def train_fn():
rnn_model = model.rnn()
# print(model.summary())
rnn_model.compile(optimizer=Adam(lr=config.LEARNING_RATE),
loss='binary_crossentropy',
metrics=['accuracy'])
train_padded, train_labels, test_padded, test_labels = data_preprocess.tokenizer_sequences(
)
callbacks = [
tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=2)
]
history = rnn_model.fit(train_padded,
train_labels,
validation_data=(test_padded, test_labels),
epochs=config.NUM_EPOCHS,
verbose=2,
callbacks=callbacks)
rnn_model.save(f"{config.MODEL_PATH}my_model.h5")
np.save(f'{config.MODEL_PATH}my_history.npy', history.history)
joblib.dump(test_padded, f"{config.MODEL_PATH}test_padded.pkl")
joblib.dump(test_labels, f"{config.MODEL_PATH}test_labels.pkl")
def train_fn():
train_padded, train_label_seq, valid_padded, valid_label_seq = data_preprocess.tokenizer_sequences(
)
rnn_model = model.rnn()
rnn_model.compile(optimizer=Adam(lr=config.LEARNING_RATE),
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
# print(model.summary())
callbacks = [
ReduceLROnPlateau(monitor='val_loss', patience=5, cooldown=0),
EarlyStopping(monitor='val_accuracy', min_delta=1e-4, patience=5)
]
history = rnn_model.fit(train_padded,
train_label_seq,
validation_data=(valid_padded, valid_label_seq),
epochs=config.NUM_EPOCHS,
batch_size=config.BATCH_SIZE,
verbose=2,
callbacks=callbacks)
rnn_model.save(f"{config.MODEL_PATH}my_model.h5")
np.save(f'{config.MODEL_PATH}my_history.npy', history.history)
tf.app.flags.DEFINE_string('resource_info_file', os.path.abspath(os.path.join(os.path.dirname(__file__), '.', 'resource_info')),
'Filename containing cluster information')
tf.app.flags.DEFINE_integer('max_steps', 1000000,
"""Number of iterations to run for each workers.""")
tf.app.flags.DEFINE_integer('log_frequency', 50,
"""How many steps between two runop logs.""")
tf.app.flags.DEFINE_integer('batch_size', 64,
"""Batch size""")
tf.app.flags.DEFINE_boolean('sync', True, '')
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# Build single-GPU rnn model
single_gpu_graph = tf.Graph()
with single_gpu_graph.as_default():
ops = rnn()
train_op = ops['train_op']
loss = ops['loss']
acc = ops['acc']
x = ops['images']
y = ops['labels']
is_training = ops['is_training']
parallax_config = parallax.Config()
ckpt_config = parallax.CheckPointConfig(ckpt_dir='parallax_ckpt',
save_ckpt_steps=1)
parallax_config.ckpt_config = ckpt_config
sess, num_workers, worker_id, num_replicas_per_worker = parallax.parallel_run(
single_gpu_graph,
FLAGS.resource_info_file,
def main(_):
with tf.Session() as sess:
cells = get_lstm_cells(num_hidden, keep_prob)
init_states = cells.zero_state(batch_size, tf.float32)
outputs, final_states = rnn(rnn_inputs, cells, num_hidden[-1],
num_steps, num_class, init_states)
predicts = tf.argmax(outputs, -1, name='predict_op')
softmax_out = tf.nn.softmax(outputs, name='softmax_op')
top_k = tf.nn.top_k(softmax_out, k=k, sorted=False, name='top_k_op')
with tf.variable_scope('train'):
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(
labels=labels, logits=outputs),
name='loss_op')
global_step = tf.Variable(0,
name='global_step',
trainable=False,
collections=[
tf.GraphKeys.GLOBAL_VARIABLES,
tf.GraphKeys.GLOBAL_STEP
])
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=0.9)
train_op = optimizer.minimize(loss,
global_step=global_step,
name='train_op')
arg_labels = tf.argmax(labels, -1)
acc = tf.reduce_mean(tf.cast(tf.equal(predicts, arg_labels),
tf.float32),
name='acc_op')
sess.run(tf.global_variables_initializer())
global_step_tensor = sess.graph.get_tensor_by_name(
'train/global_step:0')
train_op = sess.graph.get_operation_by_name('train/train_op')
acc_op = sess.graph.get_tensor_by_name('train/acc_op:0')
loss_tensor = sess.graph.get_tensor_by_name('train/loss_op:0')
print('Start training ...')
