def compare_callback(exe, benchmark, elapsed): for report_name in args.compare_to: v = model.get_result(report_name, benchmark) if v is None: print "(no %s)" % report_name, else: print "%s: %.2f (%+0.1f%%)" % (report_name, v, (elapsed - v) / v * 100),
def compare_callback(exe, benchmark, elapsed, size): for report_name in args.compare_to: v = model.get_result(report_name, benchmark) if v is None: print "(no %s)" % report_name, else: print "%s: %.2fs (%s%%)" % ( report_name, v[0], "{:5.1f}".format( (elapsed - v[0]) / v[0] * 100)),
def predict(): # whenever the predict method is called, we're going # to input the user drawn character as an image into the model # perform inference, and return the classification # get the raw data format of the image imgData = request.get_data() convertImage(imgData) keras.backend.clear_session() my_model = mod.get_mnist_model() res = mod.get_result('output.png', my_model) print(np.round(res*100,2)) # probabilites of all other digits return str(np.argmax(res))
def create_widget(self): self.target_mlb = MultiListbox(self, (('mRNAname', 20),('score',20)), height=25) self.function_mlb = MultiListbox(self, (('target family', 20),('function',20)), height=25) self.RNA_in_input = SearchInput(self, text='输入microRNA名字:', textvariable=self.search_string, button_function=lambda:get_result(self.search_string, self.target_select_value, self.target_mlb, self.function_mlb)) self.split_frame = Frame(self, height=1, width=680, bg='black') self.split_frame_2 = Frame(self, height=1, width=680, bg='black') self.query_target_label = Label(self, text='查询target') self.query_function_label = Label(self, text='查询功能') self.target_select_radiobutton = [] self.target_select_radiobutton.append(Radiobutton(self, variable = self.target_select_value, value = 'targetscan', text = 'targetscan')) self.target_select_radiobutton.append(Radiobutton(self, variable = self.target_select_value, value = 'miRanda', text = 'miRanda')) self.target_select_radiobutton.append(Radiobutton(self, variable = self.target_select_value, value = 'picTar', text = 'picTar')) for radiobutton in self.target_select_radiobutton: radiobutton['width'] = 6
def repeated_filter(exe, benchmark): v = model.get_result(skip_report_name(exe), benchmark) if v: return v return False
def save_report_callback(exe, benchmark, elapsed, size): old_val = model.get_result(args.save_report, benchmark) model.save_result(args.save_report, benchmark, elapsed, size) if old_val is not None and args.take_min: print "(prev min: %.2fs / %2.1fMB)" % (old_val[0], old_val[1]),
def view_filter(exe, benchmark): v = model.get_result(args.view, benchmark) if v is not None: return v return True
def save_report_callback(exe, benchmark, elapsed): old_val = model.get_result(args.save_report, benchmark) model.save_result(args.save_report, benchmark, elapsed) if old_val is not None and args.take_min: print "(prev min: %.2fs)" % (old_val,),
import os from utils import getFiles, plot_confusion_matrix from model import HMM_Model, get_result, train, load_models, evaluate from dataloader import Dataloader, single_loader import numpy as np genre_list = [ 'blues', 'classical', 'jazz', 'country', 'pop', 'rock', 'metal', 'disco', 'hiphop', 'reggae' ] # dl = Dataloader(genre_list, root='genres') # train(dl) models = load_models(genre_list) # Evaluate method 1: cm, real, pred = evaluate('genres_small', genre_list, models) plot_confusion_matrix(cm, genre_list, True) # get plot print(classification_report(real, pred, target_names=genre_list)) # get report # Evaluate method 2: fl = getFiles('genres_small') for f in fl: X = single_loader(f, is_print_info=False, is_vision=False) print('Truth:{}, predict:{}'.format(f, get_result(X, models))) # Single test: X = single_loader('blues.00000.wav') result = get_result(X, models) print(result)