def getAdministratorInfo():
new = False #boolean to allow for using new/load old data
socketChat = False #boolean to allow for socket convo
adminIn = input(
"Would you like to train the agent on new data/create a new agent? (y/n) "
)
#adminIn = 'y'
if adminIn == 'y':
new = True
if new:
data.read()
config.createModel()
model.create(config.training, config.output)
print("Model created")
else:
data.load()
config.createModel()
model.load()
print("Model loaded")
socket = input(
"Would you like to watch a conversation with another chatbot? (y/n) ")
if socket == 'y':
socketChat = True
if socketChat:
config.socketchat = True
else:
config.socketchat = False
def __init__(self):
super().__init__()
self.cache = CacheML(self.disk)
self.ts = 0
self.accbmps_proc = {}
self.accbmp = AccBmp()
model.load()
def login():
model.load()
account = view.get_acct()
pin = view.get_pin()
if pin == model.pin_check(account, pin):
mainmenu(account)
else:
view.bad_input()
def run():
model.load()
while True:
user_account = login_menu(
) # returns the logged in user or None for quit
if user_account == None: # login menu returns None if 'quit' is selected
break
else:
main_menu(
user_account
) # when the user exits the main menu they will go back to login
def create_account():
while True:
model.load()
view.create_account()
account = random.randint(1, 10000)
first_name = view.first_name()
last_name = view.last_name()
create_pin = view.create_pin()
model.create_acct(account, first_name, last_name, create_pin)
view.new_account(account)
model.save()
return
def eval(args):
# load model
model = get_model(args, 0, args.r, from_ckpt=True, train=False)
model.load(args.logname) # from default checkpoint
if args.wav_file_list:
with open(args.wav_file_list) as f:
for line in f:
try:
print line.strip()
upsample_wav(line.strip(), args, model)
except EOFError:
print 'WARNING: Error reading file:', line.strip()
def run():
model.load()
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
bytes2boxes_pb2_grpc.add_Bytes2BoxesServicer_to_server(
Bytes2Boxes(), server)
server.add_insecure_port('[::]:%i' % args.port)
server.start()
print('started image2boxes service on port %i' % args.port)
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
def create_model(self):
import model, rnn
if 'model_path' in self.train_info and os.path.exists(
self.train_info['model_path']):
model = model.AudioModel(rnn.BatchRNNLayers, config=None)
model.load(self.train_info['model_path'])
else:
model = model.AudioModel(rnn.BatchRNNLayers,
config=self.args.__dict__)
if torch.cuda.is_available():
if self.args.gpu == -1:
model = torch.nn.DataParallel(model).cuda()
else:
model = model.cuda()
return model
def run():
# Load the model.
model.load(args.pretrained_model_path)
# Initialize the server.
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10))
image2faces_pb2_grpc.add_Image2FacesServicer_to_server(
Image2Faces(), server)
server.add_insecure_port('[::]:%i' % args.port)
server.start()
print('started image2faces service on port %i' % args.port)
