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

class tf.contrib.bayesflow.stochastic_tensor.BinomialTensor BinomialTensor is a StochasticTensor backed

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tf.contrib.distributions.MultivariateNormalFull.is_reparameterized
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

tf.contrib.distributions.MultivariateNormalFull.is_reparameterized

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tf.python_io.TFRecordWriter.
  • References/Big Data/TensorFlow/TensorFlow Python/Data IO

tf.python_io.TFRecordWriter.__enter__() Enter a with block.

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tf.contrib.framework.with_shape()
  • References/Big Data/TensorFlow/TensorFlow Python/Framework

tf.contrib.framework.with_shape(expected_shape, tensor) Asserts tensor has expected shape. If

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tf.contrib.metrics.streaming_precision()
  • References/Big Data/TensorFlow/TensorFlow Python/Metrics

tf.contrib.metrics.streaming_precision(*args, **kwargs) Computes the precision of the predictions with respect to the labels.

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tf.contrib.distributions.Uniform
  • References/Big Data/TensorFlow/TensorFlow Python/Statistical distributions

class tf.contrib.distributions.Uniform Uniform distribution with a and b parameters.

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

tf.contrib.layers.apply_regularization(regularizer, weights_list=None) Returns the summed penalty by applying regularizer

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

tf.contrib.bayesflow.stochastic_tensor.LaplaceTensor.clone(name=None, **dist_args)

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

tf.contrib.learn.TensorFlowRNNClassifier.__repr__()

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tf.contrib.learn.monitors.ExportMonitor.step_end()
  • References/Big Data/TensorFlow/TensorFlow Python/Monitors

tf.contrib.learn.monitors.ExportMonitor.step_end(step, output) Overrides BaseMonitor.step_end. When

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