keras中的GNN结构形状

Question

我的数据集是X。X的形状是(423,320，3)。数据个数为423，数据长度为320。我使用python的spektral包。

X.shape # (423,320,3)

调整矩阵为A。A的形状为(423,423)

A.shape # (423,423)

我的y标签是y。y的形状是(320,1)

y.shape # (320,1)

我的模型如下所示。我觉得我的模型很简单。但它不起作用。

N = A.shape[0]
F = X.shape[-1]
n_classes = 1

X_in = Input(shape=(423,320,))
A_in = Input((N, ), sparse=True)

X_1 = GraphConv(16, 'relu')([X_in, A_in])
X_1 = Dropout(0.5)(X_1)
X_2 = GraphConv(n_classes, 'relu')([X_1, A_in])

model = Model(inputs=[X_in, A_in], outputs=X_2)
A = GraphConv.preprocess(A).astype('f4')

model.compile(optimizer='adam',
              loss='mean_squared_error',
              weighted_metrics=['accuracy'])
model.summary()

model.fit([X, A], y)

模型摘要如下

Model: "model_32"
__________________________________________________________________________________________________
Layer (type)                    Output Shape         Param #     Connected to                     
==================================================================================================
input_63 (InputLayer)           [(None, 423, 320)]   0                                            
__________________________________________________________________________________________________
input_64 (InputLayer)           [(None, None)]       0                                            
__________________________________________________________________________________________________
graph_conv_52 (GraphConv)       (None, 423, 16)      5136        input_63[0][0]                   
                                                                 input_64[0][0]                   
__________________________________________________________________________________________________
dropout_25 (Dropout)            (None, 423, 16)      0           graph_conv_52[0][0]              
__________________________________________________________________________________________________
graph_conv_53 (GraphConv)       (None, 423, 1)       17          dropout_25[0][0]                 
                                                                 input_64[0][0]                   
==================================================================================================
Total params: 5,153
Trainable params: 5,153
Non-trainable params: 0
__________________________________________________________________________________________________

错误在下面

ValueError                                Traceback (most recent call last)
<ipython-input-272-d00160881a92> in <module>
     18 model.summary()
     19 
---> 20 model.fit([X, A], y)

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/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, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    817         max_queue_size=max_queue_size,
    818         workers=workers,
--> 819         use_multiprocessing=use_multiprocessing)
    820 
    821   def evaluate(self,

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in fit(self, model, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_freq, max_queue_size, workers, use_multiprocessing, **kwargs)
    233           max_queue_size=max_queue_size,
    234           workers=workers,
--> 235           use_multiprocessing=use_multiprocessing)
    236 
    237       total_samples = _get_total_number_of_samples(training_data_adapter)

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_training_inputs(model, x, y, batch_size, epochs, sample_weights, class_weights, steps_per_epoch, validation_split, validation_data, validation_steps, shuffle, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    591         max_queue_size=max_queue_size,
    592         workers=workers,
--> 593         use_multiprocessing=use_multiprocessing)
    594     val_adapter = None
    595     if validation_data:

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_v2.py in _process_inputs(model, mode, x, y, batch_size, epochs, sample_weights, class_weights, shuffle, steps, distribution_strategy, max_queue_size, workers, use_multiprocessing)
    644     standardize_function = None
    645     x, y, sample_weights = standardize(
--> 646         x, y, sample_weight=sample_weights)
    647   elif adapter_cls is data_adapter.ListsOfScalarsDataAdapter:
    648     standardize_function = standardize

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, batch_size, check_steps, steps_name, steps, validation_split, shuffle, extract_tensors_from_dataset)
   2381         is_dataset=is_dataset,
   2382         class_weight=class_weight,
-> 2383         batch_size=batch_size)
   2384 
   2385   def _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs,

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training.py in _standardize_tensors(self, x, y, sample_weight, run_eagerly, dict_inputs, is_dataset, class_weight, batch_size)
   2408           feed_input_shapes,
   2409           check_batch_axis=False,  # Don't enforce the batch size.
-> 2410           exception_prefix='input')
   2411 
   2412     # Get typespecs for the input data and sanitize it if necessary.

~/anaconda3/envs/tensor2/lib/python3.6/site-packages/tensorflow_core/python/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    580                              ': expected ' + names[i] + ' to have shape ' +
    581                              str(shape) + ' but got array with shape ' +
--> 582                              str(data_shape))
    583   return data
    584 

**ValueError: Error when checking input: expected input_63 to have shape (423, 320) but got array with shape (320, 3)**

Answer 1

不确定您是否还需要帮助，但问题出在输入上。

您的节点要素X具有形状( 423,320，3)，但您的数据仅表示一个包含423个节点的图。Spektral不支持多维节点属性，因此应将X重塑为(423,320 * 3)：

X = X.reshape(423, 320 * 3)

此外，由于您使用的是model.fit()，因此应该将批处理大小设置为N或执行以下操作：

for epoch in range(epochs): 
    model.train_on_batch([X, A], y)