如何为多类别细分初始化样本权重? [英] How to initialize sample weights for multi-class segmentation?
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问题描述
我正在使用Keras和U-net进行多类别细分.
I'm working on multi-class segmentation using Keras and U-net.
我使用soft max Activation函数作为NN 12类的输出.我的输出的形状是(N，288,288,12).
I have as output of my NN 12 classes using soft max Activation function. the shape of my output is (N,288,288,12).
为适应我的模型，我使用sparse_categorical_crossentropy.
to fit my model I use sparse_categorical_crossentropy.
我想为我的不平衡数据集初始化模型的权重.
I want to initialize weights of my model for my unbalanced dataset.
我发现此有用的链接并尝试实现它；由于Keras中的class_weight不适用于2个以上的类，因此我使用了样本权重
I found this useful link and try it to implement it; since class_weight in Keras does not work for more than 2 classes, I used sample weights
我的代码是:
inputs = tf.keras.layers.Input((IMG_WIDHT, IMG_HEIGHT, IMG_CHANNELS))
smooth = 1.
s = tf.keras.layers.Lambda(lambda x: x / 255)(inputs)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
s) # Kernelsize : start with some weights initial value
c1 = tf.keras.layers.Dropout(0.1)(c1)
c1 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
c1) # Kernelsize : start with some weights initial value
p1 = tf.keras.layers.MaxPool2D((2, 2))(c1)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
p1) # Kernelsize : start with some weights initial value
c2 = tf.keras.layers.Dropout(0.1)(c2)
c2 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
c2) # Kernelsize : start with some weights initial value
p2 = tf.keras.layers.MaxPool2D((2, 2))(c2)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
p2) # Kernelsize : start with some weights initial value
c3 = tf.keras.layers.Dropout(0.1)(c3)
c3 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
c3) # Kernelsize : start with some weights initial value
p3 = tf.keras.layers.MaxPool2D((2, 2))(c3)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
p3) # Kernelsize : start with some weights initial value
c4 = tf.keras.layers.Dropout(0.1)(c4)
c4 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
c4) # Kernelsize : start with some weights initial value
p4 = tf.keras.layers.MaxPool2D((2, 2))(c4)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
p4) # Kernelsize : start with some weights initial value
c5 = tf.keras.layers.Dropout(0.1)(c5)
c5 = tf.keras.layers.Conv2D(256, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(
c5) # Kernelsize : start wi
u6 = tf.keras.layers.Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same')(c5)
u6 = tf.keras.layers.concatenate([u6, c4])
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u6)
c6 = tf.keras.layers.Dropout(0.2)(c6)
c6 = tf.keras.layers.Conv2D(128, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c6)
u7 = tf.keras.layers.Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same')(c6)
u7 = tf.keras.layers.concatenate([u7, c3])
c7 = tf.keras.layers.Conv2D(64, (2, 2), activation='relu', kernel_initializer='he_normal', padding='same')(u7)
c7 = tf.keras.layers.Dropout(0.2)(c7)
c7 = tf.keras.layers.Conv2D(64, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c7)
u8 = tf.keras.layers.Conv2DTranspose(32, (2, 2), strides=(2, 2), padding='same')(c7)
u8 = tf.keras.layers.concatenate([u8, c2])
c8 = tf.keras.layers.Conv2D(32, (2, 2), activation='relu', kernel_initializer='he_normal', padding='same')(u8)
c8 = tf.keras.layers.Dropout(0.1)(c8)
c8 = tf.keras.layers.Conv2D(32, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c8)
u9 = tf.keras.layers.Conv2DTranspose(16, (2, 2), strides=(2, 2), padding='same')(c8)
u9 = tf.keras.layers.concatenate([u9, c1], axis=3)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(u9)
c9 = tf.keras.layers.Dropout(0.1)(c9)
c9 = tf.keras.layers.Conv2D(16, (3, 3), activation='relu', kernel_initializer='he_normal', padding='same')(c9)
outputs = tf.keras.layers.Conv2D(12, (1, 1), activation='softmax')(c9)
outputs = tf.keras.layers.Flatten(data_format=None) (outputs)
model = tf.keras.Model(inputs=[inputs], outputs=[outputs])
cc = tf.keras.optimizers.Adam(learning_rate=0.0001, beta_1=0.9, beta_2=0.999, amsgrad=False)
model.compile(optimizer=cc, loss='sparse_categorical_crossentropy',
metrics=['sparse_categorical_accuracy'],sample_weight_mode="temporal") # metrics =[dice_coeff] model.summary()
model.summary()
checkpointer = tf.keras.callbacks.ModelCheckpoint('chek12class3.h5', verbose = 1, save_best_only = True)
#
print('############## Initial weights ############## : ', model.get_weights())
#callbacks = [
# tf.keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'), tf.keras.callbacks.TensorBoard(log_dir='logs')]
#history = model.fit(train_generator, validation_split=0.1, batch_size=4,epochs = 100 ,callbacks = callbacks) #,callbacks = callbacks
class_weights = np.zeros((82944, 12))
class_weights[:, 0] += 7
class_weights[:, 1] += 10
class_weights[:, 2] += 2
class_weights[:, 3] += 3
class_weights[:, 4] += 4
class_weights[:, 5] += 5
class_weights[:, 6] += 6
class_weights[:, 7] += 50
class_weights[:, 8] += 8
class_weights[:, 9] += 9
class_weights[:, 10] += 50
class_weights[:, 11] += 11
history = model.fit(X_train, Y_train, validation_split=0.18, batch_size=1,epochs = 60 ,sample_weight=class_weights) #class_weight=clas
82944是288 * 288 h，我的样本的w和12是班级数.
