基于多位数预测的Keras阵列误差

我正试图实现这个架构，从SVHN数据集中的图像中预测数字，我需要从图像中预测多个数字

我已经准备好了以下数据形状：

• 列车：（188602,32,32,1）
• 列车假人：（188602,5,11）
• X_val：（47151,32,32,1）
• y_val_假人：（47151,5,11）

然而，在我的代码中，当我尝试适应时，我遇到了这个错误（在此之前一切都正常）

---

这不是全部错误。我也无法识别代码中链接图像的尺寸。很容易看出，

y\u train\u dummy

就是问题所在。有关损失维度的假设，请查看keras文档。@sascha我添加了全部错误。问题似乎出在y_train_dummy上，我可能有5个输出（每个输出有10个类），但只有1个带标签的数组（有10个类）。

    ---------------------------------------------------------------------------
Exception                                 Traceback (most recent call last)
<ipython-input-136-c031c9f027b5> in <module>()
----> 1 model.fit(X_train, y_train_dummy, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_val, y_val_dummy))

/Users/home/anaconda3/lib/python3.5/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, nb_epoch, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight)
   1032                                                            class_weight=class_weight,
   1033                                                            check_batch_dim=False,
-> 1034                                                            batch_size=batch_size)
   1035         # prepare validation data
   1036         if validation_data:

/Users/home/anaconda3/lib/python3.5/site-packages/keras/engine/training.py in _standardize_user_data(self, x, y, sample_weight, class_weight, check_batch_dim, batch_size)
    963                                    output_shapes,
    964                                    check_batch_dim=False,
--> 965                                    exception_prefix='model target')
    966         sample_weights = standardize_sample_weights(sample_weight,
    967                                                     self.output_names)

/Users/home/anaconda3/lib/python3.5/site-packages/keras/engine/training.py in standardize_input_data(data, names, shapes, check_batch_dim, exception_prefix)
     74             raise Exception('The model expects ' + str(len(names)) +
     75                             ' input arrays, but only received one array. '
---> 76                             'Found: array with shape ' + str(data.shape))
     77         arrays = [data]
     78 

Exception: The model expects 5 input arrays, but only received one array. Found: array with shape (188602, 5, 11)

batch_size = 128
nb_classes = 10
nb_epoch = 2

_, img_rows, img_cols, img_channels = X_train.shape

model_input = Input(shape=(img_rows, img_cols, img_channels))
x = Convolution2D(32, 3, 3, border_mode='same')(model_input)
x = Activation('relu')(x)
x = Convolution2D(32, 3, 3)(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Dropout(0.25)(x)
conv_out = Flatten()(x)

x1 = Dense(nb_classes, activation='softmax')(conv_out)
x2 = Dense(nb_classes, activation='softmax')(conv_out)
x3 = Dense(nb_classes, activation='softmax')(conv_out)
x4 = Dense(nb_classes, activation='softmax')(conv_out)
x5 = Dense(nb_classes, activation='softmax')(conv_out)

lst = [x1, x2, x3, x4, x5]

model = Model(input=model_input, output=lst)

sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy', optimizer=sgd)
model.fit(X_train, y_train_dummy, batch_size=batch_size, nb_epoch=nb_epoch, verbose=1, validation_data=(X_val, y_val_dummy))