我在使用 kerase lib 运行深度学习时遇到了问题。在代码下面的第二行。
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)
深度学习中的完整代码是：
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)
错误文字：
Epoch 1/10
--------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-89-8da781e9d890> in <module>
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_outputs` contains 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:805 train_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))
使用
Flatteninput_shape=[32, 18]Flattenkeras.layersmodel.add(Flatten(input_shape=[32, 18]))model.add(LSTM(lstm_out, dropout=0.2, recurrent_dropout=0.2))
model.add(Flatten())
model.add(Dense(2,activation='softmax'))
你的暗淡不一样。正如@Anurag Dhadse建议的那样，你也可以在这个地方添加一个展平。
构建模型时，您可以使用
model.summary()