train_data = cifargen(batchsize) valid_data = cifargen(batchsize, train=False) h = train_data.x.shape[1] w = train_data.x.shape[2] if train_data.x.ndim > 3: c = train_data.x.shape[3] else: c = 1 epochs = 100 archi = VGG(brickname='vgg16') clf = CLF(archi, height=h, width=w, colors=c, learning_rate=0.01) print(clf) trainlvals = [] trainaccvals = [] validlvals = [] validaccvals = [] for e in range(epochs): print('EPOCH: ' + str(e + 1) + str('/') + str(epochs)) print('TRAIN')
valid_data = cifargen(batchsize, train=False) print('Y shape: ', train_data.y.shape) h = train_data.x.shape[1] w = train_data.x.shape[2] if train_data.x.ndim > 3: c = train_data.x.shape[3] else: c = 1 epochs = 1 archi = Classifier(brickname='reference') clf = CLF(archi, height=h, width=w, colors=c, learning_rate=0.001) print(clf) trainlvals = [] trainaccvals = [] validlvals = [] validaccvals = [] for e in range(epochs): print('EPOCH: ' + str(e + 1) + str('/') + str(epochs)) print('TRAIN')
os.environ["CUDA_VISIBLE_DEVICES"] = args.device learning_rate = args.lr train_data = cifargen(batchsize) valid_data = cifargen(batchsize, train=False) h = train_data.x.shape[1] w = train_data.x.shape[2] if train_data.x.ndim > 3: c = train_data.x.shape[3] else: c = 1 archi = Classifier(brickname='lenet', dropouts=[0.5, 0.5], fcdropouts=[0.5]) clf = CLF(archi, height=h, width=w, colors=c, learning_rate=learning_rate, optimizer='SGD') print(clf) trainlvals = [] trainaccvals = [] validlvals = [] validaccvals = [] accplot = [] for e in range(epochs): print('EPOCH: ' + str(e + 1) + str('/') + str(epochs))
print('Y shape: ', train_data.y.shape) h = train_data.x.shape[1] w = train_data.x.shape[2] if train_data.x.ndim > 3: c = train_data.x.shape[3] else: c = 1 for n in range(lenet_number): epochs = 10 archi = Classifier(brickname='dependency' + str(n), dropouts=[0.5, 0.5], fcdropouts=[0.5]) clf = CLF(archi, height=h, width=w, colors=c, learning_rate=0.0001) print(clf) trainlvals = [] trainaccvals = [] validlvals = [] validaccvals = [] for e in range(epochs): print('EPOCH: ' + str(e + 1) + str('/') + str(epochs)) print('TRAIN')
dirname = os.path.join(logfolder, 'dependency' + str(n)) # first of all, evaluate reference alone if n == 0: archidep = Classifier(brickname='dependency' + str(n), dropouts=[0.5, 0.5], fcdropouts=[0.5]) archiref = Classifier(brickname='reference', dropouts=[0.5, 0.5], fcdropouts=[0.5]) clf = CLF(archiref, height=h, width=w, colors=c, learning_rate=0.0001, model_path=os.path.join(refnetwork_folder, 'model.ckpt')) print('SCORE REFERENCE') validprogressbar = tqdm(valid_data, total=valid_data.steps) accvals = [] for x, y in validprogressbar: _, accval = clf.validate(x, y) accvals.append(accval) metrics = [('acc', numpy.mean(accvals))] desc = monitor(metrics, 4)
refarchi = Classifier(brickname='reference', filters=[32, 64, 128], kernels=[4, 5, 6], strides=[1, 1, 1], dropouts=[0.1, 0.2, 0.25], fc=[1024, 1024], fcdropouts=[0.5, 0.5], conv_activations=['relu', 'relu', 'relu'], fc_activations=['relu', 'relu'], end_activation='softmax', output_channels=3) clf = CLF(refarchi, height=h, width=w, colors=c, n_classes=3, learning_rate=0.001, model_path=ref_path, optimizer="SGD") outputdir = os.path.join(experimentfolder, 'slides_prediction') if not os.path.isdir(outputdir): os.makedirs(outputdir) # predict slides predict_slides(clf, slidedir, outputdir) clf.close()
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" os.environ["CUDA_VISIBLE_DEVICES"] = args.device # load dataset (CIFAR10 here) # --------------------------- xtrain, ytrain, xtest, ytest = cifar10.load_data() ytrain = to_categorical(ytrain, 10) ytest = to_categorical(ytest, 10) # load trained classifier (reference) # ----------------------------------- # first, build architecture refarchi = Classifier(brickname='lenet', dropouts=[0.5, 0.5], fcdropouts=[0.5]) # then, create the model refclf = CLF(refarchi, model_path=basenet) # predict with classifier predref = refclf.predict(xtest) # load trained classifier (dependency) # ------------------------------------ # first, build architecture deparchi = Classifier(brickname='explorer', dropouts=[0.5, 0.5], fcdropouts=[0.5]) # then, create the model depclf = CLF(deparchi, model_path=depnet) # predict with classifier preddep = depclf.predict(xtest) predrefclass = numpy.argmax(predref, axis=1)