add custom sequential to fix #491

Author: LarsKue

bayesflow/diagnostics/metrics/classifier_two_sample_test.py

@@ -5,7 +5,7 @@
 import keras
 
 from bayesflow.utils.exceptions import ShapeError
-from bayesflow.networks import MLP
+from bayesflow.networks import MLP, Sequential
 
 
 def classifier_two_sample_test(
@@ -96,7 +96,7 @@ def classifier_two_sample_test(
     labels = labels[shuffle_idx]
 
     # Create and train classifier with optional stopping
-    classifier = keras.Sequential(
+    classifier = Sequential(
         [MLP(widths=mlp_widths, **kwargs.get("mlp_kwargs", {})), keras.layers.Dense(1, activation="sigmoid")]
     )
 

bayesflow/networks/__init__.py

@@ -12,6 +12,7 @@
 from .point_inference_network import PointInferenceNetwork
 from .mlp import MLP
 from .fusion_network import FusionNetwork
+from .sequential import Sequential
 from .summary_network import SummaryNetwork
 from .time_series_network import TimeSeriesNetwork
 from .transformers import SetTransformer, TimeSeriesTransformer, FusionTransformer

bayesflow/networks/mlp/mlp.py

@@ -3,14 +3,15 @@
 
 import keras
 
-from bayesflow.utils import sequential_kwargs
+from bayesflow.utils import layer_kwargs
 from bayesflow.utils.serialization import deserialize, serializable, serialize
 
+from ..sequential import Sequential
 from ..residual import Residual
 
 
 @serializable("bayesflow.networks")
-class MLP(keras.Sequential):
+class MLP(Sequential):
     """
     Implements a simple configurable MLP with optional residual connections and dropout.
 
@@ -67,40 +68,44 @@ def __init__(
         self.norm = norm
         self.spectral_normalization = spectral_normalization
 
-        layers = []
+        blocks = []
 
         for width in widths:
-            layer = self._make_layer(
+            block = self._make_block(
                 width, activation, kernel_initializer, residual, dropout, norm, spectral_normalization
             )
-            layers.append(layer)
+            blocks.append(block)
 
-        super().__init__(layers, **sequential_kwargs(kwargs))
+        super().__init__(*blocks, **kwargs)
 
     def build(self, input_shape=None):
         if self.built:
             # building when the network is already built can cause issues with serialization
             # see https://github.com/keras-team/keras/issues/21147
             return
 
-        # we only care about the last dimension, and using ... signifies to keras.Sequential
-        # that any number of batch dimensions is valid (which is what we want for all sublayers)
-        # we also have to avoid calling super().build() because this causes
-        # shape errors when building on non-sets but doing inference on sets
-        # this is a work-around for https://github.com/keras-team/keras/issues/21158
-        input_shape = (..., input_shape[-1])
-
         for layer in self._layers:
             layer.build(input_shape)
             input_shape = layer.compute_output_shape(input_shape)
 
+    def call(self, x, training=None, mask=None):
+        for layer in self._layers:
+            kwargs = {}
+            if layer._call_has_mask_arg:
+                kwargs["mask"] = mask
+            if layer._call_has_training_arg and training is not None:
+                kwargs["training"] = training
+
+            x = layer(x, **kwargs)
+        return x
+
     @classmethod
     def from_config(cls, config, custom_objects=None):
         return cls(**deserialize(config, custom_objects=custom_objects))
 
     def get_config(self):
         base_config = super().get_config()
-        base_config = sequential_kwargs(base_config)
+        base_config = layer_kwargs(base_config)
 
         config = {
             "widths": self.widths,
@@ -115,7 +120,7 @@ def get_config(self):
         return base_config | serialize(config)
 
     @staticmethod
-    def _make_layer(width, activation, kernel_initializer, residual, dropout, norm, spectral_normalization):
+    def _make_block(width, activation, kernel_initializer, residual, dropout, norm, spectral_normalization):
         layers = []
 
         dense = keras.layers.Dense(width, kernel_initializer=kernel_initializer)
@@ -148,4 +153,4 @@ def _make_layer(width, activation, kernel_initializer, residual, dropout, norm,
         if residual:
             return Residual(*layers)
 
