[no ci] bf 1.1 to 2.0 notebook: minor additions/edits

vpratz · vpratz · commit ec938ed40b61 · 2025-05-04T18:50:57.000Z
diff --git a/examples/From_BayesFlow_1.1_to_2.0.ipynb b/examples/From_BayesFlow_1.1_to_2.0.ipynb
@@ -15,9 +15,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Long-time users of BayesFlow will notice that with the update to version 2.0 many things have changed. This short guide aims to clarify some of those changes. Users familiar with the previous Quickstart guide will notice that this notebook follows a similar structure, but assumes that users are already familiar with BayesFlow. We omit many of the the mathematical explanations in favor of demonstrating the differences in workflow. For a more detailed explanation of the BayesFlow framework, users should read, for example, the Starter Notebook on Bayesian Linear Regression.\n",
+    "Long-time users of BayesFlow will notice that with the update to version 2.0 many things have changed. This short guide aims to clarify some of those changes. Users familiar with the previous Quickstart guide will notice that this notebook follows a similar structure, but assumes that users are already familiar with BayesFlow. We omit many of the mathematical explanations in favor of demonstrating the differences in workflow. For a more detailed explanation of the BayesFlow framework, users should read, for example, the Starter Notebook on Bayesian Linear Regression.\n",
     "\n",
-    "Additionally to avoid confusion, similarly named objects from _BayesFlow v1.1_ will have `1.1` after their name, whereas those from _BayesFlow v2.0_ will not. Finally, a short table with a summary of the function call changes is provided at the end of the guide. "
+    "Additionally, to avoid confusion, similarly named objects from _BayesFlow v1.1_ will have `1.1` after their name, whereas those from _BayesFlow v2.0_ will not. Finally, a short table with a summary of the function call changes is provided at the end of the guide. "
    ]
   },
   {
@@ -26,7 +26,7 @@
    "source": [
     "## Keras Framework\n",
     "\n",
-    "BayesFlow 2.0 looks quite different from BayesFlow 1.1 because the library was refactored to replace the old backend, TensorFlow, with the new [Keras](https://keras.io) API. Users can now choose their preferred backend among the machine learning frameworks `TensorFlow`, `JAX` and `PyTorch`."
+    "BayesFlow 2.0 looks quite different from BayesFlow 1.1 because the library was refactored to replace the old backend, TensorFlow, with the new [Keras3](https://keras.io) API. Users can now choose their preferred backend among the machine learning frameworks [TensorFlow](https://github.com/tensorflow/tensorflow), [JAX](https://github.com/google/jax) and [PyTorch](https://github.com/pytorch/pytorch)."
    ]
   },
   {
@@ -52,7 +52,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In general, BayesFlow 2.0 relies much more on dictionaries since parameters are now named by convention. Many objects now expect a dictionary, and parameters and data are returned as dictionaries as well. "
+    "In general, BayesFlow 2.0 relies much more on dictionaries since parameters and data are now named by convention. Many objects now expect a dictionary, and parameters and data are returned as dictionaries as well. "
    ]
   },
   {
@@ -67,7 +67,7 @@
     "\n",
     "Previously users would define a prior function and pass it to a `Prior1.1` object to sample prior values. The likelihood would also be specified via a function and passed to a `Simulator1.1` wrapper to produce observations for given parameter values. These were then combined in the `GenerativeModel1.1`.  \n",
     "\n",
-    "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.simulator.make_simulator` offers a convenient wrapper to create the appropriate simulator for different settings."
+    "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.make_simulator` offers a convenient wrapper to create the appropriate simulator for different settings."
