我正在编写用于 RGB 256 * 256p图像-> 灰度图像的图像分割的深度学习UNet模型。 灵感来自 https://github.com/zhixuhao/unet， 所以我的神经网络具有以下结构：
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 256, 256, 3) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 256, 256, 16) 448 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 256, 256, 16) 64 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 256, 256, 16) 2320 batch_normalization_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 256, 256, 16) 64 conv2d_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 128, 128, 16) 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 128, 128, 32) 4640 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 128, 128, 32) 128 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 128, 128, 32) 9248 batch_normalization_3[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 128, 128, 32) 128 conv2d_4[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 64, 64, 32) 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 64, 64, 64) 18496 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 64, 64, 64) 256 conv2d_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 64, 64, 64) 36928 batch_normalization_5[0][0]
__________________________________________________________________________________________________
batch_normalization_6 (BatchNor (None, 64, 64, 64) 256 conv2d_6[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 32, 32, 64) 0 batch_normalization_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 32, 32, 128) 73856 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
batch_normalization_7 (BatchNor (None, 32, 32, 128) 512 conv2d_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 32, 32, 128) 147584 batch_normalization_7[0][0]
__________________________________________________________________________________________________
batch_normalization_8 (BatchNor (None, 32, 32, 128) 512 conv2d_8[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 128) 0 batch_normalization_8[0][0]
__________________________________________________________________________________________________
max_pooling2d_4 (MaxPooling2D) (None, 16, 16, 128) 0 dropout_1[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 16, 16, 256) 295168 max_pooling2d_4[0][0]
__________________________________________________________________________________________________
batch_normalization_9 (BatchNor (None, 16, 16, 256) 1024 conv2d_9[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 16, 16, 256) 590080 batch_normalization_9[0][0]
__________________________________________________________________________________________________
batch_normalization_10 (BatchNo (None, 16, 16, 256) 1024 conv2d_10[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 16, 16, 256) 0 batch_normalization_10[0][0]
__________________________________________________________________________________________________
up_sampling2d_1 (UpSampling2D) (None, 32, 32, 256) 0 dropout_2[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 32, 32, 128) 131200 up_sampling2d_1[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 32, 32, 256) 0 dropout_1[0][0]
conv2d_11[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 32, 32, 128) 295040 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_11 (BatchNo (None, 32, 32, 128) 512 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 32, 32, 128) 147584 batch_normalization_11[0][0]
__________________________________________________________________________________________________
batch_normalization_12 (BatchNo (None, 32, 32, 128) 512 conv2d_13[0][0]
__________________________________________________________________________________________________
up_sampling2d_2 (UpSampling2D) (None, 64, 64, 128) 0 batch_normalization_12[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 64, 64, 64) 32832 up_sampling2d_2[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 64, 64, 128) 0 conv2d_6[0][0]
