# Errors when fine-tuning using Keras **URL:** \ **Category:** 🤗Transformers\ **Created:** [February 18, 2022, 7:37pm UTC](https://discuss.huggingface.co/t/errors-when-fine-tuning-using-keras/14879 "2022-02-18T19:37:09Z")\ **Posts on this page:** 1\ **Page:** 1
**Author:** ![jwei4](https://avatars.discourse-cdn.com/v4/letter/j/ccd318/32.png) [@jwei4](https://discuss.huggingface.co/u/jwei4)\ **Post date:** [February 18, 2022, 7:37pm UTC](https://discuss.huggingface.co/t/errors-when-fine-tuning-using-keras/14879/1 "2022-02-18T19:37:09Z")
Hi community, I am in the process of fine-tuning a pegasus using Keras with my custom dataset for abstractive text summarization task. But, it raised the errors of asking to specify either decoder\_input\_ids or decoder\_inputs\_embeds. I think that I am having trouble to feed to Keras model the right format of data inputs. I am processing my input data without using data\_collector function provided by HuggingFace on purpose. I have attached my code and errors msg below. If anyone could share any thoughts or feedback on this matter, I would really appreciate it. Thank you so much! ```auto import pandas as pd #from sklearn.model_selection import train_test_split import numpy as np import json from typing import Dict, List, Tuple from tqdm import tqdm import tensorflow as tf print(tf. __version__ ) import transformers from transformers import AutoTokenizer, TFAutoModelForSeq2SeqLM # PegasusTokenizer, TFPegasusForConditionalGeneration, import warnings warnings.filterwarnings('ignore') pd.set_option("display.max_rows", None, "display.max_columns", None) # Download model and tokenizer from Huggingface model_id = "google/pegasus-xsum" model = TFAutoModelForSeq2SeqLM.from_pretrained(model_id) tokenizer = AutoTokenizer.from_pretrained(model_id) # Read in text-summary dataset from local non_null_summary_df = pd.read_excel("baseline_data.xlsx") phrase_list = non_null_summary_df['phrase'].apply(lambda x: str(x)).tolist() summary_list = non_null_summary_df['summarization'].apply(lambda x: str(x)).tolist() # Shuffle the dataset shuffle_df = non_null_summary_df.sample(frac=1) # Define a size for train set train_size = int(0.7 * len(non_null_summary_df)) # Split the dataset train_set = shuffle_df[:train_size] test_set = shuffle_df[train_size:] X_train = train_set['phrase'].apply(lambda x: str(x)).tolist() y_train = train_set['summarization'].apply(lambda x: str(x)).tolist() X_test = test_set['phrase'].apply(lambda x: str(x)).tolist() y_test = test_set['summarization'].apply(lambda x: str(x)).tolist() # Tokenized the text and summaries and return tf tensors train_input_tf_dataset = tokenizer(X_train, max_length=1024, truncation=True, padding=True, return_tensors="tf") train_output_tf_ts = tokenizer(y_train, max_length=1024, truncation=True, padding=True, return_tensors="tf").input_ids eval_input_tf_dataset = tokenizer(X_test, max_length=1024, truncation=True, padding=True, return_tensors="tf") eval_output_tf_ts = tokenizer(y_test, max_length=1024, truncation=True, padding=True, return_tensors="tf").input_ids # Create train and eval features train_features = {'input_ids': train_input_tf_dataset.input_ids, 'attention_mask': train_input_tf_dataset.attention_mask} eval_features = {'input_ids': eval_input_tf_dataset.input_ids, 'attention_masks': eval_input_tf_dataset.attention_mask} # Shuffle and batch the features train_tf_dataset = tf.data.Dataset.from_tensor_slices((train_features, train_output_tf_ts)) train_tf_dataset = train_tf_dataset.shuffle(len(X_train)).batch(8) eval_tf_dataset = tf.data.Dataset.from_tensor_slices((eval_features, eval_output_tf_ts)) eval_tf_dataset = train_tf_dataset.shuffle(len(X_test)).batch(8) # compile model model.compile(loss='categorical_crossentropy', optimizer='adam') # Train the model model.fit(train_tf_dataset, validation_data=eval_tf_dataset, epochs=3) ``` Error msg: ```auto --------------------------------------------------------------------------- ValueError Traceback (most recent call last) Input In [18], in ----> 1 model.fit(train_tf_dataset, validation_data=eval_tf_dataset, epochs=3) File ~/work/.local/lib/python3.8/site-packages/keras/engine/training.py:1184, in Model.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) 1177 with tf.profiler.experimental.Trace( 1178 'train', 1179 epoch_num=epoch, 1180 step_num=step, 1181 batch_size=batch_size, 1182 _r=1): 1183 callbacks.on_train_batch_begin(step) -> 1184 tmp_logs = self.train_function(iterator) 1185 if data_handler.should_sync: 1186 context.async_wait() File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:885, in Function. __call__ (self, *args, **kwds) 882 compiler = "xla" if self._jit_compile else "nonXla" 884 with OptionalXlaContext(self._jit_compile): --> 885 result = self._call(*args, **kwds) 887 new_tracing_count = self.experimental_get_tracing_count() 888 without_tracing = (tracing_count == new_tracing_count) File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:933, in Function._call(self, *args, **kwds) 930 try: 931 # This is the first call of __call__ , so we have to initialize. 