weighted_cross_entropy_with_logits(targets, logits, pos_weight, name=None): 此函数功能以及计算方式基本与tf_nn_sigmoid_cross_entropy_with_logits差不多,但是加上了权重的功能,是计算具有权重的sigmoid交叉熵函数 计算方法 :posweight∗targets∗−log(sigmoid(logits))+(1−targets)∗−log(1−...
I have a training dataset of train_data and train_labels which is train_data_node and train_labels_node in the graph of tensorflow. As you know, I can use the loss function of tensorflow as bellows: logits = model(train_data_node) loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_e...
import tensorflow as tf input_data = tf.Variable(np.random.rand(3,3), dtype=tf.float32) # np.random.rand()传入一个shape,返回一个在[0,1)区间符合均匀分布的arrayoutput= tf.nn.weighted_cross_entropy_with_logits(logits=input_data, targets=[[1.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, ...
However, you will need to implement your own version of the weighted mean squared error (WMSE) loss function and call it instead of the “crossentropy” function. Note that if you want to apply the weights on the mini-batches, you would need to extract the...
tf.nn.softmax_cross_entropy_with_logits函数在TensorFlow中计算分类问题交叉熵损失函数时会用到。 这个函数的返回值不是一个数,而是一个向量。如果要求最终的交叉熵损失,我们需要再做一步tf.reduce_sum操作,即对向量中的所有元...tf.nn.sigmoid_cross_entropy_with_logits()函数解析 最近学习中碰到了以前学过...
TensorFlow.js tf.losses.computeWeightedLoss()函数介绍 描述 tf.losses.computeWeightedLoss()函数用于计算加权损失。损失的计算是通过将权重乘以损失值的方式进行加权的。该函数支持各种损失函数。 语法 tf.losses.computeWeightedLoss(losses:Tensor|Tensor[],weights?:Tensor|Tensor[],reduction?:tf.losses.Reduction)...
Because the problem is multi-class, we will use the categorical cross entropy loss function to optimize the model and the efficient Adam flavor of stochastic gradient descent. 1 2 3 4 5 # define model model = Sequential() model.add(Dense(25, input_dim=2, activation='relu'))...
我去看了下 tf 里面的 binary_crossentropy,如下: 关键在这两句: 其实,因为 keras 里面对 ten...二元分类loss函数binary_crossentropy和BinaryCrossentropy的区别 在自定义训练模式里: 1.loss函数的声明及输出维度 BinaryCrossentropy(官网链接)可以直接申明,如下: 其在作为独立函数使用时,输出的时...
where, 𝑝𝑡pt is the probability that the prediction is a true label, 𝛾γ is the shape of the controlling focal loss curve, and 𝛼α is the inter-category weighting control factor. 4. Improved Entropy-Weighted Topsis Method Multi-Source Data Decision-Level Fusion Evaluation System and...