# Start infinite loop.
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
def generate_text(input_keywords):
path = os.getcwd()
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--data-dir',
type=str,
default=path,
help='data directory containing input.txt')
parser.add_argument('--seed',
type=str,
default=input_keywords,
help='seed string for sampling')
parser.add_argument('--length',
type=int,
default=int(1.5 * len(input_keywords)),
help='length of the sample to generate'
) #change the '8' to change number of words
parser.add_argument('--diversity',
type=float,
default=0.01,
help='Sampling diversity')
args, unknown = parser.parse_known_args()
model = load(args.data_dir)
del args.data_dir
sentence = model.sample(**vars(args))
return sentence
def show_unit_tasks(path, parameters, env):
group_container = parameters['groups']
group_path = model.parent(path)+'/'+group_container
groups = model.load(group_path)
person_groups = {}
for g in groups:
model.traverse(group_path+'/'+g, lambda (path, task, d): collect_persons(g, path, person_groups), all_nodes=True )
persons = set()
for group in person_groups.values():
for p in group:
persons.add(p)
dates, slots, s = prepare_people_tasks( persons )
schedules = {}
for i in s:
schedules[i[0]]=i
group_schedules = []
for g in person_groups.keys():
group_sched = [ g, len(slots)*[0.0], 0, 0, {'url': group_path+'/'+g+'/planning/'} ]
for _, person in person_groups[g]:
group_sched[1] = [ group_sched[1][i] + schedules[person][1][i] for i in range(0,len(slots)) ]
group_sched[2] += schedules[person][2]
group_sched[3] += schedules[person][3]
n = len(person_groups[g])*1.0
group_sched[1] = [ i/n for i in group_sched[1] ]
group_sched[2] /= n
group_sched[3] /= n
group_schedules.append(group_sched)
return visualize.render(dates, slots, sorted(group_schedules), variables={'qs':urlparse.parse_qs(env['QUERY_STRING']), 'context':'/', 'path':path, 'sum':True, 'url': path+'/', 'refreshable': False }), "text/html"
def test_normal(self):
mdl = model.load('models/oneanim', 60)
self.assertIsNotNone(mdl['default'])
anim = mdl['default']
self.assertTrue(anim.loop)
self.assertEquals(anim.ticks_per_frame, 20)
self.assertEquals(len(anim.frames), 3)
def receive(theModel, src, tag):
weights = model.save(theModel) # will be overwritten
for i in range(len(weights)):
dist.recv(tensor=weights[i], src=src, tag=tag * 100 + i)
theModel = model.load(weights, theModel)
print("Model received from", src)
return theModel
def test_model_on_static_examples(model_fname, training_examples_fname, m=0):
modelInstance = model.load(model_fname)
# n = model9x9['n']
W = modelInstance['W']
b = modelInstance['b']
trainingExamples = training.read_training_examples(m, fname=training_examples_fname)
if m == 0:
m = trainingExamples['X'].shape[1]
for i in range(1000):
# i = round(np.random.rand() * m)
x = trainingExamples['X'].T[i]
nextPosition = position.transform_vector_into_position(x)
position.print_position(nextPosition)
print(' predicted ')
x = position.transform_position_into_vector(nextPosition)
movement = model.predict(W, b, x)
position.print_movement(movement)
print(' expected ')
y = trainingExamples['Y'].T[i]
position.print_movement(y.reshape(9, 1))
raw_input("Press Enter to continue...")
def show_loginmenu(): #When the user logs in, and is prompted what todo
account = input('Enter Account #: ')
pin_num = input("Enter Pin #: ")
# check if account number is in the json file
if not account.isnumeric():
print("Invalid input. Account Number must have numeric values")
return
if not pin_num.isnumeric():
print("Invalid input. Pin # must have numeric values!")
