本文整理汇总了Python中constants.BATCH_SIZE属性的典型用法代码示例。如果您正苦于以下问题:Python constants.BATCH_SIZE属性的具体用法?Python constants.BATCH_SIZE怎么用?Python constants.BATCH_SIZE使用的例子?那么, 这里精选的属性代码示例或许可以为您提供帮助。您也可以进一步了解该属性所在类constants
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在下文中一共展示了constants.BATCH_SIZE属性的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: get_train_batch
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def get_train_batch():
"""
Loads c.BATCH_SIZE clips from the database of preprocessed training clips.
@return: An array of shape
[c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
"""
clips = np.empty([c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))],
dtype=np.float32)
for i in xrange(c.BATCH_SIZE):
path = c.TRAIN_DIR_CLIPS + str(np.random.choice(c.NUM_CLIPS)) + '.npz'
clip = np.load(path)['arr_0']
clips[i] = clip
return clips
示例2: train
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def train(self):
"""
Runs a training loop on the model.
"""
while True:
inputs, targets = self.data_reader.get_train_batch(c.BATCH_SIZE, c.SEQ_LEN)
print 'Training model...'
feed_dict = {self.model.inputs: inputs, self.model.targets: targets}
global_step, loss, _ = self.sess.run([self.model.global_step,
self.model.loss,
self.model.train_op],
feed_dict=feed_dict)
print 'Step: %d | loss: %f' % (global_step, loss)
if global_step % c.MODEL_SAVE_FREQ == 0:
print 'Saving model...'
self.saver.save(self.sess, join(c.MODEL_SAVE_DIR, self.artist_name + '.ckpt'),
global_step=global_step)
示例3: main2
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def main2():
num_utterances_per_speaker = 50
num_speakers = 100
num_samples = num_speakers * num_utterances_per_speaker
kx_train = np.zeros(shape=(num_samples, 32, 64, 1))
ky_train = np.zeros(shape=(num_samples, num_speakers))
for i in range(num_samples):
speaker_id = i % num_speakers
ky_train[i, speaker_id] = 1
kx_train[i] = speaker_id
kx_test = np.array(kx_train)
ky_test = np.array(ky_train)
tpshn = TripletBatcherSelectHardNegatives(kx_train, ky_train, kx_test, ky_test, None)
tp = TripletBatcher(kx_train, ky_train, kx_test, ky_test)
avg = []
avg2 = []
while True:
bx, by = tp.get_batch(BATCH_SIZE, is_test=False)
avg.append(float(triplet_loss.deep_speaker_loss(predict(bx), predict(bx))))
bx, by = tpshn.get_batch(BATCH_SIZE, is_test=False, predict=predict)
avg2.append(float(triplet_loss.deep_speaker_loss(predict(bx), predict(bx))))
print(np.mean(avg), np.mean(avg2))
示例4: create_txs
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def create_txs(ipc_path, rpc_host, rpc_port, signer_addr, airdropper_addr, omgtoken_addr, verify_eth,
processed_file, unsigned_file):
if ipc_path and (rpc_host or rpc_port):
raise Exception("both ipc and rpc cannot be specified")
if ipc_path:
web3 = Web3(IPCProvider(ipc_path))
else:
web3 = Web3(RPCProvider(host=rpc_host,
port=rpc_port))
airdropper, omgToken = get_contracts(web3,
airdropper_addr=airdropper_addr,
omgtoken_addr=omgtoken_addr)
creator = Creator(signer_addr, airdropper, omgToken, GAS_LIMIT, GAS_PRICE, GAS_RESERVE,
verify_eth=verify_eth)
airdrops = json.loads(processed_file.read())
unsigned = creator.create_txs(airdrops, BATCH_SIZE)
unsigned_file.write(json.dumps(unsigned, sort_keys=True))
示例5: test_recover_sent_airdrops
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def test_recover_sent_airdrops(web3, prepared_contracts, transactions, signed, airdrops,
creator):
"""
Assuming partially sent airdrops, when there's need to sign transactions again
e.g. when it turned out that too little gas was allowed (unlikely)
"""
airdropper, omg_token = prepared_contracts
Sender(web3).send_transactions(signed[:1], transactions[:1])
# airdrop partially done by now
check_entirely_airdropped(airdrops[0:BATCH_SIZE], omg_token)
not_airdropped = Sender(web3).recover_unsent_airdrops(airdrops, signed, airdropper, omg_token)
assert not_airdropped == airdrops[BATCH_SIZE:]
unsigned = creator.create_txs(not_airdropped, BATCH_SIZE)
new_signed = Signer(web3).sign_transactions(unsigned)
Sender(web3).send_transactions(new_signed, unsigned)
check_entirely_airdropped(airdrops, omg_token)
示例6: get_train_batch
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def get_train_batch():
"""
Loads c.BATCH_SIZE clips from the database of preprocessed training clips.
