir='./run/VAE6'\n",
- "run_id, run_dir, datasets_dir = fidle.init('VAE6', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('VAE6')"
]
},
{
@@ -76,7 +73,7 @@
" - `<datasets_dir>/celeba/enhanced` to add clusters in your datasets dir. \n",
" \n",
"`cluster_size` : number of images in a cluster, 10000 is fine. (will be adjust by scale) \n",
- "`progress_verbosity`: Verbosity of progress bar: 0=no progress, 1: progress bar, 2: One line\n",
+ "`progress_verbosity`: Verbosity of progress bar: 0=silent, 1=progress bar, 2=One line \n",
"\n",
"**Note :** If the target folder is not empty and exit_if_exist is True, the construction is blocked. "
]
@@ -203,8 +200,7 @@
" nb_clusters : Number of clusters\n",
" duration: total duration\n",
" '''\n",
- " global pwk\n",
- " \n",
+ "\n",
" def save_cluster(imgs,desc,cols,id):\n",
" file_img = f'{cluster_dir}/{cluster_name}-{id:03d}.npy'\n",
" file_desc = f'{cluster_dir}/{cluster_name}-{id:03d}.csv'\n",
@@ -213,7 +209,8 @@
" df.to_csv(file_desc, index=False)\n",
" return [],[],id+1\n",
" \n",
- " pwk.chrono_start()\n",
+ " chrono = fidle.Chrono()\n",
+ " chrono.start()\n",
" \n",
" # ---- Seed\n",
" #\n",
@@ -287,7 +284,7 @@
" # ---- Save uncomplete cluster\n",
" if len(imgs)>0 : imgs,desc,cluster_id=save_cluster(imgs,desc,cols,cluster_id)\n",
"\n",
- " duration=pwk.chrono_stop()\n",
+ " duration=chrono.get_delay('seconds')\n",
" return cluster_id,duration\n"
]
},
@@ -353,7 +350,7 @@
],
"metadata": {
"kernelspec": {
- "display_name": "Python 3",
+ "display_name": "Python 3.9.2 ('fidle-env')",
"language": "python",
"name": "python3"
},
@@ -367,7 +364,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/07-Check-CelebA.ipynb b/VAE/07-Check-CelebA.ipynb
index d0227baf61c2df031cc714d7e21fa0129577ec51..81e98261588f11ba5bb63718d5375c404ba25079 100644
--- a/VAE/07-Check-CelebA.ipynb
+++ b/VAE/07-Check-CelebA.ipynb
@@ -45,11 +45,10 @@
"\n",
"from modules.datagen import DataGenerator\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir='./run/VAE7'\n",
- "run_id, run_dir, datasets_dir = fidle.init('VAE7', run_dir)"
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('VAE7')"
]
},
{
@@ -59,10 +58,10 @@
"### 1.2 - Parameters\n",
"(Un)comment the right lines to be in accordance with the VAE6 notebook \n",
"`image_size` : images size in the clusters, should be 128x128 or 192,160 - original size is (218,178) \n",
- "`enhanced_dir` : the place where clustered dataset was saved. Maybe :\n",
- " - `./data`, for tests purpose\n",
- " - `f{datasets_dir}/celeba/enhanced` in your datasets dir. \n",
- "`progress_verbosity`: Verbosity of progress bar: 0=no progress, 1: progress bar, 2: One line"
+ "`progress_verbosity`: Verbosity of progress bar: 0=silent, 1=progress bar, 2=One line \n",
+ "`enhanced_dir` : the place where clustered dataset was saved, can be :\n",
+ "- `./data`, for tests purpose\n",
+ "- `f{datasets_dir}/celeba/enhanced` in your datasets dir. "
]
},
{
@@ -122,7 +121,8 @@
" if v==1 : legend.append(cols[i])\n",
" return str('\\n'.join(legend))\n",
"\n",
- "pwk.chrono_start()\n",
+ "chrono = fidle.Chrono()\n",
+ "chrono.start()\n",
"\n",
"# ---- the place of the clusters files\n",
"#\n",
@@ -170,10 +170,10 @@
" \n",
" fidle.utils.update_progress('Load clusters :',i,imax, redraw=True, verbosity=progress_verbosity)\n",
"\n",
- "d=pwk.chrono_stop()\n",
+ "d=chrono.get_delay('seconds')\n",
"\n",
"fidle.utils.subtitle('Few stats :')\n",
- "print(f'Loading time : {d:.2f} s or {fidle.utils.hdelay(d)}')\n",
+ "print(f'Loading time : {d} s or {fidle.utils.hdelay(d)}')\n",
"print(f'Number of cluster : {i}')\n",
"print(f'Number of images : {n1}')\n",
"print(f'Number of desc. : {n2}')\n",
@@ -245,11 +245,9 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -262,7 +260,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/08-VAE-with-CelebA.ipynb b/VAE/08-VAE-with-CelebA.ipynb
index e9bfa05d32a78218ca810e878c0540f6d821a318..5e0cd0d99467979982c7ef66d7ce8a210e270edb 100644
--- a/VAE/08-VAE-with-CelebA.ipynb
+++ b/VAE/08-VAE-with-CelebA.ipynb
@@ -79,9 +79,10 @@
"metadata": {},
"source": [
"## Step 2 - Parameters\n",
- "`scale` : With scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU ! \n",
+ "`scale` : with scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU ! \n",
"`latent_dim` : 2 dimensions is small, but usefull to draw ! \n",
- "`fit_verbosity` : verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch \n",
+ "`fit_verbosity`: Verbosity of training progress bar: 0=silent, 1=progress bar, 2=One line \n",
