![]() ![]() Do not specify batch size because the dataset takes care of that. pile(optimizer="adam", loss="sparse_categorical_crossentropy") import tensorflow as tf def gen (): raggedtensor tf.nstant ( 1, 2, 3) yield 42, raggedtensor dataset tf. ( gen, outputsignature ( tf.TensorSpec (shape (), dtypetf.int32), tf.RaggedTensorSpec (shape (2, None), dtypetf.int32))) print (dataset. Model = tf.16(weights=None, input_shape=x_shape, classes=classes) Return x * tf.random.normal(shape=x_shape), yĭataset = tf._generator(ĭataset = dataset.batch(batch_size, drop_remainder=True) Y = np.random.randint(0, classes, size=y_shape, dtype=np.int32) X = np.random.random_sample(x_shape).astype(np.float32) """Return a function that takes no arguments and returns a generator.""" Y_shape = () # A single item (not array). In the code below, I have demonstrated how you can parallelize augmentation and add prefetching. You can also parallelize augmentation, and you can prefetch data as you train, so your GPU (or other hardware) is never hungry for data. Depending on how your data are stored and read, you can parallelize reading. With a tf.data pipeline, there are several spots where you can parallelize. How to make a generator / iterator in tensorflow(keras) 2.x that can easily be parallelized across multiple CPU processes? Deadlocks and data order are not important. It also have memory leak every epoch, so traning will stops after several epochs. "data loaded" was printed once(should 8 times). For high performance data pipelines tf.data is recommended. ![]() WARNING: tensorflow: multiprocessing can interact badly with TensorFlow, causing nondeterministic deadlocks. The processor and video card were loaded perfectly. This code worked fine with tensorflow 1.x. Model.fit_generator(data_generator_train, validation_data=data_generator_test, X, y = get_random_augmented_sample(item_list)ĭata_generator_train = get_data_generator(False)ĭata_generator_test = get_data_generator(True) With tensorflow 1.x, I did this: def get_data_generator(test_flag): I want to make my own data generator for training. ![]()
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