def load_checkpoint(path):
checkpoint = torch.load(path, map_location=torch.device('cpu'))
model = LSTMModel()
model.load_state_dict(checkpoint['model'])
optimizer = optim.Adam(model.parameters(), lr=0.0001)
criterion = nn.CTCLoss()
return model, optimizer, criterion
class InferenceWrapper(object):
"""Model wrapper class for performing inference with a ShowAndTellModel."""
def __init__(self, config, model_dir,
ses_threads=2,
length_normalization_factor=0.0,
gpu_memory_fraction=0.3,
gpu=1,
with_image_embedding=True):
self.config = copy.deepcopy(config)
self.config.batch_size = 1
self.flag_load_model = False
self.model_dir = model_dir
self.gpu= gpu
self.gpu_memory_fraction = gpu_memory_fraction
self.with_image_embedding = with_image_embedding
def build_model(self):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.gpu_memory_fraction)
config_proto = tf.ConfigProto( gpu_options=gpu_options, allow_soft_placement=True)
self.session = session = tf.Session(config=config_proto)
with tf.device('/gpu:%d'%self.gpu):
with tf.variable_scope("LSTMModel", reuse=None):
if self.with_image_embedding:
self.model = LSTMModel(config=self.config, mode="inference",
model_dir = self.model_dir,
flag_with_saver=True,
num_steps = 1,
gpu=self.gpu)
else:
print ('Please use with_image_embeddind=1')
sys.exit(-1)
self.model.build()
def load_model(self, model_path):
self.model.saver.restore(self.session, model_path)
self.flag_load_model = True
self.model_path = model_path
logger.info('Load model from %s', model_path)
def feed_visual_feature(self, visual_feature):
assert visual_feature.shape[0] == self.config.vf_size
#assert self.flag_load_model, 'Must call local_model first'
sess = self.session
initial_state = sess.run(fetches="LSTMModel/lstm/initial_state:0",
feed_dict={"LSTMModel/visual_feature:0": visual_feature})
return initial_state
def inference_step(self, input_feed, state_feed):
sess = self.session
softmax_output, state_output = sess.run(
fetches=["LSTMModel/softmax:0", "LSTMModel/lstm/state:0"],
feed_dict={
"LSTMModel/input_feed:0": input_feed,
"LSTMModel/lstm/state_feed:0": state_feed,
})
return softmax_output, state_output, None
def doRun(path):
# # 设置 GPU 按需增长
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# np.set_printoptions(threshold=10000) #全部输出
# sess = tf.Session(config=config)
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(sess=sess, coord=coord)
JavaBean = jclass("com.diy.edu.rd.Model.JavaBean")
jb = JavaBean("python")
jb.println("---------------------------->start<----------------")
jb.println("---------------------------->" + path + "<----------------")
np.set_printoptions(threshold=10000) # 全部输出
sess = tf.Session()
# model = HARClassifier(class_num=class_num)
# model = HARClassifier()
model = LSTMModel()
sess.run(tf.global_variables_initializer())
data = DataGenerater.DataGenerater(path)
for i in range(50):
train(sess, model, data)
test(sess, model, data)
# coord.request_stop()
# coord.join(threads)
saver = tf.train.Saver()
save_dir = path + 'models/'
saver.save(sess, save_dir + '6')
def main():
# # 设置 GPU 按需增长
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# np.set_printoptions(threshold=10000) #全部输出
# sess = tf.Session(config=config)
# coord = tf.train.Coordinator()
# threads = tf.train.start_queue_runners(sess=sess, coord=coord)
np.set_printoptions(threshold=10000) # 全部输出
sess = tf.Session()
# model = HARClassifier(class_num=class_num)
# model = HARClassifier()
model = LSTMModel()
sess.run(tf.global_variables_initializer())
data = DataGenerater.DataGenerater()
for i in range(50):
train(sess, model, data)
test(sess, model, data)
# coord.request_stop()
# coord.join(threads)
saver = tf.train.Saver()
save_dir = 'logs/models/'
saver.save(sess, save_dir + '6')
def load_model(model_save_fn, model_type):
if model_type == 'lstm':
model = LSTMModel.load(model_save_fn)
elif model_type == 'rnn':
model = RNNModel.load(model_save_fn)
elif model_type == 'cnn':
model = CNNModel.load(model_save_fn)
return model
def build_model(self):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.gpu_memory_fraction)
config_proto = tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True)
self.session = session = tf.Session(config=config_proto)
with tf.device('/gpu:%d' % self.gpu):
with tf.variable_scope("LSTMModel", reuse=None):
if self.with_image_embedding:
self.model = LSTMModel(config=self.config,
mode="inference",
model_dir=self.model_dir,
flag_with_saver=True,
num_steps=1,
gpu=self.gpu)
else:
print('Please use with_image_embeddind=1')
sys.exit(-1)
self.model.build()
def train_model(train_data, train_target, word_to_idx, target_to_idx, model_file = "model.pth",
model_type = "LSTM", embedding_dim = 32, hidden_dim = 16, epochs = 10, learning_rate = 0.1, seed = 19):
torch.manual_seed(seed)
## initialize model
if model_type == "LSTM":
model = LSTMModel(embedding_dim, hidden_dim, len(word_to_idx), len(target_to_idx))
loss_function = nn.NLLLoss()
optimizer = optim.SGD(model.parameters(), lr = learning_rate)
st = time.time()
print("training model ...")
# reference: http://pytorch.org/tutorials/beginner/nlp/sequence_models_tutorial.html
count = 0
loss_mean = 0
for epoch in range(epochs):
for sentence, next_word in zip(train_data, train_target):