82944 is 288*288 h and w of my sample and 12 is number of classes.
我遇到此错误:
ValueError: Found a sample_weight array with shape (82944, 12) for an input with shape (481, 288, 288). sample_weight cannot be broadcast.
通过此链接此处 sample_weight应为(nbr_of_training_data，shape_of_training_data)
from this link here sample_weight should work as (nbr_of_training_data, shape_of_training_data)
然后我在输出之前添加了Flatten层，但它不起作用
Then I added Flatten layer before output and it steel does not work
我的模型的架构:
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) [(None, 288, 288, 3) 0
__________________________________________________________________________________________________
lambda (Lambda) (None, 288, 288, 3) 0 input_1[0][0]
__________________________________________________________________________________________________
conv2d (Conv2D) (None, 288, 288, 16) 448 lambda[0][0]
__________________________________________________________________________________________________
dropout (Dropout) (None, 288, 288, 16) 0 conv2d[0][0]
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 288, 288, 16) 2320 dropout[0][0]
__________________________________________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 144, 144, 16) 0 conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 144, 144, 32) 4640 max_pooling2d[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 144, 144, 32) 0 conv2d_2[0][0]
__________________________________________________________________________________________________
conv2d_3 (Conv2D) (None, 144, 144, 32) 9248 dropout_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 72, 72, 32) 0 conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_4 (Conv2D) (None, 72, 72, 64) 18496 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 72, 72, 64) 0 conv2d_4[0][0]
__________________________________________________________________________________________________
conv2d_5 (Conv2D) (None, 72, 72, 64) 36928 dropout_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 36, 36, 64) 0 conv2d_5[0][0]
__________________________________________________________________________________________________
conv2d_6 (Conv2D) (None, 36, 36, 128) 73856 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 36, 36, 128) 0 conv2d_6[0][0]
__________________________________________________________________________________________________
conv2d_7 (Conv2D) (None, 36, 36, 128) 147584 dropout_3[0][0]
__________________________________________________________________________________________________
max_pooling2d_3 (MaxPooling2D) (None, 18, 18, 128) 0 conv2d_7[0][0]
__________________________________________________________________________________________________
conv2d_8 (Conv2D) (None, 18, 18, 256) 295168 max_pooling2d_3[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 18, 18, 256) 0 conv2d_8[0][0]
__________________________________________________________________________________________________
conv2d_9 (Conv2D) (None, 18, 18, 256) 590080 dropout_4[0][0]
__________________________________________________________________________________________________
conv2d_transpose (Conv2DTranspo (None, 36, 36, 128) 131200 conv2d_9[0][0]
__________________________________________________________________________________________________
concatenate (Concatenate) (None, 36, 36, 256) 0 conv2d_transpose[0][0]
conv2d_7[0][0]
__________________________________________________________________________________________________
conv2d_10 (Conv2D) (None, 36, 36, 128) 295040 concatenate[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 36, 36, 128) 0 conv2d_10[0][0]
__________________________________________________________________________________________________
conv2d_11 (Conv2D) (None, 36, 36, 128) 147584 dropout_5[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 72, 72, 64) 32832 conv2d_11[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 72, 72, 128) 0 conv2d_transpose_1[0][0]
conv2d_5[0][0]
__________________________________________________________________________________________________
conv2d_12 (Conv2D) (None, 72, 72, 64) 32832 concatenate_1[0][0]
__________________________________________________________________________________________________
dropout_6 (Dropout) (None, 72, 72, 64) 0 conv2d_12[0][0]
__________________________________________________________________________________________________
conv2d_13 (Conv2D) (None, 72, 72, 64) 36928 dropout_6[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 144, 144, 32) 8224 conv2d_13[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 144, 144, 64) 0 conv2d_transpose_2[0][0]
conv2d_3[0][0]
