You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
94 lines
3.2 KiB
94 lines
3.2 KiB
import paddle.v2.framework.layers as layers
|
|
import paddle.v2.framework.nets as nets
|
|
from paddle.v2.framework.framework import Program, g_program
|
|
import paddle.v2.framework.core as core
|
|
import unittest
|
|
|
|
|
|
class TestBook(unittest.TestCase):
|
|
def test_fit_a_line(self):
|
|
program = Program()
|
|
x = layers.data(
|
|
name='x', shape=[13], data_type='float32', program=program)
|
|
y_predict = layers.fc(input=x, size=1, act=None, program=program)
|
|
|
|
y = layers.data(
|
|
name='y', shape=[1], data_type='float32', program=program)
|
|
cost = layers.square_error_cost(
|
|
input=y_predict, label=y, program=program)
|
|
|
|
avg_cost = layers.mean(x=cost, program=program)
|
|
self.assertIsNotNone(avg_cost)
|
|
program.append_backward(avg_cost)
|
|
print str(program)
|
|
|
|
def test_recognize_digits_mlp(self):
|
|
program = Program()
|
|
|
|
# Change g_program, so the rest layers use `g_program`
|
|
images = layers.data(
|
|
name='pixel', shape=[784], data_type='float32', program=program)
|
|
label = layers.data(
|
|
name='label', shape=[1], data_type='int32', program=program)
|
|
hidden1 = layers.fc(input=images, size=128, act='relu', program=program)
|
|
hidden2 = layers.fc(input=hidden1, size=64, act='relu', program=program)
|
|
predict = layers.fc(input=hidden2,
|
|
size=10,
|
|
act='softmax',
|
|
program=program)
|
|
cost = layers.cross_entropy(input=predict, label=label, program=program)
|
|
avg_cost = layers.mean(x=cost, program=program)
|
|
self.assertIsNotNone(avg_cost)
|
|
print str(program)
|
|
|
|
def test_simple_conv2d(self):
|
|
program = Program()
|
|
images = layers.data(
|
|
name='pixel', shape=[3, 48, 48], data_type='int32', program=program)
|
|
layers.conv2d(
|
|
input=images, num_filters=3, filter_size=[4, 4], program=program)
|
|
|
|
print str(program)
|
|
|
|
def test_recognize_digits_conv(self):
|
|
program = Program()
|
|
|
|
images = layers.data(
|
|
name='pixel',
|
|
shape=[1, 28, 28],
|
|
data_type='float32',
|
|
program=program)
|
|
label = layers.data(
|
|
name='label', shape=[1], data_type='int32', program=program)
|
|
conv_pool_1 = nets.simple_img_conv_pool(
|
|
input=images,
|
|
filter_size=5,
|
|
num_filters=2,
|
|
pool_size=2,
|
|
pool_stride=2,
|
|
act="relu",
|
|
program=program)
|
|
conv_pool_2 = nets.simple_img_conv_pool(
|
|
input=conv_pool_1,
|
|
filter_size=5,
|
|
num_filters=4,
|
|
pool_size=2,
|
|
pool_stride=2,
|
|
act="relu",
|
|
program=program)
|
|
|
|
predict = layers.fc(input=conv_pool_2,
|
|
size=10,
|
|
act="softmax",
|
|
program=program)
|
|
cost = layers.cross_entropy(input=predict, label=label, program=program)
|
|
avg_cost = layers.mean(x=cost, program=program)
|
|
|
|
program.append_backward(avg_cost)
|
|
|
|
print str(program)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
unittest.main()
|