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#Data generator keras example how to
#Data generator keras example code

The above code doesn’t work in the sense that once it enters into the above function from fit_generator(), it just stays in the while 1 loop forever. (X_train, y_train) = LOAD_HDF5_OF_10K_SAMPLES(fileIndex)įileIndex=0 #so that fileIndex wraps back and loop goes on indefinitely # following loads data from HDF5 file numbered with fileIndex My approach for building a data generator (for a very large data) which loops indefinitely is as follows (which fails): def myGenerator() #this will give chunk of 10K pictures, 100 such chunks form entire dataset: This and this answer mentions data generators, but a concrete example of it will be more helpful. Now, the data generator has to run in an infinite loop. Since the data is so large, it won’t fit into one big HDF5 file, so we split it into 8 files.

This is about fetching data from the manually written generator.

Note that we are not running datagen.fit() again, so will our augmented data contain only 1,000,000 + 2 * 10,000 samples? How do we augment entire data (i.e. After augmentation, suppose we get 2 * 10,000 more pictures. flipping, width/height shift) happen on partial data? For example, X_sample = 10000 out of total 1,000,000 pictures.

From the same previous link, Another thing is, X_sample cannot obviously be the entire data, so will the augmentation (i.e.

From this link: when we do datagen.fit(X_sample), do we assume that X_sample is a big enough chunk of data to calculate mean, perform feature centering/normalization and whitening on?.

Specifically, I have the following four questions: However, for my scenario, I can’t figure out the appropriate way to use the ImageDataGenerator or write my own dataGenerator.

#Data generator keras example how to

There is no error but the training error is all over the place (jumping up and down instead of consistently going down like it did with the other approach), and the model doesn't learn to make good predictions.I am already aware of some discussions on how to use Keras for very large datasets (>1,000,000 images) such as this and this. Window_length=WINDOW_LENGTH, batch_size=1)īut now the strange thing is that when I train the model as before, but using this generator as the input, model.fit_generator(generator=data_gen_custom, def get_generator(data, targets, window_length = 5, batch_size = 32):ĭata_gen = TimeseriesGenerator(data, targets, length=window_length,ĭata_gen_custom = get_generator(data, data,

As a step towards this, I create a generator function which just wraps the TimeseriesGenerator used previously. Now the problem is, in my non-toy situation I want to process the data coming out from the TimeseriesGenerator before feeding the data into the fit_generator. pile(loss='mse', optimizer='rmsprop', metrics=)Īnd train it using the fit_generator function: model.fit_generator(generator=data_gen,Īnd this trains perfectly, and the model makes predictions as expected. Model = Model(inputs=input1, outputs=output) Output = Dense(data_dim, activation='linear')(hidden) Hidden = Dense(20, activation='relu')(lstm1) Input1 = Input(shape=(WINDOW_LENGTH, data_dim)) I use a simple LSTM network: data_dim = 1 To illustrate the problem, I have created a toy example trying to predict the next number in a simple ascending sequence, and I use the Keras TimeseriesGenerator to create a Sequence instance: WINDOW_LENGTH = 4ĭata_gen = TimeseriesGenerator(data, data, length=WINDOW_LENGTH, So I'm trying to use Keras' fit_generator with a custom data generator to feed into an LSTM network.