tf.contrib.distributions.TransformedDistribution.pdf(value, name='pdf') Probability density function. Args: value: float or double Tensor. name: The name to give this op. Returns: prob: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if not is_continuous.

tf.contrib.learn.TensorFlowEstimator.predict_proba(x, batch_size=None) Predict class probability of the input samples x. Args: x: array-like matrix, [n_samples, n_features...] or iterator. batch_size: If test set is too big, use batch size to split it into mini batches. By default the batch_size member variable is used. Returns: y: array of shape [n_samples, n_classes]. The predicted probabilities for each class.

TTypes< T, NDIMS >::ConstTensor tensorflow::Tensor::bit_casted_shaped(gtl::ArraySlice< int64 > new_sizes) const Return the tensor data to an Eigen::Tensor with the new shape specified in new_sizes and cast to a new dtype T. Using a bitcast is useful for move and copy operations. The allowed bitcast is the only difference from shaped().

tf.contrib.distributions.Binomial.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.

tensorflow::Env::Env()

tf.contrib.losses.mean_pairwise_squared_error(*args, **kwargs) Adds a pairwise-errors-squared loss to the training procedure. (deprecated) THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-01. Instructions for updating: Use mean_pairwise_squared_error. Unlike the sum_of_squares loss, which is a measure of the differences between corresponding elements of predictions and targets, sum_of_pairwise_squares is a measure of the differences between pairs of corresponding elements of predi

class tf.contrib.distributions.Gamma The Gamma distribution with parameter alpha and beta. The parameters are the shape and inverse scale parameters alpha, beta. The PDF of this distribution is: pdf(x) = (beta^alpha)(x^(alpha-1))e^(-x*beta)/Gamma(alpha), x > 0 and the CDF of this distribution is: cdf(x) = GammaInc(alpha, beta * x) / Gamma(alpha), x > 0 where GammaInc is the incomplete lower Gamma function. WARNING: This distribution may draw 0-valued samples for small alpha values. See

tf.contrib.distributions.Dirichlet.allow_nan_stats Python boolean describing behavior when a stat is undefined. Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is

tf.add_n(inputs, name=None) Adds all input tensors element-wise. Args: inputs: A list of Tensor objects, each with same shape and type. name: A name for the operation (optional). Returns: A Tensor of same shape and type as the elements of inputs. Raises: ValueError: If inputs don't all have same shape and dtype or the shape cannot be inferred.

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