conv2d_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 64, 64, 64) 73792 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_13 (BatchNo (None, 64, 64, 64) 256 conv2d_15[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 64, 64, 64) 36928 batch_normalization_13[0][0]
__________________________________________________________________________________________________
batch_normalization_14 (BatchNo (None, 64, 64, 64) 256 conv2d_16[0][0]
__________________________________________________________________________________________________
up_sampling2d_3 (UpSampling2D) (None, 128, 128, 64) 0 batch_normalization_14[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 128, 128, 32) 8224 up_sampling2d_3[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 128, 128, 64) 0 conv2d_4[0][0]
conv2d_17[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 128, 128, 32) 18464 concatenate_3[0][0]
__________________________________________________________________________________________________
batch_normalization_15 (BatchNo (None, 128, 128, 32) 128 conv2d_18[0][0]
__________________________________________________________________________________________________
conv2d_19 (Conv2D) (None, 128, 128, 32) 9248 batch_normalization_15[0][0]
__________________________________________________________________________________________________
batch_normalization_16 (BatchNo (None, 128, 128, 32) 128 conv2d_19[0][0]
__________________________________________________________________________________________________
up_sampling2d_4 (UpSampling2D) (None, 256, 256, 32) 0 batch_normalization_16[0][0]
__________________________________________________________________________________________________
conv2d_20 (Conv2D) (None, 256, 256, 16) 2064 up_sampling2d_4[0][0]
__________________________________________________________________________________________________
concatenate_4 (Concatenate) (None, 256, 256, 32) 0 conv2d_2[0][0]
conv2d_20[0][0]
__________________________________________________________________________________________________
conv2d_21 (Conv2D) (None, 256, 256, 16) 4624 concatenate_4[0][0]
__________________________________________________________________________________________________
batch_normalization_17 (BatchNo (None, 256, 256, 16) 64 conv2d_21[0][0]
__________________________________________________________________________________________________
conv2d_22 (Conv2D) (None, 256, 256, 16) 2320 batch_normalization_17[0][0]
__________________________________________________________________________________________________
batch_normalization_18 (BatchNo (None, 256, 256, 16) 64 conv2d_22[0][0]
__________________________________________________________________________________________________
conv2d_23 (Conv2D) (None, 256, 256, 2) 290 batch_normalization_18[0][0]
__________________________________________________________________________________________________
batch_normalization_19 (BatchNo (None, 256, 256, 2) 8 conv2d_23[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 256, 256, 2) 0 batch_normalization_19[0][0]
__________________________________________________________________________________________________
MLP_layer (Conv2D) (None, 256, 256, 1) 3 dropout_3[0][0]
==================================================================================================
但是，收敛非常困难，它仅适用于非常有限的参数集：
-学习率不大于1e-3，然后在某些文章中使用带有衰变的1e-2
-第一个卷积过滤器编号仅适用于16（32个下一层，依此类推）。
-批量大小是8或16，而32和64则不起作用
- batch_normalization 是必需的，而不是在示例基础模型中。如... https://towardsdatascience.com/batch-normalization-theory-and-how-to-use-it-with-tensorflow-1892ca0173ad中所述，这应该有助于网络以较少受限的参数进行学习？ https://arxiv.org/pdf/1502.03167.pdf
其他详细信息：
-我检查了输入的np.float32范围是0到1
-我正在尝试学习卫星图像cadasters
所以我的问题是：
为什么我的网络无法使用参考文章中使用的相同参数？
->我必须放置“ slow”参数以使其起作用（较低的学习速度，较低的批处理大小，较少的卷积层...）。否则，它会输出带有单个像素值的灰度图像
使用的代码：
SHAPE=256
DIM=3
INITIALIZER='glorot_uniform'
BASE_SIZE=16
LR=0.001
def get_model(pretrained_model: str = None, input_size: tuple_int = (SHAPE, SHAPE, DIM)) -> Sequential:
"""
Machine learning model for image learning, here the purpose is segmentation,
thus there should be upsampling !!
