训练模型的 main graph，如何查看？

您可以使用Tensorboard来查看训练模型的main graph。Tensorboard是一个可视化工具，可以帮助您直观地了解模型训练的各个方面。在Tensorboard中，您可以查看模型的图表（包括main graph），张量的值和梯度等信息。如果您已经在您的代码中使用了Tensorflow，那么您可以通过以下命令启动Tensorboard，并在浏览器中查看和分析您的模型：

tensorboard --logdir=path/to/log-directory

其中，path/to/log-directory是您保存训练数据的目录。在Tensorboard中，您可以在"Graphs"选项卡下查看main graph。

相关问题

猫狗训练模型代码实例

这篇博客使用TensorFlow框架，使用预训练模型进行猫狗分类。代码在Github上已经公开，可以从[这里](https://github.com/jmhIcoding/dogsVScats)获取。

使用预训练模型进行微调的代码如下，其中包括了数据处理、模型构建和训练三个部分：

数据处理部分[^1]：

import tensorflow as tf
slim = tf.contrib.slim
...
def get_dataset(dataset_name, split_name, dataset_dir, file_pattern):
  """
  获取指定数据集和指定数据集中的数据切分
  """
  file_pattern = os.path.join(dataset_dir, file_pattern % split_name)
  if dataset_name == 'imagenet':
    return dataset.get_split(split_name, dataset_dir, file_pattern)
  elif dataset_name == 'flowers':
    return flowers.get_split(split_name, dataset_dir, file_pattern)
  elif dataset_name == 'cifar10':
    return cifar10.get_split(split_name, dataset_dir, file_pattern)
  elif dataset_name == 'mnist':
    return mnist.get_split(split_name, dataset_dir, file_pattern)
  elif dataset_name == 'cats_vs_dogs':
    return dogs.get_split(split_name, dataset_dir, file_pattern)
  else:
    raise ValueError('Invalid dataset name %s.' % dataset_name)

def load_batch(dataset, batch_size, height, width, is_training=True):
  """
  加载一批数据
  """
  data_provider = slim.dataset_data_provider.DatasetDataProvider(
        dataset,
        shuffle=is_training,
        common_queue_capacity=2 * batch_size,
        common_queue_min=batch_size)
  image_raw, label = data_provider.get(['image', 'label'])
  image = inception_preprocessing.preprocess_image(
        image_raw,
        height,
        width,
        is_training=is_training)
  images, labels = tf.train.batch(
        [image, label],
        batch_size=batch_size,
        num_threads=4,
        capacity=5 * batch_size)
  return images, labels

模型构建部分：

import tensorflow as tf
slim = tf.contrib.slim
...
def build_model(inputs, num_classes, is_training=True, scope='vgg_16'):
  """
  构建VGG16模型
  """
  with tf.variable_scope(scope, 'vgg_16', [inputs]) as sc:
    end_points_collection = sc.name + '_end_points'
    with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.max_pool2d],
                        outputs_collections=end_points_collection):
      net = slim.repeat(inputs, 2, slim.conv2d, 64, [3, 3], scope='conv1')
      net = slim.max_pool2d(net, [2, 2], scope='pool1')
      ...
      net = slim.repeat(net, 2, slim.conv2d, 512, [3, 3], scope='conv5')
      net = slim.max_pool2d(net, [2, 2], scope='pool5')
      net = slim.conv2d(net, 4096, [7, 7], padding='VALID', scope='fc6')
      net = slim.dropout(net, 0.5, is_training=is_training,
                         scope='dropout6')
      net = slim.conv2d(net, 4096, [1, 1], scope='fc7')
      net = slim.dropout(net, 0.5, is_training=is_training,
                         scope='dropout7')
      net = slim.conv2d(net, num_classes, [1, 1],
                        activation_fn=None,
                        normalizer_fn=None,
                        scope='fc8')
      end_points = slim.utils.convert_collection_to_dict(end_points_collection)
      return net, end_points

