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