我正在尝试在keras中构建u-net以进行多类语义细分。我在下面的模型没有学到任何东西。它总是只预测背景（第一类）。
我对最后一个“ softmax”层的使用正确吗？ documentation显示一个axis参数，但是我不确定如何设置该参数或应设置为什么。
def unet(input_shape=(572, 572, 1), classes=2):
input_image = KL.Input(shape=input_shape)
contracting_1, pooled_1 = blocks.contracting(input_image, filters=64, block_name="block1")
contracting_2, pooled_2 = blocks.contracting(pooled_1, filters=128, block_name="block2")
contracting_3, pooled_3 = blocks.contracting(pooled_2, filters=256, block_name="block3")
contracting_4, pooled_4 = blocks.contracting(pooled_3, filters=512, block_name="block4")
contracting_5, _ = blocks.contracting(pooled_4, filters=1024, block_name="block5")
dropout = KL.Dropout(rate=0.5)(contracting_5)
expanding_1 = blocks.expanding(dropout, merge_layer=contracting_4, filters=512, block_name="block6")
expanding_2 = blocks.expanding(expanding_1, merge_layer=contracting_3, filters=256, block_name="block7")
expanding_3 = blocks.expanding(expanding_2, merge_layer=contracting_2, filters=128, block_name="block8")
expanding_4 = blocks.expanding(expanding_3, merge_layer=contracting_1, filters=64, block_name="block9")
class_output = KL.Conv2D(classes, kernel_size=(1, 1), activation='softmax', name='class_output')(expanding_4)
model = KM.Model(inputs=[input_image], outputs=[class_output])
return model
块：
def contracting(input_layer, filters, kernel_size=(3, 3), padding='same',
block_name=""):
conv_a = KL.Conv2D(filters, kernel_size, activation='relu', padding=padding,
name='{}_contracting_conv_a'.format(block_name))(input_layer)
conv_b = KL.Conv2D(filters, kernel_size, activation='relu', padding=padding,
name='{}_contracting_conv_b'.format(block_name))(conv_a)
pool = KL.MaxPooling2D(pool_size=(2, 2), padding=padding,
name='{}_contracting_pool'.format(block_name))(conv_b)
batch_normalization = KL.BatchNormalization()(pool)
return conv_b, batch_normalization
def expanding(input_layer, merge_layer, filters, kernel_size=(3, 3), padding='same',
block_name=""):
input_layer = KL.UpSampling2D(size=(2, 2))(input_layer)
conv_up = KL.Conv2D(filters, kernel_size=(2, 2), activation='relu',
padding='same', name='{}_expanding_conv_up'.format(block_name))(input_layer)
conv_up_height, conv_up_width = int(conv_up.shape[1]), int(conv_up.shape[2])
merge_height, merge_width = int(merge_layer.shape[1]), int(merge_layer.shape[2])
crop_top = (merge_height - conv_up_height) // 2
crop_bottom = (merge_height - conv_up_height) - crop_top
crop_left = (merge_width - conv_up_width) // 2
crop_right = (merge_width - conv_up_width) - crop_left
cropping = ((crop_top, crop_bottom), (crop_left, crop_right))
merge_layer = KL.Cropping2D(cropping)(merge_layer)
merged = KL.concatenate([merge_layer, conv_up])
conv_a = KL.Conv2D(filters, kernel_size, activation='relu', padding=padding,
name='{}_expanding_conv_a'.format(block_name))(merged)
conv_b = KL.Conv2D(filters, kernel_size, activation='relu', padding=padding,
name='{}_expanding_conv_b'.format(block_name))(conv_a)
batch_normalization = KL.BatchNormalization()(conv_b)
return batch_normalization
编译：
optimizer = keras.optimizers.SGD(lr=0.0001, momentum=0.9)
loss = keras.losses.categorical_crossentropy
metrics = [keras.metrics.categorical_accuracy]
model.compile(optimizer, loss, metrics)
模型摘要：
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) (None, 96, 96, 3) 0
__________________________________________________________________________________________________
block1_contracting_conv_a (Conv (None, 96, 96, 64) 1792 input_2[0][0]
__________________________________________________________________________________________________
block1_contracting_conv_b (Conv (None, 96, 96, 64) 36928 block1_contracting_conv_a[0][0]
__________________________________________________________________________________________________
block1_contracting_pool (MaxPoo (None, 48, 48, 64) 0 block1_contracting_conv_b[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 48, 48, 64) 256 block1_contracting_pool[0][0]
__________________________________________________________________________________________________
block2_contracting_conv_a (Conv (None, 48, 48, 128) 73856 batch_normalization_10[0][0]
__________________________________________________________________________________________________
block2_contracting_conv_b (Conv (None, 48, 48, 128) 147584 block2_contracting_conv_a[0][0]
__________________________________________________________________________________________________
block2_contracting_pool (MaxPoo (None, 24, 24, 128) 0 block2_contracting_conv_b[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 24, 24, 128) 512 block2_contracting_pool[0][0]
__________________________________________________________________________________________________
block3_contracting_conv_a (Conv (None, 24, 24, 256) 295168 batch_normalization_11[0][0]
