keras ValueError:生成器的输出应为元组(x，y，sample_weight)或(x，y).找到:无 [英] keras ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None

问题描述

我有 Retina Unet 的Unet模型，但是我也增加了图像以及面具.现在?它给了我这个错误ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None我想在增强的图像和蒙版上进行训练，并在增强的图像和蒙版上进行验证.

I have Unet model from Retina Unet, However I have augmented the images as well as the masks. Now? it gives me this error ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None I want to train on augmented (images and masks) and validate on augmented images and masks.

批量生成功能:

    def batch_generator(X_gen,Y_gen):
            yield(X_batch,Y_batch)



model = get_unet(1,img_width,img_hight)  #the U-net model
print("Model Summary")
print(model.summary())
print "Check: final output of the network:"
print model.output_shape

#============  Training ==================================
checkpointer = ModelCheckpoint(filepath='./'+'SAEED'+'_best_weights.h5', verbose=2, monitor='val_acc', mode='auto', save_best_only=True) #save at each epoch if the validation decreased
print("Now augumenting training")
datagen = ImageDataGenerator(rotation_range=120)
#traing augumentation.
train_images_generator = datagen.flow_from_directory(train_images_dir,target_size=(img_width,img_hight),batch_size=30,class_mode=None)
train_mask_generator = datagen.flow_from_directory(train_masks_dir,target_size=(img_width,img_hight),batch_size=30,class_mode=None)
print("Now augumenting val")
#val augumentation.
val_images_generator = datagen.flow_from_directory(val_images_dir,target_size=(img_width,img_hight),batch_size=30,class_mode=None)
val_masks_generator = datagen.flow_from_directory(val_masks_dir,target_size=(img_width,img_hight),batch_size=30,class_mode=None)

print("Now augumenting test")
#test augumentation
test_images_generator = datagen.flow_from_directory(test_images_dir,target_size=(img_width,img_hight),batch_size=25,class_mode=None)
test_masks_generator = datagen.flow_from_directory(test_masks_dir,target_size=(img_width,img_hight),batch_size=25,class_mode=None)
#fitting model.
print("Now fitting the model ")
#model.fit_generator(train_generator,samples_per_epoch = nb_train_samples*2,nb_epoch=nb_epoch,validation_data=val_generator,nb_val_samples=nb_val_samples,callbacks=[checkpointer])
print("train_images_generator size {} and type is {}".format(next(train_images_generator).shape,type(next(train_images_generator))))
print("train_masks_generator size {} and type is {}".format(next(train_mask_generator).shape,type(next(train_mask_generator))))

model.fit_generator(batch_generator(train_images_generator,train_mask_generator),samples_per_epoch = nb_train_samples,nb_epoch=nb_epoch,validation_data=batch_generator(val_images_generator,val_masks_generator),nb_val_samples=nb_val_samples,callbacks=[checkpointer])
print("Finished fitting the model")

` 型号摘要:

`

Model Summary
____________________________________________________________________________________________________
Layer (type)                     Output Shape          Param #     Connected to
====================================================================================================
input_1 (InputLayer)             (None, 1, 160, 160)   0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D)  (None, 32, 160, 160)  320         input_1[0][0]
____________________________________________________________________________________________________
dropout_1 (Dropout)              (None, 32, 160, 160)  0           convolution2d_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D)  (None, 32, 160, 160)  9248        dropout_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D)    (None, 32, 80, 80)    0           convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D)  (None, 64, 80, 80)    18496       maxpooling2d_1[0][0]
____________________________________________________________________________________________________
dropout_2 (Dropout)              (None, 64, 80, 80)    0           convolution2d_3[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D)  (None, 64, 80, 80)    36928       dropout_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D)    (None, 64, 40, 40)    0           convolution2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D)  (None, 128, 40, 40)   73856       maxpooling2d_2[0][0]
____________________________________________________________________________________________________
dropout_3 (Dropout)              (None, 128, 40, 40)   0           convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D)  (None, 128, 40, 40)   147584      dropout_3[0][0]
____________________________________________________________________________________________________
upsampling2d_1 (UpSampling2D)    (None, 128, 80, 80)   0           convolution2d_6[0][0]
____________________________________________________________________________________________________
merge_1 (Merge)                  (None, 192, 80, 80)   0           upsampling2d_1[0][0]
                                                                   convolution2d_4[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D)  (None, 64, 80, 80)    110656      merge_1[0][0]
____________________________________________________________________________________________________
dropout_4 (Dropout)              (None, 64, 80, 80)    0           convolution2d_7[0][0]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D)  (None, 64, 80, 80)    36928       dropout_4[0][0]
____________________________________________________________________________________________________
upsampling2d_2 (UpSampling2D)    (None, 64, 160, 160)  0           convolution2d_8[0][0]
____________________________________________________________________________________________________
merge_2 (Merge)                  (None, 96, 160, 160)  0           upsampling2d_2[0][0]
                                                                   convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D)  (None, 32, 160, 160)  27680       merge_2[0][0]
____________________________________________________________________________________________________
dropout_5 (Dropout)              (None, 32, 160, 160)  0           convolution2d_9[0][0]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, 32, 160, 160)  9248        dropout_5[0][0]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, 2, 160, 160)   66          convolution2d_10[0][0]
____________________________________________________________________________________________________
reshape_1 (Reshape)              (None, 2, 25600)      0           convolution2d_11[0][0]
____________________________________________________________________________________________________
permute_1 (Permute)              (None, 25600, 2)      0           reshape_1[0][0]
____________________________________________________________________________________________________
activation_1 (Activation)        (None, 25600, 2)      0           permute_1[0][0]
====================================================================================================
Total params: 471,010
Trainable params: 471,010
Non-trainable params: 0

`

有什么主意吗?谢谢.

Any idea? Thanks.

推荐答案

以防以后有人遇到相同问题.

In case someone run to the same issue later.

问题是发电机问题.固定在下面

The problem is generator issue. fixed below

def batch_generator(X_gen,Y_gen): while true: yield(X_gen.next(),Y_gen.next())

def batch_generator(X_gen,Y_gen): while true: yield(X_gen.next(),Y_gen.next())

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