ValueError: A `Concatenate` layer requires inputs with matching shapes except for the concat axis. Got inputs shapes: `[(None, 36, 36, 128), (None, 37, 37, 128)]`
def conv2d_block(input_tensor, n_filters, kernel_size=3, batchnorm=True):
# first layer
x = Conv2D(filters=n_filters, kernel_size=(kernel_size, kernel_size), kernel_initializer="he_normal",
padding="same")(input_tensor)
if batchnorm:
x = BatchNormalization()(x)
x = Activation("relu")(x)
# second layer
x = Conv2D(filters=n_filters, kernel_size=(kernel_size, kernel_size), kernel_initializer="he_normal",
padding="same")(x)
if batchnorm:
x = BatchNormalization()(x)
x = Activation("relu")(x)
return x
def get_unet(input_img, n_filters=16, dropout=0.5, batchnorm=True):
# contracting path
c1 = conv2d_block(input_img, n_filters=n_filters*1, kernel_size=3, batchnorm=batchnorm)
p1 = MaxPooling2D((2, 2)) (c1)
p1 = Dropout(dropout*0.5)(p1)
c2 = conv2d_block(p1, n_filters=n_filters*2, kernel_size=3, batchnorm=batchnorm)
p2 = MaxPooling2D((2, 2)) (c2)
p2 = Dropout(dropout)(p2)
c3 = conv2d_block(p2, n_filters=n_filters*4, kernel_size=3, batchnorm=batchnorm)
p3 = MaxPooling2D((2, 2)) (c3)
p3 = Dropout(dropout)(p3)
c4 = conv2d_block(p3, n_filters=n_filters*8, kernel_size=3, batchnorm=batchnorm)
p4 = MaxPooling2D(pool_size=(2, 2)) (c4)
p4 = Dropout(dropout)(p4)
c5 = conv2d_block(p4, n_filters=n_filters*16, kernel_size=3, batchnorm=batchnorm)
# expansive path
u6 = Conv2DTranspose(n_filters*8, (3, 3), strides=(2, 2), padding='same') (c5)
u6 = concatenate([u6, c4])
u6 = Dropout(dropout)(u6)
c6 = conv2d_block(u6, n_filters=n_filters*8, kernel_size=3, batchnorm=batchnorm)
u7 = Conv2DTranspose(n_filters*4, (3, 3), strides=(2, 2), padding='same') (c6)
u7 = concatenate([u7, c3])
u7 = Dropout(dropout)(u7)
c7 = conv2d_block(u7, n_filters=n_filters*4, kernel_size=3, batchnorm=batchnorm)
u8 = Conv2DTranspose(n_filters*2, (3, 3), strides=(2, 2), padding='same') (c7)
u8 = concatenate([u8, c2])
u8 = Dropout(dropout)(u8)
c8 = conv2d_block(u8, n_filters=n_filters*2, kernel_size=3, batchnorm=batchnorm)
u9 = Conv2DTranspose(n_filters*1, (3, 3), strides=(2, 2), padding='same') (c8)
u9 = concatenate([u9, c1], axis=3)
u9 = Dropout(dropout)(u9)
c9 = conv2d_block(u9, n_filters=n_filters*1, kernel_size=3, batchnorm=batchnorm)
outputs = Conv2D(1, (1, 1), activation='sigmoid') (c9)
model = Model(inputs=[input_img], outputs=[outputs])
return model
你知道我错过了什么吗?异常出现在u6 = concatenate([u6, c4])
我使用python 3.9.1，我的导入主要涉及使用TensorFlow后端的Keras。我还尝试删除一些MaxPooling，但这没有帮助，以及改变一些maxpull变量。
我的图像输入是:input_img = Input((300, 300, 1), name="img")
你的问题是,图片没有尺寸为2的幂,例如有一个维度就像256年或512年不会创造这个问题,这是因为在你的网络汇聚层生成的一半大小的图像,当你把300次2得到系列:150年和75年和75年不整除2,所以当你试图分裂75 37码,当你的traspose水平下得到大小36。
我不知道它是否足够清楚:你正试图用奇数像素的图像做maxpooling，图像将达到300 ->150→75→37→18和得到加倍18 ->STOP, 36和37不相等，网络无法连接
所以，快速的解决方案:调整图像的尺寸，使其尺寸为2的幂，例如512，或者至少可以被8整除(因为您有4 maxpooling)