def teacher_details():
# init a basic bar chart:
# http://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html#bars
# grab the static resources
js_resources = INLINE.render_js()
css_resources = INLINE.render_css()
# render template
script, div1 = plotdata()
script2, div2 = plotdata2()
html = render_template('teacher_details.html',
script=script, div1=div1,
script2=script2, div2=div2,
js_resources=js_resources,
css_resources=css_resources,
)
return encode_utf8(html)
help='eval batch size')
parser.add_argument('--seed', type=int, default=1234, help='set random seed')
parser.add_argument('--cuda', action='store_true', help='use CUDA device')
parser.add_argument('--gpu_id', type=int, help='GPU device id used')
args = parser.parse_args()
if args.model_type == 'baseline':
# data preprocess and prepare
data_path = './data/dev.txt'
split_ratio = 0.3
preprocess(data_path, split_ratio)
# dataset load and plot
train_dataset = EmojiDataset('./data/Xtrain.npy', './data/ytrain.npy')
plotdata(np.load('./data/Xtrain.npy', allow_pickle=True),
np.load('./data/ytrain.npy', allow_pickle=True))
test_dataset = EmojiDataset('./data/Xtest.npy', './data/ytest.npy')
train_dataloader = DataLoader(train_dataset,
batch_size=args.train_batch_size,
shuffle=False,
collate_fn=collate_fn)
test_dataloader = DataLoader(test_dataset,
batch_size=args.eval_batch_size,
shuffle=False,
collate_fn=collate_fn)
torch.manual_seed(args.seed)
# model prepare
use_gpu = False
def graph():
# graph 그리는 부분
from data import plotdata
script, div = plotdata()
return render_template('graph.html',script=script, div=div)