model_cifar10.py 文件源码

def compute(self, inputs):
    """Compute a batch of outputs of the neural network from a batch of inputs.
      Args:
        inputs: a tensorflow tensor, a batch of input images. Each image is of
          size InputReaderCifar10.IMAGE_SIZE x InputReaderCifar10.IMAGE_SIZE x
          InputReaderCifar10.NUM_CHANNELS.
      Returns:
        net: a tensorflow op, output of the network.
        embedding: a tensorflow op, output of the embedding layer (the second
          last fully connected layer).
    """
    hparams = self._hparams
    net = None
    num_pool_conv_layers = len(hparams.nums_conv_filters)
    for i in xrange(num_pool_conv_layers):
      net = slim.conv2d(inputs if i == 0 else net,
                        hparams.nums_conv_filters[i],
                        [hparams.conv_filter_sizes[i], hparams.conv_filter_sizes[i]],
                        padding="SAME",
                        biases_initializer=tf.constant_initializer(0.1 * i),
                        scope="conv_{0}".format(i))
      net = slim.max_pool2d(net,
                            [hparams.pooling_size, hparams.pooling_size],
                            hparams.pooling_stride,
                            scope="pool_{0}".format(i))

    net = slim.flatten(net, scope="flatten")
    net = slim.fully_connected(net,
                               384,
                               biases_initializer=tf.constant_initializer(0.1),
                               scope="fc_{0}".format(num_pool_conv_layers))

    net = slim.dropout(net,
                       hparams.dropout_prob,
                       scope="dropout_{0}".format(num_pool_conv_layers))

    embedding = slim.fully_connected(net,
                                     192,
                                     biases_initializer=tf.constant_initializer(0.1),
                                     scope="fc_{0}".format(num_pool_conv_layers + 1))

    net = slim.fully_connected(embedding,
                               InputReaderCifar10.NUM_CLASSES,
                               activation_fn=None,
                               biases_initializer=tf.constant_initializer(0.0),
                               scope="fc_{0}".format(num_pool_conv_layers + 2))

    return net, embedding