Tf data generator
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Dense ( num_classes, activation = 'softmax' )) model. Dense ( 32, input_shape = input_shape, activation = 'relu' )) model. to_categorical ( y ) generator = make_generator ( x, y, batch_size ) model = tf. randint ( low = 0, high = num_classes, size = num_samples ) y = tf. rand ( num_samples, * input_shape ) y = np. fit ( generator, steps_per_epoch = steps_per_epoch ) Example: Multiclass Classification import numpy as np import tensorflow as tf from keras_balanced_batch_generator import make_generator num_samples = 100 num_classes = 3 input_shape = ( 2 ,) batch_size = 16 x = np. ) generator = make_generator ( x, y, batch_size ) model. Usage import tensorflow as tf from keras_balanced_batch_generator import make_generator x =. Returns a Keras-compatible generator yielding batches as (x, y) tuples.Otherwise, generates class vectors (i.e., shape (num_samples,)). categorical (bool) If true, generates binary class matrices (i.e., shape (num_samples, num_classes)) for batch targets.You can use tf._categorical to convert a class vector to a binary class matrix. Must be a binary class matrix (i.e., shape (num_samples, num_classes)). API make_generator ( x, y, batch_size, categorical = True, seed = None ) The generator can be easily used with Keras models'Ĭurrently, only NumPy arrays for single-input, single-output models are supported. It generates balanced batches, i.e., batches in which the number of samples from each class is on average the same. This module implements an over-sampling algorithm to address the issue of class imbalance.
#Tf data generator install
Installation pip install keras-balanced-batch-generator Keras-balanced-batch-generator: A Keras-compatible generator for creating balanced batches