# Step 1. Remember that Pytorch accumulates gradients.
# We need to clear them out before each instance
model.zero_grad()
# Also, we need to clear out the hidden state of the LSTM,
# detaching it from its history on the last instance.
model.hidden = model.init_hidden()
# Step 2. Get our inputs ready for the network, that is, turn them into
# Variables of word indices.
sentence_in = prepare_sequence(sentence, word_to_idx)
targets = prepare_sequence(next_word, target_to_idx)
# Step 3. Run our forward pass.
scores = model(sentence_in)
# Step 4. Compute the loss, gradients, and update the parameters by
# calling optimizer.step()
loss = loss_function(scores, targets)
loss.backward()
optimizer.step()
loss_mean += loss.data[0]
if count % 100 == 0 and count > 0:
print("%d sentence done. loss mean: %f" % (count,loss_mean/100))
loss_mean = 0
count += 1
print("%d th epoch done. %f sec" % (epoch, time.time() - st))
return model
def build_model(self):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.gpu_memory_fraction)
config_proto = tf.ConfigProto( gpu_options=gpu_options, allow_soft_placement=True)
self.session = session = tf.Session(config=config_proto)
with tf.device('/gpu:%d'%self.gpu):
with tf.variable_scope("LSTMModel", reuse=None):
if self.with_image_embedding:
self.model = LSTMModel(config=self.config, mode="inference",
model_dir = self.model_dir,
flag_with_saver=True,
num_steps = 1,
gpu=self.gpu)
else:
print ('Please use with_image_embeddind=1')
sys.exit(-1)
self.model.build()
x_train, y_train = DataSplitter.split_to_x_and_y(data_train, timesteps=timesteps)
x_val, y_val = DataSplitter.split_to_x_and_y(data_val, timesteps=timesteps)
x_test, y_test = DataSplitter.split_to_x_and_y(data_test, timesteps=timesteps)
print("Train dataset has {} samples.".format(*x_train.shape))
# print(x_train[:3])
# print(y_train[:3])
print("Validation dataset has {} samples.".format(*x_val.shape))
# print(x_val[:3])
# print(y_val[:3])
print("Test dataset has {} samples.".format(*x_test.shape))
# print(x_test[:3])
# print(y_test[:3])
# Build & train model
model = LSTMModel(timesteps, hidden_neurons).build()
print("Fitting the model...")
history = model.fit(x_train, y_train,
batch_size=batchsize, epochs=epochs, validation_data=(x_val, y_val))
# Output training history to csv
history_df = pd.DataFrame(history.history)
history_df.to_csv("training_history.csv")
print("Predicting...")
result = model.predict(x_test)
predicted = pd.DataFrame(result)
predicted.columns = ['predicted_nikkei']
predicted['actual_nikkei'] = y_test
print("Completed Prediction.")
one_hot(c),
one_hot(b),
one_hot(a), plus_vector, equals_vector
])
y.append([
one_hot(c),
one_hot(b),
one_hot(a),
one_hot((a + b + c) % 10),
one_hot((a + b + c) // 10)
])
x = np.array(x)
y = np.array(y)
model = LSTMModel(5, [10, 10], 12, 12)
model.train(x, y, x, y, 10, 5000)
results = model.predict(x)
activations_0 = model.getActivations(0, x)
activations_1 = model.getActivations(1, x)
count = 0
for index, result in enumerate(results):
left = argmax(result[3])
right = argmax(result[4])
leftTarget = argmax(y[index][3])
rightTarget = argmax(y[index][4])
if left == leftTarget and right == rightTarget:
def main(unused_args):
model_dir=utility.get_model_dir(FLAGS)
if os.path.exists(model_dir) and not FLAGS.overwrite:
logger.info('%s exists. quit', model_dir)
sys.exit(0)
# Load model configuration
config_path = os.path.join(os.path.dirname(__file__), 'model_conf', FLAGS.model_name + '.py')
config = utility.load_config(config_path)
# pdb.set_trace()
FLAGS.vf_dir = os.path.join(FLAGS.rootpath, FLAGS.train_collection, 'FeatureData', FLAGS.vf_name)
vocab_file = utility.get_vocab_file(FLAGS.train_collection, FLAGS.word_cnt_thr, FLAGS.rootpath)
textbank = TextBank(vocab_file)
config.vocab_size = len(textbank.vocab)
config.vf_size = int(open(os.path.join(FLAGS.vf_dir, 'shape.txt')).read().split()[1])
if hasattr(config,'num_epoch_save'):
num_epoch_save = config.num_epoch_save
else:
num_epoch_save = 1
# if FLAGS.fluency_method == 'None':
# FLAGS.fluency_method = None
# config.fluency_method = FLAGS.fluency_method
# if config.fluency_method == 'weighted':
# config.use_weighted_loss = True
# else:
# config.use_weighted_loss = False
train_image_embedding = True
try:
if config.train_image_embedding == False:
assert('freeze' in FLAGS.model_name)
train_image_embedding = False
logger.info('Not training image embedding')
except:
pass
with_image_embedding = True if FLAGS.with_image_embedding != 0 else False
# Start model training
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config_proto = tf.ConfigProto(
intra_op_parallelism_threads=FLAGS.ses_threads, gpu_options=gpu_options, allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config=config_proto) as session:
writer = tf.train.SummaryWriter("logs/", session.graph)
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
assert len(config.buckets) >= 1
assert config.buckets[-1] == config.max_num_steps
models = []
with tf.device('gpu:%s'%FLAGS.gpu):
with tf.variable_scope("LSTMModel", reuse=None, initializer=initializer):
if with_image_embedding:
m = LSTMModel(mode='train',
num_steps=config.buckets[0],
config=config,
model_dir=model_dir,
flag_with_saver=True,
train_image_embedding=train_image_embedding)
#model_root=FLAGS.model_root)
else:
# deprecating this function
logger.info('Plz use with_image_embedding=1')
sys.exit(-1)
m.build()
models.append(m)
pre_trained_iter=0
if FLAGS.pre_trained_model_path:
pre_trained_iter = int(FLAGS.pre_trained_model_path.split('model_')[1].split('.')[0])
hdlr = logging.FileHandler(os.path.join(m.model_dir, 'log%d.txt'%pre_trained_iter))
hdlr.setLevel(logging.INFO)
hdlr.setFormatter(logging.Formatter(formatter_log))
logger.addHandler(hdlr)
if FLAGS.pre_trained_model_path:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
models[0].saver.restore(session, FLAGS.pre_trained_model_path)
logger.info('Continue to train from %s', FLAGS.pre_trained_model_path)
elif FLAGS.pre_trained_imembedding_path:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
models[0].imemb_saver.restore(session, FLAGS.pre_trained_imembedding_path)
logger.info('Init image-embedding from %s', FLAGS.pre_trained_imembedding_path)
elif FLAGS.pre_trained_lm_path:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
models[0].lm_saver.restore(session, FLAGS.pre_trained_lm_path)
logger.info('Init language from %s', FLAGS.pre_trained_lm_path)
else:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
# print([v.name for v in tf.trainable_variables()])
iters_done = 0
data_provider = BucketDataProvider(FLAGS.train_collection, vocab_file, FLAGS.vf_name,
language=FLAGS.language, # method=config.fluency_method,
rootpath=FLAGS.rootpath)
for i in range(config.num_epoch):
logger.info('epoch %d', i)
data_provider.shuffle_data_queue() ####################################
train_cost, iters_done, result = run_epoch(session, iters_done, config, models, data_provider , verbose=True)
logger.info("Train cost for epoch %d is %.3f" % (i, train_cost))
writer.add_summary(result, i)
# save the current model if necessary
if (i+1)% num_epoch_save == 0:
models[0].saver.save(session, os.path.join(m.variable_dir,
'model_%d.ckpt' % (iters_done+pre_trained_iter)))
if with_image_embedding:
models[0].imemb_saver.save(session, os.path.join(m.variable_dir, \
'imembedding_model_%d.ckpt' % (iters_done)))
logger.info("Model saved at iteration %d", iters_done)
# copy the configure file in to checkpoint direction
os.system("cp %s %s" % (config_path, model_dir))
if FLAGS.pre_trained_model_path:
os.system("echo %s > %s" % (FLAGS.pre_trained_model_path, os.path.join(model_dir, 'pre_trained_model_path.txt')))
if FLAGS.pre_trained_imembedding_path:
os.system("echo %s > %s" % (FLAGS.pre_trained_imembedding_path, os.path.join(model_dir, 'pre_trained_imembedding_path.txt')))
def main():
"""Program execution starts here."""
