#Keras data augmentation 3d generator#
#Keras data augmentation 3d code#

#Keras data augmentation 3d generator#

That is, it would be great if we have a generator that would read the frames of each subfolder under each class (and zero pads it if necessary) and return it as a training example. However, usually for timeseries prediction (video recognition), each sequence of frames (timesteps) is stored in a folder, and each sequence need not have the same length of other sequences. However, if I understand correctly, it seems that you are reading frames_per_step images assuming that all of the images are stored consecutively and that each instance has the same number of timesteps. Do you have any clue what's going Thanks a lot for your contribution. That is each training example in each batch is a sequence of the same image repeated. I find plenty of sequences of frames that are of the same repeated image.

#Keras data augmentation 3d code#

So, I tried the original code with shuffle=False and the same problem is still happening. I have added print statements around 1073 to see the names of the files read, and it seems that a lot of files are read multiple times and the order isn't preserved. However, when I tried it out, the shuffling works fine, but the output of the generator is a bit absurd. Then it shuffles those buckets so that the instances in the dataset are shuffled, but the relative order of each sequence is preserved.

However, I am trying to use the ImageDataLoader to use the data augmentation and preprocessing I have forked the script here and added a function shuffle_buckets which divides the index_array into buckets, each of frames_per_step size. Hope this I have already written a script like that that does the job. Note that the images inside each folder is better to be a multiple of 'frames_per_step' value, otherwise you can write a small script that either deletes the increment or add some default images in each folder.

In this way it will iterate over the images/frames inside each folder in the same order as they are and will pass through them folder by folder (even if it will not iterate over folders in there same order). However, I got your point and the thing you want to do can be achieved by setting an option shuffle=False as follows: train_data=datagen.flow_from_directory('path/to/data', target_size=(x, y), batch_size=32, frames_per_step=4, shuffle=False)

I'm not assuming that images are stored consecutively in one folder, because even if they are stored in subfolders inside each class, ImageDataGenerator class will read them all as if they are together and will loop through the images/frames inside each folder. pile(loss=categorical_crossentropy, optimizer=Adam(), metrics=) Model.add(Dense(8, activation='softmax')) Model.add(Dense(512, activation='sigmoid')) Test_data=datagen.flow_from_directory('C:\Users\AA\Data\Testing', target_size=(80, 100), color_mode='grayscale', classes=None, class_mode='categorical', batch_size=32, interpolation='nearest') Train_data=datagen.flow_from_directory('C:\Users\AA\Data\Training', target_size=(80, 100), color_mode='grayscale', classes=None, class_mode='categorical', batch_size=32, interpolation='nearest') From import ImageDataGeneratorР”Р‡rom keras.layers import Dense, Dropout, Activation, Flatten, Conv3D, MaxPooling3DР”Р‡rom _activations import LeakyReLUР”Р‡rom keras.optimizers import SGD, RMSpropР”Р‡rom keras.utils import np_utils, generic_utilsР”Р‡rom keras.losses import categorical_crossentropyР”Р‡rom sklearn.cross_validation import train_test_split