Lines Matching refs:inx
97 inx = ops.convert_to_tensor(x)
99 y = 1.1 * tf_func(inx)
102 y = tf_func(inx)
116 inx, s, y, s, x_init_value=x)
127 inx, s, y, s, x_init_value=x, delta=1e-3)
132 inx, s, y, s, x_init_value=x, delta=1e-5)
534 inx = ops.convert_to_tensor(x)
536 out = tf_func(inx, iny)
540 np_right = tf_func(inx, y).eval()
577 inx = ops.convert_to_tensor(x)
580 out = 1.1 * tf_func(inx, iny)
582 out = tf_func(inx, iny)
585 inx, xs, out, zs, x_init_value=x)
607 inx = ops.convert_to_tensor(x)
610 out = 1.1 * tf_func(inx, iny)
612 out = tf_func(inx, iny)
631 inx = ops.convert_to_tensor(x)
633 out = tf_func(inx, iny)
694 inx = ops.convert_to_tensor(x)
696 s = math_ops.reduce_sum(inx * iny)
697 gx, gy = sess.run(gradients_impl.gradients(s, [inx, iny]))
1338 inx = ops.convert_to_tensor(x)
1340 out = tf_func(inx, iny)
1441 inx = ops.convert_to_tensor(x)
1443 out = array_ops.where(c, inx, iny)
1446 inx, s, out, s, x_init_value=x)
1465 inx = ops.convert_to_tensor(x)
1467 out = array_ops.where(c, inx, iny)
1584 inx = ops.convert_to_tensor(x)
1586 out = array_ops.where(c, inx, iny)
1589 inx, s, out, s, x_init_value=x)
1608 inx = ops.convert_to_tensor(x)
1610 out = array_ops.where(c, inx, iny)
1682 inx = ops.convert_to_tensor(x)
1684 omin, omax = math_ops.minimum(inx, iny), math_ops.maximum(inx, iny)
1713 inx = ops.convert_to_tensor(x)
1715 out = func(inx, iny)
1718 inx, s, out, s, x_init_value=x)
1728 inx = ops.convert_to_tensor(x)
1730 out = func(inx, iny)
1755 inx = ops.convert_to_tensor(x, dtype=dtype)
1756 z = func(inx, y) # Should use __add__, __sub__, etc.
1844 inx = ops.convert_to_tensor(x)
1845 ofinite, oinf, onan = math_ops.is_finite(inx), math_ops.is_inf(
1846 inx), math_ops.is_nan(inx)
1906 inx = ops.convert_to_tensor(x)
1907 ofloor, oceil = math_ops.floor(inx), math_ops.ceil(inx)
1959 inx = ops.convert_to_tensor(cplx)
1960 tf_real = math_ops.real(inx)
1961 tf_imag = math_ops.imag(inx)
1986 inx = ops.convert_to_tensor(cplx)
1987 tf_angle = math_ops.angle(inx)
2020 inx = ops.convert_to_tensor(cplx)
2021 tf_conj = math_ops.conj(inx)
2060 inx = ops.convert_to_tensor(x)
2061 real, imag = array_ops.split(value=inx, num_or_size_splits=2, axis=1)
2070 inx, list(x.shape), loss, [1], x_init_value=x, delta=epsilon)
