How to use my_generator method in Slash

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stackRNN_tests.py

Source:stackRNN_tests.py Github

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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)...

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25_generator.py

Source:25_generator.py Github

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1# generator2# iterator 를 생성해주는 함수3# 함수내에서 yield 명령으로 표현되며, 별도로 __iter__, __next__, __getitem__ 등의 함수를 만들 필요가 없다.4# 1. generator5def my_generator():6 yield 07 yield 18 yield 29for i in my_generator():10 print("my_generator :", i)11# generator 가 __iter__ 있는지 확인12print("my_generator :", dir(my_generator()))13print("#"+"-"*20+"#")14# next 함수 사용15def 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 의 예외 그리고 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)) # 횟수를 넘어가면 동일하게 StopIteration 발생34print("#"+"-"*20+"#")35# return 명령을 통해 원하는 메시지 출력 가능36def my_generator():37 yield 038 yield 139 yield 240 return "횟수 초과" # StopIteration 과 함께 메시지 출력41a = 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)) # 횟수를 넘어가면 동일하게 StopIteration 발생46print("#"+"-"*20+"#")47# 3. yield 와 함수48def 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))...

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12_generators_summary.py

Source:12_generators_summary.py Github

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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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