the data will be cycled through indefinitely.
num_readers: a integer, number of Readers to create.
reader_kwargs: an optional dict, of kwargs for the reader.
- shuffle: boolean, wether should shuffle the files and the records by using
+ shuffle: boolean, whether should shuffle the files and the records by using
RandomShuffleQueue as common_queue.
dtypes: A list of types. The length of dtypes must equal the number
of elements in each record. If it is None it will default to
with self.test_session() as sess:
x = random_ops.random_normal(shape=(2, 2, 3, 4))
- # This LinearOperatorDiag will be brodacast to (2, 2, 3, 3) during solve
+ # This LinearOperatorDiag will be broadcast to (2, 2, 3, 3) during solve
# and matmul with 'x' as the argument.
diag = random_ops.random_uniform(shape=(2, 1, 3))
operator = linalg.LinearOperatorDiag(diag, is_self_adjoint=True)
# Compute x for p <= exp(-2): x = z - log(z)/z - (1/z) P(1/z) / Q(1/z),
# where z = sqrt(-2. * log(p)), and P/Q are chosen between two different
- # arrays based on wether p < exp(-32).
+ # arrays based on whether p < exp(-32).
z = math_ops.sqrt(-2. * math_ops.log(sanitized_mcp))
first_term = z - math_ops.log(z) / z
second_term_small_p = (_create_polynomial(1. / z, p2)
operator = LinearOperatorDiag(diag)
# Create a shape [2, 1, 4, 2] vector. Note that this shape is compatible
- # since the batch dimensions, [2, 1], are brodcast to
+ # since the batch dimensions, [2, 1], are broadcast to
# operator.batch_shape = [2, 3].
y = tf.random_normal(shape=[2, 1, 4, 2])
x = operator.solve(y)