[英]Keras ValueError: Dimensions must be equal LSTM
I'm creating a Bidirectional LSTM but I faced following error我正在创建一个双向 LSTM,但我遇到了以下错误
ValueError: Dimensions must be equal, but are 5 and 250 for '{{node Equal}} = Equal[T=DT_INT64, incompatible_shape_error=true](ArgMax, ArgMax_1)' with input shapes: [?,5], [?,250]
I have no idea what is wrong and how to fix it!我不知道出了什么问题以及如何解决它!
I have a text dataset with 59k row for train the model and i would divid them into 15 classes which then I would use for text similarity base on classes for the received new text.我有一个包含 59k 行的文本数据集来训练模型,我会将它们分成 15 个类,然后我将根据接收到的新文本的类将它们用于文本相似性。 Based on the other post I played with loss but still it doesn't solve the issue.根据我玩过的另一篇文章,但它仍然没有解决问题。
Here is the model plot:这是模型图:
Also sequential model would be as follow:顺序模型也如下:
model_lstm = Sequential()
model_lstm.add(InputLayer(250,))
model_lstm.add(Embedding(input_dim=max_words+1, output_dim=200, weights=[embedding_matrix],
mask_zero=True, trainable= True, name='corpus_embed'))
enc_lstm = Bidirectional(LSTM(128, activation='sigmoid', return_sequences=True, name='LSTM_Encod'))
model_lstm.add(enc_lstm)
model_lstm.add(Dropout(0.25))
model_lstm.add(Bidirectional(LSTM( 128, activation='sigmoid',dropout=0.25, return_sequences=True, name='LSTM_Decod')))
model_lstm.add(Dropout(0.25))
model_lstm.add(Dense(15, activation='softmax'))
model_lstm.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['Accuracy'])
## Feed the model
history = model_lstm.fit(x=corpus_seq_train,
y=target_seq_train,
batch_size=128,
epochs=50,
validation_data=(corpus_seq_test,target_seq_test),
callbacks=[tensorboard],
sample_weight= sample_wt_mat)
This is the model summary:这是模型摘要:
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
corpus_embed (Embedding) (None, 250, 200) 4000200
bidirectional (Bidirectiona (None, 250, 256) 336896
l)
dropout (Dropout) (None, 250, 256) 0
bidirectional_1 (Bidirectio (None, 250, 256) 394240
nal)
dropout_1 (Dropout) (None, 250, 256) 0
dense (Dense) (None, 250, 15) 3855
=================================================================
Total params: 4,735,191
Trainable params: 4,735,191
Non-trainable params: 0
_________________________________
and dataset shape:和数据集形状:
corpus_seq_train.shape, target_seq_train.shape
((59597, 250), (59597, 5, 8205))
Finally, here is the error:最后,这是错误:
Epoch 1/50
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
C:\Users\AMIRSH~1\AppData\Local\Temp/ipykernel_10004/3838451254.py in <module>
9 ## Feed the model
10
---> 11 history = model_lstm.fit(x=corpus_seq_train,
12 y=target_seq_train,
13 batch_size=128,
C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\traceback_utils.py in error_handler(*args, **kwargs)
65 except Exception as e: # pylint: disable=broad-except
66 filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67 raise e.with_traceback(filtered_tb) from None
68 finally:
69 del filtered_tb
C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py in tf__train_function(iterator)
13 try:
14 do_return = True
---> 15 retval_ = ag__.converted_call(ag__.ld(step_function), (ag__.ld(self), ag__.ld(iterator)), None, fscope)
16 except:
17 do_return = False
ValueError: in user code:
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1051, in train_function *
return step_function(self, iterator)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1040, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 1030, in run_step **
outputs = model.train_step(data)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 894, in train_step
return self.compute_metrics(x, y, y_pred, sample_weight)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\training.py", line 987, in compute_metrics
self.compiled_metrics.update_state(y, y_pred, sample_weight)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\engine\compile_utils.py", line 501, in update_state
metric_obj.update_state(y_t, y_p, sample_weight=mask)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\metrics_utils.py", line 70, in decorated
update_op = update_state_fn(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\metrics\base_metric.py", line 140, in update_state_fn
return ag_update_state(*args, **kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\metrics\base_metric.py", line 646, in update_state **
matches = ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\metrics\metrics.py", line 3295, in categorical_accuracy
return metrics_utils.sparse_categorical_matches(
File "C:\ProgramData\Anaconda3\lib\site-packages\keras\utils\metrics_utils.py", line 893, in sparse_categorical_matches
matches = tf.cast(tf.equal(y_true, y_pred), backend.floatx())
ValueError: Dimensions must be equal, but are 5 and 250 for '{{node Equal}} = Equal[T=DT_INT64, incompatible_shape_error=true](ArgMax, ArgMax_1)' with input shapes: [?,5], [?,250].
the problem is because of the Loss function and y-label shape.问题在于损失函数和 y 标签形状。 we should not pad y_label and it should fit the model directly without any other process我们不应该填充 y_label 并且它应该直接适合模型而不需要任何其他过程
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.