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How to use my_generator method in Slash

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stackRNN_tests.py

Source:stackRNN_tests.py

1"""2Unit tests for StackAugmentedRNN class3"""4import sys5sys.path.append('./')6import pytest7import torch8from stackRNN import StackAugmentedRNN9from data import GeneratorData10gen_data_path = './data/logP_labels.csv'11gen_data = GeneratorData(training_data_path=gen_data_path, delimiter=',',12                         cols_to_read=[1], keep_header=False)13hidden_size = 5014stack_width = 5015stack_depth = 1016lr = 0.00117optimizer_instance = torch.optim.Adadelta18use_cuda = True19def test_bidirectional_stack_gru():20    layer_type = 'GRU'21    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,22                                     hidden_size=hidden_size,23                                     output_size=gen_data.n_characters,24                                     layer_type=layer_type,25                                     n_layers=1, is_bidirectional=True,26                                     has_stack=True,27                                     stack_width=stack_width,28                                     stack_depth=stack_depth,29                                     use_cuda=use_cuda,30                                     optimizer_instance=optimizer_instance,31                                     lr=lr)32    my_generator = my_generator.cuda()33    losses = my_generator.fit(gen_data, 100)34    my_generator.evaluate(gen_data)35def test_unidirectional_stack_gru():36    layer_type = 'GRU'37    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,38                                     hidden_size=hidden_size,39                                     output_size=gen_data.n_characters,40                                     layer_type=layer_type,41                                     n_layers=1, is_bidirectional=False,42                                     has_stack=True,43                                     stack_width=stack_width,44                                     stack_depth=stack_depth,45                                     use_cuda=use_cuda,46                                     optimizer_instance=optimizer_instance,47                                     lr=lr)48    my_generator = my_generator.cuda()49    losses = my_generator.fit(gen_data, 100)50    my_generator.evaluate(gen_data)51def test_unidirectional_gru_no_stack():52    layer_type = 'GRU'53    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,54                                     hidden_size=hidden_size,55                                     output_size=gen_data.n_characters,56                                     layer_type=layer_type,57                                     n_layers=1, is_bidirectional=False,58                                     has_stack=False,59                                     stack_width=stack_width,60                                     stack_depth=stack_depth,61                                     use_cuda=use_cuda,62                                     optimizer_instance=optimizer_instance,63                                     lr=lr)64    my_generator = my_generator.cuda()65    losses = my_generator.fit(gen_data, 100)66    my_generator.evaluate(gen_data)67def test_bidirectional_gru_no_stack():68    layer_type = 'GRU'69    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,70                                     hidden_size=hidden_size,71                                     output_size=gen_data.n_characters,72                                     layer_type=layer_type,73                                     n_layers=1, is_bidirectional=True,74                                     has_stack=False,75                                     stack_width=stack_width,76                                     stack_depth=stack_depth,77                                     use_cuda=use_cuda,78                                     optimizer_instance=optimizer_instance,79                                     lr=lr)80    my_generator = my_generator.cuda()81    losses = my_generator.fit(gen_data, 100)82    my_generator.evaluate(gen_data)83def test_bidirectional_stack_lstm():84    layer_type = 'LSTM'85    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,86                                     hidden_size=hidden_size,87                                     output_size=gen_data.n_characters,88                                     layer_type=layer_type,89                                     n_layers=1, is_bidirectional=True,90                                     has_stack=True,91                                     stack_width=stack_width,92                                     stack_depth=stack_depth,93                                     use_cuda=use_cuda,94                                     optimizer_instance=optimizer_instance,95                                     lr=lr)96    my_generator = my_generator.cuda()97    losses = my_generator.fit(gen_data, 100)98    my_generator.evaluate(gen_data)99def test_unidirectional_stack_lstm():100    layer_type = 'LSTM'101    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,102                                     hidden_size=hidden_size,103                                     output_size=gen_data.n_characters,104                                     layer_type=layer_type,105                                     n_layers=1, is_bidirectional=False,106                                     has_stack=True,107                                     stack_width=stack_width,108                                     stack_depth=stack_depth,109                                     use_cuda=use_cuda,110                                     optimizer_instance=optimizer_instance,111                                     lr=lr)112    my_generator = my_generator.cuda()113    losses = my_generator.fit(gen_data, 100)114    my_generator.evaluate(gen_data)115def test_unidirectional_lstm_no_stack():116    layer_type = 'LSTM'117    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,118                                     hidden_size=hidden_size,119                                     output_size=gen_data.n_characters,120                                     layer_type=layer_type,121                                     n_layers=1, is_bidirectional=False,122                                     has_stack=False,123                                     stack_width=stack_width,124                                     stack_depth=stack_depth,125                                     use_cuda=use_cuda,126                                     optimizer_instance=optimizer_instance,127                                     lr=lr)128    my_generator = my_generator.cuda()129    losses = my_generator.fit(gen_data, 100)130    my_generator.evaluate(gen_data)131def test_bidirectional_lstm_no_stack():132    layer_type = 'LSTM'133    my_generator = StackAugmentedRNN(input_size=gen_data.n_characters,134                                     hidden_size=hidden_size,135                                     output_size=gen_data.n_characters,136                                     layer_type=layer_type,137                                     n_layers=1, is_bidirectional=True,138                                     has_stack=False,139                                     stack_width=stack_width,140                                     stack_depth=stack_depth,141                                     use_cuda=use_cuda,142                                     optimizer_instance=optimizer_instance,143                                     lr=lr)144    my_generator = my_generator.cuda()145    losses = my_generator.fit(gen_data, 100)...

