tf.contrib.distributions.MultivariateNormalFull.std()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.MultivariateNormalFull.std(name='std') Standard deviation.

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tf.contrib.learn.LinearClassifier.weights_
  • References/Big Data/TensorFlow/TensorFlow Python/Learn

tf.contrib.learn.LinearClassifier.weights_

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

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

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tf.contrib.learn.monitors.EveryN.end()
  • References/Big Data/TensorFlow/TensorFlow Python/Monitors

tf.contrib.learn.monitors.EveryN.end(session=None)

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tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n
  • References/Big Data/TensorFlow/TensorFlow Python/BayesFlow Stochastic Tensors

tf.contrib.bayesflow.stochastic_tensor.SampleAndReshapeValue.n

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tf.image.adjust_saturation()
  • References/Big Data/TensorFlow/TensorFlow Python/Images

tf.image.adjust_saturation(image, saturation_factor, name=None) Adjust saturation of an RGB image. This

2025-01-10 15:47:30
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.graph_editor.detach_outputs()
  • References/Big Data/TensorFlow/TensorFlow Python/Graph Editor

tf.contrib.graph_editor.detach_outputs(sgv, control_outputs=None) Detach the outputa of a subgraph view.

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

tf.contrib.graph_editor.get_ops_ios(ops, control_inputs=False, control_outputs=None, control_ios=None) Return all the tf.Operation

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tf.contrib.distributions.ExponentialWithSoftplusLam.pdf()
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.ExponentialWithSoftplusLam.pdf(value, name='pdf') Probability density function.

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