Unet Keras ValueError：尺寸必须相等

Question

我使用具有 512x512x1 输入形状的 unet 模型 但我有一个问题。我收到一个 ValueError ：尺寸必须相等。我知道输入形状出了点问题。我需要解决这个问题。也许模型丢失有问题

X 火车形状 : (512, 512, 1) Y 火车形状：(512, 512, 1)

型号：

from keras.models import Model
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import concatenate, Conv2D, MaxPooling2D, Conv2DTranspose
from keras.layers import Input, merge, UpSampling2D,BatchNormalization
from keras.callbacks import ModelCheckpoint
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras import backend as K
import tensorflow as tf

K.set_image_data_format('channels_last')


def dice_coef(y_true, y_pred):
    smooth = 0.005 
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f)
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)


def dice_coef_loss(y_true, y_pred):
    return 1-dice_coef(y_true, y_pred)
    
def unet_model():
    
    inputs = Input((512 , 512, 1))
    
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (inputs)
    batch1 = BatchNormalization(axis=1)(conv1)
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch1)
    batch1 = BatchNormalization(axis=1)(conv1)
    pool1 = MaxPooling2D((2, 2)) (batch1)
    
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (pool1)
    batch2 = BatchNormalization(axis=1)(conv2)
    conv2 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch2)
    batch2 = BatchNormalization(axis=1)(conv2)
    pool2 = MaxPooling2D((2, 2)) (batch2)
    
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (pool2)
    batch3 = BatchNormalization(axis=1)(conv3)
    conv3 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch3)
    batch3 = BatchNormalization(axis=1)(conv3)
    pool3 = MaxPooling2D((2, 2)) (batch3)
    
    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (pool3)
    batch4 = BatchNormalization(axis=1)(conv4)
    conv4 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch4)
    batch4 = BatchNormalization(axis=1)(conv4)
    pool4 = MaxPooling2D(pool_size=(2, 2)) (batch4)
    
    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (pool4)
    batch5 = BatchNormalization(axis=1)(conv5)
    conv5 = Conv2D(1024, (3, 3), activation='relu', padding='same') (batch5)
    batch5 = BatchNormalization(axis=1)(conv5)
    
    up6 = Conv2DTranspose(512, (2, 2), strides=(2, 2), padding='same') (batch5)
    up6 = concatenate([up6, conv4], axis=1)
    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (up6)
    batch6 = BatchNormalization(axis=1)(conv6)
    conv6 = Conv2D(512, (3, 3), activation='relu', padding='same') (batch6)
    batch6 = BatchNormalization(axis=1)(conv6)
    
    up7 = Conv2DTranspose(256, (2, 2), strides=(2, 2), padding='same') (batch6)
    up7 = concatenate([up7, conv3], axis=1)
    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (up7)
    batch7 = BatchNormalization(axis=1)(conv7)
    conv7 = Conv2D(256, (3, 3), activation='relu', padding='same') (batch7)
    batch7 = BatchNormalization(axis=1)(conv7)
    
    up8 = Conv2DTranspose(128, (2, 2), strides=(2, 2), padding='same') (batch7)
    up8 = concatenate([up8, conv2], axis=1)
    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (up8)
    batch8 = BatchNormalization(axis=1)(conv8)
    conv8 = Conv2D(128, (3, 3), activation='relu', padding='same') (batch8)
    batch8 = BatchNormalization(axis=1)(conv8)
    
    up9 = Conv2DTranspose(64, (2, 2), strides=(2, 2), padding='same') (batch8)
    up9 = concatenate([up9, conv1], axis=1)
    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (up9)
    batch9 = BatchNormalization(axis=1)(conv9)
    conv9 = Conv2D(64, (3, 3), activation='relu', padding='same') (batch9)
    batch9 = BatchNormalization(axis=1)(conv9)

    conv10 = Conv2D(1, (1, 1), activation='sigmoid')(batch9)

    model = Model(inputs=[inputs], outputs=[conv10])

    model.compile(optimizer=Adam(lr=1e-4), loss=dice_coef_loss, metrics=[dice_coef])

    return model

model = unet_model()

Answer 1

如果您检查模型摘要 (model.summary())，您会看到最后一层输出形状是：

Layer (type)              Output Shape      Param #      Connected to                
===============================================================================
...
...
conv2d_18 (Conv2D)    (None, 2560, 512, 1)    65      batch_normalization_17[0][0]     

输出形状为(None,2560,512,1)，与所需的输出形状(None, 512, 512, 1)不匹配

这种不匹配是因为您的concatenate 层。您在concatenate 层中设置了axis=1 参数，用于连接第二个轴，但您需要根据最后一个轴（axis=-1 和默认值）连接层。

所以，从你的连接层中移除轴参数，让它保持默认，或者在你的所有连接层中设置axis=-1。

例如：

#up6 = concatenate([up6, conv4], axis=1) # remove axis
up6 = concatenate([up6, conv4])

然后，您将获得与您的输入和您的 y_train 相同的最后一层形状：

Layer (type)              Output Shape      Param #      Connected to                
===============================================================================
...
...
conv2d_18 (Conv2D)    (None, 512, 512, 1)    65      batch_normalization_17[0][0]