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65 lines
2.3 KiB
65 lines
2.3 KiB
import paddle.v2.framework.layers as layers
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from paddle.v2.framework.framework import Program, g_program
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import paddle.v2.framework.core as core
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import unittest
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class TestBook(unittest.TestCase):
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def test_fit_a_line(self):
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program = Program()
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x = layers.data(
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name='x', shape=[13], data_type='float32', program=program)
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y_predict = layers.fc(input=x, size=1, act=None, program=program)
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y = layers.data(
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name='y', shape=[1], data_type='float32', program=program)
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cost = layers.square_error_cost(
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input=y_predict, label=y, program=program)
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avg_cost = layers.mean(x=cost, program=program)
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self.assertIsNotNone(avg_cost)
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program.append_backward(avg_cost, set())
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print str(program)
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def test_recognize_digits_mlp(self):
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program = Program()
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# Change g_program, so the rest layers use `g_program`
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images = layers.data(
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name='pixel', shape=[784], data_type='float32', program=program)
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label = layers.data(
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name='label', shape=[1], data_type='int32', program=program)
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hidden1 = layers.fc(input=images, size=128, act='relu', program=program)
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hidden2 = layers.fc(input=hidden1, size=64, act='relu', program=program)
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predict = layers.fc(input=hidden2,
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size=10,
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act='softmax',
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program=program)
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cost = layers.cross_entropy(input=predict, label=label, program=program)
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avg_cost = layers.mean(x=cost, program=program)
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self.assertIsNotNone(avg_cost)
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# print str(program)
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def test_simple_conv2d(self):
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pd = core.ProgramDesc.__create_program_desc__()
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program = Program(desc=pd)
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images = data_layer(
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name='pixel', shape=[3, 48, 48], data_type='int32', program=program)
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conv2d_layer(
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input=images, num_filters=3, filter_size=[4, 4], program=program)
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# print str(program)
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def test_simple_conv2d(self):
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program = Program()
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images = layers.data(
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name='pixel', shape=[3, 48, 48], data_type='int32', program=program)
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layers.conv2d(
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input=images, num_filters=3, filter_size=[4, 4], program=program)
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print str(program)
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if __name__ == '__main__':
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unittest.main()
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