@return: An array of shape
[c.BATCH_SIZE, c.TRAIN_HEIGHT, c.TRAIN_WIDTH, (3 * (c.HIST_LEN + 1))].
"""
clips = np.empty([c.BATCH_SIZE, (3 * (c.HIST_LEN + 1)),c.TRAIN_HEIGHT, c.TRAIN_WIDTH],
dtype=np.float32)
print('batchsize', c.BATCH_SIZE)
print('test dir clips', c.TRAIN_DIR_CLIPS)
# for i in xrange(c.BATCH_SIZE):
for i in range(c.BATCH_SIZE):
path = c.TRAIN_DIR_CLIPS + str(np.random.choice(c.NUM_CLIPS - 1)) + '.npz'
print('path:', path)
clip = np.load(path)['arr_0']
clips[i] = clip
return clips
示例7: test
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def test(self):
"""
Runs one test step on the generator network.
"""
batch = get_test_batch(c.BATCH_SIZE, num_rec_out=self.num_test_rec)
self.g_model.test_batch(
batch, self.global_step, num_rec_out=self.num_test_rec)
示例8: save
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def save(artist, model_path, num_save):
sample_save_dir = c.get_dir('../save/samples/')
sess = tf.Session()
print artist
data_reader = DataReader(artist)
vocab = data_reader.get_vocab()
print 'Init model...'
model = LSTMModel(sess,
vocab,
c.BATCH_SIZE,
c.SEQ_LEN,
c.CELL_SIZE,
c.NUM_LAYERS,
test=True)
saver = tf.train.Saver()
sess.run(tf.initialize_all_variables())
saver.restore(sess, model_path)
print 'Model restored from ' + model_path
artist_save_dir = c.get_dir(join(sample_save_dir, artist))
for i in xrange(num_save):
print i
path = join(artist_save_dir, str(i) + '.txt')
sample = model.generate()
processed_sample = process_sample(sample)
with open(path, 'w') as f:
f.write(processed_sample)
示例9: main
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def main():
select = True
try:
sys.argv[1]
except:
select = False
print('select', select)
working_dir = '/media/philippe/8TB/deep-speaker'
# by construction this losses should be much higher than the normal losses.
# we select batches this way.
batch_input_shape = [None, NUM_FRAMES, NUM_FBANKS, 1]
print('Testing with the triplet losses.')
dsm = DeepSpeakerModel(batch_input_shape, include_softmax=False)
triplet_checkpoint = load_best_checkpoint(CHECKPOINTS_TRIPLET_DIR)
pre_training_checkpoint = load_best_checkpoint(CHECKPOINTS_SOFTMAX_DIR)
if triplet_checkpoint is not None:
print(f'Loading triplet checkpoint: {triplet_checkpoint}.')
dsm.m.load_weights(triplet_checkpoint)
elif pre_training_checkpoint is not None:
print(f'Loading pre-training checkpoint: {pre_training_checkpoint}.')