训练部分：

import tensorflow as tf
slim = tf.contrib.slim
...
def run_training(dataset_name, train_dir, dataset_dir, num_classes=2, batch_size=32, num_epochs=10, initial_learning_rate=0.0001):
  """
  训练模型
  """
  with tf.Graph().as_default():
    tf.logging.set_verbosity(tf.logging.INFO)

    # 获取数据集
    dataset = get_dataset(dataset_name, 'train', dataset_dir, '%s_*.tfrecord')
    images, labels = load_batch(dataset, batch_size=batch_size, height=224, width=224, is_training=True)

    # 构建网络
    logits, end_points = build_model(images, num_classes=num_classes, is_training=True)

    # 定义损失函数
    one_hot_labels = slim.one_hot_encoding(labels, num_classes)
    slim.losses.softmax_cross_entropy(logits=logits, onehot_labels=one_hot_labels)
    total_loss = slim.losses.get_total_loss()

    # 定义优化器
    global_step = tf.train.get_or_create_global_step()
    learning_rate = tf.train.exponential_decay(
          initial_learning_rate,
          global_step,
          decay_steps=1000,
          decay_rate=0.96,
          staircase=True)
    optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
    train_op = slim.learning.create_train_op(total_loss, optimizer, global_step=global_step)

    # 进行训练
    saver = tf.train.Saver(tf.global_variables())
    slim.learning.train(
          train_op,
          train_dir,
          log_every_n_steps=1,
          save_summaries_secs=20,
          saver=saver,
          number_of_steps=num_epochs * dataset.num_samples // batch_size,
          save_interval_secs=120)

if __name__ == '__main__':
  run_training('cats_vs_dogs', '/tmp/cats_vs_dogs', '/path/to/dataset')

多个graph训练VGAE代码示例

下面是一个多个graph训练VGAE的代码示例：

import dgl
import torch
import torch.nn.functional as F
from dgl.nn import GraphConv
from dgl.data import MiniGCDataset

class VGAE(torch.nn.Module):
    def __init__(self, in_feats, hidden_size):
        super(VGAE, self).__init__()
        self.conv1 = GraphConv(in_feats, hidden_size)
        self.conv2 = GraphConv(hidden_size, hidden_size)

        self.mean_fc = torch.nn.Linear(hidden_size, hidden_size)
        self.logstd_fc = torch.nn.Linear(hidden_size, hidden_size)

    def forward(self, g):
        h = g.ndata['feat']
        h = F.relu(self.conv1(g, h))
        h = F.relu(self.conv2(g, h))

        mean = self.mean_fc(h)
        logstd = self.logstd_fc(h)

        return mean, logstd

def train(dataset):
    model = VGAE(10, 5)
    optimizer = torch.optim.Adam(model.parameters(), lr=0.01)

    for epoch in range(10):
        for graph, _ in dataset:
            optimizer.zero_grad()
            h = graph.ndata['feat']
            mean, logstd = model(graph)
            z = mean + torch.randn([graph.num_nodes(), 5]) * torch.exp(logstd)
            recon = torch.sigmoid(torch.matmul(z, z.t()))
            loss = -torch.mean(
                torch.sum(graph.adjacency_matrix().to_dense() * torch.log(recon)
                          + (1 - graph.adjacency_matrix().to_dense()) * torch.log(1 - recon), dim=1))
            kl_loss = -0.5 * torch.mean(torch.sum(1 + 2 * logstd - mean.pow(2) - torch.exp(logstd).pow(2), dim=1))
            loss += kl_loss
            loss.backward()
            optimizer.step()

if __name__ == '__main__':
    dataset = MiniGCDataset(80, 10, 20)
    graphs = []
    for graph, _ in dataset:
        graphs.append(graph)
    batched_graph = dgl.batch(graphs)
    train(batched_graph)

在这个示例中，我们首先定义了一个VGAE模型，然后在`train()`函数中，我们对数据集中的每个图进行了训练。注意，这里我们将所有图合并成一个大图，然后在训练时使用这个大图进行训练。这里的损失函数包括重构误差和KL散度，用于衡量生成的图与原始图之间的相似度。最后，我们将所有图合并到一个大图中，然后使用`train()`函数进行训练。