vocab = len(dic)
print("Vocab=%d" %vocab)
X_train_batch = drop_tail(X_train_batch, seq_len)
X_val_batch = drop_tail(X_val_batch, seq_len)
#print 'ddddd'
print X_train_batch.shape
print X_val_batch.shape
model = lstm.setup_rnn_model(mx.cpu(),
num_lstm_layer=num_lstm_layer,
seq_len=seq_len,
num_hidden=num_hidden,
num_embed=num_embed,
num_label=vocab,
batch_size=batch_size,
input_size=vocab,
initializer=mx.initializer.Uniform(0.1),dropout=0.5)
# max_grad_norm=5.0 | update_period=1 | wd=0 | learning_rate=0.1 | num_roud=25
lstm.train_lstm(model, X_train_batch, X_val_batch,
num_round=num_round,
half_life=2,
max_grad_norm = max_grad_norm,
update_period=update_period,
learning_rate=learning_rate,
wd=wd)
# momentum=momentum)
# A simple two GPU placement plan
group2ctx = {'embed': mx.gpu(0), 'decode': mx.gpu(ngpu - 1)}
for i in range(num_lstm_layer):
group2ctx['layer%d' % i] = mx.gpu(i * ngpu // num_lstm_layer)
# whether do group-wise concat
concat_decode = False
use_loss = True
model = lstm.setup_rnn_model(mx.gpu(),
group2ctx=group2ctx,
concat_decode=concat_decode,
use_loss=use_loss,
num_lstm_layer=num_lstm_layer,
seq_len=X_train_batch.default_bucket_key,
num_hidden=num_hidden,
num_embed=num_embed,
num_label=vocab,
batch_size=batch_size,
input_size=vocab,
initializer=mx.initializer.Uniform(0.1),
dropout=0.5,
buckets=buckets)
lstm.train_lstm(model,
X_train_batch,
X_val_batch,
num_round=num_round,
concat_decode=concat_decode,
use_loss=use_loss,
half_life=2,
max_grad_norm=max_grad_norm,
# A simple two GPU placement plan
group2ctx = {'embed': mx.gpu(0),
'decode': mx.gpu(ngpu - 1)}
for i in range(num_lstm_layer):
group2ctx['layer%d' % i] = mx.gpu(i * ngpu // num_lstm_layer)
# whether do group-wise concat
concat_decode = False
use_loss=True
model = lstm.setup_rnn_model(mx.gpu(), group2ctx=group2ctx,
concat_decode=concat_decode,
use_loss=use_loss,
num_lstm_layer=num_lstm_layer,
seq_len=X_train_batch.default_bucket_key,
num_hidden=num_hidden,
num_embed=num_embed,
num_label=vocab,
batch_size=batch_size,
input_size=vocab,
initializer=mx.initializer.Uniform(0.1),dropout=0.5, buckets=buckets)
lstm.train_lstm(model, X_train_batch, X_val_batch,
num_round=num_round,
concat_decode=concat_decode,
use_loss=use_loss,
half_life=2,
max_grad_norm = max_grad_norm,
update_period=update_period,
learning_rate=learning_rate,
batch_size = batch_size,
X_train, dic = load_data("./data/ptb.train.txt")
X_val, _ = load_data("./data/ptb.valid.txt", dic)
X_train_batch = replicate_data(X_train, batch_size)
X_val_batch = replicate_data(X_val, batch_size)
vocab = len(dic)
print("Vocab=%d" % vocab)
X_train_batch = drop_tail(X_train_batch, seq_len)
X_val_batch = drop_tail(X_val_batch, seq_len)
model = lstm.setup_rnn_model(mx.gpu(),
num_lstm_layer=num_lstm_layer,
seq_len=seq_len,
num_hidden=num_hidden,
num_embed=num_embed,
num_label=vocab,
batch_size=batch_size,
input_size=vocab,
initializer=mx.initializer.Uniform(0.1),
dropout=0.5)
lstm.train_lstm(model,
X_train_batch,
X_val_batch,
num_round=num_round,
half_life=2,
max_grad_norm=max_grad_norm,
update_period=update_period,
learning_rate=learning_rate,
wd=wd,
momentum=momentum)
ngpu = 1
# A simple two GPU placement plan
group2ctx = {'embed': mx.gpu(0),
'decode': mx.gpu(ngpu - 1)}
for i in range(num_lstm_layer):
group2ctx['layer%d' % i] = mx.gpu(i * ngpu // num_lstm_layer)
# whether do group-wise concat
concat_decode = True
model = lstm.setup_rnn_model(mx.gpu(), group2ctx=group2ctx,
concat_decode=concat_decode,
num_lstm_layer=num_lstm_layer,
seq_len=seq_len,
num_hidden=num_hidden,
num_embed=num_embed,
num_label=vocab,
batch_size=batch_size,
input_size=vocab,
initializer=mx.initializer.Uniform(0.1),dropout=0.5)
lstm.train_lstm(model, X_train_batch, X_val_batch,
num_round=num_round,
concat_decode=concat_decode,
half_life=2,
max_grad_norm = max_grad_norm,
update_period=update_period,
learning_rate=learning_rate,
wd=wd)