25_generator.py

Source:25_generator.py

1ï»¿# generator2# iterator ë¥¼ ìì±í´ì£¼ë í¨ì3# í¨ìë´ìì yield ëªë ¹ì¼ë¡ ííëë©°, ë³ëë¡ __iter__, __next__, __getitem__ ë±ì í¨ìë¥¼ ë§ë¤ íìê° ìë¤.4# 1. generator5def my_generator():6    yield 07    yield 18    yield 29for i in my_generator():10    print("my_generator :", i)11# generator ê° __iter__ ìëì§ íì¸12print("my_generator :", dir(my_generator()))13print("#"+"-"*20+"#")14# next í¨ì ì¬ì©15def my_generator():16    yield 017    yield 118    yield 219a = my_generator()20print("my_generator next :", next(a))21print("my_generator next :", next(a))22print("my_generator next :", next(a))23print("#"+"-"*20+"#")24# 2. generator ì ìì¸ ê·¸ë¦¬ê³  return25def my_generator():26    yield 027    yield 128    yield 229a = my_generator()30print("my_generator next :", next(a))31print("my_generator next :", next(a))32print("my_generator next :", next(a))33#print("my_generator next :", next(a))   # íìë¥¼ ëì´ê°ë©´ ëì¼íê² StopIteration ë°ì34print("#"+"-"*20+"#")35# return ëªë ¹ì íµí´ ìíë ë©ìì§ ì¶ë ¥ ê°ë¥36def my_generator():37    yield 038    yield 139    yield 240    return "íì ì´ê³¼"  # StopIteration ê³¼ í¨ê» ë©ìì§ ì¶ë ¥41a = my_generator()42print("my_generator next :", next(a))43print("my_generator next :", next(a))44print("my_generator next :", next(a))45# print("my_generator next :", next(a))   # íìë¥¼ ëì´ê°ë©´ ëì¼íê² StopIteration ë°ì46print("#"+"-"*20+"#")47# 3. yield ì í¨ì48def my_generator(a):49    for i in a:50        yield i.upper()51a = ["a", "b", "c"]52for i in my_generator(a):53    print("my_generator :", i)54print("#"+"-"*20+"#")55# 4. yield, from56def my_generator():57    a = [10,20,30]58    for i in a:59        yield i60a = my_generator()61print("my_generator from :", next(a))62print("my_generator from :", next(a))63print("my_generator from :", next(a))...

12_generators_summary.py

Source:12_generators_summary.py

1# summary of generator behaviors (can and cannot do)....2def my_generator():3    values = [1, 2, 3]4    for i in values:5        yield i678# things you cannot do...9# print(my_generator())       # this doesn't behave like a function10# print(my_generator()[0])11# print(len(my_generator()))1213# 4 ways to invoke (work with) a generator...14# 115for i in my_generator():16    print(i)1718# 219results = list(my_generator())20print(results)2122# 323print(*my_generator())2425# 426g = my_generator()27print(g.__next__())28print(next(g))29print(next(g))3031# You can...32if 1 in my_generator():
...

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