tf.contrib.distributions.Normal.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 sample_shape(x) + self.b

tf.image.extract_glimpse(input, size, offsets, centered=None, normalized=None, uniform_noise=None, name=None) Extracts a glimpse from the input tensor. Returns a set of windows called glimpses extracted at location offsets from the input tensor. If the windows only partially overlaps the inputs, the non overlapping areas will be filled with random noise. The result is a 4-D tensor of shape [batch_size, glimpse_height, glimpse_width, channels]. The channels and batch dimensions are the same as

tf.contrib.distributions.Mixture.survival_function(value, name='survival_function') Survival function. Given random variable X, the survival function is defined: survival_function(x) = P[X > x] = 1 - P[X <= x] = 1 - cdf(x). Args: value: float or double Tensor. name: The name to give this op. Returns: Tensorof shapesample_shape(x) + self.batch_shapewith values of typeself.dtype`.

tf.contrib.losses.sum_of_pairwise_squares(*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 predictio

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

tf.contrib.learn.TensorFlowEstimator.export(*args, **kwargs) Exports inference graph into given dir. (deprecated arguments) SOME ARGUMENTS ARE DEPRECATED. They will be removed after 2016-09-23. Instructions for updating: The signature of the input_fn accepted by export is changing to be consistent with what's used by tf.Learn Estimator's train/evaluate. input_fn (and in most cases, input_feature_key) will become required args, and use_deprecated_input_fn will default to False and be removed al

tf.contrib.framework.has_arg_scope(func) Checks whether a func has been decorated with @add_arg_scope or not. Args: func: function to check. Returns: a boolean.

tf.contrib.distributions.InverseGammaWithSoftplusAlphaBeta.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.Normal.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.histogram_fixed_width(values, value_range, nbins=100, dtype=tf.int32, name=None) Return histogram of values. Given the tensor values, this operation returns a rank 1 histogram counting the number of entries in values that fell into every bin. The bins are equal width and determined by the arguments value_range and nbins. Args: values: Numeric Tensor. value_range: Shape [2] Tensor. new_values <= value_range[0] will be mapped to hist[0], values >= value_range[1] will be mapped to hist