intersections = read_intersections("intersections.data")
intersection_list = []
for i in range(len(intersections)):
intersection_list.append(intersections[i]['name'].strip(' '))
#sel_intersection = intersections[0]['name']
sel_intersection = "4589"
df = get_dataset(sel_intersection, intersections)
x_train, x_test, y_train, y_test = preprocessing(df)
model_file = search(intersections, 'name', sel_intersection)
main_menu = [
"CAPSTONE TRAFFIC PREDICTION",
"Select Intersection",
"List of Intersections",
"Train Intersection",
"Test Intersection (Accuracy)",
"Route Check"
]
train_menu = [
"Train Mode:",
"Train from scratch w/ events",
"Train from file w/ events",
"Train from scratch",
"Train from file",
]
route_check_menu = [
"Train Mode:",
"Train from scratch w/ events",
"Train from file w/ events",
"Train from scratch",
"Train from file",
]
while True:
print("Currently Selected Intersection:", sel_intersection)
choice = menu.do_menu(main_menu)
model = LSTMModel(x_train.shape[1], y_train.shape[1])
if choice is None:
return # Exit main() (and program).
if choice == 1:
# Select Intersection
temp_menu = ["Please Select a New Intersection"]
for line in intersections:
option = line["name"] + ": " + line["street"]
temp_menu.append(option)
choice = menu.do_menu(temp_menu)
if choice is not None:
sel_intersection = intersections[choice - 1]['name']
print(sel_intersection, "set as current intersection.")
df = get_dataset(sel_intersection, intersections)
x_train, x_test, y_train, y_test = preprocessing(df)
model_file = search(intersections, 'name', sel_intersection)
if model_file is not None:
model_file = intersections[model_file]['model']
#try:
model.load_network('./model/'+str(sel_intersection)+'.hdf')
#except:
# print("File for loading model is not found! Creating a new model from scratch")
# model.init_network(hidden_size=50)
elif choice == 2:
# List Intersections
print_intersections(intersections)
elif choice == 3:
model = LSTMModel(x_train.shape[1], y_train.shape[1])
# Train Intersections
# TODO test each option
choice = menu.do_menu(train_menu)
if choice == 1:
x_train, x_test, y_train, y_test = preprocessing(df, events=True)
model = LSTMModel(x_train.shape[1], y_train.shape[1])
intersection_idx = search(intersections, 'name', sel_intersection)
model_file = "model/" + sel_intersection + ".hdf"
#if os.path.exists(model_file):
#os.remove(model_file)
model.init_network(hidden_size=50)
elif choice == 2:
x_train, x_test, y_train, y_test = preprocessing(df, events=True)
model = LSTMModel(x_train.shape[1], y_train.shape[1])
intersection_idx = search(intersections, 'name', sel_intersection)
model_file = "model/" + sel_intersection + ".hdf"
if os.path.exists(model_file):
model.load_network(model_file)
else:
print("Model does not exist, starting from scratch")
model.init_network(hidden_size=50)
elif choice == 3:
x_train, x_test, y_train, y_test = preprocessing(df, events=False)
model = LSTMModel(x_train.shape[1], y_train.shape[1])
intersection_idx = search(intersections, 'name', sel_intersection)
model_file = "model/" + sel_intersection + ".hdf"
if os.path.exists(model_file):
os.remove(model_file)
model.init_network(hidden_size=50)
elif choice == 4:
x_train, x_test, y_train, y_test = preprocessing(df, events=False)
model = LSTMModel(x_train.shape[1], y_train.shape[1])
intersection_idx = search(intersections, 'name', sel_intersection)
model_file = "model/" + sel_intersection + ".hdf"
if os.path.exists(model_file):
model.load_network(model_file)
else:
print("Model does not exist, starting from scratch")
model.init_network(hidden_size=50)
try:
e = int(input("Enter Epochs to train for (Default=50): "))
model.epochs = e
except ValueError:
print("Invalid number entered, using default value")
model.train(x_train, y_train, './'+ model_file)
intersections[intersection_idx]['model'] = model_file
save_intersections(intersections, "intersections.data")
elif choice == 4:
# Test Intersections
model_file = "model/" + sel_intersection + ".hdf"
model = LSTMModel(x_train.shape[1], y_train.shape[1])
if os.path.exists(model_file):
model.load_network(model_file)
test_output = model.get_accuracy(x_test, y_test)
N, S, E, W = confusion_matrix(test_output, y_test, True)
else:
print("Please train intersection first")
elif choice == 5:
model = LSTMModel(x_train.shape[1], y_train.shape[1], intersection_list)
# Route Check
x_data = []
day_week = [1,2,3,4,5,6,7]
season=[1,2,3,4]
time_str = ''
flag = 0
x_data = []
peak = 0
while flag == 0:
num_inter = input("Please enter intersection number: ")
if num_inter in intersection_list:
flag = 1
num_inter = int(num_inter)
#x_data.append(int(num_inter))
else:
print("Intersection not found!")
