loss_func.py

import tensorflow as tf

def cross_entropy_loss(inputs, true_w):
    """
    ==========================================================================

    inputs: The embeddings for context words. Dimension is [batch_size, embedding_size].
    true_w: The embeddings for predicting words. Dimension of true_w is [batch_size, embedding_size].

    Write the code that calculate A = log(exp({u_o}^T v_c))

    A =


    And write the code that calculate B = log(\sum{exp({u_w}^T v_c)})


    B =

    ==========================================================================
    """
    return tf.subtract(B, A)

def nce_loss(inputs, weights, biases, labels, sample, unigram_prob):
    """
    ==========================================================================

    inputs: Embeddings for context words. Dimension is [batch_size, embedding_size].
    weigths: Weights for nce loss. Dimension is [Vocabulary, embeeding_size].
    biases: Biases for nce loss. Dimension is [Vocabulary, 1].
    labels: Word_ids for predicting words. Dimesion is [batch_size, 1].
    samples: Word_ids for negative samples. Dimension is [num_sampled].
    unigram_prob: Unigram probability. Dimesion is [Vocabulary].

    Implement Noise Contrastive Estimation Loss Here

    ==========================================================================
    """