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

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(*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

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

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

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.contrib.learn.monitors.NanLoss.__init__(loss_tensor, every_n_steps=100, fail_on_nan_loss=True) Initializes NanLoss monitor. Args: loss_tensor: Tensor, the loss tensor. every_n_steps: int, run check every this many steps. fail_on_nan_loss: bool, whether to raise exception when loss is NaN.

int64 tensorflow::TensorShapeDim::size

tf.contrib.distributions.WishartFull.is_reparameterized

tf.contrib.training.bucket(tensors, which_bucket, batch_size, num_buckets, num_threads=1, capacity=32, shapes=None, dynamic_pad=False, allow_smaller_final_batch=False, keep_input=None, shared_name=None, name=None) Lazy bucketing of input tensors according to which_bucket. The argument tensors can be a list or a dictionary of tensors. The value returned by the function will be of the same type as tensors. The tensors entering this function are put into the bucket given by which_bucket. Each buc