return
data = model.load()
if account in data and pin_num in data[account][
"pin_num"]: #checks account num and pin number in data
print("Please choose an option:")
print("1. Withdraw")
print("2. Deposit")
print("3. Check Balance")
print("4. Exit")
option = input()
while option not in ['1', '2', '3', '4']:
option = input(
"Sorry, that's an invalid option! Please choose 1, 2 or 3: ")
if option == '1':
model.withdraw(account)
elif option == '2':
model.deposit(account)
elif option == '3':
model.balance(account)
elif option == '4':
return
def main():
dictionary = data.Corpus(args.data,
cuda=args.cuda,
yield_sentences=True,
rng=None).dictionary
if False:
# Old way of loading a model
with open(args.model, 'rb') as f:
mdl = torch.load(f)
print(mdl)
else:
mdl = model.load(args.model)
mdl.softmax = nn.Softmax()
mdl = mdl.cuda() if args.cuda else mdl.cpu()
mdl.eval()
sampler = Sampler(dictionary, mdl)
seed_texts = []
if args.seed_text != '':
seed_texts += [args.seed_text]
if args.seed_file:
with codecs.open(args.seed_file, 'r', 'utf-8') as f:
seed_texts += [line.strip() for line in f]
if seed_texts == []:
seed_texts += ['']
for seed_text in seed_texts:
if args.print_seed_text:
print(seed_text)
if not args.seed_without_eos:
seed_text = '<eos> ' + seed_text + ' <eos>'
constraints = eval(args.constraint_list)
tokenizer_fn = lambda s: dictionary.words_to_ids(
data.tokenize(s, add_bos=False, add_eos=False), cuda=args.cuda)
for c in constraints:
if type(c) is SeedTextDictConstraint:
c.set_seed_text(seed_text, tokenizer_fn, dictionary)
if args.num_words:
# Generate N words
out_file = sys.stdout
out_string = sampler.string(seed_text,
args.prefix_text,
args.num_words,
constraints=constraints)
for i, word in enumerate(out_string):
out_file.write(word + ('\n' if i % 20 == 19 else ' '))
if i % 20 != 19:
print('')
else:
# Beam search on sentences
for batch in sampler.sentences(seed_text,
args.prefix_text,
constraints=constraints):
for sent_tokens in batch:
#print( "len = %d, %s" % (len(sent_tokens), ' '.join(sent_tokens)) )
print(' '.join(sent_tokens))
def run():
model.load()
server = grpc.server(futures.ThreadPoolExecutor(max_workers=10),
options=[('grpc.max_send_message_length',
_MAX_MESSAGE_LENGTH),
('grpc.max_receive_message_length',
_MAX_MESSAGE_LENGTH)])
image2pose_pb2_grpc.add_Image2PoseServicer_to_server(Image2Pose(), server)
server.add_insecure_port('[::]:%i' % args.port)
server.start()
print('started image2pose service on port %i' % args.port)
try:
while True:
time.sleep(_ONE_DAY_IN_SECONDS)
except KeyboardInterrupt:
server.stop(0)
def train_model_scenario_3(n, model_fname, training_examples_fname, m=0, alpha=0.001, beta=0.9, iterations=10000):
debug = False
modelInstance = model.load(model_fname)
W = modelInstance['W']
b = modelInstance['b']
# (W, b) = model.initializeWeights(n)
vdW = np.zeros(W.shape)
vdb = np.zeros(b.shape)
ex = training.read_training_examples(m, fname=training_examples_fname)
X = ex['X']
# assert X.shape == (9, 500)
Y = ex['Y']
# assert Y.shape == (9, 500)
# L is a number of NN layers
# (L = 3 for a model 9x18x18x9)
L = len(n) - 1
assert len(W) == L
for i in range(0, iterations):
# (dW, db) = model.calcGradients(W, b, X, Y)
(dW, db, _) = model.back_propagation(n, W, b, X, Y)
vdW = beta * vdW + (1 - beta) * dW
vdb = beta * vdb + (1 - beta) * db
# model.updateWeights(W, dW, b, db, alpha)
W = W - alpha * vdW
b = b - alpha * vdb
if i % 30 == 0:
print('iteration ' + str(i))
(aL, _) = model.forward_propagation(W, b, X)
cost = model.cost_function(Y, aL)
# cost = model.costFunction(Y, A[L])
# print('alpha')
# print(alpha)
print('cost')
print(cost)
if debug:
if i > 0 and i % 3000 == 0:
is_back_prop_correct = model.check_back_propagation(n, W, b, X, Y)
