def run_experiment(algo, average_gradients, batch_size, iterations, verbose):
batch_size = batch_size
tf.reset_default_graph()
net = ConvNet()
validation_batch = mnist.test.images
val_count = validation_batch.shape[0]
validation_batch = np.reshape(validation_batch, (val_count, 28, 28, 1))
validation_labels = mnist.test.labels
net.setup_train(algo, average_gradients=average_gradients)
training_log = []
listloss = []
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for i in range(iterations):
batch = mnist.train.next_batch(batch_size)
input_batch = np.reshape(batch[0], (batch_size, 28, 28, 1))
loss = net.train(sess, input_batch, batch[1])
listloss.append(loss)
if (i + 1) % 100 == 0:
accuracy = net.evaluate(sess, validation_batch,
validation_labels)
training_log.append((accuracy, i + 1))
if verbose:
print('[{:d}/{:d}] loss: {:.3g}, accuracy: {:.3g}%'.format(
i + 1, iterations, loss, accuracy))
accuracy = net.evaluate(sess, validation_batch, validation_labels)
training_log.append((accuracy, iterations))
best = sorted(training_log, key=lambda x: x[0], reverse=True)[0]
print('Training finished. Best accuracy: {:.3g} at iteration {:d}.'.
format(best[0], best[1]))
return listloss
