[英]Failed to convert numpy array to tensor
我尝试在 python 中使用 keras/tensorflow 使用 model.fit 并收到此错误。 所有软件包安装正常,但收到转换为 numpy 阵列的问题
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name)
259 ValueError: if called on a symbolic tensor.
260 """
--> 261 return _constant_impl(value, dtype, shape, name, verify_shape=False,
262 allow_broadcast=True)
263
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast)
268 ctx = context.context()
269 if ctx.executing_eagerly():
--> 270 t = convert_to_eager_tensor(value, ctx, dtype)
271 if shape is None:
272 return t
~\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\framework\constant_op.py in convert_to_eager_tensor(value, ctx, dtype)
94 dtype = dtypes.as_dtype(dtype).as_datatype_enum
95 ctx.ensure_initialized()
---> 96 return ops.EagerTensor(value, ctx.device_name, dtype)
97
98
ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int).
我的代码:
X = trainingpreds.values
y = traininglabels.values
# define the keras model model = Sequential()
model.add(Dense(20, input_dim=24, activation='relu'))
model.add(Dense(12, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
# compile the keras model model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(X, y)
假设x
和y
应该是int
类型并且您在model.fit
遇到错误,您可以使用转换为dtype
x = tf.cast(trainingpreds.values, tf.int32)
y = tf.cast(traininglabels, tf.int32)
要了解更多信息,请参阅此
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