protected virtual bool RunWithBuiltGraph(Session session)
{
Console.WriteLine("Building dataset...");
var(x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit);
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
ITextClassificationModel model = null;
switch (model_name) // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn
{
case "word_cnn":
case "char_cnn":
case "word_rnn":
case "att_rnn":
case "rcnn":
throw new NotImplementedException();
break;
case "vd_cnn":
model = new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
break;
}
// todo train the model
return(false);
}
public void Run()
{
download_dbpedia();
Console.WriteLine("Building dataset...");
var(x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN);
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15);
}
public void Run()
{
download_dbpedia();
Console.WriteLine("Building dataset...");
var(x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN);
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
with(tf.Session(), sess =>
{
new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
});
}
public bool Run()
{
PrepareData();
Console.WriteLine("Building dataset...");
var(x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN, DataLimit);
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
return(with(tf.Session(), sess =>
{
new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
return false;
}));
}
protected virtual bool RunWithImportedGraph(Session sess)
{
var graph = tf.Graph().as_default();
Console.WriteLine("Building dataset...");
var(x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit);
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
var meta_file = model_name + "_untrained.meta";
tf.train.import_meta_graph(Path.Join("graph", meta_file));
//sess.run(tf.global_variables_initializer()); // not necessary here, has already been done before meta graph export
var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS);
var num_batches_per_epoch = (len(train_x) - 1); // BATCH_SIZE + 1
double max_accuracy = 0;
Tensor is_training = graph.get_operation_by_name("is_training");
Tensor model_x = graph.get_operation_by_name("x");
Tensor model_y = graph.get_operation_by_name("y");
Tensor loss = graph.get_operation_by_name("Variable");
Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy");
foreach (var(x_batch, y_batch) in train_batches)
{
var train_feed_dict = new Hashtable
{
[model_x] = x_batch,
[model_y] = y_batch,
[is_training] = true,
};
//_, step, loss = sess.run([model.optimizer, model.global_step, model.loss], feed_dict = train_feed_dict)
}
return(false);
}
protected virtual bool RunWithImportedGraph(Session sess, Graph graph)
{
var stopwatch = Stopwatch.StartNew();
Console.WriteLine("Building dataset...");
var path = UseSubset ? SUBSET_PATH : TRAIN_PATH;
var(x, y, alphabet_size) = DataHelpers.build_char_dataset(path, model_name, CHAR_MAX_LEN, DataLimit = null, shuffle: !UseSubset);
Console.WriteLine("\tDONE ");
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
Console.WriteLine("Training set size: " + train_x.len);
Console.WriteLine("Test set size: " + valid_x.len);
Console.WriteLine("Import graph...");
var meta_file = model_name + ".meta";
tf.train.import_meta_graph(Path.Join("graph", meta_file));
Console.WriteLine("\tDONE " + stopwatch.Elapsed);
sess.run(tf.global_variables_initializer());
var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS);
var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1;
double max_accuracy = 0;
Tensor is_training = graph.get_tensor_by_name("is_training:0");
Tensor model_x = graph.get_tensor_by_name("x:0");
Tensor model_y = graph.get_tensor_by_name("y:0");
Tensor loss = graph.get_tensor_by_name("loss/value:0");
Tensor optimizer = graph.get_tensor_by_name("loss/optimizer:0");
Tensor global_step = graph.get_tensor_by_name("global_step:0");
Tensor accuracy = graph.get_tensor_by_name("accuracy/value:0");
stopwatch = Stopwatch.StartNew();
int i = 0;
foreach (var(x_batch, y_batch, total) in train_batches)
{
i++;
var train_feed_dict = new FeedDict
{
[model_x] = x_batch,
[model_y] = y_batch,
[is_training] = true,
};
//Console.WriteLine("x: " + x_batch.ToString() + "\n");
//Console.WriteLine("y: " + y_batch.ToString());
// original python:
//_, step, loss = sess.run([model.optimizer, model.global_step, model.loss], feed_dict = train_feed_dict)
var result = sess.run(new ITensorOrOperation[] { optimizer, global_step, loss }, train_feed_dict);
loss_value = result[2];
var step = (int)result[1];
if (step % 10 == 0 || step < 10)
{
var estimate = TimeSpan.FromSeconds((stopwatch.Elapsed.TotalSeconds / i) * total);
Console.WriteLine($"Training on batch {i}/{total}. Estimated training time: {estimate}");
Console.WriteLine($"Step {step} loss: {loss_value}");
}
if (step % 100 == 0)
{
// # Test accuracy with validation data for each epoch.