if not is_back_prop_correct:
print("BP is not correct")
exit()
if i % 1000 == 0:
model.save(n, W, b, model_fname)
print('------ end -------')
model.save(n, W, b, model_fname)
def spy_on_training_process(model_fname):
model_instance = model.load(model_fname)
n = model_instance['n']
W = model_instance['W']
b = model_instance['b']
vdW = np.zeros(W.shape)
vdb = np.zeros(b.shape)
alpha0 = 0.3
beta=0.9
iterations = 100000
decay_rate = 4.0 / iterations
pos_trains = 0
x_to_investigate = None
for i in range(0, iterations):
make_movement_fn = lambda x: model.predict2(W, b, x)
ex = make_training_examples(make_movement_fn)
X = ex['X']
Y = ex['Y']
x = X[:, 0].reshape(9, 1)
if x_to_investigate == None:
x_to_investigate = x
position_to_investigate = x_to_investigate.reshape(3, 3)
if (x == x_to_investigate).all():
position.print_position(position_to_investigate)
(_, _, aLbefore) = model.predict3(W, b, x_to_investigate)
(dW, db, _) = model.back_propagation(n, W, b, X, Y)
alpha = alpha0 / (1.0 + decay_rate * i)
vdW = beta * vdW + (1 - beta) * dW
vdb = beta * vdb + (1 - beta) * db
W = W - alpha * dW
b = b - alpha * db
if i > 0 and i % 1000 == 0:
model.save(n, W, b, model_fname)
print('========saved=======')
if (x == x_to_investigate).all():
pos_trains += 1
position.print_position(position_to_investigate)
(_, _, aLafter) = model.predict3(W, b, x_to_investigate)
print('\niteration: %d, position trained times: %d' % (i, pos_trains))
y = Y[:, 0].reshape(9, 1)
table = np.concatenate((aLbefore, aLafter, y), axis = 1)
print(table)
def hello_world():
print('request received')
model = load()
content = request.get_json()
query = content['query']
# TODO try-except blocks to check that everything is correct
print('returning ans')
output = {'answer': infer(model, query)}
return jsonify(output)
def process(self, image_array):
model = model.load('my_model.h5')
digit = prep.crop_image(image_array)
digit = prep.crop_image(digit)
digit = prep.center_image(digit)
digit = prep.resize_image(digit)
digit = prep.min_max_scaler(digit)
digit = prep.reshape_array(digit)
digit = model.predict(digit)
return digit
def handle(env, start_response, handler, m=None):
path = get_path(env)
if len(path) > 0 and not path[-1] == '/':
start_response('302 Redirect',
[('Location', model.normalize(path) + "/")])
return
path = model.normalize(path)
try:
d = model.describe(path)
qs = urlparse.parse_qs(env['QUERY_STRING'])
if not m and 'cache' in d:
if 'r' in qs and qs['r'][0] == '1':
if d['cache'] == 'normal':
model.invalidate_cache(path)
if d['cache'] == 'parent':
model.invalidate_cache(model.parent(path))
if d and d['type'] == 'render' and env['REQUEST_METHOD'] == 'GET':
parameters = {}
if 'parameters' in d:
parameters = d['parameters']
content, mime = render_handlers[d['function']](path, parameters,
env)
start_response('200 OK', [('Content-Type', mime),
('Content-Length', str(len(content)))])
return content
if not m:
m = model.load(path)
content, redirect = handler(path, d, m, env)
if redirect:
redirect = model.normalize(redirect)
close = ''
if 'c' in qs:
if qs['c'][0] == '0':
close = '?c=1'
if qs['c'][0] == '1':
close = '?c=2'
start_response('302 Redirect',
[('Location', redirect + "/" + close)])
return
else:
start_response('200 OK',
[('Content-Type', 'text/html;charset=utf-8'),
('Content-Length', str(len(content)))])
return content
except model.NotFoundException as e:
import traceback
traceback.print_exc()
raise restlite.Status, '404 Not Found'
except model.ParseException as e:
d = {}
d.update({'e': e.errors})
d.update(e.attributes)
start_response('302 Redirect', [('Location', model.normalize(path) +
"/?" + urllib.urlencode(d, True))])
return
def test_wrapper(model):
'''Test your code.'''
