Python 带有Model.Fit（）的Keras InvalidArgumeInterror

我试图对顺序Keras模型调用

model.fit（）

，但出现以下错误：

---------------------------------------------------------------------------
InvalidArgumentError                      Traceback (most recent call last)
<ipython-input-30-3fc420144082> in <module>
     15     return model
     16 
---> 17 trained_model = build_model()

<ipython-input-30-3fc420144082> in build_model()
     10     # fit model
     11     es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1)
---> 12     model.fit(train_data[0], train_data[1], epochs=100,verbose=1)
     13     # validation_data = (val_data[0], val_data[1])
     14     print(model.summary())

~/.local/lib/python3.6/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, max_queue_size, workers, use_multiprocessing, **kwargs)
    878           initial_epoch=initial_epoch,
    879           steps_per_epoch=steps_per_epoch,
--> 880           validation_steps=validation_steps)
    881 
    882   def evaluate(self,

~/.local/lib/python3.6/site-packages/tensorflow/python/keras/engine/training_arrays.py in model_iteration(model, inputs, targets, sample_weights, batch_size, epochs, verbose, callbacks, val_inputs, val_targets, val_sample_weights, shuffle, initial_epoch, steps_per_epoch, validation_steps, mode, validation_in_fit, **kwargs)
    327 
    328         # Get outputs.
--> 329         batch_outs = f(ins_batch)
    330         if not isinstance(batch_outs, list):
    331           batch_outs = [batch_outs]

~/.local/lib/python3.6/site-packages/tensorflow/python/keras/backend.py in __call__(self, inputs)
   3074 
   3075     fetched = self._callable_fn(*array_vals,
-> 3076                                 run_metadata=self.run_metadata)
   3077     self._call_fetch_callbacks(fetched[-len(self._fetches):])
   3078     return nest.pack_sequence_as(self._outputs_structure,

~/.local/lib/python3.6/site-packages/tensorflow/python/client/session.py in __call__(self, *args, **kwargs)
   1437           ret = tf_session.TF_SessionRunCallable(
   1438               self._session._session, self._handle, args, status,
-> 1439               run_metadata_ptr)
   1440         if run_metadata:
   1441           proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)

~/.local/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
    526             None, None,
    527             compat.as_text(c_api.TF_Message(self.status.status)),
--> 528             c_api.TF_GetCode(self.status.status))
    529     # Delete the underlying status object from memory otherwise it stays alive
    530     # as there is a reference to status from this from the traceback due to

InvalidArgumentError: data[0].shape = [3] does not start with indices[0].shape = [2]
     [[{{node training_40/Adam/gradients/loss_21/dense_21_loss/MeanSquaredError/Mean_grad/DynamicStitch}}]]

---

我不确定为什么，但是当我调用

model.fit（train\u data，epochs=100）

并且没有将其分解为点和标签时，一切都很好。任何见解都将不胜感激

根据tensorflow关于tf.keras.models.Model的文档，这是有意义的：

它精确到：

y：目标数据。与输入数据x一样，它可以是Numpy数组或TensorFlow张量。它应该与x一致（不能有Numpy输入和张量目标，或者相反）如果x是数据集、数据集迭代器、生成器或keras.utils.Sequence实例，则不应指定y（因为目标将从x获得）。

您的lstm是一个顺序模型，我想您准备的

train\u数据

是keras.utils.Sequence类型

请注意您的tensorflow版本，上面的文档链接是针对r1.13的

编辑：

尝试以下方式准备数据集：

features_type = tf.float32
target_type = tf.int32

train_dataset = tf.data.Dataset.from_tensor_slices(
    tf.cast(train_data[0].values, features_type),
    tf.cast(train_data[1].values, target_type)
)

model.fit(train_dataset, epochs=100, verbose=1)

---

确保将功能类型（所有功能铸造为float32）和目标类型（int32用于分类）调整为您当前要解决的问题。

train\u data

包含两个numpy数组刚刚编辑了答案，向您展示了如何从功能和目标中准备tensorflow数据集。请参见上文，并根据需要自定义功能和目标类型。

 fit(x=None, y=None, batch_size=None, epochs=1, ...)

features_type = tf.float32
target_type = tf.int32

train_dataset = tf.data.Dataset.from_tensor_slices(
    tf.cast(train_data[0].values, features_type),
    tf.cast(train_data[1].values, target_type)
)

model.fit(train_dataset, epochs=100, verbose=1)