您正在尝试将包含 36 层的权重文件加载到具有 19 层的模型中

如何解决您正在尝试将包含 36 层的权重文件加载到具有 19 层的模型中

import numpy as np
import keras
from keras.models import Model
from keras.layers import Input,Conv2D,MaxPooling2D,UpSampling2D,concatenate,Cropping2D,ZeroPadding2D,Conv2DTranspose
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint,LearningRateScheduler,TensorBoard
from keras import backend as K
import os
from skimage.transform import resize
from skimage.io import imsave

K.set_image_data_format('channels_first')  # Theano dimension ordering in this code

working_path = '../luna/output/'
main_path = '../luna/'
unet_weight = '../luna/unet.hdf5'



BATCH_SIZE=8
EPOCHS=60
img_rows = 512
img_cols = 512

smooth = 1.


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


def dice_coef_loss(y_true,y_pred):
    return 1 - dice_coef(y_true,y_pred)


def get_model():

    inputs = Input((1,img_rows,img_cols))

    conv1 = Conv2D(32,(3,3),activation='relu',padding='same')(inputs)
    conv1 = Conv2D(32,padding='same')(conv1)
    pool1 = MaxPooling2D(pool_size=(2,2))(conv1)

    conv2 = Conv2D(64,padding='same')(pool1)
    conv2 = Conv2D(64,padding='same')(conv2)
    pool2 = MaxPooling2D(pool_size=(2,2))(conv2)

    conv3 = Conv2D(128,padding='same')(pool2)
    conv3 = Conv2D(128,padding='same')(conv3)
    pool3 = MaxPooling2D(pool_size=(2,2))(conv3)

    conv4 = Conv2D(256,padding='same')(pool3)
    conv4 = Conv2D(256,padding='same')(conv4)
    pool4 = MaxPooling2D(pool_size=(2,2))(conv4)

    conv5 = Conv2D(512,padding='same')(pool4)
    conv5 = Conv2D(512,padding='same')(conv5)

    up6 = concatenate([UpSampling2D(size=(2,2))(conv5),conv4],axis=1)
    conv6 = Conv2D(256,padding='same')(up6)
    conv6 = Conv2D(256,padding='same')(conv6)

    up7 = concatenate([UpSampling2D(size=(2,2))(conv6),conv3],axis=1)
    conv7 = Conv2D(128,padding='same')(up7)
    conv7 = Conv2D(128,padding='same')(conv7)

    up8 = concatenate([UpSampling2D(size=(2,2))(conv7),conv2],axis=1)
    conv8 = Conv2D(64,padding='same')(up8)
    conv8 = Conv2D(64,padding='same')(conv8)

    up9 = concatenate([UpSampling2D(size=(2,2))(conv8),conv1],axis=1)
    conv9 = Conv2D(32,padding='same')(up9)
    conv9 = Conv2D(32,padding='same')(conv9)
    conv10 = Conv2D(1,(1,1),activation='sigmoid')(conv9)

    model = Model(inputs=inputs,outputs=conv10)
    print (model.summary())
#     model.compile(optimizer=Adam(lr=1.0e-5),loss=dice_coef_loss,metrics=[dice_coef])
    model.compile(optimizer=Adam(lr=1e-5),loss='binary_crossentropy',metrics = ['accuracy'])
    return model


def get_unet():
    
    K.set_image_data_format('channels_first')  # Theano dimension ordering in this code
    inputs = Input((1,padding='same')(conv5)

    up6 = concatenate([Conv2DTranspose(256,(2,2),strides=(2,padding='same')(conv5),padding='same')(conv6)

    up7 = concatenate([Conv2DTranspose(128,padding='same')(conv6),padding='same')(conv7)

    up8 = concatenate([Conv2DTranspose(64,padding='same')(conv7),padding='same')(conv8)

    up9 = concatenate([Conv2DTranspose(32,padding='same')(conv8),padding='same')(conv9)

    conv10 = Conv2D(1,activation='sigmoid')(conv9)

    model = Model(inputs=[inputs],outputs=[conv10])
    print (model.summary())
    model.compile(optimizer=Adam(lr=1e-5),metrics=[dice_coef])

    return model


def get_unet():
    
    K.set_image_data_format('channels_first')  # Theano dimension ordering in this code
    inputs = Input((1,metrics=[dice_coef])

    return model

def train_and_predict(use_existing):
    print('-' * 30)
    print('Loading and preprocessing train data...')
    print ('BATCH_SIZE : {}'.format(BATCH_SIZE))
    print ('EPOCHS : {}'.format(EPOCHS))
    imgs_train = np.load(main_path + "trainImages.npy").astype(np.float32)
    imgs_mask_train = np.load(main_path + "trainMasks.npy").astype(np.float32)

imgs_test = np.load(main_path + "testImages.npy").astype(np.float32)
    imgs_mask_test_true = np.load(main_path + "testMasks.npy").astype(np.float32)

    mean = np.mean(imgs_train)  # mean for data centering
    std = np.std(imgs_train)  # std for data normalization

    imgs_train -= mean  # images should already be standardized,but just in case
    imgs_train /= std

    print('-' * 30)
    print('Creating and compiling model...')

    model = get_model()
    # Saving weights to unet.hdf5 at checkpoints

    best_weight_path = '/root/sharedfolder/luna16/model/best_unet_upsampling.hdf5'
    model_checkpoint = ModelCheckpoint(best_weight_path,monitor='val_loss',save_best_only=True)
    tb = TensorBoard(log_dir="../logs_281118",batch_size=BATCH_SIZE)

    best_weight_path = '../model/best_unet_upsampling_{}.hdf5'.format(BATCH_SIZE)
    model_checkpoint = ModelCheckpoint(best_weight_path,save_best_only=True)
    tb = TensorBoard(log_dir="../logs_upsampling",batch_size=BATCH_SIZE)

    # Set argument for call to train_and_predict to true at end of script
    if use_existing:
        print('loading weights...')
        model.load_weights(unet_weight)

    print('-' * 30)
    print('Fitting model...')

    model.fit(imgs_train,imgs_mask_train,validation_split=0.15,batch_size=BATCH_SIZE,epochs=EPOCHS,verbose=1,shuffle=True,callbacks=[model_checkpoint,tb])


    # loading best weights from training session
    print('-' * 30)
    print('Loading saved weights...')


    model.load_weights(unet_weight)

    model.load_weights(best_weight_path)

    print('-' * 30)
    print('Predicting masks on test data...')

    num_test = len(imgs_test)
    imgs_mask_test = np.ndarray([num_test,1,512,512],dtype=np.float32)
    for i in range(num_test):
        imgs_mask_test[i] = model.predict([imgs_test[i:i + 1]],verbose=0)[0]

    np.save('../masksTestPredictedAll.npy',imgs_mask_test)
    np.save('../masks_mask_test.npy',imgs_mask_test)
    
    print('-' * 30)
    print('Calculate mean dice coeff...')
    
    mean = 0.0
    for i in range(num_test):
        mean += dice_coef_np(imgs_mask_test_true[i,0],imgs_mask_test[i,0])
    mean /= num_test


#     print("Mean Dice Coeff : ",mean)

if __name__ == '__main__':
    train_and_predict(True)

我正在尝试从 GitHub 实施一个项目，但在运行后显示错误

You are trying to load a weight file containing 36 layers into a model with 19 layers

如何解决这个错误？ 有些地方他们建议在输入层中放置“输入形状”，但它也显示错误。再次有一些建议是使用 keras version==2.00 。这也不能解决问题。