test_set = data.DataSet(FLAGS.root_dir,
FLAGS.dataset,
'test',
FLAGS.batch_size,
FLAGS.n_label,
data_aug=False,
shuffle=False)
'''TODO: Your code here.'''
model.load()
tot_acc = 0.0
tot_input = 0
while test_set.has_next_batch():
test_ims, test_labels = test_set.next_batch()
_, acc = model.valid(test_ims, test_labels)
tot_acc += acc * len(test_ims)
tot_input += len(test_ims)
acc = tot_acc / tot_input
print("Test Accuracy= " + "{:.3f}".format(acc))
print("Test Finished!")
def run(self):
# Logic goes here
model_dirs = os.listdir("model") # Traverse through the models folder and get all model names
newest = 0
for model_dir in model_dirs:
name_val = int(model_dir)
if name_val > newest:
newest = name_val
print "Loading model "+str(newest)
# newest will now hold the name of the newest model in the directory
self.cur_model = model.load(newest) # Loading in the model
def train_model_scenario_2(n, model_fname, opponet_model_fname, alpha=0.1, iterations=5000):
alpha0 = alpha
decay_rate = 0.01
modelInstance = model.load(opponet_model_fname)
W0 = modelInstance['W']
b0 = modelInstance['b']
if model_fname == opponet_model_fname:
W = W0
b = b0
else:
make_movement_fn = lambda x: model.predict2(W0, b0, x)
(W, b) = model.initialize_weights(n)
for i in range(0, iterations):
if model_fname == opponet_model_fname:
make_movement_fn = lambda x: model.predict2(W, b, x)
ex = training.make_training_examples(make_movement_fn)
X = ex['X']
Y = ex['Y']
# displayTrainingExamples(X, Y)
# (dW, db) = model.calcGradients(W, b, X, Y)
(dW, db, _) = model.back_propagation(n, W, b, X, Y)
alpha = alpha0 / (1 + decay_rate * i)
model.update_weights(W, dW, b, db, alpha)
if i % 100 == 0:
print('iteration ' + str(i))
(aL, _) = model.forward_propagation(W, b, X)
cost = model.cost_function(Y, aL)
print('cost')
print(cost)
print('alpha')
print(alpha)
# if i % 1000 == 0:
# is_back_prop_correct = model.checkBackPropagation(n, W, b, X, Y)
# if not is_back_prop_correct:
# print("BP is not correct")
# exit()
print('------ end -------')
model.save(n, W, b, model_fname)
def load(config, options=None):
label = None
description = None
dataset = None
model = None
training = None
if options is None:
options = {}
if 'base' not in options or options['base'] is None:
options['base'] = os.getcwd()
if not options['base'].startswith('/'):
options['base'] = os.path.join(os.getcwd(), options['base'])
if 'label' in config:
label = config['label']
if 'description' in config:
description = config['description']
if 'model' in config:
model = model_utils.load(config['model'], options)
if 'training' in config:
training = training_utils.load(config['training'], options)
if 'dataset' in config:
dataset = dataset_utils.load(config['dataset'], model, training, options)
if 'weightsHdf5' in config:
weights_hdf5 = config['weightsHdf5']
if not weights_hdf5.startswith('/'):
weights_hdf5 = os.path.join(options['base'], weights_hdf5)
project = Project(
label=label,
description=description,
weights_hdf5=weights_hdf5,
dataset=dataset,
model=model,
training=training,
options=options)
if 'loadWeights' in options and options['loadWeights']:
project.model.load_weights_hdf5(project.weights_hdf5)
return project
def show_unit_tasks(path, parameters, env):
group_container = parameters['groups']
group_path = model.parent(path) + '/' + group_container
groups = model.load(group_path)
person_groups = {}
for g in groups:
model.traverse(
group_path + '/' + g,
lambda (path, task, d): collect_persons(g, path, person_groups),
all_nodes=True)
persons = set()
for group in person_groups.values():
for p in group:
persons.add(p)
dates, slots, s = prepare_people_tasks(persons)
schedules = {}
for i in s:
schedules[i[0]] = i
group_schedules = []
for g in person_groups.keys():
group_sched = [
g,
len(slots) * [0.0], 0, 0, {
'url': group_path + '/' + g + '/planning/'
}
]
for _, person in person_groups[g]:
group_sched[1] = [
group_sched[1][i] + schedules[person][1][i]
