diff --git a/docs/quickstart.md b/docs/quickstart.md
index f2c4d8f681d06434a4046c1bc630858e3920a076..71981ce36c2d75b478dee1aebeffed5a82f84d81 100644
--- a/docs/quickstart.md
+++ b/docs/quickstart.md
@@ -288,7 +288,10 @@ where to report it. An example:
```python
[experiment]
-metrics=[["multi-classif",{labels = [0,1,2,3,4,5,6,7,8,9]}]] # Accuracy metric
+metrics = [
+ # Multi-label Accuracy, Precision, Recall and F1-Score.
+ ["multi-classif", {labels = [0,1,2,3,4,5,6,7,8,9]}]
+]
checkpoint = "./result_custom" # Custom location for results
```
diff --git a/examples/mnist_quickrun/config.toml b/examples/mnist_quickrun/config.toml
index b01422a743b1631857f799686e13be08f60df34b..ed48c7d20e18d43933b5fb679fbd9c66e460ceb1 100644
--- a/examples/mnist_quickrun/config.toml
+++ b/examples/mnist_quickrun/config.toml
@@ -1,6 +1,6 @@
# This is a minimal TOML file for the MNIST example
# It contains the bare minimum to make the experiment run.
-# See quickstart for more details.
+# See quickstart for more details.
# The TOML is parsed by python as dictionnary with each `[header]`
# as a key. Note the "=" sign and the absence of quotes around keys.
@@ -12,7 +12,7 @@
port = 8765 # Port used, works as-is on most set ups
[data] # Where to find your data
- data_folder = "examples/mnist_quickrun/data_iid"
+ data_folder = "examples/mnist_quickrun/data_iid"
[optim] # Optimization options for both client and server
aggregator = "averaging" # Server aggregation strategy
@@ -37,5 +37,7 @@
batch_size = 128 # Evaluation batch size
[experiment] # What to report during the experiment and where to report it
- metrics=[["multi-classif",{labels = [0,1,2,3,4,5,6,7,8,9]}]] # Accuracy metric
-
+ metrics = [
+ # Multi-label Accuracy, Precision, Recall and F-Score.
+ ["multi-classif", {labels = [0,1,2,3,4,5,6,7,8,9]}]
+ ]
diff --git a/examples/mnist_quickrun/mnist.ipynb b/examples/mnist_quickrun/mnist.ipynb
index d4dc2e6249b9dc33ac19ea9388b2f20d442ec799..b44a6eb714b6826d69352945a4879df6d36782a2 100644
--- a/examples/mnist_quickrun/mnist.ipynb
+++ b/examples/mnist_quickrun/mnist.ipynb
@@ -23,7 +23,9 @@
"id": "Clzf4NTja121"
},
"source": [
- "We first clone the repo, to have both the package itself and the `examples` folder we will use in this tutorial, then naviguate to the package directory, and finally install the required dependencies"
+ "We first clone the repo, to have both the package itself and the `examples` folder we will use in this tutorial, then naviguate to the package directory, and finally install the required dependencies.\n",
+ "\n",
+ "**If you have already cloned the repository and/or installed declearn, you may skip the following commands.** Simply make sure to set your current working directory to the folder under which the `examples/mnist_quickrun` subfolder may be found (as cloned or downloaded from the repo)."
]
},
{
@@ -100,7 +102,9 @@
"source": [
"## The model\n",
"\n",
- "To do this, we will use a simple CNN, defined in `examples/mnist_quickrun/model.py`"
+ "To do this, we will use a simple CNN, defined in `examples/mnist_quickrun/model.py`.\n",
+ "\n",
+ "Here, the model is implemented in TensorFlow, which is merely an implementation detail. If you update the `config.toml` file to use the `examples/mnist_quickrun/model_torch.py`, you will train a model with the same architecture, but implemented with Torch."
]
},
{
@@ -147,7 +151,8 @@
],
"source": [
"from examples.mnist_quickrun.model import network\n",
- "network.summary()"
+ "\n",
+ "network.summary() # network is a `tensorflow.keras.Model` instance"
]
},
{
diff --git a/examples/mnist_quickrun/model.py b/examples/mnist_quickrun/model.py
index 01dd16a094fa00fab9f6c5336f2c7c2825c67cb2..6e0e76162bf022900d026883d61b63e24eb58c9a 100644
--- a/examples/mnist_quickrun/model.py
+++ b/examples/mnist_quickrun/model.py
@@ -1,9 +1,27 @@
+# coding: utf-8
+
+# Copyright 2023 Inria (Institut National de Recherche en Informatique
+# et Automatique)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
"""Simple TensorFlow-backed CNN model for the MNIST quickrun example."""
import tensorflow as tf
from declearn.model.tensorflow import TensorflowModel
+
stack = [
tf.keras.layers.InputLayer(input_shape=(28, 28, 1)),
tf.keras.layers.Conv2D(8, 3, 1, activation="relu"),
diff --git a/examples/mnist_quickrun/model_torch.py b/examples/mnist_quickrun/model_torch.py
new file mode 100644
index 0000000000000000000000000000000000000000..59ed94c75ed28a14191ed55733a8006739757798
--- /dev/null
+++ b/examples/mnist_quickrun/model_torch.py
@@ -0,0 +1,42 @@
+# coding: utf-8
+
+# Copyright 2023 Inria (Institut National de Recherche en Informatique
+# et Automatique)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Simple Torch-backed CNN model for the MNIST quickrun example."""
+
+import torch
+
+from declearn.model.torch import TorchModel
+
+
+stack = [
+ torch.nn.Unflatten(dim=0, unflattened_size=(-1, 1)),
+ torch.nn.Conv2d(1, 8, 3, 1),
+ torch.nn.ReLU(),
+ torch.nn.MaxPool2d(2),
+ torch.nn.Dropout(0.25),
+ torch.nn.Flatten(),
+ torch.nn.Linear(1352, 64),
+ torch.nn.ReLU(),
+ torch.nn.Dropout(0.5),
+ torch.nn.Linear(64, 10),
+ torch.nn.Softmax(dim=-1),
+]
+network = torch.nn.Sequential(*stack)
+
+# This needs to be called "model"; otherwise, a different name must be
+# specified via the experiment's TOML configuration file.
+model = TorchModel(network, loss=torch.nn.CrossEntropyLoss())