我正在运行一个 CNN 来检查图像但不分类。实际上，输出层是一个密集层，其参数为 1d 中标签中图像的大小。

如下代码所示，我使用的是 model.fit_generator() 而不是 model.fit 开始训练模型时出现以下错误：

ValueError: Error when checking target: expected dense_10 to have shape 
(1,) but got array with shape (19316,)

为什么这是一个错误？我的密集输出是一个包含 19316 个元素的数组，为什么它期望它具有 (1,) 的形状？

这里还附上了模型的摘要：

层（类型）输出形状参数#

conv2d_28 (Conv2D)（无、26、877、32）544

activation_37（激活）（无、26、877、32）0

max_pooling2d_28 (MaxPooling (None, 13, 438, 32) 0

conv2d_29 (Conv2D)（无、12、437、16）2064

activation_38（激活）（无、12、437、16）0

max_pooling2d_29 (MaxPooling (None, 6, 218, 16) 0

conv2d_30 (Conv2D) (None, 5, 217, 8) 520

activation_39（激活）（无、5、217、8）0

max_pooling2d_30 (MaxPooling (None, 2, 108, 8) 0

activation_40（激活）（无、2、108、8）0

flatten_10（扁平化）（无，1728）0

dropout_10（辍学）（无，1728）0

密集_10（密集）（无，19316）33397364

================================================== ================

总参数：33,400,492 可训练参数：33,400,492 不可训练参数：0

有什么建议么？

非常感谢！

def generator(data_arr, batch_size = 10):

num = len(data_arr) 

if num % batch_size != 0 : 
    num = int(num/batch_size)

# Loop forever so the generator never terminates
while True: 

for offset in range(0, num, batch_size):

    batch_samples = (data_arr[offset:offset+batch_size])

    samples = []
    labels = []

    for batch_sample in batch_samples:

        samples.append(batch_sample[0])
        labels.append((np.array(batch_sample[1].flatten)).transpose())

    X_ = np.array(samples)
    Y_ = np.array(labels)

    X_ = X_[:, :, :, newaxis]

    print(X_.shape)
    print(Y_.shape)

    yield (X_, Y_)

# compile and train the model using the generator function
train_generator = generator(training_data, batch_size = 10)
validation_generator = generator(val_data, batch_size = 10)

run_opts = tf.RunOptions(report_tensor_allocations_upon_oom = True)

model = Sequential()

model.add(Conv2D(32, (4, 4), strides=(2, 2), input_shape = (55, 1756, 
1)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(16, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Conv2D(8, (2, 2)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2, 2)))

model.add(Activation('softmax'))
model.add(Flatten())  # this converts our 3D feature maps to 1D feature 
vectors
model.add(Dropout(0.3))
model.add(Dense(19316))

model.compile(loss = 'sparse_categorical_crossentropy',
              optimizer = 'adam',
              metrics = ['accuracy'],
              options = run_opts)

model.summary()

batch_size = 20
nb_epoch = 6

model.fit_generator(train_generator, 
                    steps_per_epoch = len(training_data) ,
                    epochs = nb_epoch,
                    validation_data = validation_generator,
                    validation_steps = len(val_data))