tensorflow-datasets

The current TensorFlow dataset interleave functionality is basically a interleaved flat-map taking as input a single dataset. Given the current API, what's the best way to interleave multiple datasets together? Say they have already been constructed and I have a list of them. I want to produce elements from them alternatively and I want to support lists with more than 2 datasets (i.e., stacked zips and interleaves would be pretty ugly). Thanks! :) @mrry might be able to help. EDIT 2: See tf.contrib.data.choose_from_datasets . It performs deterministic dataset interleaving. EDIT: See tf.contrib

For an LSTM network, I've seen great improvements with bucketing. I've come across the bucketing section in the TensorFlow docs which (tf.contrib). Though in my network, I am using the tf.data.Dataset API, specifically I'm working with TFRecords, so my input pipeline looks something like this dataset = tf.data.TFRecordDataset(TFRECORDS_PATH) dataset = dataset.map(_parse_function) dataset = dataset.map(_scale_function) dataset = dataset.shuffle(buffer_size=10000) dataset = dataset.padded_batch(batch_size, padded_shapes={.....}) How can I incorporate the bucketing method into a the tf.data

Problem Summary In the following example, my NMT model has high loss because it correctly predicts target_input instead of target_output . Targetin : 1 3 3 3 3 6 6 6 9 7 7 7 4 4 4 4 4 9 9 10 10 10 3 3 10 10 3 10 3 3 10 10 3 9 9 4 4 4 4 4 3 10 3 3 9 9 3 6 6 6 6 6 6 10 9 9 10 10 4 4 4 4 4 4 4 4 4 4 4 4 9 9 9 9 3 3 3 6 6 6 6 6 9 9 10 3 4 4 4 4 4 4 4 4 4 4 4 4 9 9 10 3 10 9 9 3 4 4 4 4 4 4 4 4 4 10 10 4 4 4 4 4 4 4 4 4 4 9 9 10 3 6 6 6 6 3 3 3 10 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 9 9 3 3 10 6 6 6 6 6 3 9 9 3 3 3 3 3 3 3 10 10 3 9 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 9 3 6 6 6 6 6 6 3 5 3 3 3 3 10 10

I have the following error in my input pipeline: tensorflow.python.framework.errors_impl.InvalidArgumentError: Cannot batch tensors with different shapes in component 0. First element had shape [2,48,48,3] and element 1 had shape [27,48,48,3]. with this code dataset = tf.data.Dataset.from_generator(generator, (tf.float32, tf.int64, tf.int64, tf.float32, tf.int64, tf.float32)) dataset = dataset.batch(max_buffer_size) This is completely logical as the batch method tries to create a (batch_size, ?, 48, 48, 3) Tensor. However I want that it creates a [29,48,48,3] Tensor for this case. So

I'm trying to used the cache transformation for a dataset . Here is my current code (simplified): dataset = tf.data.TFRecordDataset(filenames, num_parallel_reads=1) dataset = dataset.apply(tf.contrib.data.shuffle_and_repeat(buffer_size=5000, count=1)) dataset = dataset.map(_parser_a, num_parallel_calls=12) dataset = dataset.padded_batch( 20, padded_shapes=padded_shapes, padding_values=padding_values ) dataset = dataset.prefetch(buffer_size=1) dataset = dataset.cache() After the first epoch, I received the following error message: The calling iterator did not fully read the dataset we were