def __init__(self,
channel_1_num,
channel_2_num,
conv_size,
hidden_size,
pool_size,
learning_rate,
x_dim=784,
y_dim=10):
self.channel_1_num = channel_1_num
self.channel_2_num = channel_2_num
self.conv_size = nni.choice(2, 3, 5, 7, name='self.conv_size')
self.hidden_size = nni.choice(124, 512, 1024, name='self.hidden_size')
self.pool_size = pool_size
self.learning_rate = nni.uniform(0.0001,
0.1,
name='self.learning_rate')
self.x_dim = x_dim
self.y_dim = y_dim
self.images = tf.placeholder(tf.float32, [None, self.x_dim],
name='input_x')
self.labels = tf.placeholder(tf.float32, [None, self.y_dim],
name='input_y')
self.keep_prob = tf.placeholder(tf.float32, name='keep_prob')
self.train_step = None
self.accuracy = None
def __init__(self,
channel_1_num=32,
channel_2_num=64,
conv_size=5,
hidden_size=1024,
pool_size=2,
learning_rate=0.0001,
x_dim=784,
y_dim=10):
self.channel_1_num = channel_1_num
self.channel_2_num = channel_2_num
self.conv_size = nni.choice({
2: 2,
3: 3,
5: 5,
7: 7
},
name='self.conv_size')
self.hidden_size = nni.choice({
124: 124,
512: 512,
1024: 1024
},
name='self.hidden_size')
self.pool_size = pool_size
self.learning_rate = nni.randint(2, 3, 5, name='self.learning_rate')
self.x_dim = x_dim
self.y_dim = y_dim
def main():
data_dir = '/tmp/tensorflow/mnist/input_data'
mnist = input_data.read_data_sets(data_dir, one_hot=True)
logger.debug('Mnist download data down.')
mnist_network = MnistNetwork()
mnist_network.build_network()
logger.debug('Mnist build network done.')
graph_location = tempfile.mkdtemp()
logger.debug('Saving graph to: %s' % graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
test_acc = 0.0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
batch_num = 200
for i in range(batch_num):
batch_size = nni.choice(50, 250, 500, name='batch_size')
batch = mnist.train.next_batch(batch_size)
dropout_rate = nni.choice(1, 5, name='dropout_rate')
mnist_network.train_step.run(feed_dict={mnist_network.x: batch[
0], mnist_network.y: batch[1], mnist_network.keep_prob:
dropout_rate})
if i % 100 == 0:
test_acc = mnist_network.accuracy.eval(feed_dict={
mnist_network.x: mnist.test.images, mnist_network.y:
mnist.test.labels, mnist_network.keep_prob: 1.0})
nni.report_intermediate_result(test_acc)
test_acc = mnist_network.accuracy.eval(feed_dict={mnist_network.x:
mnist.test.images, mnist_network.y: mnist.test.labels,
mnist_network.keep_prob: 1.0})
nni.report_final_result(test_acc)
def main(params):
'''
Main function, build mnist network, run and send result to NNI.
'''
# Import data
mnist = download_mnist_retry(params['data_dir'])
print('Mnist download data done.')
logger.debug('Mnist download data done.')
# Create the model
# Build the graph for the deep net
mnist_network = MnistNetwork(channel_1_num=params['channel_1_num'],
channel_2_num=params['channel_2_num'],
pool_size=params['pool_size'])
mnist_network.build_network()
logger.debug('Mnist build network done.')
# Write log
graph_location = tempfile.mkdtemp()
logger.debug('Saving graph to: %s', graph_location)
train_writer = tf.summary.FileWriter(graph_location)
train_writer.add_graph(tf.get_default_graph())
test_acc = 0.0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
batch_num = nni.choice(50, 250, 500, name='batch_num')
for i in range(batch_num):
batch = mnist.train.next_batch(batch_num)
dropout_rate = nni.choice(1, 5, name='dropout_rate')
mnist_network.train_step.run(
feed_dict={
mnist_network.images: batch[0],
mnist_network.labels: batch[1],
mnist_network.keep_prob: dropout_rate
})
if i % 100 == 0:
test_acc = mnist_network.accuracy.eval(
feed_dict={
mnist_network.images: mnist.test.images,
mnist_network.labels: mnist.test.labels,
mnist_network.keep_prob: 1.0
})
nni.report_intermediate_result(test_acc)
logger.debug('test accuracy %g', test_acc)
logger.debug('Pipe send intermediate result done.')
test_acc = mnist_network.accuracy.eval(
feed_dict={
mnist_network.images: mnist.test.images,
mnist_network.labels: mnist.test.labels,
mnist_network.keep_prob: 1.0
})
nni.report_final_result(test_acc)
logger.debug('Final result is %g', test_acc)
logger.debug('Send final result done.')
