tf.image.adjust_contrast(images, contrast_factor) Adjust contrast of RGB or grayscale images. This is a convenience method that converts an RGB image to float representation, adjusts its contrast, and then converts it back to the original data type. If several adjustments are chained it is advisable to minimize the number of redundant conversions. images is a tensor of at least 3 dimensions. The last 3 dimensions are interpreted as [height, width, channels]. The other dimensions only represent

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

tf.contrib.distributions.WishartCholesky.cholesky_input_output_matrices Boolean indicating if Tensor input/outputs are Cholesky factorized.

tf.image.per_image_whitening(image) Linearly scales image to have zero mean and unit norm. This op computes (x - mean) / adjusted_stddev, where mean is the average of all values in image, and adjusted_stddev = max(stddev, 1.0/sqrt(image.NumElements())). stddev is the standard deviation of all values in image. It is capped away from zero to protect against division by 0 when handling uniform images. Note that this implementation is limited: * It only whitens based on the statistics of an indivi

tf.contrib.distributions.Gamma.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.distributions.NormalWithSoftplusSigma.get_event_shape() Shape of a single sample from a single batch as a TensorShape. Same meaning as event_shape. May be only partially defined. Returns: event_shape: TensorShape, possibly unknown.

tf.contrib.learn.BaseEstimator.set_params(**params) Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it's possible to update each component of a nested object. Args: **params: Parameters. Returns: self Raises: ValueError: If params contain invalid names.

tf.contrib.distributions.TransformedDistribution.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.contrib.layers.max_pool2d(*args, **kwargs) Adds a 2D Max Pooling op. It is assumed that the pooling is done per image but not in batch or channels. Args: inputs: A Tensor of size [batch_size, height, width, channels]. kernel_size: A list of length 2: [kernel_height, kernel_width] of the pooling kernel over which the op is computed. Can be an int if both values are the same. stride: A list of length 2: [stride_height, stride_width]. Can be an int if both strides are the same. Note that pr

tf.invert_permutation(x, name=None) Computes the inverse permutation of a tensor. This operation computes the inverse of an index permutation. It takes a 1-D integer tensor x, which represents the indices of a zero-based array, and swaps each value with its index position. In other words, for an output tensor y and an input tensor x, this operation computes the following: y[x[i]] = i for i in [0, 1, ..., len(x) - 1] The values must include 0. There can be no duplicate values or negative values