Sou*_*wal 4 image-segmentation deep-learning keras
我最近开始学习图像分割和UNet。我正在尝试进行多类图像分割，其中我有 7 个类，输入是 (256, 256, 3) rgb 图像，输出是 (256, 256, 1) 灰度图像，其中每个强度值对应于一个类。我正在做像素级的softmax。我使用稀疏分类交叉熵以避免进行 One Hot Encoding。
def soft1(x):
return keras.activations.softmax(x, axis = -1)
def conv2d_block(input_tensor, n_filters, kernel_size = 3, batchnorm = True):
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)
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)
return x
def get_unet(input_img, n_classes, n_filters = 16, dropout = 0.1, batchnorm = True):
# Contracting Path
c1 = conv2d_block(input_img, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)
p1 = MaxPooling2D((2, 2))(c1)
p1 = Dropout(dropout)(p1)
c2 = conv2d_block(p1, n_filters * 2, kernel_size = 3, batchnorm = batchnorm)
p2 = MaxPooling2D((2, 2))(c2)
p2 = Dropout(dropout)(p2)
c3 = conv2d_block(p2, n_filters * 4, kernel_size = 3, batchnorm = batchnorm)
p3 = MaxPooling2D((2, 2))(c3)
p3 = Dropout(dropout)(p3)
c4 = conv2d_block(p3, n_filters * 8, kernel_size = 3, batchnorm = batchnorm)
p4 = MaxPooling2D((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 * 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 * 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 * 2, kernel_size = 3, batchnorm = batchnorm)
u9 = Conv2DTranspose(n_filters * 1, (3, 3), strides = (2, 2), padding = 'same')(c8)
u9 = concatenate([u9, c1])
u9 = Dropout(dropout)(u9)
c9 = conv2d_block(u9, n_filters * 1, kernel_size = 3, batchnorm = batchnorm)
outputs = Conv2D(n_classes, (1, 1))(c9)
outputs = Reshape((image_height*image_width, 1, n_classes), input_shape = (image_height, image_width, n_classes))(outputs)
outputs = Activation(soft1)(outputs)
model = Model(inputs=[input_img], outputs=[outputs])
print(outputs.shape)
return model
我的模型摘要是：
Model: "model_2"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_12 (InputLayer) (None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv2d_211 (Conv2D) (None, 256, 256, 16) 448 input_12[0][0]
__________________________________________________________________________________________________
batch_normalization_200 (BatchN (None, 256, 256, 16) 64 conv2d_211[0][0]
__________________________________________________________________________________________________
activation_204 (Activation) (None, 256, 256, 16) 0 batch_normalization_200[0][0]
__________________________________________________________________________________________________
max_pooling2d_45 (MaxPooling2D) (None, 128, 128, 16) 0 activation_204[0][0]
__________________________________________________________________________________________________
dropout_89 (Dropout) (None, 128, 128, 16) 0 max_pooling2d_45[0][0]
__________________________________________________________________________________________________
conv2d_213 (Conv2D) (None, 128, 128, 32) 4640 dropout_89[0][0]
__________________________________________________________________________________________________
batch_normalization_202 (BatchN (None, 128, 128, 32) 128 conv2d_213[0][0]
__________________________________________________________________________________________________
activation_206 (Activation) (None, 128, 128, 32) 0 batch_normalization_202[0][0]
__________________________________________________________________________________________________
max_pooling2d_46 (MaxPooling2D) (None, 64, 64, 32) 0 activation_206[0][0]
__________________________________________________________________________________________________
dropout_90 (Dropout) (None, 64, 64, 32) 0 max_pooling2d_46[0][0]
__________________________________________________________________________________________________
conv2d_215 (Conv2D) (None, 64, 64, 64) 18496 dropout_90[0][0]
__________________________________________________________________________________________________
batch_normalization_204 (BatchN (None, 64, 64, 64) 256 conv2d_215[0][0]
__________________________________________________________________________________________________
activation_208 (Activation) (None, 64, 64, 64) 0 batch_normalization_204[0][0]
__________________________________________________________________________________________________
max_pooling2d_47 (MaxPooling2D) (None, 32, 32, 64) 0 activation_208[0][0]
__________________________________________________________________________________________________
dropout_91 (Dropout) (None, 32, 32, 64) 0 max_pooling2d_47[0][0]
__________________________________________________________________________________________________
conv2d_217 (Conv2D) (None, 32, 32, 128) 73856 dropout_91[0][0]
__________________________________________________________________________________________________
batch_normalization_206 (BatchN (None, 32, 32, 128) 512 conv2d_217[0][0]
__________________________________________________________________________________________________
activation_210 (Activation) (None, 32, 32, 128) 0 batch_normalization_206[0][0]
__________________________________________________________________________________________________
max_pooling2d_48 (MaxPooling2D) (None, 16, 16, 128) 0 activation_210[0][0]
__________________________________________________________________________________________________
dropout_92 (Dropout) (None, 16, 16, 128) 0 max_pooling2d_48[0][0]
__________________________________________________________________________________________________
conv2d_219 (Conv2D) (None, 16, 16, 256) 295168 dropout_92[0][0]
__________________________________________________________________________________________________
batch_normalization_208 (BatchN (None, 16, 16, 256) 1024 conv2d_219[0][0]