# If `by_name` is True, weights are loaded into layers only if they share the
# same name. This is useful for fine-tuning or transfer-learning models where
# some of the layers have changed.
dsm.m.load_weights(pre_training_checkpoint, by_name=True)
dsm.m.compile(optimizer='adam', loss=deep_speaker_loss)
kc = KerasFormatConverter(working_dir)
if select:
print('TripletBatcherSelectHardNegatives()')
batcher = TripletBatcherSelectHardNegatives(kc.kx_train, kc.ky_train, kc.kx_test, kc.ky_test, dsm)
else:
print('TripletBatcher()')
batcher = TripletBatcher(kc.kx_train, kc.ky_train, kc.kx_test, kc.ky_test)
batch_size = BATCH_SIZE
losses = []
while True:
_bx, _by = batcher.get_batch(batch_size, is_test=False)
losses.append(dsm.m.evaluate(_bx, _by, verbose=0, batch_size=BATCH_SIZE))
print(np.mean(losses))
示例10: minted_and_credited
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def minted_and_credited(token, airdropper, chain, accounts):
txn_hash = token.transact().mint(accounts[0], BATCH_SIZE * LARGEST_AMOUNT)
chain.wait.for_receipt(txn_hash)
txn_hash = token.transact().transfer(airdropper.address, BATCH_SIZE * LARGEST_AMOUNT)
chain.wait.for_receipt(txn_hash)
示例11: test_flow
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def test_flow(token, airdropper, chain, accounts, minted_and_credited):
txn_hash = airdropper.transact().multisend(token.address, accounts[1:2], [10])
chain.wait.for_receipt(txn_hash)
# return to owner
remainder = token.call().balanceOf(airdropper.address)
txn_hash = airdropper.transact().multisend(token.address, [accounts[0]], [remainder])
chain.wait.for_receipt(txn_hash)
assert token.call().balanceOf(accounts[0]) == BATCH_SIZE * LARGEST_AMOUNT - 10
assert token.call().balanceOf(accounts[1]) == 10
assert token.call().balanceOf(airdropper.address) == 0
示例12: test_list_processing_and_cost
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def test_list_processing_and_cost(token, airdropper, chain, minted_and_credited):
beneficiaries = [urandom(20) for _ in xrange(BATCH_SIZE)]
txn_hash = airdropper.transact().multisend(token.address,
beneficiaries,
[LARGEST_AMOUNT] * len(beneficiaries))
peracc = chain.web3.eth.getTransactionReceipt(txn_hash)['gasUsed'] / len(beneficiaries)
for account in beneficiaries:
assert token.call().balanceOf(account) == LARGEST_AMOUNT
assert peracc <= 33000 # golden number
示例13: airdrops
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def airdrops():
"""
uses a pre-prepared json file with processed airdrops (see README.md)
it is also a truncated list of airdrops, just enough for 2 uneven transactions
"""
with open("data/processed.json") as f:
airdrops = json.loads(f.read())
return airdrops[0:BATCH_SIZE + 10]
示例14: test_entire_flow
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def test_entire_flow(web3, prepared_contracts, creator, input_file):
airdropper, omg_token = prepared_contracts
airdrops = process(input_file.read())
transactions = creator.create_txs(airdrops, BATCH_SIZE)
# this being a long-running test, the unlocking from web3 fixture might have expired
web3.personal.unlockAccount(web3.eth.accounts[0], "")
signed = Signer(web3).sign_transactions(transactions)
Sender(web3).send_transactions(signed, transactions)
check_entirely_airdropped(airdrops, omg_token)
示例15: test_small_flow
# 需要导入模块: import constants [as 别名]
# 或者: from constants import BATCH_SIZE [as 别名]
def test_small_flow(web3, prepared_contracts, creator, airdrops):
_, omg_token = prepared_contracts
transactions = creator.create_txs(airdrops, BATCH_SIZE)
signed = Signer(web3).sign_transactions(transactions)
Sender(web3).send_transactions(signed, transactions)
check_entirely_airdropped(airdrops, omg_token)