__________________________________________________________________________________________________
block3_contracting_conv_b (Conv (None, 24, 24, 256) 590080 block3_contracting_conv_a[0][0]
__________________________________________________________________________________________________
block3_contracting_pool (MaxPoo (None, 12, 12, 256) 0 block3_contracting_conv_b[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 12, 12, 256) 1024 block3_contracting_pool[0][0]
__________________________________________________________________________________________________
block4_contracting_conv_a (Conv (None, 12, 12, 512) 1180160 batch_normalization_12[0][0]
__________________________________________________________________________________________________
block4_contracting_conv_b (Conv (None, 12, 12, 512) 2359808 block4_contracting_conv_a[0][0]
__________________________________________________________________________________________________
block4_contracting_pool (MaxPoo (None, 6, 6, 512) 0 block4_contracting_conv_b[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 6, 6, 512) 2048 block4_contracting_pool[0][0]
__________________________________________________________________________________________________
block5_contracting_conv_a (Conv (None, 6, 6, 1024) 4719616 batch_normalization_13[0][0]
__________________________________________________________________________________________________
block5_contracting_conv_b (Conv (None, 6, 6, 1024) 9438208 block5_contracting_conv_a[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 6, 6, 1024) 0 block5_contracting_conv_b[0][0]
__________________________________________________________________________________________________
up_sampling2d_5 (UpSampling2D) (None, 12, 12, 1024) 0 dropout_2[0][0]
__________________________________________________________________________________________________
cropping2d_5 (Cropping2D) (None, 12, 12, 512) 0 block4_contracting_conv_b[0][0]
__________________________________________________________________________________________________
block6_expanding_conv_up (Conv2 (None, 12, 12, 512) 2097664 up_sampling2d_5[0][0]
__________________________________________________________________________________________________
concatenate_5 (Concatenate) (None, 12, 12, 1024) 0 cropping2d_5[0][0]
block6_expanding_conv_up[0][0]
__________________________________________________________________________________________________
block6_expanding_conv_a (Conv2D (None, 12, 12, 512) 4719104 concatenate_5[0][0]
__________________________________________________________________________________________________
block6_expanding_conv_b (Conv2D (None, 12, 12, 512) 2359808 block6_expanding_conv_a[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 12, 12, 512) 2048 block6_expanding_conv_b[0][0]
__________________________________________________________________________________________________
up_sampling2d_6 (UpSampling2D) (None, 24, 24, 512) 0 batch_normalization_15[0][0]
__________________________________________________________________________________________________
cropping2d_6 (Cropping2D) (None, 24, 24, 256) 0 block3_contracting_conv_b[0][0]
__________________________________________________________________________________________________
block7_expanding_conv_up (Conv2 (None, 24, 24, 256) 524544 up_sampling2d_6[0][0]
__________________________________________________________________________________________________
concatenate_6 (Concatenate) (None, 24, 24, 512) 0 cropping2d_6[0][0]
block7_expanding_conv_up[0][0]
__________________________________________________________________________________________________
block7_expanding_conv_a (Conv2D (None, 24, 24, 256) 1179904 concatenate_6[0][0]
__________________________________________________________________________________________________
block7_expanding_conv_b (Conv2D (None, 24, 24, 256) 590080 block7_expanding_conv_a[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 24, 24, 256) 1024 block7_expanding_conv_b[0][0]
__________________________________________________________________________________________________
up_sampling2d_7 (UpSampling2D) (None, 48, 48, 256) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
cropping2d_7 (Cropping2D) (None, 48, 48, 128) 0 block2_contracting_conv_b[0][0]
__________________________________________________________________________________________________
block8_expanding_conv_up (Conv2 (None, 48, 48, 128) 131200 up_sampling2d_7[0][0]
__________________________________________________________________________________________________
concatenate_7 (Concatenate) (None, 48, 48, 256) 0 cropping2d_7[0][0]
block8_expanding_conv_up[0][0]