loss_history = []
acc_history = []
batch_num = 30
a = datetime.now().replace(microsecond=0)
for i in range(epochs):
total_loss = 0
total_acc = 0
count = 0
current_states = sess.run(init_states,
feed_dict={batch_size: batch_num})
for x, y in get_batches(train_encode, batch_num, num_steps):
_, loss_value, acc_value, current_states = sess.run(
[train_op, loss_tensor, acc_op, final_states],
feed_dict={
X: x,
Y: y,
init_states: current_states,
keep_prob: 1
})
total_loss += loss_value
total_acc += acc_value
count += 1
total_loss /= count
total_acc /= count
valid_acc = 0
count = 0
current_states = sess.run(init_states,
feed_dict={batch_size: batch_num})
for x, y in get_batches(valid_encode, batch_num, num_steps):
acc_value, current_states = sess.run([acc_op, final_states],
feed_dict={
X:
x,
Y:
y,
init_states:
current_states
})
valid_acc += acc_value
count += 1
valid_acc /= count
print("Epochs: {}, loss: {:.4f}, acc: {:.4f}, val_acc: {:.4f}".
format(i + 1, total_loss, total_acc, valid_acc))
loss_history.append(total_loss)
acc_history.append([total_acc, valid_acc])
plt.plot(loss_history)
plt.xlabel("epochs")
plt.ylabel("BPC")
plt.title("Training curve")
plt.savefig("Training curve.png", dpi=100)
plt.gcf().clear()
acc_history = np.array(acc_history).T
err_history = 1 - acc_history
plt.plot(err_history[0], label='training error')
plt.plot(err_history[1], label='validation error')
plt.xlabel("epochs")
plt.ylabel("Error rate")
plt.title("Training error")
plt.legend()
plt.savefig("Training error.png", dpi=100)
# predict 500 words
seed = 'Asuka'
seed_encode = np.array([vocab_to_int[c] for c in list(seed)])
seed_encode = np.concatenate((seed_encode, np.zeros(num_steps - 5)))
current_states = sess.run(init_states, feed_dict={batch_size: 1})
index = 4
for i in range(500):
if index == num_steps - 1:
candidates, current_states = sess.run([top_k, final_states],
feed_dict={
X:
seed_encode[None, :],
init_states:
current_states
})
p = candidates.values[0, index]
p /= p.sum()
rand_idx = np.random.choice(k, p=p)
seed_encode = np.append(candidates.indices[0, index, rand_idx],
np.zeros(num_steps - 1))
else:
candidates = sess.run(top_k,
feed_dict={
X: seed_encode[None, :],
init_states: current_states
})
p = candidates.values[0, index]
p /= p.sum()
rand_idx = np.random.choice(k, p=p)
seed_encode[index + 1] = candidates.indices[0, index, rand_idx]
seed += int_to_vocab[candidates.indices[0, index, rand_idx]]
index = (index + 1) % num_steps
print(seed)
b = datetime.now().replace(microsecond=0)
print("Time cost:", b - a)
def cv_train(modelname,
config,
kfold,
lstep,
n_samples,
feature_weight,
pkl_filename="data/adni.pkl",
json_filename="conf/dataConfig.json"):
"""
Cross validation training process.
Description:
In each fold, we split data into train and validation parts.
If validation loss has no improvement for continuous 10 epoches, the current fold is stop and we go
to the next epoch. In each fold, the parameters would inherit from the saved `best-model` of the past fold. Thus, the first
fold would take longer time, and the following folds would be faster. After cross validation is done, we load the best model, and apply it to test data.