__________________________________________________________________________________________________
activation_212 (Activation) (None, 16, 16, 256) 0 batch_normalization_208[0][0]
__________________________________________________________________________________________________
conv2d_transpose_45 (Conv2DTran (None, 32, 32, 128) 295040 activation_212[0][0]
__________________________________________________________________________________________________
concatenate_45 (Concatenate) (None, 32, 32, 256) 0 conv2d_transpose_45[0][0]
activation_210[0][0]
__________________________________________________________________________________________________
dropout_93 (Dropout) (None, 32, 32, 256) 0 concatenate_45[0][0]
__________________________________________________________________________________________________
conv2d_221 (Conv2D) (None, 32, 32, 128) 295040 dropout_93[0][0]
__________________________________________________________________________________________________
batch_normalization_210 (BatchN (None, 32, 32, 128) 512 conv2d_221[0][0]
__________________________________________________________________________________________________
activation_214 (Activation) (None, 32, 32, 128) 0 batch_normalization_210[0][0]
__________________________________________________________________________________________________
conv2d_transpose_46 (Conv2DTran (None, 64, 64, 64) 73792 activation_214[0][0]
__________________________________________________________________________________________________
concatenate_46 (Concatenate) (None, 64, 64, 128) 0 conv2d_transpose_46[0][0]
activation_208[0][0]
__________________________________________________________________________________________________
dropout_94 (Dropout) (None, 64, 64, 128) 0 concatenate_46[0][0]
__________________________________________________________________________________________________
conv2d_223 (Conv2D) (None, 64, 64, 64) 73792 dropout_94[0][0]
__________________________________________________________________________________________________
batch_normalization_212 (BatchN (None, 64, 64, 64) 256 conv2d_223[0][0]
__________________________________________________________________________________________________
activation_216 (Activation) (None, 64, 64, 64) 0 batch_normalization_212[0][0]
__________________________________________________________________________________________________
conv2d_transpose_47 (Conv2DTran (None, 128, 128, 32) 18464 activation_216[0][0]
__________________________________________________________________________________________________
concatenate_47 (Concatenate) (None, 128, 128, 64) 0 conv2d_transpose_47[0][0]
activation_206[0][0]
__________________________________________________________________________________________________
dropout_95 (Dropout) (None, 128, 128, 64) 0 concatenate_47[0][0]
__________________________________________________________________________________________________
conv2d_225 (Conv2D) (None, 128, 128, 32) 18464 dropout_95[0][0]
__________________________________________________________________________________________________
batch_normalization_214 (BatchN (None, 128, 128, 32) 128 conv2d_225[0][0]
__________________________________________________________________________________________________
activation_218 (Activation) (None, 128, 128, 32) 0 batch_normalization_214[0][0]
__________________________________________________________________________________________________
conv2d_transpose_48 (Conv2DTran (None, 256, 256, 16) 4624 activation_218[0][0]
__________________________________________________________________________________________________
concatenate_48 (Concatenate) (None, 256, 256, 32) 0 conv2d_transpose_48[0][0]
activation_204[0][0]
__________________________________________________________________________________________________
dropout_96 (Dropout) (None, 256, 256, 32) 0 concatenate_48[0][0]
__________________________________________________________________________________________________
conv2d_227 (Conv2D) (None, 256, 256, 16) 4624 dropout_96[0][0]
__________________________________________________________________________________________________
batch_normalization_216 (BatchN (None, 256, 256, 16) 64 conv2d_227[0][0]
__________________________________________________________________________________________________
activation_220 (Activation) (None, 256, 256, 16) 0 batch_normalization_216[0][0]
__________________________________________________________________________________________________
conv2d_228 (Conv2D) (None, 256, 256, 7) 119 activation_220[0][0]
__________________________________________________________________________________________________
reshape_12 (Reshape) (None, 65536, 1, 7) 0 conv2d_228[0][0]
__________________________________________________________________________________________________
activation_221 (Activation) (None, 65536, 1, 7) 0 reshape_12[0][0]
==================================================================================================
Total params: 1,179,511
Trainable params: 1,178,039
Non-trainable params: 1,472
__________________________________________________________________________________________________
我的模型对吗？当我使用 softmax 时，最终输出不应该是 (65536, 1, 1) 吗？代码正在编译，但骰子系数非常低。
你的模型应该以(256,256,7).
即每个像素 7 个类，并且形状应与您的输出图像一致(256,256,1)。这仅适用于'sparse_categorical_crossentropy'自定义损失。
所以，模型conv_228看起来不错（尽管没有详细查看）。
在这个卷积之后不需要任何东西。
您可以将 softmax 直接放在前面conv_228或后面。
y_train应该是(256,256,1)为了这个。
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