tf.image.decode_jpeg(contents, channels=None, ratio=None, fancy_upscaling=None, try_recover_truncated=None, acceptable_fraction=None, name=None) Decode a JPEG-encoded image to a uint8 tensor. The attr channels indicates the desired number of color channels for the decoded image. Accepted values are: 0: Use the number of channels in the JPEG-encoded image. 1: output a grayscale image. 3: output an RGB image. If needed, the JPEG-encoded image is transformed to match the requested number of col

tf.contrib.distributions.MultivariateNormalDiagPlusVDVT.sigma Dense (batch) covariance matrix, if available.

tf.contrib.learn.DNNClassifier.weights_ DEPRECATED FUNCTION THIS FUNCTION IS DEPRECATED. It will be removed after 2016-10-13. Instructions for updating: This method inspects the private state of the object, and should not be used

tf.contrib.distributions.MultivariateNormalDiagWithSoftplusStDev.__init__(mu, diag_stdev, validate_args=False, allow_nan_stats=True, name='MultivariateNormalDiagWithSoftplusStdDev')

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

tf.contrib.distributions.WishartCholesky.pmf(value, name='pmf') Probability mass function. Args: value: float or double Tensor. name: The name to give this op. Returns: pmf: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype. Raises: TypeError: if is_continuous.

tf.contrib.distributions.WishartCholesky.dtype The DType of Tensors handled by this Distribution.

string tensorflow::Status::ToString() const Return a string representation of this status suitable for printing. Returns the string "OK" for success.

tf.contrib.distributions.Normal.param_shapes(cls, sample_shape, name='DistributionParamShapes') Shapes of parameters given the desired shape of a call to sample(). Subclasses should override static method _param_shapes. Args: sample_shape: Tensor or python list/tuple. Desired shape of a call to sample(). name: name to prepend ops with. Returns: dict of parameter name to Tensor shapes.

tf.contrib.distributions.WishartFull.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.