Arguments
---------
modelname: string
the name of chosen model class
config: dictionary
the configuration of training
kfold: int
the number of folds in cross validation
lstep: int
the value of step in forward prediction
n_samples: int
the number of samples to draw in testing stage
pkl_filename: string
the filename of pkl file storing "demo","dync","max_len"
json_filename: string
the filename of json file storing data configuration
Returns
-------
model parameters saved in `save` folder
"""
"""load data and data config
The data file *pkl contains three parts: {'demo','dync','max_len'}
'demo' : a list of dataframes storing patients' demographics information
'dync' : a list of dataframes storing patients' dynamic information, including continous features and diganosis
'max_len' : int, the maximum lengths of patient sequence
"""
adni = _pickle.load(open(pkl_filename, "rb"))
dataConfig = json.load(open(json_filename))
max_len = adni["max_len"]
"""
record val_loss change / trends;
the smallest loss value for each fold is given by the best model
"""
loss_curve = {}
# build model graph
if modelname == "rnn":
model = rnn(len(dataConfig["demo_vars"]),
len(dataConfig["input_x_vars"]),
len(dataConfig["input_y_vars"]), max_len,
config["batch_size"], config["n_h"], config["n_z"], lstep,
feature_weight)
elif modelname == "stocast":
model = stocast(len(dataConfig["demo_vars"]),
len(dataConfig["input_x_vars"]),
len(dataConfig["input_y_vars"]), max_len,
config["batch_size"], config["n_h"], config["n_z"],
lstep, feature_weight)
elif modelname == "storn":
model = storn(len(dataConfig["demo_vars"]),
len(dataConfig["input_x_vars"]),
len(dataConfig["input_y_vars"]), max_len,
config["batch_size"], config["n_h"], config["n_z"],
lstep, feature_weight)
elif modelname == "retain":
model = retain(len(dataConfig["demo_vars"]),
len(dataConfig["input_x_vars"]),
len(dataConfig["input_y_vars"]), max_len,
config["batch_size"], config["n_h"], config["n_z"],
lstep, feature_weight)
elif modelname == "tlstm":
model = tlstm(len(dataConfig["demo_vars"]),
len(dataConfig["input_x_vars"]),
len(dataConfig["input_y_vars"]), max_len,
config["batch_size"], config["n_h"], config["n_z"],
lstep, feature_weight)
# saving ...
dirname = "save/{} {}".format(
modelname,
time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time())))
if not os.path.exists(dirname):
os.makedirs(dirname)
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(
'logs/' + datetime.now().isoformat().replace(':', '-'), sess.graph)
merged = tf.summary.merge_all()
saver = tf.train.Saver(tf.global_variables(), max_to_keep=1)
start = time.time()
totaltime = 0
for k in range(kfold):
""" if k = 0, random initialization params """
""" else, inherit previous best model's params """
if k == 0:
tf.global_variables_initializer().run()
else:
ckpt = tf.train.get_checkpoint_state(dirname)
saver.restore(sess, ckpt.model_checkpoint_path)
# split into trainining, validation, testing data
train_ds, valid_ds = split_data(k, kfold, adni, dataConfig,
max_len)
# train
minVlloss = 1e10
loss_curve[k] = []
n_batchs = int(train_ds.num_examples / config["batch_size"])
# each epoch
no_improvement = 0 # number of no improvements
e = 0
while e < config["num_epochs"]:
sess.run(
tf.assign(
model.lr,
config["learning_rate"] * (config["decay_rate"]**e)))
for b in range(n_batchs):
wrap = train_ds.next_batch(config["batch_size"])
feed = {
model.input_demo: wrap['input_demo'],
model.input_x: wrap['input_x'],
model.input_y: wrap['input_y'],
model.input_dt: wrap['input_dt'],
model.seqlens: wrap['seqlens'],
model.mask: wrap['mask']
}
_, loss, summary = sess.run(
[model.train_op, model.loss, merged], feed)
summary_writer.add_summary(summary, e * n_batchs + b)
# validation
vloss = val_loss(sess, model, valid_ds, config["batch_size"])
loss_curve[k].append(vloss)
print(" |- FOLD:%d, EPOCH:%d, VLOSS:%.4f" % (k, e, vloss))
if minVlloss > vloss:
minVlloss = vloss
checkpoint_path = os.path.join(
dirname, "best_model_k={}_e={}.ckpt".format(k, e))
saver.save(sess,
checkpoint_path,
global_step=e * n_batchs +
k * config["num_epochs"] * n_batchs)
print(
" |- Best model saved to {}".format(checkpoint_path))
no_improvement = 0
else:
no_improvement += 1
# if the number of improvement reaches 10, stop running
if no_improvement < 10:
e += 1
continue
else:
break
end = time.time()
print("|- %2d fold costs %.4f seconds.\n" % (k, end - start))
totaltime += end - start
start = time.time()
print("Total train time is %.4f seconds." % totaltime)
# testing
print("Starting testing")
ckpt = tf.train.get_checkpoint_state(dirname)
if ckpt:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Loading model: ", ckpt.model_checkpoint_path)
test_ds = DataSet(dataConfig, adni["demo"], adni["dync"], max_len)
test_res = test(sess,
model,
modelname,
test_ds,
config["batch_size"],
max_len,
dataConfig["input_y_vars"],
lstep,
n_samples=n_samples)
print("Saving test results...")
