Error converting symbolic tensor object to numpy while running RELERNN_TRAIN

[brooklynnscott00]

While running RELERNN_TRAIN, I ran into the following error which appears to be the result of a failure to convert a tensor object to a numpy array. I ran into this after a fresh install of a conda environment following the same versions of dependencies specified in the documentation (tensorflow/2.2.0, cudatoolkit/10.1.243, and cudnn/7.6.5). Any help would be greatly appreciated!

2024-12-19 09:15:57.795018: I tensorflow/core/platform/cpu_feature_guard.cc:143] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2024-12-19 09:15:57.871059: I tensorflow/core/platform/profile_utils/cpu_utils.cc:102] CPU Frequency: 2944210000 Hz
2024-12-19 09:15:57.871375: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x555559e7dcf0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:
2024-12-19 09:15:57.871560: I tensorflow/compiler/xla/service/service.cc:176]   StreamExecutor device (0): Host, Default Version
2024-12-19 09:15:57.884792: I tensorflow/core/common_runtime/process_util.cc:147] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
2024-12-19 09:16:28.739187: I tensorflow/core/common_runtime/process_util.cc:147] Creating new thread pool with default inter op setting: 2. Tune using inter_op_parallelism_threads for best performance.
Traceback (most recent call last):
  File "/home/brscott4/.conda/envs/relernn/bin/ReLERNN_TRAIN", line 130, in <module>
    main()
  File "/home/brscott4/.conda/envs/relernn/bin/ReLERNN_TRAIN", line 109, in main
    runModels(ModelFuncPointer=GRU_TUNED84,
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/ReLERNN/helpers.py", line 344, in runModels
    model = ModelFuncPointer(x,y)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/ReLERNN/networks.py", line 19, in GRU_TUNED84
    model = layers.Bidirectional(layers.GRU(84,return_sequences=False))(genotype_inputs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/wrappers.py", line 531, in __call__
    return super(Bidirectional, self).__call__(inputs, **kwargs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 922, in __call__
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/wrappers.py", line 644, in call
    y = self.forward_layer(forward_inputs,
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 654, in __call__
    return super(RNN, self).__call__(inputs, **kwargs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py", line 922, in __call__
    outputs = call_fn(cast_inputs, *args, **kwargs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent_v2.py", line 408, in call
    inputs, initial_state, _ = self._process_inputs(inputs, initial_state, None)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 848, in _process_inputs
    initial_state = self.get_initial_state(inputs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 636, in get_initial_state
    init_state = get_initial_state_fn(
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 1910, in get_initial_state
    return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2926, in _generate_zero_filled_state_for_cell
    return _generate_zero_filled_state(batch_size, cell.state_size, dtype)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2944, in _generate_zero_filled_state
    return create_zeros(state_size)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/keras/layers/recurrent.py", line 2939, in create_zeros
    return array_ops.zeros(init_state_size, dtype=dtype)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2677, in wrapped
    tensor = fun(*args, **kwargs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2721, in zeros
    output = _constant_if_small(zero, shape, dtype, name)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/ops/array_ops.py", line 2662, in _constant_if_small
    if np.prod(shape) < 1000:
  File "<__array_function__ internals>", line 180, in prod
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 3045, in prod
    return _wrapreduction(a, np.multiply, 'prod', axis, dtype, out,
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/numpy/core/fromnumeric.py", line 86, in _wrapreduction
    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)
  File "/home/brscott4/.conda/envs/relernn/lib/python3.8/site-packages/tensorflow/python/framework/ops.py", line 748, in __array__
    raise NotImplementedError("Cannot convert a symbolic Tensor ({}) to a numpy"
NotImplementedError: Cannot convert a symbolic Tensor (bidirectional/forward_gru/strided_slice:0) to a numpy array.