-        return keras.Sequential(layers)
+        return Sequential(layers)

bayesflow/networks/residual/residual.py

@@ -6,9 +6,11 @@
 from bayesflow.utils import sequential_kwargs
 from bayesflow.utils.serialization import deserialize, serializable, serialize
 
+from ..sequential import Sequential
+
 
 @serializable("bayesflow.networks")
-class Residual(keras.Sequential):
+class Residual(Sequential):
     def __init__(self, *layers: keras.Layer, **kwargs):
         if len(layers) == 1 and isinstance(layers[0], Sequence):
             layers = layers[0]

bayesflow/networks/sequential/__init__.py

@@ -0,0 +1 @@
+from .sequential import Sequential

bayesflow/networks/sequential/sequential.py

@@ -0,0 +1,65 @@
+from collections.abc import Sequence
+import keras
+
+from bayesflow.utils import layer_kwargs
+from bayesflow.utils.serialization import deserialize, serializable, serialize
+
+
+@serializable("bayesflow.networks")
+class Sequential(keras.Layer):
+    def __init__(self, *layers: keras.Layer | Sequence[keras.Layer], **kwargs):
+        super().__init__(**layer_kwargs(kwargs))
+        if len(layers) == 1 and isinstance(layers[0], Sequence):
+            layers = layers[0]
+
+        self._layers = layers
+
+    def build(self, input_shape):
+        if self.built:
+            # building when the network is already built can cause issues with serialization
+            # see https://github.com/keras-team/keras/issues/21147
+            return
+
+        for layer in self._layers:
+            layer.build(input_shape)
+            input_shape = layer.compute_output_shape(input_shape)
+
+    def call(self, inputs, training=None, mask=None):
+        x = inputs
+        for layer in self._layers:
+            kwargs = self._make_kwargs_for_layer(layer, training, mask)
+            x = layer(x, **kwargs)
+        return x
+
+    def compute_output_shape(self, input_shape):
+        for layer in self._layers:
+            input_shape = layer.compute_output_shape(input_shape)
+
+        return input_shape
+
+    def get_config(self):
+        base_config = super().get_config()
+        base_config = layer_kwargs(base_config)
+
+        config = {
+            "layers": [serialize(layer) for layer in self._layers],
+        }
+
+        return base_config | config
+
+    @classmethod
+    def from_config(cls, config, custom_objects=None):
+        return cls(**deserialize(config, custom_objects=custom_objects))
+
+    @property
+    def layers(self):
+        return self._layers
+
+    @staticmethod
+    def _make_kwargs_for_layer(layer, training, mask):
+        kwargs = {}
+        if layer._call_has_mask_arg:
+            kwargs["mask"] = mask
+        if layer._call_has_training_arg and training is not None:
+            kwargs["training"] = training
+        return kwargs

tests/test_networks/test_mlp/conftest.py

@@ -3,9 +3,24 @@
 from bayesflow.networks import MLP
 
 
+@pytest.fixture(params=[None, 0.0, 0.1])
+def dropout(request):
+    return request.param
+
+
+@pytest.fixture(params=[None, "batch"])
+def norm(request):
+    return request.param
+
+
+@pytest.fixture(params=[False, True])
+def residual(request):
+    return request.param
+
+
 @pytest.fixture()
-def mlp():
-    return MLP([64, 64])
+def mlp(dropout, norm, residual):
+    return MLP([64, 64], dropout=dropout, norm=norm, residual=residual)
 
 
 @pytest.fixture()

tests/test_networks/test_mlp/test_mlp.py

@@ -2,7 +2,7 @@
 
 from bayesflow.utils.serialization import deserialize, serialize
 
-from ...utils import assert_models_equal
+from ...utils import assert_layers_equal
 
 
 def test_serialize_deserialize(mlp, build_shapes):
@@ -21,4 +21,4 @@ def test_save_and_load(tmp_path, mlp, build_shapes):
     keras.saving.save_model(mlp, tmp_path / "model.keras")
     loaded = keras.saving.load_model(tmp_path / "model.keras")
 
-    assert_models_equal(mlp, loaded)
+    assert_layers_equal(mlp, loaded)