I faced a problem when I run deep learning with kerase lib. at the second line below code.
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.15, random_state = 42)
model.fit(X_train, y_train,validation_data = (X_test,y_test),epochs = 10, batch_size=32)
the full code in deep learning is:
from keras.models import Sequential
from keras.layers import Dense, Embedding, LSTM, SpatialDropout1D
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import CountVectorizer
from keras.preprocessing.text import Tokenizer
from keras.preprocessing.sequence import pad_sequences
from keras.utils.np_utils import to_categorical
import re
embed_dim = 128
lstm_out = 196
model = Sequential()
model.add(Embedding(1500, embed_dim,input_length = 18))
model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
model.add(Dense(2,activation='softmax'))
model.compile(loss = 'binary_crossentropy', optimizer='adam',metrics = ['accuracy'])
tokenizer = Tokenizer(num_words=1500, split=' ')
tokenizer.fit_on_texts(output['text'].values)
X = tokenizer.texts_to_sequences(dataset1['text'])
X = pad_sequences(X)
from sklearn.preprocessing import LabelEncoder
Le = LabelEncoder()
y = Le.fit_transform(dataset1['sentiment'])
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.15, random_state = 42)
model.fit(X_train, y_train,validation_data = (X_test,y_test),epochs = 10, batch_size=32)
the text of error:
Epoch 1/10 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) in 1 X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.15, random_state = 42) 2 ----> 3 model.fit(X_train, y_train,validation_data = (X_test,y_test),epochs = 10, batch_size=32)
~\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) 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()~\anaconda3\lib\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()
~\anaconda3\lib\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
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to) 723 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 724 self._concrete_stateful_fn = ( --> 725 self._stateful_fn._get_concrete_function_internal_garbage_collected(
pylint: disable=protected-access
726 *args, **kwds)) 727~\anaconda3\lib\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
~\anaconda3\lib\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
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3194 arg_names = base_arg_names + missing_arg_names 3195
graph_function = ConcreteFunction( -> 3196 func_graph_module.func_graph_from_py_func( 3197 self._name, 3198 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) 988 _, original_func = tf_decorator.unwrap(python_func) 989 --> 990 func_outputs = python_func(*func_args, **func_kwargs) 991 992 # invariant:
func_outputscontains only Tensors, CompositeTensors,~\anaconda3\lib\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
~\anaconda3\lib\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:
C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:805train_function * return step_function(self, iterator) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run return self.extended.call_for_each_replica(fn, args=args, kwargs=kwargs) C:\Users\amal\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica return self.call_for_each_replica(fn, args, kwargs) C:\Users\amal\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 call_for_each_replica return fn(*args, **kwargs) C:\Users\amal\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step ** outputs = model.train_step(data) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:755 train_step loss = self.compiled_loss( C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:203 call loss_value = loss_obj(y_t, y_p, sample_weight=sw) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:152 call losses = call_fn(y_true, y_pred) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:256 call ** return ag_fn(y_true, y_pred, **self.fn_kwargs) C:\Users\amal\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper return target(*args, **kwargs) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:1608 binary_crossentropy K.binary_crossentropy(y_true, y_pred, from_logits=from_logits), axis=-1) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper return target(*args, **kwargs) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4979 binary_crossentropy return nn.sigmoid_cross_entropy_with_logits(labels=target, logits=output) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper return target(*args, **kwargs) C:\Users\amal_\anaconda3\lib\site-packages\tensorflow\python\ops\nn_impl.py:173 sigmoid_cross_entropy_with_logits
raise ValueError("logits and labels must have the same shape (%s vs %s)" %
ValueError: logits and labels must have the same shape ((32, 2) vs (32, 1))