__________________________________________________________________________________________________
block8_expanding_conv_a (Conv2D (None, 48, 48, 128) 295040 concatenate_7[0][0]
__________________________________________________________________________________________________
block8_expanding_conv_b (Conv2D (None, 48, 48, 128) 147584 block8_expanding_conv_a[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 48, 48, 128) 512 block8_expanding_conv_b[0][0]
__________________________________________________________________________________________________
up_sampling2d_8 (UpSampling2D) (None, 96, 96, 128) 0 batch_normalization_17[0][0]
__________________________________________________________________________________________________
cropping2d_8 (Cropping2D) (None, 96, 96, 64) 0 block1_contracting_conv_b[0][0]
__________________________________________________________________________________________________
block9_expanding_conv_up (Conv2 (None, 96, 96, 64) 32832 up_sampling2d_8[0][0]
__________________________________________________________________________________________________
concatenate_8 (Concatenate) (None, 96, 96, 128) 0 cropping2d_8[0][0]
block9_expanding_conv_up[0][0]
__________________________________________________________________________________________________
block9_expanding_conv_a (Conv2D (None, 96, 96, 64) 73792 concatenate_8[0][0]
__________________________________________________________________________________________________
block9_expanding_conv_b (Conv2D (None, 96, 96, 64) 36928 block9_expanding_conv_a[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 96, 96, 64) 256 block9_expanding_conv_b[0][0]
__________________________________________________________________________________________________
class_output (Conv2D) (None, 96, 96, 4) 260 batch_normalization_18[0][0]
==================================================================================================
Total params: 31,039,620
Trainable params: 31,035,780
Non-trainable params: 3,840
__________________________________________________________________________________________________
Total params: 31,031,940
Trainable params: 31,031,940
Non-trainable params: 0
数据集中的类别百分比：
{0: 0.6245757457188198,
1: 0.16082110268729075,
2: 0.1188858904157366,
3: 0.09571726117815291}
0是背景(1, 96, 96, 3) (1, 96, 96, 4) 答案 0 :(得分：2)
您的模型似乎没有什么错。
Softmax可以，因为它默认为最后一个轴，因此您显然使用'channels_last'作为配置。这样就可以了
建议是：
BatchNormalization()并降低学习速度（这可以防止relu太快地变为“全零”）。 np.unique(y_train)仅包含0和1 (np.sum(y_train, axis=-1) == 1).all() == True。 np.sum(y_train[:,:,:,0])不应大于np.sum(y_train[:,:,:,1:])。
class_weight参数，传递权重以平衡每个类的损失（有关使用方法，请查看fit上的keras文档）答案 1 :(得分：0)
对于大多数细分项目，该模型对我来说效果很好，我对多类细分使用交叉熵，对二元类使用平滑骰子
def conv_block(tensor, nfilters, size=3, padding='same', initializer="he_normal"):
x = Conv2D(filters=nfilters, kernel_size=(size, size), padding=padding, kernel_initializer=initializer)(tensor)
x = BatchNormalization()(x)
x = Activation("relu")(x)
x = Conv2D(filters=nfilters, kernel_size=(size, size), padding=padding, kernel_initializer=initializer)(x)
x = BatchNormalization()(x)
x = Activation("relu")(x)
return x
def deconv_block(tensor, residual, nfilters, size=3, padding='same', strides=(2, 2)):
y = Conv2DTranspose(nfilters, kernel_size=(size, size), strides=strides, padding=padding)(tensor)
y = concatenate([y, residual], axis=3)
y = conv_block(y, nfilters)
return y
def Unet(img_height, img_width, nclasses=3, filters=64):
# down
input_layer = Input(shape=(img_height, img_width, 3), name='image_input')
conv1 = conv_block(input_layer, nfilters=filters)
conv1_out = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = conv_block(conv1_out, nfilters=filters*2)
conv2_out = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = conv_block(conv2_out, nfilters=filters*4)
conv3_out = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = conv_block(conv3_out, nfilters=filters*8)
conv4_out = MaxPooling2D(pool_size=(2, 2))(conv4)
conv4_out = Dropout(0.5)(conv4_out)
conv5 = conv_block(conv4_out, nfilters=filters*16)
conv5 = Dropout(0.5)(conv5)
# up
deconv6 = deconv_block(conv5, residual=conv4, nfilters=filters*8)
deconv6 = Dropout(0.5)(deconv6)
deconv7 = deconv_block(deconv6, residual=conv3, nfilters=filters*4)
deconv7 = Dropout(0.5)(deconv7)
deconv8 = deconv_block(deconv7, residual=conv2, nfilters=filters*2)
deconv9 = deconv_block(deconv8, residual=conv1, nfilters=filters)
# output
output_layer = Conv2D(filters=nclasses, kernel_size=(1, 1))(deconv9)
output_layer = BatchNormalization()(output_layer)
output_layer = Activation('softmax')(output_layer)
model = Model(inputs=input_layer, outputs=output_layer, name='Unet')
return model
答案 2 :(得分：0)
有时，问题与模型架构有关。在处理复杂的数据集进行细分时，需要增强模型架构。我在新数据集上遇到了相同的问题，而模型可以在另一个数据集上很好地工作。因此，我使用Res-Unet而不是Unet作为模型架构，并解决了问题。 希望这会有所帮助