tf.contrib.graph_editor.reroute_b2a_ts()
  • References/Big Data/TensorFlow/TensorFlow Python/Graph Editor

tf.contrib.graph_editor.reroute_b2a_ts(ts0, ts1, can_modify=None, cannot_modify=None) For each tensor's pair, replace the end

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tf.contrib.util.constant_value()
  • References/Big Data/TensorFlow/TensorFlow Python/Utilities

tf.contrib.util.constant_value(tensor) Returns the constant value of the given tensor, if efficiently calculable.

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tf.contrib.learn.monitors.GraphDump.data
  • References/Big Data/TensorFlow/TensorFlow Python/Monitors

tf.contrib.learn.monitors.GraphDump.data

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tf.contrib.layers.unit_norm()
  • References/Big Data/TensorFlow/TensorFlow Python/Layers

tf.contrib.layers.unit_norm(*args, **kwargs) Normalizes the given input across the specified dimension to unit length.

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tf.contrib.distributions.Beta.entropy()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Beta.entropy(name='entropy') Shanon entropy in nats.

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tf.SparseTensorValue.shape
  • References/Big Data/TensorFlow/TensorFlow Python/Sparse Tensors

tf.SparseTensorValue.shape Alias for field number 2

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tf.contrib.distributions.StudentT.log_survival_function()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.StudentT.log_survival_function(value, name='log_survival_function') Log survival function.

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tf.contrib.distributions.Normal.log_cdf()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.Normal.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given

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tf.contrib.bayesflow.stochastic_tensor.MeanValue.popped_above()
  • References/Big Data/TensorFlow/TensorFlow Python/BayesFlow Stochastic Tensors

tf.contrib.bayesflow.stochastic_tensor.MeanValue.popped_above(unused_value_type)

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tf.contrib.distributions.StudentT.sample_n()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.StudentT.sample_n(n, seed=None, name='sample_n') Generate n samples.

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