birds_skip_thought_demo.py 文件源码

def sample_encoded_context(embeddings, model, bAugmentation=True):
    '''Helper function for init_opt'''
    # Build conditioning augmentation structure for text embedding
    # under different variable_scope: 'g_net' and 'hr_g_net'
    c_mean_logsigma = model.generate_condition(embeddings)
    mean = c_mean_logsigma[0]
    if bAugmentation:
        # epsilon = tf.random_normal(tf.shape(mean))
        epsilon = tf.truncated_normal(tf.shape(mean))
        stddev = tf.exp(c_mean_logsigma[1])
        c = mean + stddev * epsilon
    else:
        c = mean
    return c