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

tf.contrib.util.make_tensor_proto(values, dtype=None, shape=None) Create a TensorProto. Args: values: Values to put in the TensorProto. dtype: Optional tensor_pb2 DataType value. shape: List of integers representing the dimensions of tensor. Returns: A TensorProto. Depending on the type, it may contain data in the "tensor_content" attribute, which is not directly useful to Python programs. To access the values you should convert the proto back to a numpy ndarray with tensor_util.MakeNdarr

tf.contrib.crf.crf_binary_score(tag_indices, sequence_lengths, transition_params) Computes the binary scores of tag sequences. Args: tag_indices: A [batch_size, max_seq_len] matrix of tag indices. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] matrix of binary potentials. Returns: binary_scores: A [batch_size] vector of binary scores.

tf.contrib.crf.crf_log_likelihood(inputs, tag_indices, sequence_lengths, transition_params=None) Computes the log-likehood of tag sequences in a CRF. Args: inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials to use as input to the CRF layer. tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which we compute the log-likehood. sequence_lengths: A [batch_size] vector of true sequence lengths. transition_params: A [num_tags, num_tags] transition matrix, if

tf.contrib.learn.monitors.PrintTensor.step_begin(step) Overrides BaseMonitor.step_begin. When overriding this method, you must call the super implementation. Args: step: int, the current value of the global step. Returns: A list, the result of every_n_step_begin, if that was called this step, or an empty list otherwise. Raises: ValueError: if called more than once during a step.

tf.contrib.layers.separable_convolution2d(*args, **kwargs) Adds a depth-separable 2D convolution with optional batch_norm layer. This op first performs a depthwise convolution that acts separately on channels, creating a variable called depthwise_weights. If num_outputs is not None, it adds a pointwise convolution that mixes channels, creating a variable called pointwise_weights. Then, if batch_norm_params is None, it adds bias to the result, creating a variable called 'biases', otherwise it a

tf.contrib.layers.convolution2d_transpose(*args, **kwargs) Adds a convolution2d_transpose with an optional batch normalization layer. The function creates a variable called weights, representing the kernel, that is convolved with the input. If batch_norm_params is None, a second variable called 'biases' is added to the result of the operation. Args: inputs: a tensor of size [batch_size, height, width, channels]. num_outputs: integer, the number of output filters. kernel_size: a list of leng

tf.contrib.distributions.MultivariateNormalCholesky.log_cdf(value, name='log_cdf') Log cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: log_cdf(x) := Log[ P[X <= x] ] Often, a numerical approximation can be used for log_cdf(x) that yields a more accurate answer than simply taking the logarithm of the cdf when x << -1. Args: value: float or double Tensor. name: The name to give this op. Returns: logcdf: a Tensor of shape samp

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

tf.contrib.distributions.StudentTWithAbsDfSoftplusSigma.cdf(value, name='cdf') Cumulative distribution function. Given random variable X, the cumulative distribution function cdf is: cdf(x) := P[X <= x] Args: value: float or double Tensor. name: The name to give this op. Returns: cdf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.