def test_specified_name(self):
val = nni.choice({'a': 'a', '3*2+1': 3*2+1, '[1, 2]': [1, 2], '{"a", 2}': {"a", 2}}, name = 'choice1', key='test_smartparam/choice1/choice')
self.assertEqual(val, 'a')
val = nni.choice({'a': 'a', '3*2+1': 3*2+1, '[1, 2]': [1, 2], '{"a", 2}': {"a", 2}}, name = 'choice2', key='test_smartparam/choice2/choice')
self.assertEqual(val, 7)
val = nni.choice({'a': 'a', '3*2+1': 3*2+1, '[1, 2]': [1, 2], '{"a", 2}': {"a", 2}}, name = 'choice3', key='test_smartparam/choice3/choice')
self.assertEqual(val, [1, 2])
val = nni.choice({'a': 'a', '3*2+1': 3*2+1, '[1, 2]': [1, 2], '{"a", 2}': {"a", 2}}, name = 'choice4', key='test_smartparam/choice4/choice')
self.assertEqual(val, {"a", 2})
def test_specified_name(self):
val = nni.choice(
{
'a': 'a',
'3*2+1': 3 * 2 + 1,
'[1, 2]': [1, 2],
'{"a", 2}': {"a", 2}
},
name='choice1')
self.assertEqual(val, 'a')
val = nni.choice(
{
'a': 'a',
'3*2+1': 3 * 2 + 1,
'[1, 2]': [1, 2],
'{"a", 2}': {"a", 2}
},
name='choice2')
self.assertEqual(val, 7)
val = nni.choice(
{
'a': 'a',
'3*2+1': 3 * 2 + 1,
'[1, 2]': [1, 2],
'{"a", 2}': {"a", 2}
},
name='choice3')
self.assertEqual(val, [1, 2])
val = nni.choice(
{
'a': 'a',
'3*2+1': 3 * 2 + 1,
'[1, 2]': [1, 2],
'{"a", 2}': {"a", 2}
},
name='choice4')
self.assertEqual(val, {"a", 2})
def test_specified_name(self):
val = nni.choice('a', 'b', 'c', name='choice1')
self.assertEqual(val, 'c')
import nni
def max_pool(k):
pass
h_conv1 = 1
conv_size = nni.choice(2, 3, 5, 7, name='conv_size')
h_pool1 = nni.function_choice(lambda: max_pool(h_conv1),
lambda: avg_pool(h_conv2, h_conv3),
name='max_pool')
test_acc = 1
nni.report_intermediate_result(test_acc)
test_acc = 2
nni.report_final_result(test_acc)
import nni
def max_pool(k):
pass
h_conv1 = 1
nni.choice({'foo': foo, 'bar': bar})(1)
conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name='conv_size')
abc = nni.choice({'2': '2', 3: 3, '(5 * 6)': 5 * 6, 7: 7}, name='abc')
h_pool1 = nni.function_choice({
'max_pool': lambda: max_pool(h_conv1),
'h_conv1': lambda: h_conv1,
'avg_pool': lambda: avg_pool(h_conv2, h_conv3)
})
h_pool1 = nni.function_choice(
{
'max_pool(h_conv1)': lambda: max_pool(h_conv1),
'avg_pool(h_conv2, h_conv3)': lambda: avg_pool(h_conv2, h_conv3)
},
name='max_pool')
h_pool2 = nni.function_choice(
{
'max_poo(h_conv1)': lambda: max_poo(h_conv1),
'(2 * 3 + 4)': lambda: 2 * 3 + 4,
'(lambda x: 1 + x)': lambda: lambda x: 1 + x
},
name='max_poo')
tmp = nni.qlognormal(1.2, 3, 4.5)
test_acc = 1
import nni
def max_pool(k):
pass
h_conv1 = 1
conv_size = nni.choice({2: 2, 3: 3, 5: 5, 7: 7}, name='conv_size')
abc = nni.choice(
{
'2': '2',
3: 3,
'(5 * 6)': 5 * 6,
"{(1): 2, '3': 4}": {
(1): 2,
'3': 4
},
'[1, 2, 3]': [1, 2, 3]
},
name='abc')
h_pool1 = nni.function_choice(
{
'max_pool(h_conv1)': lambda: max_pool(h_conv1),
'avg_pool(h_conv2, h_conv3)': lambda: avg_pool(h_conv2, h_conv3)
},
name='max_pool')
h_pool2 = nni.function_choice(
{
'max_poo(h_conv1)': lambda: max_poo(h_conv1),
'(2 * 3 + 4)': lambda: 2 * 3 + 4,
