def testRepeatMatrixAxis1(self):
with self.session():
x = np.array([[1, 2], [3, 4]], dtype=np.int32)
v_tf = repeat_ops.repeat(constant_op.constant(x),
constant_op.constant(3),
axis=1)
v_np = np.repeat(x, 3, axis=1)
self.assertAllEqual(self.evaluate(v_tf), v_np)
def testRepeatDTypes(self):
for dtype in [
np.int8, np.int16, np.uint8, np.uint16, np.int32, np.int64
]:
with self.session():
x = np.array([[1, 2], [3, 4]], dtype=dtype)
v_tf = repeat_ops.repeat(constant_op.constant(x), 2)
v_np = np.repeat(x, 2)
self.assertAllEqual(self.evaluate(v_tf), v_np)
def testRepeatMatrixAxis0(self):
with self.session():
x = np.array([[1, 2], [3, 4]], dtype=np.int32)
for axis in (0, 1):
v_tf = repeat_ops.repeat(constant_op.constant(x),
constant_op.constant([1, 2]),
axis=axis)
v_np = np.repeat(x, [1, 2], axis=axis)
self.assertAllEqual(self.evaluate(v_tf), v_np)
def repeat_base(repeats):
"""Repeat using `tf.while_loop` with `Tensor` concatenation."""
values = tf.equal(tf.range(tf.size(repeats), dtype=tf.int32) % 2, 1)
return repeat_ops.repeat(values, repeats)
def testRepeatScalar(self):
with self.session():
v_tf = repeat_ops.repeat(constant_op.constant(3), 4)
v_np = np.repeat(3, 4)
self.assertAllEqual(self.evaluate(v_tf), v_np)