tf.contrib.bayesflow.stochastic_tensor.PoissonTensor.input_dict

tf.contrib.distributions.BernoulliWithSigmoidP.param_static_shapes(cls, sample_shape) param_shapes with static (i.e. TensorShape) shapes. Args: sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample(). Returns: dict of parameter name to TensorShape. Raises: ValueError: if sample_shape is a TensorShape and is not fully defined.

tf.is_strictly_increasing(x, name=None) Returns True if x is strictly increasing. Elements of x are compared in row-major order. The tensor [x[0],...] is strictly increasing if for every adjacent pair we have x[i] < x[i+1]. If x has less than two elements, it is trivially strictly increasing. See also: is_non_decreasing Args: x: Numeric Tensor. name: A name for this operation (optional). Defaults to "is_strictly_increasing" Returns: Boolean Tensor, equal to True iff x is strictly increa

tf.QueueBase.dequeue_many(n, name=None) Dequeues and concatenates n elements from this queue. This operation concatenates queue-element component tensors along the 0th dimension to make a single component tensor. All of the components in the dequeued tuple will have size n in the 0th dimension. If the queue is closed and there are less than n elements left, then an OutOfRange exception is raised. At runtime, this operation may raise an error if the queue is closed before or during its executio

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.parameters Dictionary of parameters used by this Distribution.

tf.contrib.layers.sum_regularizer(regularizer_list, scope=None) Returns a function that applies the sum of multiple regularizers. Args: regularizer_list: A list of regularizers to apply. scope: An optional scope name Returns: A function with signature sum_reg(weights) that applies the sum of all the input regularizers.

tf.contrib.bayesflow.stochastic_tensor.SampleValue.__init__(n=1, stop_gradient=False) Sample n times and concatenate along a new outer dimension. Args: n: A python integer or int32 tensor. The number of samples to take. stop_gradient: If True, StochasticTensors' values are wrapped in stop_gradient, to avoid backpropagation through.

tf.contrib.distributions.Categorical.sample(sample_shape=(), seed=None, name='sample') Generate samples of the specified shape. Note that a call to sample() without arguments will generate a single sample. Args: sample_shape: 0D or 1D int32 Tensor. Shape of the generated samples. seed: Python integer seed for RNG name: name to give to the op. Returns: samples: a Tensor with prepended dimensions sample_shape.

tf.contrib.distributions.Chi2WithAbsDf.batch_shape(name='batch_shape') Shape of a single sample from a single event index as a 1-D Tensor. The product of the dimensions of the batch_shape is the number of independent distributions of this kind the instance represents. Args: name: name to give to the op Returns: batch_shape: Tensor.

tf.contrib.losses.sigmoid_cross_entropy(logits, multi_class_labels, weight=1.0, label_smoothing=0, scope=None) Creates a cross-entropy loss using tf.nn.sigmoid_cross_entropy_with_logits. weight acts as a coefficient for the loss. If a scalar is provided, then the loss is simply scaled by the given value. If weight is a tensor of size [batch_size], then the loss weights apply to each corresponding sample. If label_smoothing is nonzero, smooth the labels towards 1/2: new_multiclass_labels = mult