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)