+ "\n",
"`loss_weights` : Our **loss function** is the weighted sum of two loss:\n",
" - `r_loss` which measures the loss during reconstruction. \n",
" - `kl_loss` which measures the dispersion. \n",
@@ -337,11 +338,12 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.chrono_start()\n",
+ "chrono = fidle.Chrono()\n",
+ "chrono.start()\n",
"\n",
"history = vae.fit(data_gen, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list, verbose=fit_verbosity)\n",
"\n",
- "pwk.chrono_show()"
+ "chrono.show()"
]
},
{
@@ -422,11 +424,9 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -439,7 +439,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/VAE/09-VAE-with-CelebA-192x160.ipynb b/VAE/09-VAE-with-CelebA-192x160.ipynb
index b97c49e9d6bb56abda5144bf4e3badf84c3c31ce..588cd50a449964baec923b1fbfe432933fd63ea0 100644
--- a/VAE/09-VAE-with-CelebA-192x160.ipynb
+++ b/VAE/09-VAE-with-CelebA-192x160.ipynb
@@ -54,11 +54,10 @@
"from modules.callbacks import ImagesCallback, BestModelCallback\n",
"from modules.datagen import DataGenerator\n",
"\n",
- "# Init Fidle environment\n",
"import fidle\n",
"\n",
- "run_dir = './run/VAE9'\n",
- "run_id, run_dir, datasets_dir = fidle.init('VAE9', run_dir)\n",
+ "# Init Fidle environment\n",
+ "run_id, run_dir, datasets_dir = fidle.init('VAE9')\n",
"\n",
"VAE.about()\n",
"DataGenerator.about()"
@@ -81,7 +80,8 @@
"## Step 2 - Parameters\n",
"`scale` : With scale=1, we need 1'30s on a GPU V100 ...and >20' on a CPU ! \n",
"`latent_dim` : 2 dimensions is small, but usefull to draw ! \n",
- "`fit_verbosity` : verbosity during training : 0 = silent, 1 = progress bar, 2 = one line per epoch \n",
+ "`fit_verbosity`: Verbosity of training progress bar: 0=silent, 1=progress bar, 2=One line \n",
+ "\n",
"`loss_weights` : Our **loss function** is the weighted sum of two loss:\n",
" - `r_loss` which measures the loss during reconstruction. \n",
" - `kl_loss` which measures the dispersion. \n",
@@ -95,7 +95,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
@@ -347,11 +347,12 @@
"metadata": {},
"outputs": [],
"source": [
- "pwk.chrono_start()\n",
+ "chrono = fidle.Chrono()\n",
+ "chrono.start()\n",
"\n",
"history = vae.fit(data_gen, epochs=epochs, batch_size=batch_size, callbacks=callbacks_list, verbose=fit_verbosity)\n",
"\n",
- "pwk.chrono_show()"
+ "chrono.show()"
]
},
{
@@ -432,11 +433,9 @@
}
],
"metadata": {
- "interpreter": {
- "hash": "8e38643e33497db9a306e3f311fa98cb1e65371278ca73ee4ea0c76aa5a4f387"
- },
"kernelspec": {
- "display_name": "Python 3.9.7 64-bit ('fidle-cpu': conda)",
+ "display_name": "Python 3.9.2 ('fidle-env')",
+ "language": "python",
"name": "python3"
},
"language_info": {
@@ -449,7 +448,12 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.7"
+ "version": "3.9.2"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "b3929042cc22c1274d74e3e946c52b845b57cb6d84f2d591ffe0519b38e4896d"
+ }
}
},
"nbformat": 4,
diff --git a/fidle/about.yml b/fidle/about.yml
index 21ca86230643896acf6d140b97c315f9c2bc97fa..df1da0cc38c811d7f318a541a0501bf4fee00f44 100644
--- a/fidle/about.yml
+++ b/fidle/about.yml
@@ -13,7 +13,7 @@
#
# This file describes the notebooks used by the Fidle training.
-version: 2.02b4
+version: 2.02b5
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 e52b040e9251a1d20d4c14db5af37206af76fc5c..4e4ff78e06dcdea29149d9acc7645c46ce44214c 100644
--- a/fidle/ci/default.yml
+++ b/fidle/ci/default.yml
@@ -1,6 +1,6 @@
campain:
version: '1.0'
- description: Automatically generated ci profile (10/10/22 13:07:38)
+ description: Automatically generated ci profile (10/10/22 22:30:24)
directory: ./campains/default
existing_notebook: 'remove # remove|skip'
report_template: 'fidle # fidle|default'
@@ -129,12 +129,14 @@ IMDB3:
overrides:
vocab_size: default
review_len: default
+ saved_models: default
dictionaries_dir: default
IMDB4:
notebook: IMDB/04-Show-vectors.ipynb
overrides:
vocab_size: default
review_len: default
+ saved_models: default
dictionaries_dir: default
IMDB5:
notebook: IMDB/05-LSTM-Keras.ipynb
@@ -208,6 +210,7 @@ AE2:
train_prop: default
batch_size: default
epochs: default
+ fit_verbosity: default
AE3:
notebook: AE/03-AE-with-MNIST-post.ipynb
overrides:
@@ -225,6 +228,7 @@ AE4:
train_prop: default
batch_size: default
epochs: default
+ fit_verbosity: default
AE5:
notebook: AE/05-ExtAE-with-MNIST.ipynb
overrides:
@@ -235,6 +239,7 @@ AE5:
train_prop: default
batch_size: default
epochs: default
+ fit_verbosity: default
#
# ------------ VAE