| 查看: 903 | 回复: 0 | ||
| 【悬赏金币】回答本帖问题，作者yang_12345将赠送您 5 个金币 | ||
[求助]
Tensorflow自伴特征分解不成功，输入可能无效
|
||
|
Self-adjoint eigen decomposition was not successful. The input might not be valid(Tensorflow自伴特征分解不成功，输入可能无效) def cca_loss(outdim_size, use_all_singular_values): def inner_cca_objective(y_true, y_pred): r1 = 1e-4 r2 = 1e-4 eps = 1e-12 print("y_pred:",y_pred.shape[1]) o1 = o2 = int(y_pred.shape[1] // 2) print(y_pred) print("aaaa",o1,o2) # unpack (separate) the output of networks for view 1 and view 2 H1 = tf.transpose(y_pred[:, 0  1])H2 = tf.transpose(y_pred[:, o1 1 + o2])m = tf.shape(H1)[1] H1bar = H1 - tf.cast(tf.divide(1, m), tf.float32) * tf.matmul(H1, tf.ones([m, m])) H2bar = H2 - tf.cast(tf.divide(1, m), tf.float32) * tf.matmul(H2, tf.ones([m, m])) SigmaHat12 = tf.cast(tf.divide(1, m - 1), tf.float32) * tf.matmul(H1bar, H2bar, transpose_b=True) # [dim, dim] SigmaHat11 = tf.cast(tf.divide(1, m - 1), tf.float32) * tf.matmul(H1bar, H1bar, transpose_b=True) + r1 * tf.eye( o1) SigmaHat22 = tf.cast(tf.divide(1, m - 1), tf.float32) * tf.matmul(H2bar, H2bar, transpose_b=True) + r2 * tf.eye( o2) D1, V1 = tf.self_adjoint_eig(SigmaHat11) #求特征值 特征向量 D2, V2 = tf.self_adjoint_eig(SigmaHat22) # Added to increase stability posInd1 = tf.where(tf.greater(D1, eps)) D1 = tf.gather_nd(D1, posInd1) # get eigen values that are larger than eps 选出大于eps的特征值 V1 = tf.transpose(tf.nn.embedding_lookup(tf.transpose(V1), tf.squeeze(posInd1))) posInd2 = tf.where(tf.greater(D2, eps)) D2 = tf.gather_nd(D2, posInd2) V2 = tf.transpose(tf.nn.embedding_lookup(tf.transpose(V2), tf.squeeze(posInd2))) SigmaHat11RootInv = tf.matmul(tf.matmul(V1, tf.diag(D1 ** -0.5)), V1, transpose_b=True) # [dim, dim] SigmaHat22RootInv = tf.matmul(tf.matmul(V2, tf.diag(D2 ** -0.5)), V2, transpose_b=True) Tval = tf.matmul(tf.matmul(SigmaHat11RootInv, SigmaHat12), SigmaHat22RootInv) print("use_all_singular_values",use_all_singular_values) if use_all_singular_values: corr = tf.sqrt(tf.trace(tf.matmul(Tval, Tval, transpose_a=True))) else: [U, V] = tf.self_adjoint_eig(tf.matmul(Tval, Tval, transpose_a=True)) U = tf.gather_nd(U, tf.where(tf.greater(U, eps))) kk = tf.reshape(tf.cast(tf.shape(U), tf.int32), []) K = tf.minimum(kk, outdim_size) w, _ = tf.nn.top_k(U, k=K) corr = tf.reduce_sum(tf.sqrt(w)) print("corr:",corr) return -corr return inner_cca_objective |
回复此楼» 猜你喜欢
极限拉扯，如何抉择
已经有15人回复
-
申请科研项目纯靠实力能成功吗？
已经有8人回复
-
中科院海洋所招收海洋材料相关领域博士研究生及研究助理
已经有4人回复
-
40+中年人失业后，还能找到好工作吗？
已经有3人回复
-
二审4个多月了还是Awaiting Reviewer Scores
已经有4人回复
-
OpenAI又攻克数学难题了，离AI做有机合成还有多远？
已经有13人回复
-
课题组会帮你通过考核么？
已经有7人回复
-
入职后第一次管课题组的专利缴费，对账才发现官费比别人多掏了一倍
已经有6人回复
-
售一区SCI文章T0P，我:8O.551.O54,科目全,可十急
已经有9人回复
-
售SCI一区文章，我:8O5.5.1.O5.4,科目全,可伽急
已经有10人回复
200