def main(ftrain, fdev=None, fmodel='model/model.pickle.gz'): # Load data print 'Loading training data ...' data = load(gzip.open(ftrain)) M, labels = data['data'], data['labels'] # Load dev data if fdev is not None: print 'Loading dev data ...' devdata = load(gzip.open(fdev)) devM, devlabels = devdata['data'], devdata['labels'] else: devM, devlabels = None, None # Training with specified parameters print 'Training ...' clf = Classifier() clf.train(M, labels, devM, devlabels) clf.savemodel(fmodel)
from model.nets import * from model.train_functions import * from model.classifier import Classifier from scipy import misc import numpy as np import pandas as pd params = read_params('Project_path/params.txt') data = Cifar10_Data() data.load_and_split(params['input_data_path'], params['labels_path']) #xtrain=data.train_idxs #xval=data.val_idxs #batch = data.get_train_feed_dict('X','y','train',128) #%% cls = Classifier(params, data.Ndims, net=convnet2) cls.train(data, epochs=10, batch_size=128) #cls.load_weights_from_checkpoint(params['pre-traind_model_path']) #%% Get The Test Data And Classify It test_path = params['test_data_path'] labels = [] for batch_num in range(600): X = [] for idx in range(500): img_path = test_path + str(batch_num * 500 + idx + 1) + '.png' X.append(misc.imread(img_path)) X = np.array(X) X = (X - data.mean) / (data.std + 1e-7) preds = cls.predict(X) preds = np.argmax(preds, axis=1) for i in np.arange(preds.shape[0]):