tf.python_io.TFRecordWriter.__enter__() Enter a with block.

tf.python_io.TFRecordWriter.write(record) Write a string record to the file. Args: record: str

tf.python_io.TFRecordWriter.close() Close the file.

class tf.python_io.TFRecordWriter A class to write records to a TFRecords file. This class implements __enter__ and __exit__, and can be used in with blocks like a normal file.

tf.PriorityQueue.__init__(capacity, types, shapes=None, names=None, shared_name=None, name='priority_queue') Creates a queue that dequeues elements in a first-in first-out order. A PriorityQueue has bounded capacity; supports multiple concurrent producers and consumers; and provides exactly-once delivery. A PriorityQueue holds a list of up to capacity elements. Each element is a fixed-length tuple of tensors whose dtypes are described by types, and whose shapes are optionally described by the

class tf.PriorityQueue A queue implementation that dequeues elements in prioritized order. See tf.QueueBase for a description of the methods on this class.

tf.pow(x, y, name=None) Computes the power of one value to another. Given a tensor x and a tensor y, this operation computes \(x^y\) for corresponding elements in x and y. For example: # tensor 'x' is [[2, 2], [3, 3]] # tensor 'y' is [[8, 16], [2, 3]] tf.pow(x, y) ==> [[256, 65536], [9, 27]] Args: x: A Tensor of type float32, float64, int32, int64, complex64, or complex128. y: A Tensor of type float32, float64, int32, int64, complex64, or complex128. name: A name for the operation (opti

tf.polygamma(a, x, name=None) Compute the polygamma function \(\psi^{(n)}(x)\). The polygamma function is defined as: \psi^{(n)}(x) = \frac{d^n}{dx^n} \psi(x) where \(\psi(x)\) is the digamma function. Args: a: A Tensor. Must be one of the following types: float32, float64. x: A Tensor. Must have the same type as a. name: A name for the operation (optional). Returns: A Tensor. Has the same type as a.

tf.placeholder_with_default(input, shape, name=None) A placeholder op that passes though input when its output is not fed. Args: input: A Tensor. The default value to produce when output is not fed. shape: A tf.TensorShape or list of ints. The (possibly partial) shape of the tensor. name: A name for the operation (optional). Returns: A Tensor. Has the same type as input. A placeholder tensor that defaults to input if it is not fed. For feeding SparseTensors which are composite type, there

tf.placeholder(dtype, shape=None, name=None) Inserts a placeholder for a tensor that will be always fed. Important: This tensor will produce an error if evaluated. Its value must be fed using the feed_dict optional argument to Session.run(), Tensor.eval(), or Operation.run(). For example: x = tf.placeholder(tf.float32, shape=(1024, 1024)) y = tf.matmul(x, x) with tf.Session() as sess: print(sess.run(y)) # ERROR: will fail because x was not fed. rand_array = np.random.rand(1024, 1024)