for i in range(0, len(slots))
]
group_sched[2] += schedules[person][2]
group_sched[3] += schedules[person][3]
n = len(person_groups[g]) * 1.0
group_sched[1] = [i / n for i in group_sched[1]]
group_sched[2] /= n
group_sched[3] /= n
group_schedules.append(group_sched)
return visualize.render(dates,
slots,
sorted(group_schedules),
variables={
'qs': urlparse.parse_qs(env['QUERY_STRING']),
'context': '/',
'path': path,
'sum': True,
'url': path + '/',
'refreshable': False
}), "text/html"
def main():
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--data-dir', type=str, default='data/corona',
help='data directory containing input.txt')
parser.add_argument('--seed', type=str, default=None,
help='seed string for sampling')
parser.add_argument('--length', type=int, default=1000,
help='length of the sample to generate')
parser.add_argument('--diversity', type=float, default=1.0,
help='Sampling diversity')
args = parser.parse_args()
model = load(args.data_dir)
del args.data_dir
model.sample(**vars(args))
def handle(env, start_response, handler, m=None):
path = get_path(env)
if len(path) > 0 and not path[-1] == '/':
start_response('302 Redirect', [('Location', model.normalize(path)+"/")])
return
path = model.normalize(path)
try:
d = model.describe(path)
qs = urlparse.parse_qs(env['QUERY_STRING'])
if not m and 'cache' in d:
if 'r' in qs and qs['r'][0]=='1':
if d['cache'] == 'normal':
model.invalidate_cache(path)
if d['cache'] == 'parent':
model.invalidate_cache(model.parent(path))
if d and d['type'] == 'render' and env['REQUEST_METHOD'] == 'GET':
parameters = {}
if 'parameters' in d:
parameters = d['parameters']
content, mime = render_handlers[d['function']](path, parameters, env)
start_response('200 OK', [('Content-Type', mime), ('Content-Length', str(len(content)))])
return content
if not m:
m = model.load(path)
content, redirect = handler(path, d, m, env)
if redirect:
redirect = model.normalize(redirect)
close=''
if 'c' in qs:
if qs['c'][0]=='0':
close='?c=1'
if qs['c'][0]=='1':
close='?c=2'
start_response('302 Redirect', [('Location', redirect+"/"+close)])
return
else:
start_response('200 OK', [('Content-Type', 'text/html;charset=utf-8'), ('Content-Length', str(len(content)))])
return content
except model.NotFoundException as e:
import traceback
traceback.print_exc()
raise restlite.Status, '404 Not Found'
except model.ParseException as e:
d = {}
d.update( { 'e': e.errors } )
d.update(e.attributes)
start_response('302 Redirect', [('Location', model.normalize(path)+"/?"+urllib.urlencode(d, True))])
return
def show_team_tasks(path, parameters, env):
people = model.parent(path) + '/people'
person_list = model.load(people)
dates, slots, s = prepare_people_tasks([(people, person)
for person in person_list])
return visualize.render(dates,
slots,
sorted(s),
variables={
'qs': urlparse.parse_qs(env['QUERY_STRING']),
'context': '/',
'path': path,
'sum': True,
'url': path + '/',
'refreshable': True
}), "text/html"
def ANNCertainty(start="2019-04-01",
stop="2019-05-01",
fromPickle=False,
clean=True,
load_model=False):
if clean: filename = "ANNCertainty"
else: filename = "ANNCertainty-uncleaned"
if fromPickle:
print("Loaded", filename)
return pickle.load(open(config.DATA_PATH + "" + filename, "rb"))
else:
print("Making Met-ANM dataset for certaintyPlot", filename)
met_df = pp.getMetData(start, stop).set_index("forecast_time")
anm_df = pp.getSingleDataframe(start,
"2019-05-31",
fromPickle=True,
clean=clean)
df = anm_df.join(met_df, how="inner")
if load_model:
ere_wtnn = m.load(filename=filename)
else:
df_train = pp.getEdayData()
df_full = pp.getSingleDataframe(fromPickle=True, clean=clean)
df_train = df_full.join(df_train, how="inner")
ere_wtnn = m.train_and_save_simple(
df_train[["Wind Mean (M/S)", "weekday", "hour"]].values,
df_train[["Curtailment"]].values,
kfold=False,
filename=filename)
print("Doing ERE WT-FFNN predictions...")
