I was trying to design a model for probing (if sentence length is encoded in encoder representations). However, I am getting an error after I execute the fit() method. I used a pre-trained model to make a new one (probe_model). So that I can check if any information about the sentence is encoded in the representations output by the earlier model.
I have searched on how to resolve this problem before and removing the steps_per_execution=32 parameter doesn't solve this issue.
It gives the following error:
ValueError: Layer model_2 expects 2 input(s), but it received 1 input tensors. Inputs received: [<tf.Tensor 'replicated_input_1:0' shape=(None, 15) dtype=int64>]
def get_model():
model = tf.keras.models.load_model('../input/sentlen/NLI_model.030-0.793.h5')
layer_name = "encoder_layer"
probe_intermediate_layer = tf.keras.Model(inputs=model.input, outputs=model.get_layer(layer_name).output)
probe_intermediate_layer.trainable = False
probe_input = tf.keras.layers.Input(shape=(None,), dtype="int64")
p = probe_intermediate_layer(probe_input)
p = tf.keras.layers.Dense(200, activation = 'relu')(p)
p = tf.keras.layers.Dropout(0.3)(p)
outputs = tf.keras.layers.Dense(5, activation = 'softmax')(p)
probe_model = tf.keras.Model(inputs = probe_input, outputs = outputs)
return probe_model
# detect and init the TPU
tpu = tf.distribute.cluster_resolver.TPUClusterResolver.connect()
# instantiate a distribution strategy
tpu_strategy = tf.distribute.experimental.TPUStrategy(tpu)
# instantiating the model in the strategy scope creates the model on the TPU
with tpu_strategy.scope():
probe_model = get_model()
probe_model.compile(optimizer='rmsprop',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'],steps_per_execution=32)
probe_model.fit(task_train_1,task_train_labels, epochs = 30, batch_size = 1024)
Error stack:
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-af206ebfdc18> in <module>
----> 1 probe_model.fit(task_train_1,task_train_labels, epochs = 30, batch_size = 1024)
/opt/conda/lib/python3.7/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)
1098 _r=1):
1099 callbacks.on_train_batch_begin(step)
-> 1100 tmp_logs = self.train_function(iterator)
1101 if data_handler.should_sync:
1102 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
869 # This is the first call of __call__, so we have to initialize.
870 initializers = []
--> 871 self._initialize(args, kwds, add_initializers_to=initializers)
872 finally:
873 # At this point we know that the initialization is complete (or less
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
724 self._concrete_stateful_fn = (
725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
--> 726 *args, **kwds))
727
728 def invalid_creator_scope(*unused_args, **unused_kwds):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
2967 args, kwargs = None, None
2968 with self._lock:
-> 2969 graph_function, _ = self._maybe_define_function(args, kwargs)
2970 return graph_function
2971
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
3359
3360 self._function_cache.missed.add(call_context_key)
-> 3361 graph_function = self._create_graph_function(args, kwargs)
3362 self._function_cache.primary[cache_key] = graph_function
3363
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
3204 arg_names=arg_names,
3205 override_flat_arg_shapes=override_flat_arg_shapes,
-> 3206 capture_by_value=self._capture_by_value),
3207 self._function_attributes,
3208 function_spec=self.function_spec,
/opt/conda/lib/python3.7/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)
988 _, original_func = tf_decorator.unwrap(python_func)
989
--> 990 func_outputs = python_func(*func_args, **func_kwargs)
991
992 # invariant: `func_outputs` contains only Tensors, CompositeTensors,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
632 xla_context.Exit()
633 else:
--> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds)
635 return out
636
/opt/conda/lib/python3.7/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
975 except Exception as e: # pylint:disable=broad-except
976 if hasattr(e, "ag_error_metadata"):
--> 977 raise e.ag_error_metadata.to_exception(e)
978 else:
979 raise
ValueError: in user code:
/opt/conda/lib/python3.7/site-packages/tensorflow/python/keras/engine/training.py:811 train_function *
for _ in math_ops.range(self._steps_per_execution):
/opt/conda/lib/python3.7/site-packages/tensorflow/python/autograph/operators/control_flow.py:414 for_stmt
symbol_names, opts)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/autograph/operators/control_flow.py:629 _tf_range_for_stmt
opts)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/autograph/operators/control_flow.py:1059 _tf_while_stmt
body, get_state, set_state, init_vars, nulls, symbol_names)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/autograph/operators/control_flow.py:1032 _try_handling_undefineds
_verify_loop_init_vars(init_vars, symbol_names, first_iter_vars)
/opt/conda/lib/python3.7/site-packages/tensorflow/python/autograph/operators/control_flow.py:193 _verify_loop_init_vars
raise ValueError(error_msg)
ValueError: 'outputs' must be defined before the loop.