构建模型的代码，我遇到的问题是当我尝试加载模型并应用到测试数据集时，会得到以下错误：
learning_rate=0.001epochs = 10decay_rate = learning_rate / epochsdef scheduler(epochs, lr): if epochs == 15: lr = 0.001 return lr else: lr = lr * tensorflow.math.exp(-0.1) return lrcallback = keras.callbacks.LearningRateScheduler(scheduler) wv_model = Sequential()# Add embedding layer # No of output dimenstions is 100 as we embedded with Word2Vec 100dEmbed_Layer = Embedding(vocab_size, 100, weights=[embedding_matrix], input_length=(MAX_SEQUENCE_LENGTH,), trainable=True)# define Inputsreview_input = Input(shape=(MAX_SEQUENCE_LENGTH,),dtype= 'int32',name = 'review_input')review_embedding = Embed_Layer(review_input)Flatten_Layer = Flatten()review_flatten = Flatten_Layer(review_embedding)output_size = 2dense1 = Dense(100,activation='relu')(review_flatten)dense2 = Dense(32,activation='relu')(dense1)predict = Dense(5, activation='softmax')(dense2)wv_model = Model(inputs=[review_input],outputs=[predict])# wv_model.compile(optimizer='adam',loss='categorical_crossentropy',metrics=['acc'])opt = keras.optimizers.SGD(lr = 0.01, momentum=0.8, decay=0.0)wv_model.compile(loss='mean_squared_error', optimizer=opt, metrics=['mean_squared_error'])tensorboard = TensorBoard( log_dir="logs", histogram_freq=1, write_graph=True, write_images=False, update_freq="epoch", profile_batch=2, embeddings_freq=0, embeddings_metadata=None)keras_callbacks = [tensorboard]checkpoint = ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, save_best_only=True)stp = keras.callbacks.EarlyStopping(patience=4)callbacks_list = [checkpoint,stp, tensorboard,callback]wv_model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=256, verbose=1, callbacks=callbacks_list)eval = wv_model.evaluate(X_test, y_test)[1]print(eval)wv_model.load_weights('./models/best_model.h5')print(wv_model.summary())输出：
Layer (type) Output Shape Param # =================================================================review_input (InputLayer) [(None, 100)] 0 _________________________________________________________________embedding_8 (Embedding) (None, 100, 100) 22228800 _________________________________________________________________flatten_8 (Flatten) (None, 10000) 0 _________________________________________________________________dense_24 (Dense) (None, 100) 1000100 _________________________________________________________________dense_25 (Dense) (None, 32) 3232 _________________________________________________________________dense_26 (Dense) (None, 5) 165 =================================================================Total params: 23,232,297Trainable params: 23,232,297Non-trainable params: 0_________________________________________________________________None验证数据集：
predictions = load_model('./models/best_model.h5').predict(X12_test)print("y_test", y_test)print("predictions", predictions)print("validation set RMSE ", rmse2(predictions, y_test))y_test = y_test.overall.values输出：
WARNING:tensorflow:Model was constructed with shape (None, 100) for input Tensor("review_input_13:0", shape=(None, 100), dtype=int32), but it was called on an input with incompatible shape (None, 6000).---------------------------------------------------------------------------ValueError Traceback (most recent call last)<ipython-input-80-82850281ff1c> in <module>----> 1 predictions_o = load_model('./models/best_model.h5').predict(X12_test) 2 3 print("y1_test_truth", y1_test) 4 print("predictions_o", predictions_o) 5 print("validation set RMSE ", rmse2(predictions_o, y1_test))~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in _method_wrapper(self, *args, **kwargs) 128 raise ValueError('{} is not supported in multi-worker mode.'.format( 129 method.__name__))--> 130 return method(self, *args, **kwargs) 131 132 return tf_decorator.make_decorator(~/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing) 1597 for step in data_handler.steps(): 1598 callbacks.on_predict_batch_begin(step)-> 1599 tmp_batch_outputs = predict_function(iterator) 1600 if data_handler.should_sync: 1601 context.async_wait()~/.local/lib/python3.8/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()~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds) 821 # This is the first call of __call__, so we have to initialize. 822 initializers = []--> 823 self._initialize(args, kwds, add_initializers_to=initializers) 824 finally: 825 # At this point we know that the initialization is complete (or less~/.local/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to) 694 self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph) 695 self._concrete_stateful_fn = (--> 696 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access 697 *args, **kwds)) 698 ~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 2853 args, kwargs = None, None 2854 with self._lock:-> 2855 graph_function, _, _ = self._maybe_define_function(args, kwargs) 2856 return graph_function 2857 ~/.local/lib/python3.8/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs) 3211 3212 self._function_cache.missed.add(call_context_key)-> 3213 graph_function = self._create_graph_function(args, kwargs) 3214 self._function_cache.primary[cache_key] = graph_function 3215 return graph_function, args, kwargs~/.local/lib/python3.8/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,~/.local/lib/python3.8/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,~/.local/lib/python3.8/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 ~/.local/lib/python3.8/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 raiseValueError: in user code: /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1462 predict_function * return step_function(self, iterator) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1452 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:1211 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica return fn(*args, **kwargs) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1445 run_step ** outputs = model.predict_step(data) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/training.py:1418 predict_step return self(x, training=False) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__ outputs = call_fn(inputs, *args, **kwargs) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py:385 call return self._run_internal_graph( /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph outputs = node.layer(*args, **kwargs) /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/base_layer.py:975 __call__ input_spec.assert_input_compatibility(self.input_spec, inputs, /home/x/.local/lib/python3.8/site-packages/tensorflow/python/keras/engine/input_spec.py:212 assert_input_compatibility raise ValueError( ValueError: Input 0 of layer dense_24 is incompatible with the layer: expected axis -1 of input shape to have value 10000 but received input with shape [None, 600000]我正在尝试找出需要在哪里以及如何更改，以确保维度正确工作，但我还没有弄清楚具体需要更改什么。任何帮助将不胜感激。
更新：
数据的形状：
from sklearn.model_selection import train_test_splitX_train, X_test, y_train, y_test = train_test_split(data, y, test_size=0.2, random_state = 40)[nSamp, inpShape] = X_train.shapeprint("X train shape ", X_train.shape)print("X test shape ", X_test.shape)print("y train shape ",y_train.shape)print("y test shape ",y_test.shape)print(nSamp, inpShape)输出：
X train shape (160000, 100)X test shape (40000, 100)y train shape (160000, 5)y test shape (40000, 5)160000 100回答：
从第一行的警告来看，X12_test的形状似乎不正确，根据警告，您的模型构建时预期输入形状为shape (None, 100)，而您正在使用形状为shape (None, 6000)的输入调用模型