flag = 0
while flag == 0:
week = [0,0,0,0,0,0,0]
num_day = input("Please enter the day of the week:\nOptions:\n1:Sunday\n2:Monday\n3:Tuesday\n4:Wednesday\n5:Thursday\n6:Friay\n7:Saturday\n")
num_day = int(num_day)
if num_day in day_week:
flag = 1
week[num_day-1] = 1
x_data = x_data + week
else:
print("Day of the week not found!")
flag = 0
while flag == 0:
time_day = input("Please enter the time of the day (ex. 17:30): ")
temp = time_day.split(':')
if len(temp) == 2:
hour = int(temp[0]) * 60
if hour > 9 and hour < 17:
peak = 1
time = hour + int(temp[1])
time_d = float(time) / float(1440)
if time_d > 1.0:
print("Please enter time in the proper format!")
else:
x_data.append(time_d)
flag = 1
else:
print("Please enter time in the proper format!")
flag = 0
while flag == 0:
seasons = [0,0,0,0]
season_input = input("Please enter the season:\n1:Summer\n2:Fall\n3:Winter\n4:Spring\n")
season_input = int(season_input)
if season_input in season:
flag = 1
seasons[season_input-1] = 1
x_data = x_data + seasons
else:
print("Season not found!")
# add [0,0] for events
x_data.append(peak)
x_data = x_data + [0,0]
x_test = np.array([x_data])
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
res = model.predict(x_test, num_inter)
print("Prediction: " + str(res))
class InferenceWrapper(object):
"""Model wrapper class for performing inference with a ShowAndTellModel."""
def __init__(self,
config,
model_dir,
ses_threads=2,
length_normalization_factor=0.0,
gpu_memory_fraction=0.3,
gpu=1,
with_image_embedding=True):
self.config = copy.deepcopy(config)
self.config.batch_size = 1
self.flag_load_model = False
self.model_dir = model_dir
self.gpu = gpu
self.gpu_memory_fraction = gpu_memory_fraction
self.with_image_embedding = with_image_embedding
def build_model(self):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=self.gpu_memory_fraction)
config_proto = tf.ConfigProto(gpu_options=gpu_options,
allow_soft_placement=True)
self.session = session = tf.Session(config=config_proto)
with tf.device('/gpu:%d' % self.gpu):
with tf.variable_scope("LSTMModel", reuse=None):
if self.with_image_embedding:
self.model = LSTMModel(config=self.config,
mode="inference",
model_dir=self.model_dir,
flag_with_saver=True,
num_steps=1,
gpu=self.gpu)
else:
print('Please use with_image_embeddind=1')
sys.exit(-1)
self.model.build()
def load_model(self, model_path):
self.model.saver.restore(self.session, model_path)
self.flag_load_model = True
self.model_path = model_path
logger.info('Load model from %s', model_path)
def feed_visual_feature(self, visual_feature):
assert visual_feature.shape[0] == self.config.vf_size
#assert self.flag_load_model, 'Must call local_model first'
sess = self.session
initial_state = sess.run(
fetches="LSTMModel/lstm/initial_state:0",
feed_dict={"LSTMModel/visual_feature:0": visual_feature})
return initial_state
def inference_step(self, input_feed, state_feed):
sess = self.session
softmax_output, state_output = sess.run(
fetches=["LSTMModel/softmax:0", "LSTMModel/lstm/state:0"],
feed_dict={
"LSTMModel/input_feed:0": input_feed,
"LSTMModel/lstm/state_feed:0": state_feed,
})
return softmax_output, state_output, None
from torch import Tensor
from lstm_model import LSTMModel
from saa import MovieDataLoader
from tqdm import tqdm
dataset = MovieDataLoader()
tr_dl, val_dl, test_dl = dataset.tr_dl, dataset.val_dl, dataset.test_dl
vocab_size = len(dataset.TEXT.vocab)
input_dim = 28
hidden_dim = 128
layer_dim = 1 # ONLY CHANGE IS HERE FROM ONE LAYER TO TWO LAYER
output_dim = 2
model = LSTMModel(vocab_size, hidden_dim, layer_dim, output_dim)
criterion = nn.BCELoss()
learning_rate = 0.1
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
def test(loss_fn, model, data_loader, device):
if model.training:
model.eval()
total_loss = 0
correct_count = 0
for step, batch in tqdm(enumerate(data_loader), total=len(data_loader)):
])
y.append([
one_hot(b) + [b / 10.0],
one_hot(a) + [a / 10.0],
one_hot((a + b) % 10) + [((a + b) % 10) / 10.0],
one_hot((a + b) // 10) + [((a + b) // 10) / 10.0]
])
sys.exit(0)
x = np.array(x)
y = np.array(y)
test_x = np.array(test_x)
test_y = np.array(test_y)
model = LSTMModel(4, [100, 100], 14, 11)
model.train(x, y, test_x, test_y, 10, 5000)
results = model.predict(x)
activations_0 = model.getActivations(0, x)
activations_1 = model.getActivations(1, x)
count = 0
count_ordinal = 0
for index, result in enumerate(results):
left = argmax(result[2][:-1])
right = argmax(result[3][:-1])
leftTarget = argmax(y[index][2][:-1])
rightTarget = argmax(y[index][3][:-1])
def main(unused_args):
train_collection = FLAGS.train_collection
val_collection = FLAGS.val_collection
overwrite = FLAGS.overwrite
output_dir = utility.get_sim_dir(FLAGS)
loss_info_file = os.path.join(output_dir, 'loss_info.txt')
if os.path.exists(loss_info_file) and not overwrite:
logger.info('%s exists. quit', loss_info_file)
sys.exit(0)
model_dir=utility.get_model_dir(FLAGS)
config_path = os.path.join(os.path.dirname(__file__), 'model_conf', FLAGS.model_name + '.py')
config = utility.load_config(config_path)
# if FLAGS.fluency_method == 'None':
# FLAGS.fluency_method = None
# config.fluency_method = FLAGS.fluency_method
# if config.fluency_method == 'weighted':
# config.use_weighted_loss = True
# else:
# config.use_weighted_loss = False
textbank = TextBank(utility.get_train_vocab_file(FLAGS))
config.vocab_size = len(textbank.vocab)
config.vf_size = int(open(os.path.join(utility.get_val_feat_dir(FLAGS), 'shape.txt')).read().split()[1])
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config_proto = tf.ConfigProto(