__________________________________________________________________________________________________
conv2d_14 (Conv2D) (None, 144, 144, 32) 8224 concatenate_2[0][0]
__________________________________________________________________________________________________
dropout_7 (Dropout) (None, 144, 144, 32) 0 conv2d_14[0][0]
__________________________________________________________________________________________________
conv2d_15 (Conv2D) (None, 144, 144, 32) 9248 dropout_7[0][0]
__________________________________________________________________________________________________
conv2d_transpose_3 (Conv2DTrans (None, 288, 288, 16) 2064 conv2d_15[0][0]
__________________________________________________________________________________________________
concatenate_3 (Concatenate) (None, 288, 288, 32) 0 conv2d_transpose_3[0][0]
conv2d_1[0][0]
__________________________________________________________________________________________________
conv2d_16 (Conv2D) (None, 288, 288, 16) 4624 concatenate_3[0][0]
__________________________________________________________________________________________________
dropout_8 (Dropout) (None, 288, 288, 16) 0 conv2d_16[0][0]
__________________________________________________________________________________________________
conv2d_17 (Conv2D) (None, 288, 288, 16) 2320 dropout_8[0][0]
__________________________________________________________________________________________________
conv2d_18 (Conv2D) (None, 288, 288, 12) 204 conv2d_17[0][0]
==================================================================================================
我认为此解决方案可能会起作用:
I think this solution maybe will work :
sample_weights = np.zeros(len(Y_train))
# your own weight corresponding here:
sample_weights[Y_train[Y_train==0]] = 7
sample_weights[Y_train[Y_train==1]] = 10
sample_weights[Y_train[Y_train==2]] = 2
sample_weights[Y_train[Y_train==3]] = 3
sample_weights[Y_train[Y_train==4]] = 4
sample_weights[Y_train[Y_train==5]] = 5
sample_weights[Y_train[Y_train==6]] = 6
sample_weights[Y_train[Y_train==7]] = 50
sample_weights[Y_train[Y_train==8]] = 8
sample_weights[Y_train[Y_train==9]] = 9
sample_weights[Y_train[Y_train==10]] = 50
sample_weights[Y_train[Y_train==11]] = 11
我遇到此错误:
ValueError: Found a sample_weight array with shape (481,). In order to use timestep-wise sample weighting, you should pass a 2D sample_weight array.
推荐答案
您正在滥用sample_weight.顾名思义，它在每个 sample 中分配一个权重；因此，尽管您只有481个样本，但您传递的长度为82944(此外还有2维)，因此出现了预期的错误:
You are misusing sample_weight. As its name clearly implies, it assigns a weight in each sample; so, despite you having only 481 samples, you pass something of length 82944 (and additionally, of 2 dimensions), hence the expected error:
ValueError: Found a sample_weight array with shape (82944, 12) for an input with shape (481, 288, 288). sample_weight cannot be broadcast.
因此，您实际需要的是一个长度sample_weight的一维数组，其长度等于您的训练样本，其中的每个元素都是相应样本的权重-而每个类别的权重应该相同，如图所示.
So, what you actually need is a sample_weight 1D-array of length equal to your training sample, with each element of it being the weight of the corresponding sample - which, in turn, should be the same for each class, as you show.
在这里，您可以使用12类的虚拟数据y和仅30个样本来做到这一点:
Here is how you can do it using some dummy data y of 12 classes and only 30 samples:
import numpy as np
y = np.random.randint(12, size=30) # dummy data, 12 classes
y
# array([ 8, 0, 6, 8, 9, 9, 7, 11, 6, 4, 6, 3, 10, 8, 7, 7, 11,
# 2, 5, 8, 8, 1, 7, 2, 7, 9, 5, 2, 0, 0])
sample_weights = np.zeros(len(y))
# your own weight corresponding here:
sample_weights[y==0] = 7
sample_weights[y==1] = 10
sample_weights[y==2] = 2
sample_weights[y==3] = 3
sample_weights[y==4] = 4
sample_weights[y==5] = 5
sample_weights[y==6] = 6
sample_weights[y==7] = 50
sample_weights[y==8] = 8
sample_weights[y==9] = 9
sample_weights[y==10] = 50
sample_weights[y==11] = 11
sample_weights
# result:
array([ 8., 7., 6., 8., 9., 9., 50., 11., 6., 4., 6., 3., 50.,
8., 50., 50., 11., 2., 5., 8., 8., 10., 50., 2., 50., 9.,
5., 2., 7., 7.])
让我们将它们放在一个不错的数据框中，以便更好地查看:
Let's put them in a nice dataframe, for better viewing:
import pandas as pd
d = {'y': y, 'weight': sample_weights}
df = pd.DataFrame(d)
print(df.to_string(index=False))
# result:
y weight
8 8.0
0 7.0
6 6.0
8 8.0
9 9.0
9 9.0
7 50.0
11 11.0
6 6.0
4 4.0
6 6.0
3 3.0
10 50.0
8 8.0
7 50.0
7 50.0
11 11.0
2 2.0
5 5.0
8 8.0
8 8.0
1 10.0
7 50.0
2 2.0
7 50.0
9 9.0
5 5.0
2 2.0
0 7.0
0 7.0
，当然应该将model.fit中的sample_weight=class_weights替换为sample_weight=sample_weights.
and where of course you should replace sample_weight=class_weights in your model.fit with sample_weight=sample_weights.
这篇关于如何为多类别细分初始化样本权重?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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