history = model.fit(X_tr, np.array(y_tr), batch_size=22, epochs=200, validation_split=0.1, verbose=1)

Epoch 1/200
ValueError                                Traceback (most recent call last)
 in 
----> 1 history = model.fit(X_tr, np.array(y_tr), batch_size=22, epochs=200, validation_split=0.1, verbose=1)
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in _method_wrapper(self, *args, **kwargs)
106   def _method_wrapper(self, *args, **kwargs):
107     if not self._in_multi_worker_mode():  # pylint: disable=protected-access
 → 108       return method(self, *args, **kwargs)
109
110     # Running inside run_distribute_coordinator already.
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1096                 batch_size=batch_size):
1097               callbacks.on_train_batch_begin(step)
 → 1098               tmp_logs = train_function(iterator)
1099               if data_handler.should_sync:
1100                 context.async_wait()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in call(self, *args, **kwds)
778       else:
779         compiler = “nonXla”
 → 780         result = self._call(*args, **kwds)
781
782       new_tracing_count = self._get_tracing_count()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
812       # In this case we have not created variables on the first call. So we can
813       # run the first trace but we should fail if variables are created.
 → 814       results = self._stateful_fn(*args, **kwds)
815       if self._created_variables:
816         raise ValueError(“Creating variables on a non-first call to a function”
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, *args, **kwargs)
2826     “”“Calls a graph function specialized to the inputs.”""
2827     with self._lock:
 → 2828       graph_function, args, kwargs = self._maybe_define_function(args, kwargs)
2829     return graph_function._filtered_call(args, kwargs)  # pylint: disable=protected-access
2830
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs)
3208           and self.input_signature is None
3209           and call_context_key in self._function_cache.missed):
 → 3210         return self._define_function_with_shape_relaxation(args, kwargs)
3211
3212       self._function_cache.missed.add(call_context_key)
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _define_function_with_shape_relaxation(self, args, kwargs)
3139           expand_composites=True)
3140
 → 3141     graph_function = self._create_graph_function(
3142         args, kwargs, override_flat_arg_shapes=relaxed_arg_shapes)
3143     self._function_cache.arg_relaxed[rank_only_cache_key] = graph_function
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3063     arg_names = base_arg_names + missing_arg_names
3064     graph_function = ConcreteFunction(
 → 3065         func_graph_module.func_graph_from_py_func(
3066             self._name,
3067             self._python_function,
~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes)
984         _, original_func = tf_decorator.unwrap(python_func)
985
 → 986       func_outputs = python_func(*func_args, **func_kwargs)
987
988       # invariant: func_outputs contains only Tensors, CompositeTensors,
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds)
598         # wrapped allows AutoGraph to swap in a converted function. We give
599         # the function a weak reference to itself to avoid a reference cycle.
 → 600         return weak_wrapped_fn().wrapped(*args, **kwds)
601     weak_wrapped_fn = weakref.ref(wrapped_fn)
602
~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs)
971           except Exception as e:  # pylint:disable=broad-except
972             if hasattr(e, “ag_error_metadata”):
 → 973               raise e.ag_error_metadata.to_exception(e)
974             else:
975               raise
ValueError: in user code:
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:806 train_function  *
    return step_function(self, iterator)
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:796 step_function  **
    outputs = model.distribute_strategy.run(run_step, args=(data,))
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1211 run
    return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2585 call_for_each_replica
    return self._call_for_each_replica(fn, args, kwargs)
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2945 _call_for_each_replica
    return fn(*args, **kwargs)
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:789 run_step  **
    outputs = model.train_step(data)
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:756 train_step
    _minimize(self.distribute_strategy, tape, self.optimizer, loss,
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:2736 _minimize
    gradients = optimizer._aggregate_gradients(zip(gradients,  # pylint: disable=protected-access
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:562 _aggregate_gradients
    filtered_grads_and_vars = _filter_grads(grads_and_vars)
C:\Users\BlackPearl\anaconda3\lib\site-packages\tensorflow\python\keras\optimizer_v2\optimizer_v2.py:1270 _filter_grads
    raise ValueError("No gradients provided for any variable: %s." %

ValueError: No gradients provided for any variable: ['embedding/embeddings:0', 'bidirectional/forward_lstm/lstm_cell_1/kernel:0', 'bidirectional/forward_lstm/lstm_cell_1/recurrent_kernel:0', 'bidirectional/forward_lstm/lstm_cell_1/bias:0', 'bidirectional/backward_lstm/lstm_cell_2/kernel:0', 'bidirectional/backward_lstm/lstm_cell_2/recurrent_kernel:0', 'bidirectional/backward_lstm/lstm_cell_2/bias:0', 'dense/kernel:0', 'dense/bias:0'].

from seqeval.metrics import precision_score, recall_score, f1_score, classification_report
test_pred = model.predict(X_te, verbose=1)