intra_op_parallelism_threads=FLAGS.ses_threads, gpu_options=gpu_options, allow_soft_placement=True)
with_image_embedding = True if FLAGS.with_image_embedding > 0 else False
with tf.Graph().as_default(), tf.Session(config=config_proto) as session:
assert len(config.buckets) >= 1
assert config.buckets[-1] == config.max_num_steps
with tf.device('/gpu:%d'%FLAGS.gpu):
with tf.variable_scope("LSTMModel", reuse=None):
if with_image_embedding:
model = LSTMModel(mode='eval',
num_steps=config.buckets[-1],
config=config,
model_dir=model_dir, #model_name=FLAGS.model_name,
flag_with_saver=True)
#model_root=FLAGS.model_root)
else:
# deprecating this option
print('Plz use image_embedding')
sys.exit(-1)
model.build()
model_path_list = []
_dir = os.path.join(model_dir,'variables')
for _file in os.listdir(_dir):
if _file.startswith('model_') and _file.endswith('.ckpt.meta'):
iter_n = int(_file[6:-10])
model_path = os.path.join(_dir, 'model_%d.ckpt'%iter_n)
model_path_list.append((iter_n, model_path))
data_provider = BucketDataProvider(val_collection, utility.get_train_vocab_file(FLAGS),
feature=FLAGS.vf_name,
language=FLAGS.language,
flag_shuffle=False,
# method=config.fluency_method,
rootpath=FLAGS.rootpath)
iter2loss = {}
for iter_n, model_path in model_path_list:
loss_file = os.path.join(output_dir, 'model_%d.ckpt' % iter_n, 'loss.txt')
if os.path.exists(loss_file) and not overwrite:
logger.info('load loss from %s', loss_file)
loss = float(open(loss_file).readline().strip())
iter2loss[iter_n] = loss
continue
if not os.path.exists(os.path.split(loss_file)[0]):
os.makedirs(os.path.split(loss_file)[0])
model.saver.restore(session, model_path)
# print([v.name for v in tf.trainable_variables()])
logger.info('Continue to train from %s', model_path)
val_cost = run_epoch(session, config.batch_size, config.buckets[-1], config,model, data_provider)
logger.info("Validation cost for checkpoint model_%d.ckpt is %.3f" % (iter_n, val_cost))
iter2loss[iter_n] = val_cost
with open(loss_file, "w") as fw:
fw.write('%g' % val_cost)
fw.close()
sorted_iter2loss = sorted(iter2loss.iteritems(), key=lambda x: x[1])
with open(loss_info_file, 'w') as fw:
fw.write('\n'.join(['%d %g' % (iter_n, loss) for (iter_n, loss) in sorted_iter2loss]))
fw.close()
dataLoader = DataLoader(src="MNIST_data/dataset")
[
noisyTrain,
noisySymbolsTrain,
targetsTrain,
noisyValidation,
noisySymbolsValidation,
targetsValidation,
noisyTest,
noisySymbolsTest,
targetsTest
] = dataLoader.getSequencialDataNoClassification(i, 2, 2)
model = LSTMModel(3, [512], 784, 20)
model.train(noisyTrain, targetsTrain, noisyValidation, targetsValidation, 100, 50)
result = model.evaluate(noisyTest, targetsTest)
#predictions = model.predict(noisyTest)
#utils.renderResults("results_ideal" + str(i) + ".png", noisyTest, predictions)
results.append(result[1])
total += result[1]
print(results)
print("SCORE: " + str(total / k))
#No Classification
#[0.26166666626930235, 0.4266666686534882, 0.4099999964237213, 0.2849999988079071, 0.28833332896232605]
#SCORE: 0.33433333182334896
from lstm_model import LSTMModel
if __name__ == "__main__":
lstm_model = LSTMModel.create_from_args()
lstm_model.run()
valid_batch_size = 64
n_conv_stack = 120
conv_size = 5
corpus = Corpus.load_from_file(r'imdb\imdb-prepared.pkl')
(train_x, train_mask, train_y), (valid_x, valid_mask, valid_y), (test_x, test_mask, test_y) = corpus.train_valid_test()
n_train, n_valid, n_test = len(train_x), len(valid_x), len(test_x)
class_dim = np.max(train_y) + 1
rng = np.random.RandomState(1224)
th_rng = RandomStreams(1226)
gensim_w2v = Word2Vec.load(r'w2v\enwiki.w2v')
lstm = LSTMModel(corpus, n_emb=n_emb, n_hidden=n_hidden, pooling='max', gensim_w2v=gensim_w2v)
# test whether emb values are replaced by word2vec model results
# lstm_emb = lstm.model_layers[0]
# idx = 1001
# W_values = lstm_emb.W.get_value()
# vec = w2v.wv[corpus.dic._idx2word[idx]]
# W_values[idx]
# print(W_values[idx]/vec)
# test function compile
f = theano.function([lstm.x, lstm.mask, lstm.y], lstm.cost)
batch_idx_seq = np.arange(batch_size)
print(f(train_x[batch_idx_seq], train_mask[batch_idx_seq], train_y[batch_idx_seq]))
# # x: (batch size, n_words/steps)
def main(unused_args):
model_dir = utility.get_model_dir(FLAGS)
if os.path.exists(model_dir) and not FLAGS.overwrite:
logger.info('%s exists. quit', model_dir)
sys.exit(0)
# Load model configuration
config_path = os.path.join(os.path.dirname(__file__), 'model_conf',
FLAGS.model_name + '.py')
config = utility.load_config(config_path)
FLAGS.vf_dir = os.path.join(FLAGS.rootpath, FLAGS.train_collection,
'FeatureData', FLAGS.vf_name)
vocab_file = utility.get_vocab_file(FLAGS.train_collection,
FLAGS.word_cnt_thr, FLAGS.rootpath)
textbank = TextBank(vocab_file)
config.vocab_size = len(textbank.vocab)
config.vf_size = int(
open(os.path.join(FLAGS.vf_dir, 'shape.txt')).read().split()[1])
if hasattr(config, 'num_epoch_save'):
num_epoch_save = config.num_epoch_save
else:
num_epoch_save = 1
if FLAGS.fluency_method == 'None':
FLAGS.fluency_method = None
config.fluency_method = FLAGS.fluency_method
if config.fluency_method == 'weighted':
config.use_weighted_loss = True
else:
config.use_weighted_loss = False
train_image_embedding = True
try:
if config.train_image_embedding == False:
assert ('freeze' in FLAGS.model_name)
train_image_embedding = False
logger.info('Not training image embedding')
except:
pass
with_image_embedding = True if FLAGS.with_image_embedding != 0 else False
# Start model training
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config_proto = tf.ConfigProto(
intra_op_parallelism_threads=FLAGS.ses_threads,
gpu_options=gpu_options,