Parameters
----------
pretrained_model: str
name of .hdf5 file containing pretrained weights, syntax: 'dir:weight.hfd5'
input_size: tuple_int
Returns
-------
Sequential
"""
if pretrained_model:
return read_model(pretrained_model)
else:
inputs = Input(input_size)
conv1 = Conv2D(BASE_SIZE, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(inputs)
batch_norm1 = BatchNormalization()(conv1)
conv2 = Conv2D(BASE_SIZE, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(batch_norm1)
batch_norm2 = BatchNormalization()(conv2)
pool1 = MaxPooling2D(pool_size=(2, 2))(batch_norm2)
conv3 = Conv2D(BASE_SIZE * 2, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(pool1)
batch_norm3 = BatchNormalization()(conv3)
conv4 = Conv2D(BASE_SIZE * 2, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(batch_norm3)
batch_norm4 = BatchNormalization()(conv4)
pool2 = MaxPooling2D(pool_size=(2, 2))(batch_norm4)
conv5 = Conv2D(BASE_SIZE * 4, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(pool2)
batch_norm5 = BatchNormalization()(conv5)
conv6 = Conv2D(BASE_SIZE * 4, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(batch_norm5)
batch_norm6 = BatchNormalization()(conv6)
pool3 = MaxPooling2D(pool_size=(2, 2))(batch_norm6)
conv7 = Conv2D(BASE_SIZE * 8, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(pool3)
batch_norm7 = BatchNormalization()(conv7)
conv8 = Conv2D(BASE_SIZE * 8, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(batch_norm7)
batch_norm8 = BatchNormalization()(conv8)
drop4 = Dropout(0.2)(batch_norm8)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv9 = Conv2D(BASE_SIZE * 16, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(pool4)
batch_norm9 = BatchNormalization()(conv9)
conv10 = Conv2D(BASE_SIZE * 16, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
batch_norm9)
batch_norm10 = BatchNormalization()(conv10)
drop5 = Dropout(0.5)(batch_norm10)
up6 = Conv2D(BASE_SIZE * 8, 2, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
UpSampling2D(size=(2, 2))(drop5))
merge6 = concatenate([drop4, up6], axis=3)
conv11 = Conv2D(BASE_SIZE * 8, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(merge6)
batch_norm11 = BatchNormalization()(conv11)
conv12 = Conv2D(BASE_SIZE * 8, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
batch_norm11)
batch_norm12 = BatchNormalization()(conv12)
up7 = Conv2D(BASE_SIZE * 4, 2, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
UpSampling2D(size=(2, 2))(batch_norm12))
merge7 = concatenate([conv6, up7], axis=3)
conv13 = Conv2D(BASE_SIZE * 4, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(merge7)
batch_norm13 = BatchNormalization()(conv13)
conv14 = Conv2D(BASE_SIZE * 4, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
batch_norm13)
batch_norm14 = BatchNormalization()(conv14)
up8 = Conv2D(BASE_SIZE * 2, 2, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
UpSampling2D(size=(2, 2))(batch_norm14))
merge8 = concatenate([conv4, up8], axis=3)
conv15 = Conv2D(BASE_SIZE * 2, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(merge8)
batch_norm15 = BatchNormalization()(conv15)
conv16 = Conv2D(BASE_SIZE * 2, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
batch_norm15)
batch_norm16 = BatchNormalization()(conv16)
up9 = Conv2D(BASE_SIZE, 2, activation='relu', padding='same', kernel_initializer=INITIALIZER)(
UpSampling2D(size=(2, 2))(batch_norm16))
merge9 = concatenate([conv2, up9], axis=3)
conv17 = Conv2D(BASE_SIZE, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(merge9)
batch_norm17 = BatchNormalization()(conv17)
conv18 = Conv2D(BASE_SIZE, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(batch_norm17)
batch_norm18 = BatchNormalization()(conv18)
conv19 = Conv2D(2, 3, activation='relu', padding='same', kernel_initializer=INITIALIZER)(batch_norm18)
batch_norm19 = BatchNormalization()(conv19)
# personall add
drop4 = Dropout(0.2)(batch_norm19)
conv10 = Conv2D(1, 1, activation='sigmoid', name='MLP_layer')(drop4)
model = Model(input=inputs, output=conv10)
model.compile(optimizer=Adam(lr=LR),
loss='binary_crossentropy',
metrics=['accuracy', iou_loss])
return model
谢谢
答案 0 :(得分：0)
二进制交叉熵不适用于细分问题，尤其是在班级不平衡的情况下。例如，如果蒙版平均包含的黑色像素多于白色像素，则您的神经网络可以预测所有黑色。尝试将Dice损失或Jaccard损失用作目标函数，或者可以将Dice或Jaccard的总和与二进制交叉熵或加权二进制交叉熵一起使用。最后，您可以看一下该库https://segmentation-models.readthedocs.io/en/latest/install.html，其中包含一些细分模型，包括具有不同预训练编码器的Unet和该主题上最常见的度量标准（例如Dice和Jaccard）。