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python - Unet Keras ValueError：尺寸必须相等

问题描述

我使用了具有 512x512x1 输入形状的 unet 模型，但我遇到了问题。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)

模型：

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()

标签： pythontensorflowkerasartificial-intelligencedata-science

解决方案

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

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

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

这种不匹配是因为您的concatenate图层。您已经在连接第二个轴axis=1的图层中设置了参数，但是您需要根据默认concatenate的最后一个轴连接图层。axis=-1

因此，从连接层中删除轴参数，使其保持默认值或axis=-1在所有连接层中设置。

例如：

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

然后，您将获得与输入和 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]

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