"""The results are saved in the following format.
res = pickle.load(open(filename,'rb'))
res : a list of dicts, with each dict() stores the prediction results corresponding to a specific patient
res[i] : the dict for patient i, {'curr_labels','target_labels','pred_pi','target_features','pred_mu'}
'curr_labels' : a list of labels
'target_labels' : a list of target labels
'pred_pi' : a list of predictions, the length is timesteps
- pred_pi[t] is a 1d array for deterministic methods, or a 2d array for stocast with size (n_samples, 3)
'target_features' : list, the length is timesteps
- target_features[t] : a 1d array
'pred_mu' : list, the length is timesteps
- pred_mu[t] : a 1d array for deterministic methods, or a 2d array for stocast
"""
dirname = "result_fw={}/{}".format(feature_weight, modelname)
if not os.path.exists(dirname):
os.makedirs(dirname)
_pickle.dump(
test_res,
open(
os.path.join(
dirname,
"lstep{}_nsamples{}_result.pkl".format(lstep, n_samples)),
"wb"))
_pickle.dump(
loss_curve,
open(
os.path.join(
dirname,
"lstep{}_nsamples{}_losses.pkl".format(lstep, n_samples)),
"wb"))
from model import rnn
from tensorflow.examples.tutorials.mnist import input_data
hvd.init()
FLAGS = tf.app.flags.FLAGS
tf.app.flags.DEFINE_integer(
'max_steps', 1000000, """Number of iterations to run for each workers.""")
tf.app.flags.DEFINE_integer('log_frequency', 50,
"""How many steps between two runop logs.""")
tf.app.flags.DEFINE_integer('batch_size', 32, """Batch size""")
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
ops = rnn(only_logits=True)
logits = ops['logits']
x = ops['images']
y = ops['labels']
is_training = ops['is_training']
global_step = ops['global_step']
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=logits))
loss += model.weight_decay * tf.losses.get_regularization_loss()
acc = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(logits, axis=1), tf.argmax(y, axis=1)),
tf.float32))
optimizer = tf.train.AdamOptimizer(learning_rate=model.learning_rate)
optimizer = hvd.DistributedOptimizer(optimizer)
num_workers=args.numworkers,
pin_memory=True)
else:
trainloader = DataLoader(data,
batch_size=args.mbsize,
shuffle=True,
num_workers=args.numworkers)
vocabSize = data.vocabularySize()
embeddingSize = 300
hiddenSize = 100
momentum = 0.9
if args.type in 'rnn':
print('RNN model')
model = rnn(vocabSize, embeddingSize, hiddenSize).to(device)
elif args.type in 'gru':
print('GRU model')
model = gru(vocabSize, embeddingSize, hiddenSize).to(device)
elif args.type in 'lstm':
print('LSTM model')
model = lstm(vocabSize, embeddingSize, hiddenSize).to(device)
else:
print('Invalid entry for model type. Should be one of rnn, lstm or gru')
assert False
criterion = nn.BCEWithLogitsLoss().to(device)
optimizer = optim.SGD(model.parameters(),
lr=args.lr,
momentum=momentum,
nesterov=True)
logging.info(args)
ctx = mx.gpu()
batch_size = args.batch_size
bptt = args.bptt
mx.random.seed(args.seed)
# data
corpus = Corpus(args.data)
ntokens = len(corpus.dictionary)
train_data = CorpusIter(corpus.train, batch_size, bptt)
valid_data = CorpusIter(corpus.valid, batch_size, bptt)
test_data = CorpusIter(corpus.test, batch_size, bptt)
