Merge pull request #173 from Chase-Grajeda/summary-lstnet

LSTNet Implementation

bayesflow/experimental/networks/__init__.py

@@ -2,8 +2,9 @@
 from .coupling_flow import CouplingFlow
 from .deep_set import DeepSet
 from .flow_matching import FlowMatching
-from .inference_network import InferenceNetwork
 from .mlp import MLP
 from .resnet import ResNet
 from .transformers import SetTransformer
-from .summary_network import SummaryNetwork
+
+from .inference_network import InferenceNetwork
+from .summary_network import SummaryNetwork

bayesflow/experimental/networks/lstnet/__init__.py

@@ -0,0 +1 @@
+from .lstnet import LSTNet

bayesflow/experimental/networks/lstnet/lstnet.py

@@ -0,0 +1,64 @@
+import keras
+from bayesflow.experimental.types import Tensor
+from bayesflow.experimental.utils import keras_kwargs
+from keras import layers, Sequential, regularizers
+from keras.saving import (register_keras_serializable)
+from .skip_gru import SkipGRU
+from ...networks.resnet import ResNet
+
+@register_keras_serializable(package="bayesflow.networks.lstnet")
+class LSTNet(keras.Model):
+    """
+    Implements a LSTNet Architecture as described in [1]
+    
+    [1] Y. Zhang and L. Mikelsons, Solving Stochastic Inverse Problems with Stochastic BayesFlow, 
+    2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 
+    Seattle, WA, USA, 2023, pp. 966-972, doi: 10.1109/AIM46323.2023.10196190.
+    
+    TODO: Add proper docstring
+    
+    """
+
+    def __init__(
+        self,
+        cnn_out: int = 128,
+        kernel_size: int = 4,
+        kernel_initializer: str = "glorot_uniform",
+        kernel_regularizer: regularizers.Regularizer | None = None,
+        activation: str = "relu",
+        gru_out: int = 64,
+        skip_outs: list[int] = [32],
+        skip_steps: list[int] = [2],
+        resnet_out: int = 32,
+        **kwargs
+    ):
+        if len(skip_outs) != len(skip_steps):
+            raise ValueError("hidden_out must have same length as skip_steps")
+
+        super().__init__(**keras_kwargs(kwargs))
+
+        # Define model
+        self.model = Sequential()
+        self.conv1 = layers.Conv1D(
+            filters=cnn_out,
+            kernel_size=kernel_size,
+            activation=activation,
+            kernel_initializer=kernel_initializer,
+            kernel_regularizer=kernel_regularizer
+        )        
+        self.bnorm = layers.BatchNormalization()        
+        self.skip_gru = SkipGRU(gru_out, skip_outs, skip_steps)        
+        self.resnet = ResNet(width=resnet_out)
+
+        # Aggregate layers               In:  (batch, time steps, num series)
+        self.model.add(self.conv1)       # -> (batch, reduced time steps, cnn_out)
+        self.model.add(self.bnorm)       # -> (batch, reduced time steps, cnn_out)
+        self.model.add(self.skip_gru)    # -> (batch, _)
+        self.model.add(self.resnet)      # -> (batch, resnet_out)
+
+    def call(self, x: Tensor) -> Tensor:
+        x = self.model(x)
+        return x
+
+    def build(self, input_shape):
+        self.call(keras.ops.zeros(input_shape))

bayesflow/experimental/networks/lstnet/skip_gru.py

@@ -0,0 +1,44 @@
+import keras
+from keras.saving import register_keras_serializable
+from keras import layers
+from bayesflow.experimental.types import Tensor
+from bayesflow.experimental.utils import keras_kwargs
+
+@register_keras_serializable(package="bayesflow.networks.skip_gru")
+class SkipGRU(keras.Model):
+    """
+    Implements a Skip GRU layer as described in [1]
+    
+    [1] Y. Zhang and L. Mikelsons, Solving Stochastic Inverse Problems with Stochastic BayesFlow, 
+    2023 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 
+    Seattle, WA, USA, 2023, pp. 966-972, doi: 10.1109/AIM46323.2023.10196190.
+    
+    TODO: Add proper docstring
+    
+    """
+    def __init__(self, gru_out: int, skip_outs: list[int], skip_steps: list[int], **kwargs):        
+        super().__init__(**keras_kwargs(kwargs))
+        self.gru_out = gru_out
+        self.skip_steps = skip_steps
+        self.gru = layers.GRU(gru_out)
+        self.skip_grus = [layers.GRU(skip_outs[i]) for i in range(len(self.skip_steps))]
+
+    def call(self, x: Tensor) -> Tensor:
+        sgru = self.gru(x)
+        for i, skip_step in enumerate(self.skip_steps):
+            # Reshape, remove skipped time points
+            skip_length = x.shape[1] // skip_step
+            s = x[:, -skip_length * skip_step:, :]
+            s = keras.ops.reshape(s, (-1, s.shape[2], skip_length, skip_step))
+            s = keras.ops.transpose(s, [0, 3, 2, 1])
+            s = keras.ops.reshape(s, (-1, s.shape[2], s.shape[3]))
+
+            # Reapply GRU, add to working tensor
+            s = self.skip_grus[i](s)
+            s = keras.ops.reshape(s, (-1, skip_step * s.shape[1]))
+            sgru = keras.ops.concatenate([sgru, s], axis=1)
+
+        return sgru
+
+    def build(self, input_shape):
+        self.call(keras.ops.zeros(input_shape))