df["ere_wtnn_prediction"] = [
ere_wtnn.predict([[d[["wind_speed", "weekday",
"hour"]].values]])[0][0]
for i, d in df.iterrows()
]
df["ere_wtnn_prediction_correct"] = [
int(round(d["ere_wtnn_prediction"]) == d["Curtailment"]) * 100
for i, d in df.iterrows()
]
print(df["ere_wtnn_prediction_correct"].mean())
pickle.dump(df, open(config.DATA_PATH + "" + filename, "wb"))
return df
def main(argv):
ap = ArgumentParser(prog="snow-patrol daemon")
ap.add_argument("-v",
"--verbose",
default=False,
action="store_true",
help="Turn on verbose logging.")
ap.add_argument("config_path")
ap.add_argument("--dry-run", action="store_true", default=False)
aargs = ap.parse_args(argv)
log_file = ".%s.%s.log" % (os.path.splitext(
os.path.basename(__file__))[0], os.path.basename(aargs.config_path))
setup_logging(log_file, aargs.verbose, False, True, True)
config = model.load(aargs.config_path)
logging.debug("Running under: %s" % config)
run_continuously(config, aargs.dry_run)
return 0
def main():
# Training data consits of 60000 images and 60000 labels
# Testing data consists of 10000 images and 10000 labels
# Each image consits of 784 (28x28) pixels each of which contains a value from
# 0 to 255.0 which corresponds to its darkness or lightness.
# Each input needs to be a list of numpy arrays to be valid.
# Load all of the data
print "Loading data..."
test_images = data.load_data(LIMITED)
train_images = data.load_data(LIMITED, "train-images.idx3-ubyte", "train-labels.idx1-ubyte")
print "Normalizing data..."
X_train, Y_train = data.convert_image_data(train_images)
X_test, Y_test = data.convert_image_data(test_images)
X_train = np.array(X_train)
Y_train = np.array(Y_train)
X_test = np.array(X_test)
Y_test = np.array(Y_test)
if LOAD == False:
print "Building the model..."
_model = model.build()
else:
print "Loading the model..."
elements = os.listdir("model")
if len(elements) == 0:
print "No models to load."
else:
_model = model.load(elements[len(elements)-1])
if TRAIN == True:
print "Training the model..."
model.train(_model, X_train, Y_train, X_test, Y_test)
if VISUALIZE:
model.visualize(_model, test_images, VISUALIZE_TO_FILE)
if TRAIN == True:
print "Saving the model..."