allow_soft_placement=True)
with tf.Graph().as_default(), tf.Session(config=config_proto) as session:
initializer = tf.random_uniform_initializer(-config.init_scale,
config.init_scale)
assert len(config.buckets) >= 1
assert config.buckets[-1] == config.max_num_steps
models = []
with tf.device('gpu:%s' % FLAGS.gpu):
with tf.variable_scope("LSTMModel",
reuse=None,
initializer=initializer):
if with_image_embedding:
m = LSTMModel(mode='train',
num_steps=config.buckets[0],
config=config,
model_dir=model_dir,
flag_with_saver=True,
train_image_embedding=train_image_embedding)
#model_root=FLAGS.model_root)
else:
# deprecating this function
logger.info('Plz use with_image_embedding=1')
sys.exit(-1)
'''m = PCALSTMModel(mode='train',
num_steps=config.buckets[0],
config=config,
model_name=FLAGS.model_name,
flag_with_saver=True,
train_image_embedding=train_image_embedding,
model_root=FLAGS.model_root)
'''
m.build()
models.append(m)
pre_trained_iter = 0
if FLAGS.pre_trained_model_path:
pre_trained_iter = int(
FLAGS.pre_trained_model_path.split('model_')[1].split('.')[0])
hdlr = logging.FileHandler(
os.path.join(m.model_dir, 'log%d.txt' % pre_trained_iter))
hdlr.setLevel(logging.INFO)
hdlr.setFormatter(logging.Formatter(formatter_log))
logger.addHandler(hdlr)
if FLAGS.pre_trained_model_path:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
models[0].saver.restore(session, FLAGS.pre_trained_model_path)
logger.info('Continue to train from %s',
FLAGS.pre_trained_model_path)
elif FLAGS.pre_trained_imembedding_path:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
models[0].imemb_saver.restore(session,
FLAGS.pre_trained_imembedding_path)
logger.info('Init image-embedding from %s',
FLAGS.pre_trained_imembedding_path)
elif FLAGS.pre_trained_lm_path:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
models[0].lm_saver.restore(session, FLAGS.pre_trained_lm_path)
logger.info('Init language from %s', FLAGS.pre_trained_lm_path)
else:
if tf.__version__ < '1.0':
tf.initialize_all_variables().run()
else:
tf.global_variables_initializer().run()
# print([v.name for v in tf.trainable_variables()])
iters_done = 0
data_provider = BucketDataProvider(FLAGS.train_collection,
vocab_file,
FLAGS.vf_name,
language=FLAGS.language,
method=config.fluency_method,
rootpath=FLAGS.rootpath)
for i in range(config.num_epoch):
logger.info('epoch %d', i)
data_provider.shuffle_data_queue()
train_cost, iters_done = run_epoch(session,
iters_done,
config,
models,
data_provider,
verbose=True)
logger.info("Train cost for epoch %d is %.3f" % (i, train_cost))
# save the current model if necessary
if (i + 1) % num_epoch_save == 0:
models[0].saver.save(
session,
os.path.join(
m.variable_dir,
'model_%d.ckpt' % (iters_done + pre_trained_iter)))
if with_image_embedding:
models[0].imemb_saver.save(session, os.path.join(m.variable_dir, \
'imembedding_model_%d.ckpt' % (iters_done)))
logger.info("Model saved at iteration %d", iters_done)
# copy the configure file in to checkpoint direction
os.system("cp %s %s" % (config_path, model_dir))
if FLAGS.pre_trained_model_path:
os.system("echo %s > %s" %
(FLAGS.pre_trained_model_path,
os.path.join(model_dir, 'pre_trained_model_path.txt')))
if FLAGS.pre_trained_imembedding_path:
os.system(
"echo %s > %s" %
(FLAGS.pre_trained_imembedding_path,
os.path.join(model_dir, 'pre_trained_imembedding_path.txt')))
def test_init(self):
self.assertIsInstance(LSTMModel(1, 1, 1, 1), LSTMModel)
from lstm_model import LSTMModel
if __name__ == "__main__":
lstm_model = LSTMModel.create_from_args(is_sample_mode=True)
lstm_model.run()
model.fit(session, saver)
with open(model.config.conll_output, 'w') as f:
write_conll(f, output)
with open(model.config.eval_output, 'w') as f:
for sentence, labels, predictions in output:
print_sentence(f, sentence, labels, predictions)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-t', action='store_true')
parser.add_argument('--top10eval', action='store_true')
x = parser.parse_args()
config = Config()
embeddings, tokens = loadWordVectors()
config.embed_size = embeddings.shape[1]
config.vocab_size = len(tokens)
if x.t:
graph = tf.Graph()
with graph.as_default():
model = LSTMModel(config, embeddings, tokens)
top10(config, embeddings, tokens, model)
elif x.top10eval:
graph = tf.Graph()
with graph.as_default():
model = LSTMModel(config, embeddings, tokens)
eval_test(embeddings, tokens, model)
else:
main(config, embeddings, tokens)
test_y = []
test_result = []
for x, y, r in testSequances:
test_x.append(x)
test_y.append(y)
test_result.append(r)
train_x = np.array(train_x)
train_y = np.array(train_y)
test_x = np.array(test_x)
test_y = np.array(test_y)
print(len(train_x))
print(len(test_x))
model = LSTMModel(3, [512, 512], 10, 20)
model.train(train_x, train_y, test_x, test_y, 10, 200)
results = model.predict(test_x)
count = 0
total = 0
for index, result in enumerate(results):
left = argmax(result[2][:10])
right = argmax(result[2][10:])
if left * 10 + right == test_result[index]:
count += 1
total += 1
print("SCORE: " + str(count / float(total)))
sys.dont_write_bytecode = True
from data import DataLoader
from lstm_model import LSTMModel
from feed_forward_model import FeedForwardModel
k = 5
results = []
total = 0
for i in range(k):
dataLoader = DataLoader(src="MNIST_data/dataset")
[
noisyTrain, noisySymbolsTrain, targetsTrain, noisyValidation,
noisySymbolsValidation, targetsValidation, noisyTest, noisySymbolsTest,
targetsTest
] = dataLoader.getSequencialData(i, 2, 2)
model = LSTMModel(2, [512, 512], 1568, 20)
model.train(noisySymbolsTrain, targetsTrain, noisySymbolsValidation,
targetsValidation, 100, 200)