var valid_batches = batch_iter(valid_x, valid_y, BATCH_SIZE, 1);
var(sum_accuracy, cnt) = (0.0f, 0);
foreach (var(valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches)
{
var valid_feed_dict = new FeedDict
{
[model_x] = valid_x_batch,
[model_y] = valid_y_batch,
[is_training] = false
};
var result1 = sess.run(accuracy, valid_feed_dict);
float accuracy_value = result1;
sum_accuracy += accuracy_value;
cnt += 1;
}
var valid_accuracy = sum_accuracy / cnt;
print($"\nValidation Accuracy = {valid_accuracy}\n");
// # Save model
// if valid_accuracy > max_accuracy:
// max_accuracy = valid_accuracy
// saver.save(sess, "{0}/{1}.ckpt".format(args.model, args.model), global_step = step)
// print("Model is saved.\n")
}
}
return(false);
}
protected virtual bool RunWithImportedGraph(Session sess, Graph graph)
{
Console.WriteLine("Building dataset...");
var(x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit);
Console.WriteLine("\tDONE");
var(train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
Console.WriteLine("Import graph...");
var meta_file = model_name + ".meta";
tf.train.import_meta_graph(Path.Join("graph", meta_file));
Console.WriteLine("\tDONE");
// definitely necessary, otherwize will get the exception of "use uninitialized variable"
sess.run(tf.global_variables_initializer());
var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS);
var num_batches_per_epoch = (len(train_x) - 1); // BATCH_SIZE + 1
double max_accuracy = 0;
Tensor is_training = graph.get_operation_by_name("is_training");
Tensor model_x = graph.get_operation_by_name("x");
Tensor model_y = graph.get_operation_by_name("y");
Tensor loss = graph.get_operation_by_name("loss/loss");
//var optimizer_nodes = graph._nodes_by_name.Keys.Where(key => key.Contains("optimizer")).ToArray();
Tensor optimizer = graph.get_operation_by_name("loss/optimizer");
Tensor global_step = graph.get_operation_by_name("global_step");
Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy");
int i = 0;
foreach (var(x_batch, y_batch) in train_batches)
{
i++;
Console.WriteLine("Training on batch " + i);
var train_feed_dict = new Hashtable
{
[model_x] = x_batch,
[model_y] = y_batch,
[is_training] = true,
};
// original python:
//_, step, loss = sess.run([model.optimizer, model.global_step, model.loss], feed_dict = train_feed_dict)
var result = sess.run(new Tensor[] { optimizer, global_step, loss }, train_feed_dict);
// exception here, loss value seems like a float[]
//loss_value = result[2];
var step = result[1];
if (step % 10 == 0)
{
Console.WriteLine($"Step {step} loss: {result[2]}");
}
if (step % 100 == 0)
{
continue;
// # Test accuracy with validation data for each epoch.
var valid_batches = batch_iter(valid_x, valid_y, BATCH_SIZE, 1);
var(sum_accuracy, cnt) = (0, 0);
foreach (var(valid_x_batch, valid_y_batch) in valid_batches)
{
// valid_feed_dict = {
// model.x: valid_x_batch,
// model.y: valid_y_batch,
// model.is_training: False
// }
// accuracy = sess.run(model.accuracy, feed_dict = valid_feed_dict)
// sum_accuracy += accuracy
// cnt += 1
}
// valid_accuracy = sum_accuracy / cnt
// print("\nValidation Accuracy = {1}\n".format(step // num_batches_per_epoch, sum_accuracy / cnt))
// # Save model
// if valid_accuracy > max_accuracy:
// max_accuracy = valid_accuracy
// saver.save(sess, "{0}/{1}.ckpt".format(args.model, args.model), global_step = step)
// print("Model is saved.\n")
}
}
return(false);
}
