'NoneType' object has no attribute XXX

As title, I followed the example: cifar10_cnn.py, using a subset of cifar10, loading data without using (X_train, y_train), (X_test, y_test) = cifar10.load_data() but using numpy to parse the data to be like <type 'numpy.ndarray'> shape: (5000, 32, 32, 3). Then I trained the network by setting data_augmentation = True, the training part of code was same as the example

# convert class vectors to binary class matrices
Y_train = np_utils.to_categorical(y_train, nb_classes)
Y_test = np_utils.to_categorical(y_test, nb_classes)

model = Sequential()

model.add(Convolution2D(32, 3, 3, border_mode='same',
                        input_shape=X_train.shape[1:]))
model.add(Activation('relu'))
model.add(Convolution2D(32, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))

model.add(Flatten())
model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(nb_classes))
model.add(Activation('softmax'))

# let's train the model using SGD + momentum (how original).
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])

X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255
X_test /= 255

if not data_augmentation:
    print('Not using data augmentation.')
    model.fit(X_train, Y_train,
              batch_size=batch_size,
              nb_epoch=nb_epoch,
              validation_data=(X_test, Y_test),
              shuffle=True)
else:
    print('Using real-time data augmentation.')

    # this will do preprocessing and realtime data augmentation
    datagen = ImageDataGenerator(
        featurewise_center=False,  # set input mean to 0 over the dataset
        samplewise_center=False,  # set each sample mean to 0
        featurewise_std_normalization=False,  # divide inputs by std of the dataset
        samplewise_std_normalization=False,  # divide each input by its std
        zca_whitening=False,  # apply ZCA whitening
        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)
        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)
        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)
        horizontal_flip=True,  # randomly flip images
        vertical_flip=False)  # randomly flip images

    # compute quantities required for featurewise normalization
    # (std, mean, and principal components if ZCA whitening is applied)
    datagen.fit(X_train)

    # fit the model on the batches generated by datagen.flow()
    model.fit_generator(datagen.flow(X_train, Y_train,
                        batch_size=batch_size),
                        samples_per_epoch=X_train.shape[0],
                        nb_epoch=nb_epoch,
                        validation_data=(X_test, Y_test))

but it threw the error:

Exception in thread Thread-1:
Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 754, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 435, in data_generator_task
    generator_output = next(generator)
  File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 496, in next
    x = self.image_data_generator.random_transform(x.astype('float32'))
  File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 362, in random_transform
    fill_mode=self.fill_mode, cval=self.cval)
  File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 108, in apply_transform
    final_offset, order=0, mode=fill_mode, cval=cval) for x_channel in x]
AttributeError: 'NoneType' object has no attribute 'interpolation'

and the error was different sometimes:

Exception in thread Thread-1:
Traceback (most recent call last):
  File "/usr/lib/python2.7/threading.py", line 801, in __bootstrap_inner
    self.run()
  File "/usr/lib/python2.7/threading.py", line 754, in run
    self.__target(*self.__args, **self.__kwargs)
  File "/usr/local/lib/python2.7/dist-packages/keras/engine/training.py", line 433, in data_generator_task
    generator_output = next(generator)
  File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 496, in next
    x = self.image_data_generator.random_transform(x.astype('float32'))
  File "/usr/local/lib/python2.7/dist-packages/keras/preprocessing/image.py", line 360, in random_transform
    transform_matrix = transform_matrix_offset_center(transform_matrix, h, w)
TypeError: 'NoneType' object is not callable

Any help would be very nice to me, thanks.

[likejazz]

Same here during model.save(). Keras 2.1.4 backed by TensorFlow 1.5.0 and used model = multi_gpu_model(model)

Traceback (most recent call last):
  File "train.py", line 92, in <module>
    model.save('./data/malstm.h5')
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 2576, in save
    save_model(self, filepath, overwrite, include_optimizer)
  File "/usr/local/lib/python3.5/dist-packages/keras/models.py", line 111, in save_model
    'config': model.get_config()
  File "/usr/local/lib/python3.5/dist-packages/keras/engine/topology.py", line 2417, in get_config
    return copy.deepcopy(config)
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 243, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 218, in _deepcopy_list
    y.append(deepcopy(a, memo))
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 243, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 243, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 223, in _deepcopy_tuple
    y = [deepcopy(a, memo) for a in x]
  File "/usr/lib/python3.5/copy.py", line 223, in <listcomp>
    y = [deepcopy(a, memo) for a in x]
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 223, in _deepcopy_tuple
    y = [deepcopy(a, memo) for a in x]
  File "/usr/lib/python3.5/copy.py", line 223, in <listcomp>
    y = [deepcopy(a, memo) for a in x]
  File "/usr/lib/python3.5/copy.py", line 182, in deepcopy
    y = _reconstruct(x, rv, 1, memo)
  File "/usr/lib/python3.5/copy.py", line 297, in _reconstruct
    state = deepcopy(state, memo)
  File "/usr/lib/python3.5/copy.py", line 155, in deepcopy
    y = copier(x, memo)
  File "/usr/lib/python3.5/copy.py", line 243, in _deepcopy_dict
    y[deepcopy(key, memo)] = deepcopy(value, memo)
  File "/usr/lib/python3.5/copy.py", line 182, in deepcopy
    y = _reconstruct(x, rv, 1, memo)
  File "/usr/lib/python3.5/copy.py", line 306, in _reconstruct
    y.__dict__.update(state)
AttributeError: 'NoneType' object has no attribute 'update'

[netw0rkf10w]

@lukeyeager I’m having the same problem in Keras 2.1.2. Unfortunately the code of this version is different from 1.2.0 and I couldn’t find thread.daemon as you suggested. Do you have a solution for 2.1.2 please? Thanks in advance.