def sanityCheck_GeneratorLoss(pretrain_result=None, batch_size=5):
'''test custom loss function '''
if pretrain_result is None:
x, _, reverse_vocab, _ = read_sampleFile()
pretrain_result = pretrain_LSTMCore(x, vocab_size=len(reverse_vocab))
model = pretrain_result[0]
y_pred_pretrain = pretrain_result[1].view(
[-1, SEQ_LENGTH, len(reverse_vocab)])
test_reward = y_pred_pretrain.sum(dim=2).data
params = list(filter(lambda p: p.requires_grad, model.parameters()))
optimizer = torch.optim.SGD(params, lr=0.01)
optimizer.zero_grad()
log = openLog('test.txt')
log.write('\n\nTest generator.sanityCheck_GeneratorLoss: {}\n'.format(
datetime.now()))
criterion = GeneratorLoss()
g_loss = criterion(y_pred_pretrain[0:batch_size, :, :], x[0:batch_size, :],
test_reward[0:batch_size, :])
g_loss.backward()
optimizer.step()
log.write(' generator.sanityCheck_GeneratorLoss SUCCESSFUL: ' +
str(g_loss) + '\n')
log.close()
return g_loss
def __init__(self, pretrain_model=None, start_token=0,
ignored_tokens=None):
super().__init__()
self.start_token = start_token
self.ignored_tokens = ignored_tokens
if pretrain_model is None:
x, _, reverse_vocab, _ = read_sampleFile()
self.pretrain_model, _ = pretrain_LSTMCore(train_x=x, vocab_size=len(reverse_vocab))
else:
self.pretrain_model = pretrain_model
self.softmax = nn.Softmax(dim=2)
self.loss = GeneratorLoss()
def sanityCheck_LSTMCore(batch_size=1):
''' test prtrain_LSTMCore function '''
log = openLog('test.txt')
log.write('\n\nTest lstmCore.sanityCheck_LSTMCore: {}\n'.format(datetime.now()))
log.close()
x, _, reverse_vocab, _ = read_sampleFile()
pretrain_result = pretrain_LSTMCore(train_x=x,batch_size=batch_size,vocab_size=len(reverse_vocab))
model = pretrain_result[0]
y_all_max, y_all_sample = test_genMaxSample(model,start_token=0,batch_size=batch_size)
log = openLog('test.txt')
gen_tokens_max = decode(y_all_max, reverse_vocab, log)
gen_tokens_sample = decode(y_all_sample, reverse_vocab, log)
log.close()
return gen_tokens_max, gen_tokens_sample
def sanityCheck_generator(model=None):
''' test Generator instantiation and train_generator function '''
log = openLog('test.txt')
log.write('\n\nTest generator.sanityCheck_generator: {}\n'.format(datetime.now()))
x, vocabulary, reverse_vocab, _ = read_sampleFile()
if model is None:
pretrain_result = pretrain_LSTMCore(x,vocab_size=len(vocabulary))
model = Generator(pretrain_model=pretrain_result[0])
log.write(' generator instantiated: {}\n'.format(datetime.now()))
model.to(DEVICE)
model, y_prob_all, y_output_all = train_generator(model, x, reward=None)
log.write(' trained generator outputs:\n')
log.write(' y_output_all shape: '+ str(y_output_all.shape) +'\n')
log.write(' y_prob_all shape: '+ str(y_prob_all.shape) +'\n')
log.close()
return model, y_prob_all, y_output_all
