from __future__ import print_function import unittest import paddle.v2.fluid.layers as layers import paddle.v2.fluid.nets as nets from paddle.v2.fluid.framework import Program, program_guard from paddle.v2.fluid.param_attr import ParamAttr class TestBook(unittest.TestCase): def test_fit_a_line(self): program = Program() with program_guard(program, startup_program=Program()): x = layers.data(name='x', shape=[13], dtype='float32') y_predict = layers.fc(input=x, size=1, act=None) y = layers.data(name='y', shape=[1], dtype='float32') cost = layers.square_error_cost(input=y_predict, label=y) avg_cost = layers.mean(x=cost) self.assertIsNotNone(avg_cost) program.append_backward(avg_cost) print(str(program)) def test_recognize_digits_mlp(self): program = Program() with program_guard(program, startup_program=Program()): # Change g_program, so the rest layers use `g_program` images = layers.data(name='pixel', shape=[784], dtype='float32') label = layers.data(name='label', shape=[1], dtype='int32') hidden1 = layers.fc(input=images, size=128, act='relu') hidden2 = layers.fc(input=hidden1, size=64, act='relu') predict = layers.fc(input=[hidden2, hidden1], size=10, act='softmax', param_attr=["sftmax.w1", "sftmax.w2"]) cost = layers.cross_entropy(input=predict, label=label) avg_cost = layers.mean(x=cost) self.assertIsNotNone(avg_cost) print(str(program)) def test_simple_conv2d(self): program = Program() with program_guard(program, startup_program=Program()): images = layers.data(name='pixel', shape=[3, 48, 48], dtype='int32') layers.conv2d(input=images, num_filters=3, filter_size=[4, 4]) print(str(program)) def test_conv2d_transpose(self): program = Program() with program_guard(program): img = layers.data(name='pixel', shape=[3, 2, 2], dtype='float32') layers.conv2d_transpose(input=img, num_filters=10, output_size=28) print(str(program)) def test_recognize_digits_conv(self): program = Program() with program_guard(program, startup_program=Program()): images = layers.data( name='pixel', shape=[1, 28, 28], dtype='float32') label = layers.data(name='label', shape=[1], dtype='int32') conv_pool_1 = nets.simple_img_conv_pool( input=images, filter_size=5, num_filters=2, pool_size=2, pool_stride=2, act="relu") 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") predict = layers.fc(input=conv_pool_2, size=10, act="softmax") cost = layers.cross_entropy(input=predict, label=label) avg_cost = layers.mean(x=cost) program.append_backward(avg_cost) print(str(program)) def test_word_embedding(self): program = Program() with program_guard(program, startup_program=Program()): dict_size = 10000 embed_size = 32 first_word = layers.data(name='firstw', shape=[1], dtype='int64') second_word = layers.data(name='secondw', shape=[1], dtype='int64') third_word = layers.data(name='thirdw', shape=[1], dtype='int64') forth_word = layers.data(name='forthw', shape=[1], dtype='int64') next_word = layers.data(name='nextw', shape=[1], dtype='int64') embed_first = layers.embedding( input=first_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') embed_second = layers.embedding( input=second_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') embed_third = layers.embedding( input=third_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') embed_forth = layers.embedding( input=forth_word, size=[dict_size, embed_size], dtype='float32', param_attr='shared_w') concat_embed = layers.concat( input=[embed_first, embed_second, embed_third, embed_forth], axis=1) hidden1 = layers.fc(input=concat_embed, size=256, act='sigmoid') predict_word = layers.fc(input=hidden1, size=dict_size, act='softmax') cost = layers.cross_entropy(input=predict_word, label=next_word) avg_cost = layers.mean(x=cost) self.assertIsNotNone(avg_cost) print(str(program)) def test_linear_chain_crf(self): program = Program() with program_guard(program, startup_program=Program()): label_dict_len = 10 images = layers.data(name='pixel', shape=[784], dtype='float32') label = layers.data(name='label', shape=[1], dtype='int32') hidden = layers.fc(input=images, size=128) crf = layers.linear_chain_crf( input=hidden, label=label, param_attr=ParamAttr(name="crfw")) crf_decode = layers.crf_decoding( input=hidden, param_attr=ParamAttr(name="crfw")) layers.chunk_eval( input=crf_decode, label=label, chunk_scheme="IOB", num_chunk_types=(label_dict_len - 1) / 2) self.assertNotEqual(crf, None) self.assertNotEqual(crf_decode, None) print(str(program)) def test_sigmoid_cross_entropy(self): program = Program() with program_guard(program): dat = layers.data(name='data', shape=[10], dtype='float32') lbl = layers.data(name='label', shape=[10], dtype='float32') self.assertIsNotNone( layers.sigmoid_cross_entropy_with_logits( x=dat, label=lbl)) print(str(program)) def test_sequence_expand(self): program = Program() with program_guard(program): x = layers.data(name='x', shape=[10], dtype='float32') y = layers.data( name='y', shape=[10, 20], dtype='float32', lod_level=1) self.assertIsNotNone(layers.sequence_expand(x=x, y=y)) print(str(program)) def test_lstm_unit(self): program = Program() with program_guard(program): x_t_data = layers.data( name='x_t_data', shape=[10, 10], dtype='float32') x_t = layers.fc(input=x_t_data, size=10) prev_hidden_data = layers.data( name='prev_hidden_data', shape=[10, 20], dtype='float32') prev_hidden = layers.fc(input=prev_hidden_data, size=20) prev_cell_data = layers.data( name='prev_cell', shape=[10, 30], dtype='float32') prev_cell = layers.fc(input=prev_cell_data, size=30) self.assertIsNotNone( layers.lstm_unit( x_t=x_t, hidden_t_prev=prev_hidden, cell_t_prev=prev_cell)) print(str(program)) if __name__ == '__main__': unittest.main()