【发布时间】：2020-07-24 06:33:51
【问题描述】：
我正在使用 Keras 和 U-net 进行多类分割。
我的 NN 12 个类使用 soft max Activation 函数作为输出。我的输出形状是 (N,288,288,12)。
为了适合我的模型，我使用 sparse_categorical_crossentropy。
我想为我的不平衡数据集初始化模型的权重。
我发现这很有用link 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
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 是我的样本的 288*288 h 和 w，12 是类数。
我收到了这个错误：
ValueError: Found a sample_weight array with shape (82944, 12) for an input with shape (481, 288, 288). sample_weight cannot be broadcast.
从此链接here sample_weight 应该作为 (nbr_of_training_data, shape_of_training_data)
然后我在输出之前添加了 Flatten 层，它的钢不起作用
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]
==================================================================================================
我认为这个解决方案可能会奏效：
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
我收到了这个错误：
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.
【问题讨论】：
-
你的训练样本数量是多少？
-
@desertnaut 我的训练样本数是 481
-
@desertnaut 我刚刚发布了我的问题 [stackoverflow.com/questions/61159988/…，请看一下，再次感谢 :)
标签： tensorflow machine-learning image-processing keras deep-learning