tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.loss()

tf.contrib.bayesflow.stochastic_tensor.WishartFullTensor.loss(final_loss, name='Loss')

tf.contrib.distributions.Multinomial.allow_nan_stats

tf.contrib.distributions.Multinomial.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

tf.errors.FailedPreconditionError.__init__()

tf.errors.FailedPreconditionError.__init__(node_def, op, message) Creates a FailedPreconditionError.

tf.errors.PermissionDeniedError

class tf.errors.PermissionDeniedError Raised when the caller does not have permission to run an operation. For example, running the tf.WholeFileReader.read() operation could raise PermissionDeniedError if it receives the name of a file for which the user does not have the read file permission.

tf.contrib.layers.convolution2d_in_plane()

tf.contrib.layers.convolution2d_in_plane(*args, **kwargs) Performs the same in-plane convolution to each channel independently. This is useful for performing various simple channel-independent convolution operations such as image gradients: image = tf.constant(..., shape=(16, 240, 320, 3)) vert_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[2, 1]) horz_gradients = layers.conv2d_in_plane(image, kernel=[1, -1], kernel_size=[1, 2]) Args: inputs: a 4-D tensor with dimensio

tf.errors.UnavailableError

class tf.errors.UnavailableError Raised when the runtime is currently unavailable. This exception is not currently used.

tf.nn.rnn_cell.InputProjectionWrapper.__call__()

tf.nn.rnn_cell.InputProjectionWrapper.__call__(inputs, state, scope=None) Run the input projection and then the cell.

tf.FixedLengthRecordReader.num_records_produced()

tf.FixedLengthRecordReader.num_records_produced(name=None) Returns the number of records this reader has produced. This is the same as the number of Read executions that have succeeded. Args: name: A name for the operation (optional). Returns: An int64 Tensor.

tf.FixedLenFeature.__getstate__()

tf.FixedLenFeature.__getstate__() Exclude the OrderedDict from pickling

tf.contrib.distributions.Distribution.parameters

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