matvec, in_args_tree, b_avals) tree, = treedef_children(in_args_tree) def _shape_checked(fun, name): def f(x): y = fun(x) _check_shapes(name, "b", y, b_flat) return y return f matvec_jaxpr, matvec_consts, out_tree = _initial_style_jaxpr( _shape_checked(matvec, "...
Transposing allows you to be really creative. You might transpose a song to get a specific tone from a different set of chords. You also need to transpose a song if you’re trying to arrange two different songs to flow together in the form of a medley (e.g.Somewhere Over the Rainbow–...
# 需要导入模块: from tensorflow.compat import v1 [as 别名]# 或者: from tensorflow.compat.v1 importtranspose[as 别名]def_attention_projection_and_transpose(x_flat, batch_size, seq_length, num_attention_heads, size_per_head, name, initializer_range=0.02):""" :param x_flat: [batch_size*s...
DEBUG_CSEtypedefComplex<Real> C;constReal pi =4*Atan( Real(1) );constC phi = Sqrt( C(0,omega/pi) );// Compute Gauss quadrature points and weightsMatrix<Real> d, e; Zeros( d, n,1); e.Resize( n-1,1);for( Int j=0; j<n-1; ++j ) {constReal betaInv =2*Sqrt(1-Pow(j...
import numpy as npa = np.arange(8)print(a)b = a.reshape(4, 2)print(b) 2.ndarray.flat 函数返回数组上的一维迭代器,行为类似 Python 内建的迭代器。 import numpy as npa = np.arange(0, 16, 2).reshape(2, 4)print(a)# 返回展开数组中的下标的对应元素print(list(a.flat))[[ 0 2 4 ...
Example:Transposing a melody to alto saxophone (an E-flat instrument). Your concert-pitch melody is scored in the key of A-flat Major. You’ll need to move down 1.5 steps to transpose your piece for an E-flat instrument, thus your new key is F Major. ...
That's why we say that the clarinet is a transposing instrument in B-flat. To be more accurate, not all clarinets are in B-flat: clarinets in other keys (in C, in A, in E-flat, just to name a few) also exist, but the B-flat clarinet is by far the most common variety....
transpose(tf.multiply(tf.transpose(D3), 1.0 / D3[:, -1])) v_log = tf.reduce_sum(tf.log(m + 1e-9), axis=1) # to avoid nan lids = -20 / v_log ## batch normalize lids # lids = tf.nn.l2_normalize(lids, dim=0, epsilon=1e-12) return lids...
array_ops.matrix_diag(grad_e) + f * math_ops.matmul( v, grad_v, adjoint_a=True), v, adjoint_b=True))else: grad_a = math_ops.matmul( v, math_ops.matmul( array_ops.matrix_diag(grad_e), v, adjoint_b=True))# The forward op only depends on the lower triangular part of a,...
batch_size = array_ops.shape(inputs_flat[0])[1]ifinitial_stateisNone: initial_state = cell.zero_state(batch_size, dtype) func_cell = _FunctionalRnnCell(cell, inputs, initial_state)ifsequence_lengthisnotNone: max_length = math_ops.reduce_max(sequence_length)else: ...