# model
pred, states, state_names = rnn(bptt, ntokens, args.emsize, args.nhid,
args.nlayers, args.dropout, batch_size,
args.tied)
loss = softmax_ce_loss(pred)
# module
module = CustomStatefulModule(loss,
states,
state_names=state_names,
context=ctx)
module.bind(data_shapes=train_data.provide_data,
label_shapes=train_data.provide_label)
module.init_params(initializer=mx.init.Xavier())
optimizer = mx.optimizer.create('sgd',
learning_rate=args.lr,
rescale_grad=1.0 / batch_size)
module.init_optimizer(optimizer=optimizer)
drop = 0.3
epochs = 100
batch = 128
optimizer = 'rmsprop'
seq = 5
new_words = 1000
temperature = 0.5
file = inp(text, seq)
file.text_seq()
x, y = file.rnn_input()
rnn = rnn(text,
x,
y,
layer1=layer,
dropout=drop,
epochs=epochs,
batch=batch,
optimizer=optimizer)
rnn.define()
# rnn.load()
rnn.train()
new = output(file.get_content(),
seq=seq,
words=new_words,
temp=temperature)
vocab, dict1, dict2 = file.get_vocab()
new_text = new.generate(rnn.get_model(), vocab, dict1, dict2)
# print new_text
import numpy as np
np.random.RandomState(0)
from model import rnn
from keras.datasets import imdb
from keras.preprocessing import sequence
from keras import optimizers
from keras.callbacks import EarlyStopping
from utils import output_performance, generate_figures, get_args
args = get_args()
(x_train, y_train), (x_test, y_test) = imdb.load_data(path="imdb.npz", num_words=args.vocab_size, maxlen=args.maxLen)
x_train = sequence.pad_sequences(x_train, maxlen=args.maxLen)
x_test = sequence.pad_sequences(x_test, maxlen=args.maxLen)
model = rnn(vocab_size=args.vocab_size, maxLen=args.maxLen, embedding_dim=args.embed,
hidden_dim=args.hidden, output_dim=args.output, batch_size=args.batch, keep_prob=args.keep)
model.compile(optimizer=optimizers.Adam(lr=args.lr), loss='binary_crossentropy', metrics=['accuracy'])
print(model.summary())
history = model.fit(x_train, y_train, validation_split=args.val_split, batch_size=args.batch, epochs=args.epochs,
callbacks=[EarlyStopping(monitor='val_loss')])
y_pred = model.predict(x_test)
generate_figures(history=history, model_name=args.model_name, output_dir="figures")
output_performance(model=model, y_test=y_test, y_pred=y_pred)
args = parser.parse_args()
logging.info(args)
ctx = mx.gpu()
batch_size = args.batch_size
bptt = args.bptt
mx.random.seed(args.seed)
# data
corpus = Corpus(args.data)
ntokens = len(corpus.dictionary)
train_data = CorpusIter(corpus.train, batch_size, bptt)
valid_data = CorpusIter(corpus.valid, batch_size, bptt)
test_data = CorpusIter(corpus.test, batch_size, bptt)
# model
pred, states, state_names = rnn(bptt, ntokens, args.emsize, args.nhid,
args.nlayers, args.dropout, batch_size, args.tied)
loss = softmax_ce_loss(pred)
# module
module = CustomStatefulModule(loss, states, state_names=state_names, context=ctx)
module.bind(data_shapes=train_data.provide_data, label_shapes=train_data.provide_label)
module.init_params(initializer=mx.init.Xavier())
optimizer = mx.optimizer.create('sgd', learning_rate=args.lr, rescale_grad=1.0/batch_size)
module.init_optimizer(optimizer=optimizer)
# metric
speedometer = mx.callback.Speedometer(batch_size, args.log_interval)
# train
logging.info("Training started ... ")
for epoch in range(args.epochs):
