model.py 文件源码

def rnn_layers(features, sequence_length, num_classes):
    """Build a stack of RNN layers from input features"""

    # Input features is [batchSize paddedSeqLen numFeatures]
    logit_activation = tf.nn.relu
    weight_initializer = tf.contrib.layers.variance_scaling_initializer()
    bias_initializer = tf.constant_initializer(value=0.0)

    with tf.variable_scope("rnn"):
        # Transpose to time-major order for efficiency
        rnn_sequence = tf.transpose(features, perm=[1, 0, 2], name='time_major')
        rnn1 = rnn_layer(rnn_sequence, sequence_length, rnn_size, 'bdrnn1')
        rnn2 = rnn_layer(rnn1, sequence_length, rnn_size, 'bdrnn2')
        rnn_logits = tf.layers.dense( rnn2, num_classes+1, 
                                      activation=logit_activation,
                                      kernel_initializer=weight_initializer,
                                      bias_initializer=bias_initializer,
                                      name='logits')
        return rnn_logits