Beispiel #1
0
 def basic_accuracy(self, y_true, y_pred, go_backwards=False):
     """训练过程中显示逐帧准确率的函数,排除了mask的影响
     此处y_true需要是整数形式(非one hot)
     """
     # 导出mask并转换数据类型
     mask = K.all(K.greater(y_pred, -1e6), axis=2)
     mask = K.cast(mask, K.floatx())
     # y_true需要重新明确一下shape和dtype
     y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
     y_true = K.cast(y_true, 'int32')
     # 反转相关
     if self.hidden_dim is None:
         if go_backwards:  # 是否反转序列
             y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
             trans = K.transpose(self.trans)
         else:
             trans = self.trans
         histoty = K.gather(trans, y_true)
     else:
         if go_backwards:  # 是否反转序列
             y_true, y_pred = self.reverse_sequence([y_true, y_pred], mask)
             r_trans, l_trans = self.l_trans, self.r_trans
         else:
             l_trans, r_trans = self.l_trans, self.r_trans
         histoty = K.gather(l_trans, y_true)
         histoty = tf.einsum('bnd,kd->bnk', histoty, r_trans)
     # 计算逐标签accuracy
     histoty = K.concatenate([y_pred[:, :1], histoty[:, :-1]], 1)
     y_pred = (y_pred + histoty) / 2
     y_pred = K.cast(K.argmax(y_pred, 2), 'int32')
     isequal = K.cast(K.equal(y_true, y_pred), K.floatx())
     return K.sum(isequal * mask) / K.sum(mask)
Beispiel #2
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 def sparse_accuracy(self, y_true, y_pred):
     """训练过程中显示逐帧准确率的函数,排除了mask的影响
     此处y_true需要是整数形式(非one hot)
     """
     # 导出mask并转换数据类型
     mask = K.all(K.greater(y_pred, -1e6), axis=2)
     mask = K.cast(mask, K.floatx())
     # y_true需要重新明确一下shape和dtype
     y_true = K.reshape(y_true, K.shape(y_pred)[:-1])
     y_true = K.cast(y_true, 'int32')
     # 逐标签取最大来粗略评测训练效果
     y_pred = K.cast(K.argmax(y_pred, 2), 'int32')
     isequal = K.cast(K.equal(y_true, y_pred), K.floatx())
     return K.sum(isequal * mask) / K.sum(mask)
Beispiel #3
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 def dense_accuracy(self, y_true, y_pred):
     """训练过程中显示逐帧准确率的函数,排除了mask的影响
     此处y_true需要是one hot形式
     """
     y_true = K.argmax(y_true, 2)
     return self.sparse_accuracy(y_true, y_pred)
Beispiel #4
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 def dense_loss(self, y_true, y_pred):
     """y_true需要是one hot形式
     """
     y_true = K.argmax(y_true, 2)
     return self.sparse_loss(y_true, y_pred)