result = model.evaluate(noisySymbolsTest, targetsTest)
results.append(result[1])
total += result[1]
print(results)
print("SCORE: " + str(total / k))
k = 5
tctoScores = []
tcfoScores = []
fctoScores = []
fcfoScores = []
epochs = []
k_fold_index = 0
bestModel = None
maxAcccuracy = 0
while k_fold_index < k:
[train_x, train_y, val_x, val_y, test_x,
test_y] = dataLoader.getData(k_fold_index, 4, 4, num_with_symbols)
model = LSTMModel(4, [512, 512], 784, 50)
epoch = model.train(train_x, train_y, val_x, val_y, 100, 200)
epochs.append(epoch)
results = model.predict(test_x)
tcto = 0
tcfo = 0
fcto = 0
fcfo = 0
total = 0
for index, result in enumerate(results):
left = argmax(result[3][-20:-10])
right = argmax(result[3][-10:])
temperature(0) + [0, 0, 1]
])
y.append([
temperature(b),
temperature(a),
temperature((a + b) % 10),
temperature((a + b) // 10)
])
x = np.array(x)
y = np.array(y)
test_x = np.array(test_x)
test_y = np.array(test_y)
model = LSTMModel(4, [20, 20], 12, 9)
model.train(x, y, x, y, 10, 5000)
results = model.predict(test_x)
activations_0 = model.getActivations(0, test_x)
activations_1 = model.getActivations(1, test_x)
count = 0
for index, result in enumerate(results):
right = temperatureToInt(result[2])
left = temperatureToInt(result[3])
rightTarget = temperatureToInt(test_y[index][2])
leftTarget = temperatureToInt(test_y[index][3])
if left == leftTarget and right == rightTarget:
else:
x.append([
temperature(b) + [1, 0, 0],
temperature(a) + [1, 0, 0],
temperature(0) + [0, 1, 0],
temperature(0) + [0, 0, 1]
])
y.append([[b / 10.0], [a / 10.0], [((a + b) % 10) / 10.0],
[((a + b) // 10) / 10.0]])
x = np.array(x)
y = np.array(y)
test_x = np.array(test_x)
test_y = np.array(test_y)
model = LSTMModel(4, [512, 512], 12, 1)
model.train(x, y, test_x, test_y, 10, 5000)
results = model.predict(x)
count = 0
for index, result in enumerate(results):
left = round(result[2][0], 1)
right = round(result[3][0], 1)
leftTarget = round(y[index][2][0], 1)
rightTarget = round(y[index][3][0], 1)
if left == leftTarget and right == rightTarget:
count += 1
len(trainSamples[b]) - 1)]
val_x.append([right, left, operators[key], equals])
val_y.append([
temperature(b),
temperature(a), rightTarget, leftTarget
])
train_x = np.array(train_x)
train_y = np.array(train_y)
val_x = np.array(val_x)
val_y = np.array(val_y)
test_x = np.array(test_x)
test_y = np.array(test_y)
model = LSTMModel(4, [10, 100], 784, 9)
model.train(train_x, train_y, val_x, val_y, 100, 5000)
results = model.predict(val_x)
count = 0
for index, result in enumerate(results):
left = result[2]
right = result[3]
leftTarget = val_y[index][2]
rightTarget = val_y[index][3]
if is_temperature(left, leftTarget) and is_temperature(right, rightTarget):
count += 1
def train_with_validation(train_set, valid_set, corpus,
n_hidden=128, n_emb=128, batch_size=32, conv_size=5,
pooling_type='mean', model_type='lstm', w2v_fn=None,
model_save_fn=None, disp_proc=True):
'''pooling_type: mean or max
model_type: lstm, rnn or cnn
use_w2v: whether to use pre-trained embeddings from word2vec
'''
# Only train_set is converted by theano.shared
train_x, train_mask, train_y = [theano.shared(_) for _ in train_set]
valid_x, valid_mask, valid_y = valid_set
n_train, n_valid = len(train_x.get_value()), len(valid_x)
print("%d training examples" % n_train)
print("%d validation examples" % n_valid)
rng = np.random.RandomState(1224)
th_rng = RandomStreams(1224)
if model_save_fn is None:
model_save_fn = os.path.join('model-res', '%s-%s' % (model_type, pooling_type))
# Load Word2Vec
if w2v_fn is None:
gensim_w2v = None
else:
print('Loading word2vec model...')
if not os.path.exists(w2v_fn):
raise Exception("Word2Vec model doesn't exist!", model_type)
gensim_w2v = Word2Vec.load(w2v_fn)
# Define Model
if model_type == 'lstm':
model = LSTMModel(corpus, n_emb, n_hidden, pooling_type,
rng=rng, th_rng=th_rng, gensim_w2v=gensim_w2v)
elif model_type == 'rnn':
model = RNNModel(corpus, n_emb, n_hidden, pooling_type,
rng=rng, th_rng=th_rng, gensim_w2v=gensim_w2v)
elif model_type == 'cnn':
model = CNNModel(corpus, n_emb, n_hidden, batch_size, conv_size, pooling_type,
rng=rng, th_rng=th_rng, gensim_w2v=gensim_w2v)
else:
raise Exception("Invalid model type!", model_type)
x, mask, y = model.x, model.mask, model.y
batch_idx_seq, use_noise = model.batch_idx_seq, model.use_noise
f_update_1_gr = theano.function(inputs =[batch_idx_seq],
outputs=model.cost,
updates=model.gr_updates,
givens ={x: train_x[batch_idx_seq],
mask: train_mask[batch_idx_seq],
y: train_y[batch_idx_seq]},
on_unused_input='ignore')
f_update_2_gr_sqr = theano.function(inputs=[], updates=model.gr_sqr_updates)
f_update_3_dp_sqr = theano.function(inputs=[], updates=model.dp_sqr_updates)
f_update_4_params = theano.function(inputs=[], updates=model.param_updates)
# keep validation set consistent
keep_tail = False if model_type == 'cnn' else True
valid_idx_batches = get_minibatches_idx(n_valid, batch_size, keep_tail=keep_tail)
valid_y = np.concatenate([valid_y[idx_batch] for idx_batch in valid_idx_batches])
# train the model
patience = 5000
patience_increase = 2
improvement_threshold = 0.995
disp_freq = 20
validation_freq = 100
max_epoch = 500
best_iter = 0
best_validation_err = np.inf
epoch = 0
uidx = 0
done_looping = False
start_time = time.time()
while (epoch < max_epoch) and (not done_looping):
epoch += 1
# Get new shuffled index for the training set. use rng to make result keep same with specific random-seed
for idx_batch in get_minibatches_idx(n_train, batch_size, shuffle=True, rng=rng, keep_tail=keep_tail):
uidx += 1
use_noise.set_value(1.)
