class tf.IdentityReader A Reader that outputs the queued work as both the key and value. To use, enqueue strings in a Queue. Read will take the front work string and output (work, work). See ReaderBase for supported methods.

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

tf.get_session_tensor(handle, dtype, name=None) Get the tensor of type dtype by feeding a tensor handle. This is EXPERIMENTAL and subject to change. Get the value of the tensor from a tensor handle. The tensor is produced in a previous run() and stored in the state of the session. Args: handle: The string representation of a persistent tensor handle. dtype: The type of the output tensor. name: Optional name prefix for the return tensor. Returns: A pair of tensors. The first is a placehold

tf.get_session_handle(data, name=None) Return the handle of data. This is EXPERIMENTAL and subject to change. Keep data "in-place" in the runtime and create a handle that can be used to retrieve data in a subsequent run(). Combined with get_session_tensor, we can keep a tensor produced in one run call in place, and use it as the input in a future run call. Args: data: A tensor to be stored in the session. name: Optional name prefix for the return tensor. Returns: A scalar string tensor rep

tf.get_default_session() Returns the default session for the current thread. The returned Session will be the innermost session on which a Session or Session.as_default() context has been entered. NOTE: The default session is a property of the current thread. If you create a new thread, and wish to use the default session in that thread, you must explicitly add a with sess.as_default(): in that thread's function. Returns: The default Session being used in the current thread.

tf.foldr(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None) foldr on the list of tensors unpacked from elems on dimension 0. This foldr operator repeatedly applies the callable fn to a sequence of elements from last to first. The elements are made of the tensors unpacked from elems. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initializer is None, elem

tf.foldl(fn, elems, initializer=None, parallel_iterations=10, back_prop=True, swap_memory=False, name=None) foldl on the list of tensors unpacked from elems on dimension 0. This foldl operator repeatedly applies the callable fn to a sequence of elements from first to last. The elements are made of the tensors unpacked from elems on dimension 0. The callable fn takes two tensors as arguments. The first argument is the accumulated value computed from the preceding invocation of fn. If initialize

tf.floordiv(x, y, name=None) Divides x / y elementwise, rounding down for floating point. The same as tf.div(x,y) for integers, but uses tf.floor(tf.div(x,y)) for floating point arguments so that the result is always an integer (though possibly an integer represented as floating point). This op is generated by x // y floor division in Python 3 and in Python 2.7 with from __future__ import division. Note that for efficiency, floordiv uses C semantics for negative numbers (unlike Python and Nump

tf.floor(x, name=None) Returns element-wise largest integer not greater than x. Args: x: A Tensor. Must be one of the following types: half, float32, float64. name: A name for the operation (optional). Returns: A Tensor. Has the same type as x.

tf.FixedLenSequenceFeature.__repr__() Return a nicely formatted representation string