model.save(_model)
def test_position(model_fname):
print('\ntest model: %s' % (model_fname))
model_instance = model.load(model_fname)
W = model_instance['W']
b = model_instance['b']
x = np.array([
-1,-1, 0,
0, 1, 0,
0, 0, 0,
]).reshape(9, 1)
print('\n')
position.print_position(position.transform_vector_into_position(x))
(aL, _) = model.forward_propagation(W, b, x)
print("aL")
print(aL)
movement = model.predict(W, b, x)
position.print_movement(movement)
def show_team_tasks(path, parameters, env):
people = model.parent(path)+'/people'
person_list = model.load(people)
dates, slots, s = prepare_people_tasks( [ ( people, person) for person in person_list ] )
return visualize.render(dates, slots, sorted(s), variables={'qs':urlparse.parse_qs(env['QUERY_STRING']), 'context':'/', 'path':path, 'sum':True, 'url': path+'/', 'refreshable': True }), "text/html"
#!/usr/bin/env python
import os, sys
lib_path = os.path.abspath(os.environ['TMT_ROOT'] + '/personal/mnovak/ml_framework/lib')
sys.path.append(lib_path)
import model
#from sklearn import tree
#import StringIO, pydot
name = sys.argv.pop(0)
if len(sys.argv) < 2:
print >> sys.stderr, "Usage: " + name + " <model_path> <params_path>"
exit()
model = model.Model()
model.load(sys.argv[0])
model.print_params(sys.argv[1])
plot_verts.append(verts)
barpath = path.Path(verts, codes)
patch = patches.PathPatch(barpath, facecolor='green', edgecolor='yellow', alpha=0.5)
ax.add_patch(patch)
ax.set_xlim(left[0], right[-1])
ax.set_ylim(0,1.1)
def animate(t):
time_text.set_text("t=%d"%t)
for sequence,verts in zip(plot,plot_verts):
n = sequence[t]
top = bottom + n
verts[1::5,1] = top
verts[2::5,1] = top
ani = animation.FuncAnimation(fig, animate, len(sequence), repeat=False)
ani.save(filename, fps=15,bitrate=1280)
if __name__ == "__main__":
predict = model.load(sys.argv[1])
layers = predict(load_ark(sys.argv[2]))
means = [ np.sum(l > 0.5,axis=0) for l in layers ]
order = [ np.argsort(-m)[:100] for m in means ]
plot = [ l[:,o] for l,o in zip(layers,order) ]
create_animation(plot,sys.argv[3])
def load_default_model():
trained_model_dir = os.path.join(os.path.dirname(__file__), '../trained_models')
global keras_model, input_encoding, input_decoding, output_encoding, output_decoding
keras_model, input_encoding, input_decoding, output_encoding, output_decoding = model.load(save_dir=trained_model_dir)
import os, sys, time import numpy as np from config import config import model from env import Environment ############################################# config.use_gpu = True ############################################# config.rl_model = "double_dqn" config.rl_model = "bootstrapped_double_dqn" config.rl_final_exploration_step = 20000 config.apply_batchnorm = False model = model.load() env = Environment() max_episode = 2000 total_steps = 0 exploration_rate = config.rl_initial_exploration dump_freq = 10 episode_rewards = 0 num_optimal_episodes = 0 sum_reward = 0 sum_loss = 0.0 save_freq = 100 bootstrapped = False if config.rl_model in ["bootstrapped_double_dqn"]:
print str(err) # will print something like "option -a not recognized"
usage(name)
sys.exit(2)
ranking = False
for o,a in optlist:
if o == '--ranking':
ranking = True
else:
assert False, "unhandled option"
if len(args) < 1:
usage(name)
sys.exit(2)
model_path = args[0]
model = model.Model()
model.load(model_path)
#dot_data = StringIO.StringIO()
#tree.export_graphviz(model.model, out_file=dot_data)
#graph = pydot.graph_from_dot_data(dot_data.getvalue())
#graph.write_pdf("graph.pdf")
print >> sys.stderr, "Reading the data..."
in_data = VowpalWabbitData(ranking=ranking)
(X_all, Y, tags_all) = in_data.read(sys.stdin)
print >> sys.stderr, "Making predictions..."
for X in X_all:
tags = tags_all.pop(0)
losses = model.predict_loss(X)
for i in range(0, len(X)):