video_level_models.py 文件源码

def create_model(self,
                   model_input,
                   vocab_size,
                   keep_prob,
                   num_mixtures=None,
                   l2_penalty=1e-8,
                   **unused_params):

    num_mixtures = num_mixtures or FLAGS.moe_num_mixtures

    new_model_input = slim.dropout(model_input,keep_prob=keep_prob)
    #new_model_input = model_input

    gate_activations = slim.fully_connected(
        new_model_input,
        vocab_size * (num_mixtures + 1),
        activation_fn=None,
        biases_initializer=None,
        weights_regularizer=slim.l2_regularizer(l2_penalty),
        scope="gates")
    expert_activations = slim.fully_connected(
        new_model_input,
        vocab_size * num_mixtures,
        activation_fn=None,
        weights_regularizer=slim.l2_regularizer(l2_penalty),
        scope="experts")

    gating_distribution = tf.nn.softmax(tf.reshape(
        gate_activations,
        [-1, num_mixtures + 1]))  # (Batch * #Labels) x (num_mixtures + 1)
    expert_distribution = tf.nn.sigmoid(tf.reshape(
        expert_activations,
        [-1, num_mixtures]))  # (Batch * #Labels) x num_mixtures

    final_probabilities_by_class_and_batch = tf.reduce_sum(
        gating_distribution[:, :num_mixtures] * expert_distribution, 1)
    final_probabilities = tf.reshape(final_probabilities_by_class_and_batch,
                                     [-1, vocab_size])
    return {"predictions": final_probabilities,
            "zhaofeatures": model_input}