    ]
   },
   {
@@ -217,7 +217,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For the inference network there are now several implemented architectures for users to choose from. They are `FlowMatching`, `ConsistencyModel`, and `CouplingFlow`. For this demonstration we use `FlowMatching`, for explanations on the different models please refer to the other examples and the API documentation. "
+    "The previous version only featured the `InvertibleNetwork1.1` class as inference network. As the generative modeling landscape has evolved since then, there are now several implemented architectures for users to choose from. `CouplingFlow` features an architecture similar to `InvertibleNetwork1.1`. `FlowMatching` is a continuous flow architecture, which can express more complex distributions, at the cost of higher inference time. For this demonstration we use `FlowMatching`. For explanations on the different models please refer to the other examples and the API documentation. "
    ]
   },
   {
@@ -377,10 +377,11 @@
     "| BayesFlow v1.1       | BayesFlow v2.0 usage |\n",
     "| :--------| :---------| \n",
     "| `Prior`, `Simulator` | Defunct and no longer standalone objects but incorporated into `bf.simulators.Simulator` | \n",
-    "|`GenerativeModel`     | Defunct with it's functionality having been taken over by `bf.simulators.make_simulator` | \n",
+    "| `GenerativeModel`    | Defunct with it's functionality having been taken over by `bf.make_simulator` | \n",
     "| `training.configurator` | Functionality taken over by `bf.adapters.Adapter` | \n",
     "|`Trainer` | Functionality taken over by `fit` method of `bf.approximators.Approximator` | \n",
-    "| `AmortizedPosterior`, `AmortizedLikelihood` | Functionality taken over by `ContinuousApproximator` | "
+    "| `AmortizedPosterior`, `AmortizedLikelihood` | Functionality taken over by `ContinuousApproximator` |\n",
+    "| `InvertibleNetwork` | Functionality taken over by `CouplingFlow`, but also other networks that are subclasses of `InferenceNetwork`, e.g. `FlowMatching` |"
    ]
   }
  ],

Original file line number	Diff line number	Diff line change
`@@ -15,9 +15,9 @@`
`15`	`15`	`"cell_type": "markdown",`
`16`	`16`	`"metadata": {},`
`17`	`17`	`"source": [`
`18`		- "Long-time users of BayesFlow will notice that with the update to version 2.0 many things have changed. This short guide aims to clarify some of those changes. Users familiar with the previous Quickstart guide will notice that this notebook follows a similar structure, but assumes that users are already familiar with BayesFlow. We omit many of the the mathematical explanations in favor of demonstrating the differences in workflow. For a more detailed explanation of the BayesFlow framework, users should read, for example, the Starter Notebook on Bayesian Linear Regression.\n",
	`18`	+ "Long-time users of BayesFlow will notice that with the update to version 2.0 many things have changed. This short guide aims to clarify some of those changes. Users familiar with the previous Quickstart guide will notice that this notebook follows a similar structure, but assumes that users are already familiar with BayesFlow. We omit many of the mathematical explanations in favor of demonstrating the differences in workflow. For a more detailed explanation of the BayesFlow framework, users should read, for example, the Starter Notebook on Bayesian Linear Regression.\n",
`19`	`19`	`"\n",`
`20`		- "Additionally to avoid confusion, similarly named objects from _BayesFlow v1.1_ will have `1.1` after their name, whereas those from _BayesFlow v2.0_ will not. Finally, a short table with a summary of the function call changes is provided at the end of the guide. "
	`20`	+ "Additionally, to avoid confusion, similarly named objects from _BayesFlow v1.1_ will have `1.1` after their name, whereas those from _BayesFlow v2.0_ will not. Finally, a short table with a summary of the function call changes is provided at the end of the guide. "
`21`	`21`	`]`
`22`	`22`	`},`
`23`	`23`	`{`
`@@ -26,7 +26,7 @@`
`26`	`26`	`"source": [`
`27`	`27`	`"## Keras Framework\n",`
`28`	`28`	`"\n",`
`29`		- "BayesFlow 2.0 looks quite different from BayesFlow 1.1 because the library was refactored to replace the old backend, TensorFlow, with the new [Keras](https://keras.io) API. Users can now choose their preferred backend among the machine learning frameworks `TensorFlow`, `JAX` and `PyTorch`."
