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Is Tensorflow Dataset.from_generator deprecated in tensorflow 2.0 ? It throws tf.py_func deprecation error


Is Tensorflow Dataset.from_generator deprecated in tensorflow 2.0 ? It throws tf.py_func deprecation error

By : Patric
Date : October 24 2020, 06:10 AM
I wish this help you No, tf.data.Dataset.from_generator won't be deprecated in TensorFlow 2.0. What you see is a warning message, it's used to inform users about future changes. In case you need to use py_func directly, the most straightforward way is to use tf.compat.v1.py_func. TF2.0 has it's own wrapper, called tf.py_function.
code :


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Calling TensorFlow's Dataset.from_generator method

Calling TensorFlow's Dataset.from_generator method


By : chinnu
Date : March 29 2020, 07:55 AM
around this issue This is a known issue with TensorFlow 1.4.0rc0 when running on Windows. The bug has been fixed in the nightly build of TensorFlow and cherry-picked into the next release candidate of TensorFlow 1.4.0, which is now available.
In the meantime, there are a few options:
TensorFlow Dataset API from_generator End of Sequence Error

TensorFlow Dataset API from_generator End of Sequence Error


By : Walker
Date : March 29 2020, 07:55 AM
help you fix your problem This is caused by incorrect instantiation of a generator.
The error is caused by makeRand() running out of elements to yield. This is fixed by changing it to:
code :
def makeRand():
   while True:
      yield np.random.rand(1)
Tensorflow, replacing feed_dict with Dataset.from_generator

Tensorflow, replacing feed_dict with Dataset.from_generator


By : nck
Date : March 29 2020, 07:55 AM
wish help you to fix your issue You get to drop the placeholders altogether. If you have a placeholder defined, you did it wrong. It should just be this, as you have it:
code :
self.X, self.Y = dsi.get_next()
# continue your network
Proper way to stop a TensorFlow Dataset `from_generator`?

Proper way to stop a TensorFlow Dataset `from_generator`?


By : yongjiacheng
Date : March 29 2020, 07:55 AM
Hope that helps I would like to use a TensorFlow Dataset built with from_generator to access a formatted file. Most everything works except I don't know how to stop the Dataset iterator when the generator runs out of data (the generator just returns empty lists forever when you go out of range). , Return when you meet your stop criteria:
code :
def make_batch_generator_fn(batch_size=10, dset_size=100):
    feats, targs = range(dset_size), range(1, dset_size + 1)

    def batch_generator_fn():
        start_idx, stop_idx = 0, batch_size
        while True:
            if stop_idx > dset_size:
                return
            else:
                yield feats[start_idx: stop_idx], targs[start_idx: stop_idx]
                start_idx, stop_idx = start_idx + batch_size, stop_idx + batch_size

    return batch_generator_fn
TensorFlow DataSet `from_generator` with variable batch size

TensorFlow DataSet `from_generator` with variable batch size


By : java-monk
Date : March 29 2020, 07:55 AM
should help you out When specifying Tensor shapes in from_generator, you can use None as an element to specify variable-sized dimensions. This way you can accommodate batches of different sizes, in particular "leftover" batches that are a bit smaller than your requested batch size. So you would use
code :
def make_fashion_dset(file_name, batch_size, shuffle=False):
    dgen = _make_fashion_generator_fn(file_name, batch_size)
    features_shape = [None, 28, 28, 1]
    labels_shape = [None, 10]
    ds = tf.data.Dataset.from_generator(
        dgen, (tf.float32, tf.uint8),
        (tf.TensorShape(features_shape), tf.TensorShape(labels_shape))
    )
    ...
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