我使用 pandas 将文件转换为数据帧，现在我想通过 TensorFlow 训练深度学习模型。我没有成功训练模型:划分训练集和测试集后，当我去编译模型时，它告诉我
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type
numpy.ndarray).
我认为问题在于 numpy 数组具有不同的大小，但尽管执行了填充(通过这种方式，所有数组在列内具有相同的维度)，但问题并未解决。 下面我插入了数据集中的列示例:如果我想将其转换为张量，我应该怎么做？
df = pd.read_parquet('example.parquet')
df['column']
0 [0, 1, 1, 1, 0, 1, 0, 1, 0]
1 [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0]
2 [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1]
3 [0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1]
4 [0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0]
...
115 [0, 1, 0, 0, 1, 1, 1, 1, 1]
116 [0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, ...
117 [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1]
118 [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, ...
119 [0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1]
显然我已经插入了原始列，而不是我填充失败的列。
这些是我训练模型的步骤(如果有用的话)
from sklearn.preprocessing import LabelEncoder
label_encoder = LabelEncoder()
Y = label_encoder.fit_transform(Y)
X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.3, random_state = 42)
#create model
model = Sequential()
#add model layers
model.add(BatchNormalization())
model.add(Dense(20, activation='softmax', input_shape=(X_train.shape)))
# compile model
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50)
更新:完整回溯
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported
object type numpy.ndarray).
---------------------------------------------------------------------
------
ValueError Traceback (most recent call
last)
~\AppData\Local\Temp/ipykernel_16380/3421148994.py in <module>
1 from livelossplot import PlotLossesKeras
2
----> 3 model.fit(X_train, y_train, validation_data=(X_test, y_test),
epochs=50, callbacks=[PlotLossesKeras()])
~\AppData\Local\Programs\Python\Python39\lib\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)
1132 training_utils.RespectCompiledTrainableState(self):
1133 # Creates a `tf.data.Dataset` and handles batch and epoch
iteration.
-> 1134 data_handler = data_adapter.get_data_handler(
1135 x=x,
1136 y=y,
~\AppData\Local\Programs\Python\Python39\lib\site-
packages\keras\engine\data_adapter.py in get_data_handler(*args,
**kwargs)
1381 if getattr(kwargs["model"], "_cluster_coordinator", None):
1382 return _ClusterCoordinatorDataHandler(*args, **kwargs)
-> 1383 return DataHandler(*args, **kwargs)
1384
1385
~\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution, distribute)
1136
1137 adapter_cls = select_data_adapter(x, y)
-> 1138 self._adapter = adapter_cls(
1139 x,
1140 y,
~\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs)
228 **kwargs):
229 super(TensorLikeDataAdapter, self).__init__(x, y, **kwargs)
--> 230 x, y, sample_weights = _process_tensorlike((x, y, sample_weights))
231 sample_weight_modes = broadcast_sample_weight_modes(
232 sample_weights, sample_weight_modes)
~\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\data_adapter.py in _process_tensorlike(inputs)
1029 return x
1030
-> 1031 inputs = tf.nest.map_structure(_convert_numpy_and_scipy, inputs)
1032 return tf.__internal__.nest.list_to_tuple(inputs)
1033
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\util\nest.py in map_structure(func, *structure, **kwargs)
867
868 return pack_sequence_as(
--> 869 structure[0], [func(*x) for x in entries],
870 expand_composites=expand_composites)
871
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\util\nest.py in <listcomp>(.0)
867
868 return pack_sequence_as(
--> 869 structure[0], [func(*x) for x in entries],
870 expand_composites=expand_composites)
871
~\AppData\Local\Programs\Python\Python39\lib\site-packages\keras\engine\data_adapter.py in _convert_numpy_and_scipy(x)
1024 if issubclass(x.dtype.type, np.floating):
1025 dtype = backend.floatx()
-> 1026 return tf.convert_to_tensor(x, dtype=dtype)
1027 elif _is_scipy_sparse(x):
1028 return _scipy_sparse_to_sparse_tensor(x)
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs)
204 """Call target, and fall back on dispatchers if there is a TypeError."""
