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我正在构建一个命名实体识别模型。在进行预处理（建模和验证集中的标记化、填充和分离）之后，我在训练模型时出错。据我了解，错误是由于训练集和测试集的尺寸造成的。

我的模型如下

model = tf.keras.Sequential([
    tf.keras.layers.Embedding(input_dim=2109,output_dim=64),
    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(16, activation='relu'),
    #tf.keras.layers.Dropout(rate, noise_shape=None, seed=None, **kwargs)
    tf.keras.layers.Dense(3, activation='softmax')
])

我的训练数据具有以下格式。您看到的第一个包含随机数和 -1 的向量是我的向量 x，而您看到的仅包含 1 和 0 的第二个向量是我的向量，它采用一种热编码格式

print(train_pad_seq[1])
print('\n')
print(train_tag[1])

print('\n')

print(len(test_pad_seq[1]))
print('\n')
print(len(test_tag[1]))

[269   2   9   6  28  21   2  53   3 142 368  23 132  12  23 698   2  88
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1
  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1  -1]


[[1. 0. 0.]
 [1. 0. 0.]
 [0. 1. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [1. 0. 0.]
 [0. 1. 0.]]

我的训练和测试集的维度如下

print('lad dimensiones de train_seq es: ')
print(train_pad_seq.shape)

print('lad dimensiones de train_tag es: ')
print(train_tag.shape)

print('\n')

print('lad dimensiones de test_seq es: ')
print(test_seq.shape)

print('lad dimensiones de test_tag es: ')
print(test_tag.shape)

lad dimensiones de train_seq es: 
(1104, 158)
lad dimensiones de train_tag es: 
(1104,)


lad dimensiones de test_seq es: 
(368,)
lad dimensiones de test_tag es: 
(368,)

我编译模型的代码如下

#ajustamos los parametros de la red creada y entrenamos el modelo
num_epochs = 10

#model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy'])
model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
#detencion temprana
history = model.fit(train_pad_seq,train_pad_tag, epochs=num_epochs, validation_data=(test_pad_seq,test_pad_tag))

最后抛出我的错误如下

Epoch 1/10

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-29-3ba21e640097> in <module>()
      5 model.compile(loss='categorical_crossentropy',optimizer='adam',metrics=['accuracy'])
      6 #detencion temprana
----> 7 history = model.fit(train_pad_seq,train_pad_tag, epochs=num_epochs, validation_data=(test_pad_seq,test_pad_tag))

9 frames

/usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in 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:

    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:853 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:842 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1286 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2849 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3632 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:835 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:789 train_step
        y, y_pred, sample_weight, regularization_losses=self.losses)
    /usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__
        loss_value = loss_obj(y_t, y_p, sample_weight=sw)
    /usr/local/lib/python3.7/dist-packages/keras/losses.py:141 __call__
        losses = call_fn(y_true, y_pred)
    /usr/local/lib/python3.7/dist-packages/keras/losses.py:245 call  **
        return ag_fn(y_true, y_pred, **self._fn_kwargs)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/losses.py:1666 categorical_crossentropy
        y_true, y_pred, from_logits=from_logits, axis=axis)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.7/dist-packages/keras/backend.py:4839 categorical_crossentropy
        target.shape.assert_is_compatible_with(output.shape)
    /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with
        raise ValueError("Shapes %s and %s are incompatible" % (self, other))

    ValueError: Shapes (None, 158, 3) and (None, 3) are incompatible

有人可以帮我理解这个错误的原因吗？

4

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