MatMul' Op has type float32 that does not match type int32 of argument 'a'. loss = tf.reduce_mean( tf.nn.nce_loss
(embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) ncs_loss_test=tf.nn.nce_loss...inputs=embed, num_sampled=num_sampled, num_classes=vocabulary_size) loss = tf.reduce_mean(tf.nn.nce_loss
定义损失函数 TensorFlow已经为我们实现了NCE损失函数: tf.nn.nce_loss( weights, biases, labels, inputs,...nce_bias = tf.get_variable('nce_bias', initializer=tf.zeros([VOCAB_SIZE])) 损失函数定义如下: loss = tf.reduce_mean(tf.nn.nce_loss
说的是logits,其实内部实现是relu tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels, num_sampled, vocabulary_size
再来看看TF里word2vec的实现,他用到nce_loss的代码如下: loss = tf.reduce_mean( tf.nn.nce_loss(nce_weights, nce_biases
函数原型: tf.nn.nce_loss( weights, biases, labels, inputs, num_sampled, num_classes...initializer=tf.zeros([VOCAB_SIZE])) # define loss function to be NCE loss function loss = tf.reduce_mean(tf.nn.nce_loss...# define loss function to be NCE loss function self.loss = tf.reduce_mean(tf.nn.nce_loss
值得注意的是,TensorFlow 里面把上面的两个过程合并了,合并在tf.nn.nce_loss这个函数里面。...你可以看到 TensorFlow 的教程里面的损失函数就是使用的tf.nn.nce_loss作为损失函数。
TensorFlow 已经在此帮助过我们,并为我们提供了 NCE 损失函数,即 tf.nn.nce_loss()。我们可以将权重和偏差变量输入 tf.nn.nce_loss()。...embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) nce_loss = tf.reduce_mean( tf.nn.nce_loss
embedding_size))) nce_biases = tf.Variable(tf.zeros([vocabulary_size])) loss = tf.reduce_mean(tf.nn.nce_loss
nce_bias = tf.get_variable('nce_bias', initializer=tf.zeros([VOCAB_SIZE])) loss = tf.reduce_mean(tf.nn.nce_loss
tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/word2vec/word2vec_basic.py 其中的抽样算法封装在了tf.nn.nce_loss
loss = tf.reduce_mean( tf.nn.nce_loss(weights=nce_weights, biases=nce_biases
loss = tf.reduce_mean( tf.nn.nce_loss(nce_weights, nce_biases, embed, train_labels,
# 计算批处理的平均NCE损失 y = tf.cast(y, tf.int64) loss = tf.reduce_mean( tf.nn.nce_loss
在实践中,我们采用了TensorFlow提供的函数tf.nn.nce_loss去做候选采样并计算NCE损失。
操作 描述 Sampled Loss Functions tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled, num_classes
) #定义loss,损失函数,tf.reduce_mean求平均值,# 得到NCE损失(负采样得到的损失) loss = tf.reduce_mean(tf.nn.nce_loss
操作 描述 Sampled Loss Functions tf.nn.nce_loss(weights, biases, inputs, labels, num_sampled,num_classes
://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/ loss = tf.reduce_mean( tf.nn.nce_loss
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