Buidling a network from a binary parse tree

I’m experimenting with the Stanford Natural Language Inference Corpus. I want to create a network whose structure is based on the parse trees given in the columns sentence1_parse and sentence2_parse, with weights shared between the nodes of the tree. This will convert the sentence to a vector, and I am hoping to train the network so that for a pair of sentences, the vectors produced will be close to parallel if the gold_label is entailment, close to antiparallel if it is contradiction, and close to perpendicular if the gold_label is neutral’ My code looks like this so far.

def seminormal(x):
    return x/tf.sqrt(1.0+tf.reduce_sum(x**2.0))

class Tree2Vec(object):
    def __init__(self,Dictionary):
        self.Dictionary = Dictionary
        n = len(self.Dictionary)
        initialiser = tf.random_normal_initializer()
        self.embedding = tf.Variable(initialiser((n,256)))
        layer_0_weights = tf.Variable(initialiser((444,768)))
        def layer_0(Input):
            return tf.nn.silu(tf.Dot(layer_0_weights,Input))
        Input = tf.raw_ops.Placeholder(dtype=tf.float32,shape=768)
        layer_1_weights = tf.Variable(initialiser((256,444)))
        def layer_1(Input):
            return seminormal(tf.Dot(layer_1_weights,layer_0(Input)))
        self.fn = layer_1(Input)
    def __call__(self,node):
        level = 0
        splits = []

        for (i,char) in enumerate(node):
            if char == '(':
                if level==0:
            elif char == ')':
                if level == 0:
        tensors = [self.embedding[i] 
                   for i in self.Dictionary.doc2idx(node.split('(')[0].strip().split())]
        for i in range(0,len(splits),2):
        if len(tensors)<3:
        return self.fn(tf.concat(tensors,axis=0))

training = pandas.read_csv('/kaggle/input/stanford-natural-language-inference-corpus/snli_1.0_train.csv').fillna('')
dev = pandas.read_csv('/kaggle/input/stanford-natural-language-inference-corpus/snli_1.0_dev.csv').fillna('')
test = pandas.read_csv('/kaggle/input/stanford-natural-language-inference-corpus/snli_1.0_test.csv').fillna('')
dictionary = gensim.corpora.Dictionary(sentence_corpus(read_corpus(training,
tree2vec = Tree2Vec(dictionary)

def vectorize(series):
    return tf.stack(series.str[1:-1].lower().apply(tree2vec))

left = keras.layers.Input(shape=(1,))
right = keras.layers.Input(shape=(1,))

dotprod = keras.layers.Dot(axes=1)(vectorize(left),vectorize(right))

with tpu_strategy.scope():
    #embedding = ConsistencyEmbedding(82,256,len(dictionary))
    #model = ConsistencyModel(embedding,82)
    model = keras.Model(inputs = [left,right],outputs=[dotprod])

However, I am getting the following error

2023-01-02 16:52:18.814732: W tensorflow/core/distributed_runtime/eager/remote_tensor_handle_data.cc:76] Unable to destroy remote tensor handles. If you are running a tf.function, it usually indicates some op in the graph gets an error: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [768]

InvalidArgumentError                      Traceback (most recent call last)
/tmp/ipykernel_20/2348231237.py in <module>
      5                                                                       dev,
      6                                                                       test)))
----> 7 tree2vec = Tree2Vec(dictionary)
      9 def vectorize(series):

/tmp/ipykernel_20/3505782308.py in __init__(self, Dictionary)
     14             return tf.nn.silu(tf.Dot(layer_0_weights,Input))
     15         Input = tf.raw_ops.Placeholder(dtype=tf.float32,shape=768)
---> 16         layer_1_weights = tf.Variable(initialiser((256,444)))
     17         @tf.function
     18         def layer_1(Input):

/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/init_ops_v2.py in __call__(self, shape, dtype, **kwargs)
    418       shape = kwargs[_PARTITION_SHAPE]
    419     return self._random_generator.random_normal(shape, self.mean, self.stddev,
--> 420                                                 dtype)
    422   def get_config(self):

/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/init_ops_v2.py in random_normal(self, shape, mean, stddev, dtype)
   1071       op = random_ops.random_normal
   1072     return op(
-> 1073         shape=shape, mean=mean, stddev=stddev, dtype=dtype, seed=self.seed)
   1075   def random_uniform(self, shape, minval, maxval, dtype):

/opt/conda/lib/python3.7/site-packages/tensorflow/python/util/dispatch.py in wrapper(*args, **kwargs)
    199     """Call target, and fall back on dispatchers if there is a TypeError."""
    200     try:
--> 201       return target(*args, **kwargs)
    202     except (TypeError, ValueError):
    203       # Note: convert_to_eager_tensor currently raises a ValueError, not a

/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/random_ops.py in random_normal(shape, mean, stddev, dtype, seed, name)
     93     seed1, seed2 = random_seed.get_seed(seed)
     94     rnd = gen_random_ops.random_standard_normal(
---> 95         shape_tensor, dtype, seed=seed1, seed2=seed2)
     96     mul = rnd * stddev_tensor
     97     value = math_ops.add(mul, mean_tensor, name=name)

/opt/conda/lib/python3.7/site-packages/tensorflow/python/ops/gen_random_ops.py in random_standard_normal(shape, dtype, seed, seed2, name)
    635       return _result
    636     except _core._NotOkStatusException as e:
--> 637       _ops.raise_from_not_ok_status(e, name)
    638     except _core._FallbackException:
    639       pass

/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/ops.py in raise_from_not_ok_status(e, name)
   6860   message = e.message + (" name: " + name if name is not None else "")
   6861   # pylint: disable=protected-access
-> 6862   six.raise_from(core._status_to_exception(e.code, message), None)
   6863   # pylint: enable=protected-access

/opt/conda/lib/python3.7/site-packages/six.py in raise_from(value, from_value)

InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder' with dtype float and shape [768] [Op:RandomStandardNormal]

I have two questions

  1. How do I resolve this error?
  2. Is this approach to building the network from the parse trees valid, or is there something I need to revise to make it work?