205 try:
--> 206 return target(*args, **kwargs)
207 except (TypeError, ValueError):
208 # Note: convert_to_eager_tensor currently raises a ValueError, not a
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor_v2_with_dispatch(value, dtype, dtype_hint, name)
1428 ValueError: If the `value` is a tensor not of given `dtype` in graph mode.
1429 """
-> 1430 return convert_to_tensor_v2(
1431 value, dtype=dtype, dtype_hint=dtype_hint, name=name)
1432
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name)
1434 def convert_to_tensor_v2(value, dtype=None, dtype_hint=None, name=None):
1435 """Converts the given `value` to a `Tensor`."""
-> 1436 return convert_to_tensor(
1437 value=value,
1438 dtype=dtype,
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\profiler\trace.py in wrapped(*args, **kwargs)
161 with Trace(trace_name, **trace_kwargs):
162 return func(*args, **kwargs)
--> 163 return func(*args, **kwargs)
164
165 return wrapped
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types)
1564
1565 if ret is None:
-> 1566 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
1567
1568 if ret is NotImplemented:
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\tensor_conversion_registry.py in _default_conversion_function(***failed resolving arguments***)
50 def _default_conversion_function(value, dtype, name, as_ref):
51 del as_ref # Unused.
---> 52 return constant_op.constant(value, dtype, name=name)
53
54
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name)
269 ValueError: if called on a symbolic tensor.
270 """
--> 271 return _constant_impl(value, dtype, shape, name, verify_shape=False,
272 allow_broadcast=True)
273
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
281 with trace.Trace("tf.constant"):
282 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
--> 283 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
284
285 g = ops.get_default_graph()
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_eager_impl(ctx, value, dtype, shape, verify_shape)
306 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape):
307 """Creates a constant on the current device."""
--> 308 t = convert_to_eager_tensor(value, ctx, dtype)
309 if shape is None:
310 return t
~\AppData\Local\Programs\Python\Python39\lib\site-packages\tensorflow\python\framework\constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
104 dtype = dtypes.as_dtype(dtype).as_datatype_enum
105 ctx.ensure_initialized()
--> 106 return ops.EagerTensor(value, ctx.device_name, dtype)
107
108
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type numpy.ndarray).
最佳答案
我假设您当前正在处理填充数据。因此，现在填充数据后，您将进行缩放。完成此操作后，训练和测试的 X 形状分别为 (120,3) 和 (84,3)。
现在第一个明显的错误在下面一行
model.add(Dense(20, activation='softmax', input_shape=(X_train.shape)))
您未在 input_shape 中指定 batch 的尺寸。更简单地说，假设您要向模型提供图像，那么如果是 1 channel 图像，您将在 input_shape 中写入什么内容？如下所示。
height = 224
width = 224
model.add(Dense(20, activation='softmax', input_shape=(height, width)))
# In your case you have written
model.add(Dense(20, activation='softmax', input_shape=(120, 3)))
这告诉模型，对应于形状 (120,3) 的每个输入，有一些标签，但情况并非如此，因此您应该只传递如下所示的特征维度
model.add(Dense(20, activation='softmax', input_shape=(3,)))
此后错误应该被删除。另外，我没有看到您在 model.fit 中使用 batch_size 参数，您应该使用它。
我看到的第二件事不是语法错误，而是下面代码中的方法错误。
#create model
model = Sequential()
#add model layers
model.add(BatchNormalization()) # RED FLAG
model.add(Dense(20, activation='softmax', input_shape=(X_train.shape)))
您不应该对输入使用BatchNormalization。使用 BatchNormalization 的主要原因是提高模型的训练速度，甚至不需要提高输入的训练速度。另外，需要注意的重要一点是，BatchNormalization 是针对训练批处理而不是整个数据集的标准化，因此如果您不使用大批量大小，则没什么用处。可以代表全体人口。
更新:
您没有正确填充。填充后 X.shape 的输出应为 ( _ , _ ) 而不是 ( _ , )。因此，请执行以下操作
# Creating some random data
random_array = []
for i in range(20):
random_array.append([i for i in range(i+1)])
df = pd.DataFrame()
df['values'] = random_array
for i in range (0, len(df['values'])):
pad_size = 21 - len(df['values'][i])
df['values'][i] = np.pad(df['values'][i], (pad_size, 0))
final_array = np.array([np.array(i) for i in df['values']])
print(final_array.shape) # This will give (20, 21) and not (20,)
关于python - 将 Numpy 数组转换为张量，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/68785329/