Describe the bug
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When Converting a Keras model containing a Bidirectional LSTM layer, the activation function attribute (activations) is not set in the corresponding lstm onnx operator.
The bug does not seem to appear if we are not in the "Bidirectional" context.

System information

• OS Platform and Distribution (e.g., Linux Ubuntu 20.04*):
• TensorFlow Version: 2.11
• tf2onnx: 1.14.0
• Python version: 3.10

To Reproduce

1: Creating the Keras network with the activation functions set to "relu":

#! /usr/bin/env python3 from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, LSTM, Bidirectional input = Input(shape=(1,1)) model = Bidirectional(LSTM(units=1, return_sequences=True, activation="relu", recurrent_activation="relu"))(input) model = Model(input, model) model.save(f"lstm_issue_activation_function.h5") 

2: converting the model

 #! /usr/bin/env python3 import tensorflow as tf import onnx import sys import tf2onnx target_model = sys.argv[1] model = tf.keras.models.load_model(target_model) spec = (tf.TensorSpec((None,1, 1), tf.float32, name="input"),) output_path = model.name + ".onnx" model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=15, output_path=output_path) onnx.save(model_proto, output_path) 

3: checking the converted model
While inspecting the resulting onnx model, we can see that the attribute "activations" of the lstm node is not set and will default to "sigmoid/tanh" rather than the expected "relu" functions

Screenshots