| Commit message (Collapse) | Author | Age |
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Update protobuf commit
Change: 114990608
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Change: 114470777
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Fixes #1035
Change: 114439527
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of a cached version.
Change: 114388651
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Change: 114328737
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tf.tensor_util was already not public (not documented), now it is not part of
tf.__all__. The one function that was in tf.unsupported is now
tf.contrib.util.constant_value, and there is also
tf.contrib.util.make_tensor_proto.
Change: 114118183
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Fixes #1001.
Change: 113956896
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moved inside of cwise_ops_test; and this code is outdated.
Change: 113857436
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stability improvements
Change: 113791782
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What this means is that when passing sequence_length to either rnn() or
bidirectional_rnn(), the following hold for any given row r of output and state:
if time < sequence_lengths(r):
output(r) is calculated
state(r) is calculated
if time >= sequence_lengths(r):
output(r) is zero
state(r) is carried over from sequence_lengths(r) - 1.
What this means when minibatching with sequence_length is that:
* When t < min(sequence_length), all the outputs are calculated
* When t >= max(sequence_length), all the outputs are zero (and gradients to
these outputs will not be propagated)
* When min(sequence_length) < t < max(sequence_length), the outputs are
a mix of zeros and calculated values (and gradients are conditionally
propagated to only those entries which are still calculating)
* The final state's rows correspond to:
state[r] = state calculated at row r at t = sequence_lengths(r) - 1
and gradients are properly propagated to start updating weights
accordingly for each row.
Change: 113752785
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Change: 113646664
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the Google-internal version.
Change: 113552310
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Change: 113064195
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The two functions already have the same behavior.
Change: 112959229
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014b9012d32c6409bf2f4f13ff4608a2cbc7cb9a
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supports python3
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956a770adc772184e5911f964b866028465d1727 for bazel 0.1.2
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TensorFlow is an open source software library for numerical computation
using data flow graphs.
Base CL: 107276108
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