3D Unet сцепленные слои не совпадают

Question

Я пытаюсь преобразовать свой 2D Unet в 3D Unet. Когда код достигает первой свертки, я получаю сообщение об ошибке:

ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: [(None, 42, 42, 2, 256), (None, 43, 43, 2, 256)]

Может кто-нибудь сказать мне, почему формы ввода не совпадают?

Нейронная сеть:

def get_model(optimizer, loss_metric, metrics, lr=1e-3):
    inputs = Input((sample_width, sample_height, sample_depth, 1))
    conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)
    drop1 = Dropout(0.5)(pool1)

    conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(drop1)
    conv2 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)
    drop2 = Dropout(0.5)(pool2)

    conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(drop2)
    conv3 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)
    drop3 = Dropout(0.3)(pool3)

    conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(drop3)
    conv4 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv4)
    pool4 = MaxPooling3D(pool_size=(2, 2, 2))(conv4)
    drop4 = Dropout(0.3)(pool4)

    conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(drop4)
    conv5 = Conv3D(512, (3, 3, 3), activation='relu', padding='same')(conv5)

    up6 = concatenate([Conv3DTranspose(256, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv5), conv4], axis=3)
    conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(up6)
    conv6 = Conv3D(256, (3, 3, 3), activation='relu', padding='same')(conv6)

    up7 = concatenate([Conv3DTranspose(128, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv6), conv3], axis=3)
    conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(up7)
    conv7 = Conv3D(128, (3, 3, 3), activation='relu', padding='same')(conv7)

    up8 = concatenate([Conv3DTranspose(64, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv7), conv2], axis=3)
    conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(up8)
    conv8 = Conv3D(64, (3, 3, 3), activation='relu', padding='same')(conv8)

    up9 = concatenate([Conv3DTranspose(32, (2, 2, 2), strides=(2, 2, 2), padding='same')(conv8), conv1], axis=3)
    conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(up9)
    conv9 = Conv3D(32, (3, 3, 3), activation='relu', padding='same')(conv9)

    conv10 = Conv3D(1, (1, 1, 1), activation='sigmoid')(conv9)

    model = Model(inputs=[inputs], outputs=[conv10])
    model.compile(optimizer=optimizer(lr=lr), loss=loss_metric, metrics=metr

ектронное)