public override void Train()
{
// Number of training iterations in each epoch
var num_tr_iter = mnist.Train.Labels.shape[0] / batch_size;
var init = tf.global_variables_initializer();
sess.run(init);
float loss_val = 100.0f;
float accuracy_val = 0f;
var sw = new Stopwatch();
sw.Start();
foreach (var epoch in range(epochs))
{
print($"Training epoch: {epoch + 1}");
// Randomly shuffle the training data at the beginning of each epoch
var(x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels);
foreach (var iteration in range(num_tr_iter))
{
var start = iteration * batch_size;
var end = (iteration + 1) * batch_size;
var(x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
// Run optimization op (backprop)
sess.run(optimizer, (x, x_batch), (y, y_batch));
if (iteration % display_freq == 0)
{
// Calculate and display the batch loss and accuracy
(loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_batch), (y, y_batch));
print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms");
sw.Restart();
}
}
// Run validation after every epoch
(loss_val, accuracy_val) = sess.run((loss, accuracy), (x, mnist.Validation.Data), (y, mnist.Validation.Labels));
print("---------------------------------------------------------");
print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
print("---------------------------------------------------------");
}
}
public void Train(Session sess)
{
// Number of training iterations in each epoch
var num_tr_iter = mnist.Train.Labels.len / batch_size;
var init = tf.global_variables_initializer();
sess.run(init);
float loss_val = 100.0f;
float accuracy_val = 0f;
foreach (var epoch in range(epochs))
{
print($"Training epoch: {epoch + 1}");
// Randomly shuffle the training data at the beginning of each epoch
var(x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels);
foreach (var iteration in range(num_tr_iter))
{
var start = iteration * batch_size;
var end = (iteration + 1) * batch_size;
var(x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
// Run optimization op (backprop)
sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
if (iteration % display_freq == 0)
{
// Calculate and display the batch loss and accuracy
var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
loss_val = result[0];
accuracy_val = result[1];
print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
}
}
// Run validation after every epoch
var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, mnist.Validation.Data), new FeedItem(y, mnist.Validation.Labels));
loss_val = results1[0];
accuracy_val = results1[1];
print("---------------------------------------------------------");
print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
print("---------------------------------------------------------");
}
}
public override void Train()
{
float loss_val = 100.0f;
float accuracy_val = 0f;
// Number of training iterations in each epoch
var n_batches = y_train.shape[0] / batch_size;
var init = tf.global_variables_initializer();
sess.run(init);
foreach (var epoch in range(n_epochs))
{
print($"Training epoch: {epoch + 1}");
// Randomly shuffle the training data at the beginning of each epoch
(x_train, y_train) = mnist.Randomize(x_train, y_train);
foreach (var iteration in range(n_batches))
{
var start = iteration * batch_size;
var end = (iteration + 1) * batch_size;
var(X_train, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
X_train = X_train.reshape(-1, n_steps, n_inputs);
y_batch = np.argmax(y_batch, axis: 1);
// Run optimization op (backprop)
sess.run(optimizer, new FeedItem(X, X_train), new FeedItem(y, y_batch));
if (iteration % display_freq == 0)
{
// Calculate and display the batch loss and accuracy
(loss_val, accuracy_val) = sess.run((loss, accuracy), (X, X_train), (y, y_batch));
print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
}
}
// Run validation after every epoch
(loss_val, accuracy_val) = sess.run((loss, accuracy), (X, x_valid), (y, y_valid));
print("---------------------------------------------------------");
print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
print("---------------------------------------------------------");
}
}