932 initializers = [] --> 933 self._initialize(args, kwds, add_initializers_to=initializers) 934 finally: 935 # At this point we know that the initialization is complete (or less 936 # interestingly an exception was raised) so we no longer need a lock. 937 self._lock.release() File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:759, in Function._initialize(self, args, kwds, add_initializers_to) 756 self._lifted_initializer_graph = lifted_initializer_graph 757 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 758 self._concrete_stateful_fn = ( --> 759 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access 760 *args, **kwds)) 762 def invalid_creator_scope(*unused_args, **unused_kwds): 763 """Disables variable creation.""" File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3066, in Function._get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 3064 args, kwargs = None, None 3065 with self._lock: -> 3066 graph_function, _ = self._maybe_define_function(args, kwargs) 3067 return graph_function File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3463, in Function._maybe_define_function(self, args, kwargs) 3459 return self._define_function_with_shape_relaxation( 3460 args, kwargs, flat_args, filtered_flat_args, cache_key_context) 3462 self._function_cache.missed.add(call_context_key) -> 3463 graph_function = self._create_graph_function(args, kwargs) 3464 self._function_cache.primary[cache_key] = graph_function 3466 return graph_function, filtered_flat_args File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py:3298, in Function._create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3293 missing_arg_names = [ 3294 "%s_%d" % (arg, i) for i, arg in enumerate(missing_arg_names) 3295 ] 3296 arg_names = base_arg_names + missing_arg_names 3297 graph_function = ConcreteFunction( -> 3298 func_graph_module.func_graph_from_py_func( 3299 self._name, 3300 self._python_function, 3301 args, 3302 kwargs, 3303 self.input_signature, 3304 autograph=self._autograph, 3305 autograph_options=self._autograph_options, 3306 arg_names=arg_names, 3307 override_flat_arg_shapes=override_flat_arg_shapes, 3308 capture_by_value=self._capture_by_value), 3309 self._function_attributes, 3310 function_spec=self.function_spec, 3311 # Tell the ConcreteFunction to clean up its graph once it goes out of 3312 # scope. This is not the default behavior since it gets used in some 3313 # places (like Keras) where the FuncGraph lives longer than the 3314 # ConcreteFunction. 3315 shared_func_graph=False) 3316 return graph_function File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:1007, 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, acd_record_initial_resource_uses) 1004 else: 1005 _, original_func = tf_decorator.unwrap(python_func) -> 1007 func_outputs = python_func(*func_args, **func_kwargs) 1009 # invariant: `func_outputs` contains only Tensors, CompositeTensors, 1010 # TensorArrays and `None`s. 1011 func_outputs = nest.map_structure(convert, func_outputs, 1012 expand_composites=True) File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py:668, in Function._defun_with_scope..wrapped_fn(*args, **kwds) 664 with default_graph._variable_creator_scope(scope, priority=50): # pylint: disable=protected-access 665 # __wrapped__ allows AutoGraph to swap in a converted function. We give 666 # the function a weak reference to itself to avoid a reference cycle. 667 with OptionalXlaContext(compile_with_xla): --> 668 out = weak_wrapped_fn(). __wrapped__ (*args, **kwds) 669 return out File ~/work/.local/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py:994, in func_graph_from_py_func..wrapper(*args, **kwargs) 992 except Exception as e: # pylint:disable=broad-except 993 if hasattr(e, "ag_error_metadata"): --> 994 raise e.ag_error_metadata.to_exception(e) 995 else: 996 raise ValueError: in user code: /home/jovyan/work/.local/lib/python3.8/site-packages/keras/engine/training.py:853 train_function * return step_function(self, iterator) /home/jovyan/work/.local/lib/python3.8/site-packages/transformers/models/pegasus/modeling_tf_pegasus.py:1433 call * outputs = self.model( /home/jovyan/work/.local/lib/python3.8/site-packages/transformers/models/pegasus/modeling_tf_pegasus.py:1190 call * decoder_outputs = self.decoder( /home/jovyan/work/.local/lib/python3.8/site-packages/transformers/models/pegasus/modeling_tf_pegasus.py:974 call * raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds") ValueError: You have to specify either decoder_input_ids or decoder_inputs_embeds ```