	`29`	`+ "BayesFlow 2.0 looks quite different from BayesFlow 1.1 because the library was refactored to replace the old backend, TensorFlow, with the new [Keras3](https://keras.io) API. Users can now choose their preferred backend among the machine learning frameworks [TensorFlow](https://github.com/tensorflow/tensorflow), [JAX](https://github.com/google/jax) and [PyTorch](https://github.com/pytorch/pytorch)."`
`30`	`30`	`]`
`31`	`31`	`},`
`32`	`32`	`{`
`@@ -52,7 +52,7 @@`
`52`	`52`	`"cell_type": "markdown",`
`53`	`53`	`"metadata": {},`
`54`	`54`	`"source": [`
`55`		`- "In general, BayesFlow 2.0 relies much more on dictionaries since parameters are now named by convention. Many objects now expect a dictionary, and parameters and data are returned as dictionaries as well. "`
	`55`	`+ "In general, BayesFlow 2.0 relies much more on dictionaries since parameters and data are now named by convention. Many objects now expect a dictionary, and parameters and data are returned as dictionaries as well. "`
`56`	`56`	`]`
`57`	`57`	`},`
`58`	`58`	`{`
`@@ -67,7 +67,7 @@`
`67`	`67`	`"\n",`
`68`	`68`	"Previously users would define a prior function and pass it to a `Prior1.1` object to sample prior values. The likelihood would also be specified via a function and passed to a `Simulator1.1` wrapper to produce observations for given parameter values. These were then combined in the `GenerativeModel1.1`. \n",
`69`	`69`	`"\n",`
`70`		- "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.simulator.make_simulator` offers a convenient wrapper to create the appropriate simulator for different settings."
	`70`	+ "In 2.0 we no longer make use of the `Prior1.1`, `Simulator1.1` or `GenerativeModel1.1` objects. Instead, the `Simulator` class comprises the whole functionality, taking the role of the `GenerativeModel1.1`. It directly produces joint samples from prior and likelihood, without creating separate `Prior1.1` and `Simulator1.1` objects first. The `bf.make_simulator` offers a convenient wrapper to create the appropriate simulator for different settings."
`71`	`71`	`]`
`72`	`72`	`},`
`73`	`73`	`{`
`@@ -217,7 +217,7 @@`
`217`	`217`	`"cell_type": "markdown",`
`218`	`218`	`"metadata": {},`
`219`	`219`	`"source": [`
`220`		- "For the inference network there are now several implemented architectures for users to choose from. They are `FlowMatching`, `ConsistencyModel`, and `CouplingFlow`. For this demonstration we use `FlowMatching`, for explanations on the different models please refer to the other examples and the API documentation. "
	`220`	+ "The previous version only featured the `InvertibleNetwork1.1` class as inference network. As the generative modeling landscape has evolved since then, there are now several implemented architectures for users to choose from. `CouplingFlow` features an architecture similar to `InvertibleNetwork1.1`. `FlowMatching` is a continuous flow architecture, which can express more complex distributions, at the cost of higher inference time. For this demonstration we use `FlowMatching`. For explanations on the different models please refer to the other examples and the API documentation. "
`221`	`221`	`]`
`222`	`222`	`},`
`223`	`223`	`{`
`@@ -377,10 +377,11 @@`
`377`	`377`	`"\| BayesFlow v1.1 \| BayesFlow v2.0 usage \|\n",`
`378`	`378`	`"\| :--------\| :---------\| \n",`
`379`	`379`	"\| `Prior`, `Simulator` \| Defunct and no longer standalone objects but incorporated into `bf.simulators.Simulator` \| \n",
`380`		- "\|`GenerativeModel` \| Defunct with it's functionality having been taken over by `bf.simulators.make_simulator` \| \n",
	`380`	+ "\| `GenerativeModel` \| Defunct with it's functionality having been taken over by `bf.make_simulator` \| \n",
`381`	`381`	"\| `training.configurator` \| Functionality taken over by `bf.adapters.Adapter` \| \n",
`382`	`382`	"\|`Trainer` \| Functionality taken over by `fit` method of `bf.approximators.Approximator` \| \n",
`383`		- "\| `AmortizedPosterior`, `AmortizedLikelihood` \| Functionality taken over by `ContinuousApproximator` \| "
	`383`	+ "\| `AmortizedPosterior`, `AmortizedLikelihood` \| Functionality taken over by `ContinuousApproximator` \|\n",
	`384`	+ "\| `InvertibleNetwork` \| Functionality taken over by `CouplingFlow`, but also other networks that are subclasses of `InferenceNetwork`, e.g. `FlowMatching` \|"
`384`	`385`	`]`
`385`	`386`	`}`
`386`	`387`	`],`