我正在尝试建立一个暹罗网络，该网络需要两个输入，并且有一个培训标签。
使用model.fit()时出现上述错误。
请告诉我该怎么办才能纠正错误。
请在下面找到代码。
input_shape = (data.shape[1],) #input_shape --> (474,)
print(input_shape)
left_input = Input(input_shape)
right_input = Input(input_shape)
from keras.optimizers import SGD,Adam
dropoutRate=0.0
numNeurons=40
optimizer='adam'
numNeuronsFirstTwo=40
convnet = Sequential()
convnet.add(Dense(numNeuronsFirstTwo, kernel_regularizer=l2(0.001)))
convnet.add(Dense(numNeuronsFirstTwo, kernel_regularizer=l2(0.001)))
convnet.add(Dropout(dropoutRate))
convnet.add(Dense(numNeurons, kernel_regularizer=l2(0.001)))
encoded_l = convnet(left_input)
encoded_r = convnet(right_input)
#layer to merge two encoded inputs with the l1 distance between them
L1_layer = Lambda(lambda tensors:K.abs(tensors[0] - tensors[1]))
#call this layer on list of two input tensors.
L1_distance = L1_layer([encoded_l, encoded_r])
prediction = Dense(1,activation='sigmoid',bias_initializer='zeros')(L1_distance)
siamese_net = Model(inputs=[left_input,right_input],outputs=prediction)
optimizer = Adam(0.00006)
siamese_net.compile(loss="binary_crossentropy",optimizer=optimizer)
这是模型：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 474) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (None, 474) 0
__________________________________________________________________________________________________
sequential_1 (Sequential) (None, 40) 22280 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
lambda_1 (Lambda) (None, 40) 0 sequential_1[1][0]
sequential_1[2][0]
__________________________________________________________________________________________________
dense_4 (Dense) (None, 1) 41 lambda_1[0][0]
==================================================================================================
Total params: 22,321
Trainable params: 22,321
Non-trainable params: 0
batch_size = 32
epochs = 50
class_weight = {0:1, 1:15}
#print(len(trainValiData))
labels = [1,0]
labels = np.asarray(labels)
print(labels)
siamese_net.summary()
siamese_net.fit((trainValiData[1], trainValiData[2]), labels)
我得到的错误如下：
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-18-b55790c3f858> in <module>()
13
14 siamese_net.summary()
---> 15 siamese_net.fit((trainValiData[1], trainValiData[2]), labels)
~\Anaconda3\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, **kwargs)
953 sample_weight=sample_weight,
954 class_weight=class_weight,
--> 955 batch_size=batch_size)
956 # Prepare validation data.
957 do_validation = False
~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
752 feed_input_shapes,
753 check_batch_axis=False, # Don't enforce the batch size.
--> 754 exception_prefix='input')
755
756 if y is not None:
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
88 data = data.values if data.__class__.__name__ == 'DataFrame' else data
89 data = [data]
---> 90 data = [standardize_single_array(x) for x in data]
91
92 if len(data) != len(names):
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in <listcomp>(.0)
88 data = data.values if data.__class__.__name__ == 'DataFrame' else data
89 data = [data]
---> 90 data = [standardize_single_array(x) for x in data]
91
92 if len(data) != len(names):
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_single_array(x)
23 'Got tensor with shape: %s' % str(shape))
24 return x
---> 25 elif x.ndim == 1:
26 x = np.expand_dims(x, 1)
27 return x
AttributeError: 'tuple' object has no attribute 'ndim'
请告诉我该如何解决。 非常感谢。
编辑：新错误：
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-20-93dfa1f77351> in <module>()
15
16 siamese_net.summary()
---> 17 siamese_net.fit([trainValiData[1], trainValiData[2]], labels)
~\Anaconda3\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, **kwargs)
953 sample_weight=sample_weight,
954 class_weight=class_weight,
--> 955 batch_size=batch_size)
956 # Prepare validation data.
957 do_validation = False
~\Anaconda3\lib\site-packages\keras\engine\training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_array_lengths, batch_size)
752 feed_input_shapes,
753 check_batch_axis=False, # Don't enforce the batch size.
--> 754 exception_prefix='input')
755
756 if y is not None:
~\Anaconda3\lib\site-packages\keras\engine\training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
134 ': expected ' + names[i] + ' to have shape ' +
135 str(shape) + ' but got array with shape ' +
--> 136 str(data_shape))
137 return data
138
ValueError: Error when checking input: expected input_1 to have shape (474,) but got array with shape (1,)