import unittest import paddle.v2.fluid.layers as layers import paddle.v2.fluid.nets as nets from paddle.v2.fluid.framework import Program class TestBook(unittest.TestCase): def test_fit_a_line(self): program = Program() x = layers.data( name='x', shape=[13], dtype='float32', main_program=program) y_predict = layers.fc(input=x, size=1, act=None, main_program=program) y = layers.data( name='y', shape=[1], dtype='float32', main_program=program) cost = layers.square_error_cost( input=y_predict, label=y, main_program=program) avg_cost = layers.mean(x=cost, main_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], dtype='float32', main_program=program) label = layers.data( name='label', shape=[1], dtype='int32', main_program=program) hidden1 = layers.fc(input=images, size=128, act='relu', main_program=program) hidden2 = layers.fc(input=hidden1, size=64, act='relu', main_program=program) predict = layers.fc(input=hidden2, size=10, act='softmax', main_program=program) cost = layers.cross_entropy( input=predict, label=label, main_program=program) avg_cost = layers.mean(x=cost, main_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], dtype='int32', main_program=program) layers.conv2d( input=images, num_filters=3, filter_size=[4, 4], main_program=program) print str(program) def test_recognize_digits_conv(self): program = Program() images = layers.data( name='pixel', shape=[1, 28, 28], dtype='float32', main_program=program) label = layers.data( name='label', shape=[1], dtype='int32', main_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", main_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", main_program=program) predict = layers.fc(input=conv_pool_2, size=10, act="softmax", main_program=program) cost = layers.cross_entropy( input=predict, label=label, main_program=program) avg_cost = layers.mean(x=cost, main_program=program) program.append_backward(avg_cost) print str(program) def test_word_embedding(self): program = Program() dict_size = 10000 embed_size = 32 first_word = layers.data( name='firstw', shape=[1], dtype='int64', main_program=program) second_word = layers.data( name='secondw', shape=[1], dtype='int64', main_program=program) third_word = layers.data( name='thirdw', shape=[1], dtype='int64', main_program=program) forth_word = layers.data( name='forthw', shape=[1], dtype='int64', main_program=program) next_word = layers.data( name='nextw', shape=[1], dtype='int64', main_program=program) embed_first = layers.embedding( input=first_word, size=[dict_size, embed_size], dtype='float32', param_attr={'name': 'shared_w'}, main_program=program) embed_second = layers.embedding( input=second_word, size=[dict_size, embed_size], dtype='float32', param_attr={'name': 'shared_w'}, main_program=program) embed_third = layers.embedding( input=third_word, size=[dict_size, embed_size], dtype='float32', param_attr={'name': 'shared_w'}, main_program=program) embed_forth = layers.embedding( input=forth_word, size=[dict_size, embed_size], dtype='float32', param_attr={'name': 'shared_w'}, main_program=program) concat_embed = layers.concat( input=[embed_first, embed_second, embed_third, embed_forth], axis=1, main_program=program) hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid', main_program=program) predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax', main_program=program) cost = layers.cross_entropy( input=predict_word, label=next_word, main_program=program) avg_cost = layers.mean(x=cost, main_program=program) self.assertIsNotNone(avg_cost) print str(program) def test_linear_chain_crf(self): program = Program() # Change g_program, so the rest layers use `g_program` images = layers.data( name='pixel', shape=[784], dtype='float32', main_program=program) label = layers.data( name='label', shape=[1], dtype='int32', main_program=program) hidden = layers.fc(input=images, size=128, main_program=program) crf = layers.linear_chain_crf( input=hidden, label=label, main_program=program) print str(program) if __name__ == '__main__': unittest.main()