Keras convolution form has dimensions out of order (error checking model input)

Question

I checked all similar messages, but my error was not fixed with the proposed fixes. Thanks in advance for your help!

I use an endorflow server with Keras, and my images are 1185 by 676 in size. Most of the code refers to one Keras example.

I get ValueError: Negative dimension size caused by subtracting 2 from 1 for 'MaxPool' (op: 'MaxPool') with input shapes: [?,1,1183,32].This error disappears when I switch to dim_ordering = "th", which is odd considering I use shadoworflow and not anano.

Code up to this point:

img_width, img_height = 1185, 676

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 32
nb_validation_samples = 8
nb_epoch = 3

model = Sequential()
model.add(Convolution2D(32, 3, 3, input_shape=(3, img_width, img_height)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), dim_ordering="tf"))

And just in case, data generation is part of the problem:

model.compile(loss='binary_crossentropy',
              optimizer='rmsprop',
              metrics=['accuracy'])

train_datagen = ImageDataGenerator(
        rescale=1./255,
        shear_range=0.2,
        zoom_range=0.2,
        horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
        train_data_dir,
        batch_size=4,
        target_size=(img_width, img_height),
        class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
        validation_data_dir,
        batch_size=4,
        target_size=(img_width, img_height),
        class_mode='binary')

model.fit_generator(
        train_generator,
        samples_per_epoch=nb_train_samples,
        nb_epoch=nb_epoch,
        validation_data=validation_generator,
        nb_val_samples=nb_validation_samples)

Answer 1

. .

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from keras import backend as K
K.set_image_dim_ordering('tf')

.