cost = f_update_1_gr(idx_batch)
f_update_2_gr_sqr()
f_update_3_dp_sqr()
f_update_4_params()
if uidx % disp_freq == 0 and disp_proc:
print('epoch %i, minibatch %i, train cost %f' % (epoch, uidx, cost))
if uidx % validation_freq == 0:
use_noise.set_value(0.)
valid_y_pred = [model.predict_func(valid_x[idx_batch], valid_mask[idx_batch]) for idx_batch in valid_idx_batches]
valid_y_pred = np.concatenate(valid_y_pred)
this_validation_err = (valid_y_pred != valid_y).mean()
print('epoch %i, minibatch %i, validation error %f %%' % (epoch, uidx, this_validation_err*100))
if this_validation_err < best_validation_err:
if this_validation_err < best_validation_err*improvement_threshold:
patience = max(patience, uidx*patience_increase)
best_validation_err = this_validation_err
best_iter = uidx
model.save(model_save_fn)
if patience < uidx:
done_looping = True
break
end_time = time.time()
print('Optimization complete with best validation score of %f %%, at iter %d' % (best_validation_err * 100, best_iter))
print('The code run for %d epochs, with %f epochs/sec' % (epoch, epoch / (end_time - start_time)))
def main(unused_args):
train_collection = FLAGS.train_collection
val_collection = FLAGS.val_collection
overwrite = FLAGS.overwrite
output_dir = utility.get_sim_dir(FLAGS)
loss_info_file = os.path.join(output_dir, 'loss_info.txt')
if os.path.exists(loss_info_file) and not overwrite:
logger.info('%s exists. quit', loss_info_file)
sys.exit(0)
model_dir = utility.get_model_dir(FLAGS)
config_path = os.path.join(os.path.dirname(__file__), 'model_conf',
FLAGS.model_name + '.py')
config = utility.load_config(config_path)
if FLAGS.fluency_method == 'None':
FLAGS.fluency_method = None
config.fluency_method = FLAGS.fluency_method
if config.fluency_method == 'weighted':
config.use_weighted_loss = True
else:
config.use_weighted_loss = False
textbank = TextBank(utility.get_train_vocab_file(FLAGS))
config.vocab_size = len(textbank.vocab)
config.vf_size = int(
open(os.path.join(utility.get_val_feat_dir(FLAGS),
'shape.txt')).read().split()[1])
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=FLAGS.gpu_memory_fraction)
config_proto = tf.ConfigProto(
intra_op_parallelism_threads=FLAGS.ses_threads,
gpu_options=gpu_options,
allow_soft_placement=True)
with_image_embedding = True if FLAGS.with_image_embedding > 0 else False
with tf.Graph().as_default(), tf.Session(config=config_proto) as session:
assert len(config.buckets) >= 1
assert config.buckets[-1] == config.max_num_steps
with tf.device('/gpu:%d' % FLAGS.gpu):
with tf.variable_scope("LSTMModel", reuse=None):
if with_image_embedding:
model = LSTMModel(
mode='eval',
num_steps=config.buckets[-1],
config=config,
model_dir=model_dir, #model_name=FLAGS.model_name,
flag_with_saver=True)
#model_root=FLAGS.model_root)
else:
# deprecating this option
print('Plz use image_embedding')
sys.exit(-1)
model.build()
model_path_list = []
_dir = os.path.join(model_dir, 'variables')
for _file in os.listdir(_dir):
if _file.startswith('model_') and _file.endswith('.ckpt.meta'):
iter_n = int(_file[6:-10])
model_path = os.path.join(_dir, 'model_%d.ckpt' % iter_n)
model_path_list.append((iter_n, model_path))
data_provider = BucketDataProvider(val_collection,
utility.get_train_vocab_file(FLAGS),
feature=FLAGS.vf_name,
language=FLAGS.language,
flag_shuffle=False,
method=config.fluency_method,
rootpath=FLAGS.rootpath)
iter2loss = {}
for iter_n, model_path in model_path_list:
loss_file = os.path.join(output_dir, 'model_%d.ckpt' % iter_n,
'loss.txt')
if os.path.exists(loss_file) and not overwrite:
logger.info('load loss from %s', loss_file)
loss = float(open(loss_file).readline().strip())
iter2loss[iter_n] = loss
continue
if not os.path.exists(os.path.split(loss_file)[0]):
os.makedirs(os.path.split(loss_file)[0])
model.saver.restore(session, model_path)
# print([v.name for v in tf.trainable_variables()])
logger.info('Continue to train from %s', model_path)
val_cost = run_epoch(session, config.batch_size,
config.buckets[-1], config, model,
data_provider)
logger.info(
"Validation cost for checkpoint model_%d.ckpt is %.3f" %
(iter_n, val_cost))
iter2loss[iter_n] = val_cost
with open(loss_file, "w") as fw:
fw.write('%g' % val_cost)
fw.close()
sorted_iter2loss = sorted(iter2loss.iteritems(), key=lambda x: x[1])
with open(loss_info_file, 'w') as fw:
fw.write('\n'.join(
['%d %g' % (iter_n, loss) for (iter_n, loss) in sorted_iter2loss]))
fw.close()
dataLoader = DataLoader(src="MNIST_data/dataset")
[
noisyTrain,
noisySymbolsTrain,
targetsTrain,
noisyValidation,
noisySymbolsValidation,
targetsValidation,
noisyTest,
noisySymbolsTest,
targetsTest
] = dataLoader.getSequencialDataWithDontCare(0, 2, 2)
model = LSTMModel(2, [512, 512, 512], 1568, 20)
model.train(noisyTrain, targetsTrain, noisyValidation, targetsValidation, 100, 100)
result = model.evaluate(noisyTest, targetsTest)
predictions = model.predict(noisyTest)
count = 0
for index in range(len(predictions)):
a = utils.argmax(predictions[index][:10])
b = utils.argmax(predictions[index][10:])
predicted = a * 10 + b
a = utils.argmax(targetsTest[index][:10])
b = utils.argmax(targetsTest[index][10:])
actual = a * 10 + b
if actual == predicted:
count += 1