title: keras ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None
body: <p>I have Unet model from <a href="https://github.com/orobix/retina-unet" rel="nofollow noreferrer">Retina Unet</a>, However I have augmented the images as well as the  masks. Now? it gives me this error <code>ValueError: output of generator should be a tuple (x, y, sample_weight) or (x, y). Found: None</code> I want to train on augmented (images and masks) and validate on augmented images and masks.</p>
  
  <p>batch generating function:</p>
  
  <pre><code>    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")
  </code></pre>
  
  <p>`
  Model summary :</p>
  
  <p>`</p>
  
  <pre><code>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
  </code></pre>
  
  <p>`</p>
  
  <p>Any idea? Thanks. </p>