def run_experiment(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(average_gradients=average_gradients)
training_log = []
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])
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 best[0]
def experiment(threshold,
iterations,
train_loss,
n_conv,
optimizer,
batch_size=1,
batch_norm=False,
learning_rate=1e-3,
summary_dir=None):
model = ConvNet(filters=4,
n_conv=n_conv,
train_loss=train_loss,
batch_norm=batch_norm,
optimizer=optimizer,
learning_rate=learning_rate,
summary_dir=summary_dir)
print('train_loss:', train_loss.value, 'optimizer:', optimizer.value,
'n_conv:', n_conv, 'batch_norm:', batch_norm, 'batch_size:',
batch_size, 'learning_rate:', learning_rate)
ret = dict()
val_input_batch, val_output_batch = get_data(threshold, 100, verbose=True)
best_accuracy = (0.0, 0)
for i in tqdm(range(iterations)):
input_batch, output_batch = get_data(threshold, batch_size)
out = model.train(input_batch, output_batch)
if i == 0:
for k in out.keys():
ret[k] = []
ret['accuracy'] = []
for k, v in out.items():
ret[k].append(v)
if i % 250 == 0:
accuracy = model.accuracy(val_input_batch, val_output_batch)
if accuracy > best_accuracy[0]:
best_accuracy = (accuracy, i)
ret['accuracy'].append((i, accuracy))
#print('[%d] accuracy: %.3g' % (i, accuracy))
print('Best accuracy %.3g at iteration %d.' % best_accuracy)
return ret