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Я видел несколько вопросов с аналогичной проблемой, и мне еще предстоит выяснить, что не так с моим кодом. Я пытаюсь обучить и настроить базовую модель Keras. У меня есть следующее:

 from keras import layers
from keras import models
import pandas as pd

model = models.Sequential()
model.add(layers.Dense(2, activation='relu', input_shape=(2,)))
model.add(layers.Dense(1, activation='relu'))

 

И некоторые данные, которые выглядят так:

 housing_final
value   floorArea   bedrooms
0   458000  990 3
1   775000  1022    3
2   875000  1259    3
3   280000  699 3
4   410000  1184    3
... ... ... ...
24559   500000  1119    3
24560   560000  1593    4
24561   400000  1388    3
24562   530000  990 3
24563   350000  990 3
24564 rows × 3 columns
 

Затем я делаю:

 val_dataframe = housing_final.sample(frac=0.2, random_state=1337)
train_dataframe = housing_final.drop(val_dataframe.index)

print(
    "Using %d samples for training and %d for validation"
    % (len(train_dataframe), len(val_dataframe))
)

import tensorflow as tf
def dataframe_to_dataset(dataframe):
    dataframe = dataframe.copy()
    labels = dataframe.pop("value")
    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    ds = ds.shuffle(buffer_size=len(dataframe))
    return ds


train_ds = dataframe_to_dataset(train_dataframe)
val_ds = dataframe_to_dataset(val_dataframe)

train_ds = train_ds.batch(32)
val_ds = val_ds.batch(32)
model.compile("adam", "binary_crossentropy", metrics=["accuracy"])

model.fit(train_ds, epochs=50, validation_data=val_ds)
 

Но я понял ошибку в названии. Я совсем не уверен, что я здесь делаю не так. Я бы предположил некоторое несоответствие размеров входным данным, но это не ясно. Я вставлю полную трассировку стека ниже:

 ---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-47-43091e144d61> in <module>
----> 1 model.fit(train_ds, epochs=50, validation_data=val_ds)

~/.local/lib/python3.9/site-packages/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)
   1156                 _r=1):
   1157               callbacks.on_train_batch_begin(step)
-> 1158               tmp_logs = self.train_function(iterator)
   1159               if data_handler.should_sync:
   1160                 context.async_wait()

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
    887 
    888       with OptionalXlaContext(self._jit_compile):
--> 889         result = self._call(*args, **kwds)
    890 
    891       new_tracing_count = self.experimental_get_tracing_count()

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
    931       # This is the first call of __call__, so we have to initialize.
    932       initializers = []
--> 933       self._initialize(args, kwds, add_initializers_to=initializers)
    934     finally:
    935       # At this point we know that the initialization is complete (or less

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in _initialize(self, args, kwds, add_initializers_to)
    761     self._graph_deleter = FunctionDeleter(self._lifted_initializer_graph)
    762     self._concrete_stateful_fn = (
--> 763         self._stateful_fn._get_concrete_function_internal_garbage_collected(  # pylint: disable=protected-access
    764             *args, **kwds))
    765 

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs)
   3048       args, kwargs = None, None
   3049     with self._lock:
-> 3050       graph_function, _ = self._maybe_define_function(args, kwargs)
   3051     return graph_function
   3052 

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _maybe_define_function(self, args, kwargs)
   3442 
   3443           self._function_cache.missed.add(call_context_key)
-> 3444           graph_function = self._create_graph_function(args, kwargs)
   3445           self._function_cache.primary[cache_key] = graph_function
   3446 

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes)
   3277     arg_names = base_arg_names   missing_arg_names
   3278     graph_function = ConcreteFunction(
-> 3279         func_graph_module.func_graph_from_py_func(
   3280             self._name,
   3281             self._python_function,

~/.local/lib/python3.9/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)
    997         _, original_func = tf_decorator.unwrap(python_func)
    998 
--> 999       func_outputs = python_func(*func_args, **func_kwargs)
   1000 
   1001       # invariant: `func_outputs` contains only Tensors, CompositeTensors,

~/.local/lib/python3.9/site-packages/tensorflow/python/eager/def_function.py in wrapped_fn(*args, **kwds)
    670         # the function a weak reference to itself to avoid a reference cycle.
    671         with OptionalXlaContext(compile_with_xla):
--> 672           out = weak_wrapped_fn().__wrapped__(*args, **kwds)
    673         return out
    674 

~/.local/lib/python3.9/site-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    984           except Exception as e:  # pylint:disable=broad-except
    985             if hasattr(e, "ag_error_metadata"):
--> 986               raise e.ag_error_metadata.to_exception(e)
    987             else:
    988               raise

ValueError: in user code:

    /Users/logankilpatrick/.local/lib/python3.9/site-packages/keras/engine/training.py:830 train_function  *
        return step_function(self, iterator)
    /Users/logankilpatrick/.local/lib/python3.9/site-packages/keras/engine/training.py:813 run_step  *
        outputs = model.train_step(data)
    /Users/logankilpatrick/.local/lib/python3.9/site-packages/keras/engine/training.py:770 train_step  *
        y_pred = self(x, training=True)
    /Users/logankilpatrick/.local/lib/python3.9/site-packages/keras/engine/base_layer.py:989 __call__  *
        input_spec.assert_input_compatibility(self.input_spec, inputs, self.name)
    /Users/logankilpatrick/.local/lib/python3.9/site-packages/keras/engine/input_spec.py:197 assert_input_compatibility  *
        raise ValueError('Layer '   layer_name   ' expects '  

    ValueError: Layer sequential_1 expects 1 input(s), but it received 2 input tensors. Inputs received: [<tf.Tensor 'ExpandDims:0' shape=(None, 1) dtype=int64>, <tf.Tensor 'ExpandDims_1:0' shape=(None, 1) dtype=float64>]
 

Вам нужно объединить функции floorArea и bedrooms сделать так, чтобы конечная форма пакета была ( batch_size , 2 ) . Ваша текущая реализация дает вам два тензора, или форму ( batch_size , 1 ) и ( batch_size , 1 ) .

 def dataframe_to_dataset(dataframe):
    labels = dataframe[ 'value' ].values
    features = np.concatenate( ( dataframe[ 'bedrooms' ].values , dataframe[ 'floorArea' ].values ) , axis=1 )
    ds = tf.data.Dataset.from_tensor_slices(( features , labels ))
    ds = ds.shuffle(buffer_size=len(dataframe))
    return ds