plt.legend() plt.show() if __name__ == '__main__': params = {"max_iter": 100, "alpha": 0.07} train_data, train_label, test_data, test_label = load_data2() ################# data1数据集有10类,需要二分。如果想把10个类全部分出来,需要进行多次二分 # train_label = binary(train_label) # test_label = binary(test_label) # 增加b部分 train_b = np.ones((train_data.shape[0], 1)) train_data = np.hstack((train_b, train_data)) test_b = np.ones((test_data.shape[0], 1)) test_data = np.hstack((test_b, test_data)) from load_data3 import load_data train_data, train_label = load_data() test_data, test_label = load_data( file_path='./horseColic/horseColicTest.txt') weight_result, loss = grad_ascent1(train_data, train_label, params, test_data, test_label) draw_loss(loss) # # for ind in xrange(weight_result.shape[0]): # print weight_result[ind][0] # data1 error rate: 0.0263 # data2 error rate: 0.0235294117647
import sys sys.path.insert( 0, "/common/home/deeplearning/studdocs/gerasimov_d/LAB2/mxnet-cuda") import mxnet as mx from mxnet import gluon, autograd, ndarray import numpy as np import time start_time = time.time() from load_data3 import load_data all_data, train_data, test_data = load_data() elapsed_time = time.time() - start_time print 'Time load data: ', elapsed_time start_time = time.time() model_ctx = mx.gpu(0) shape_input = train_data[0][0].shape size_inputs = train_data[0][0].size num_outputs = train_data[0][1].size num_hidden = train_data[0][1].size from autoencoder_conf import prepare_autoencoder3 encoder, loss_encoder, decoder, loss_decoder = prepare_autoencoder3( num_hidden, num_outputs, model_ctx) elapsed_time = time.time() - start_time print 'Time of initializing data: ', elapsed_time num_epochs = 15
import load_data3 import load_data (x_train1, y_train1), (x_test1, y_test1) = load_data.load_data(0.7,10) (x_train3, y_train3), (x_test3, y_test3) = load_data3.load_data(0.3,10) print(len(x_train1)) print(len(x_train3)) for i in range(0,len(x_train1)): if(x_train1[i]==x_train3[i]): print(0) else: print(0000000000000)