from util import load_parameter, load_lists import json import numpy as np train_news_id,train_news_label,train_user_id,\ test_news_id,test_news_label,test_user_id,\ user_pos,test_user_pos,test_pos_loc = load_lists('lists.json') print("文件lists.json加载完毕。\n") word2id, news_title, news_key = load_parameter('parameter.json') print("文件parameters.json加载完毕。\n") train_news_id = np.array(train_news_id, dtype='int32') train_news_label = np.array(train_news_label, dtype='int32') train_user_id = np.array(train_user_id, dtype='int32') test_news_id = np.array(test_news_id, dtype='int32') test_news_label = np.array(test_news_label, dtype='int32') test_user_id = np.array(test_user_id, dtype='int32') test_pos_loc = np.array(test_pos_loc, dtype='int32') user_pos = np.array(user_pos, dtype='int32') test_user_pos = np.array(test_user_pos, dtype='int32') news_title = np.array(news_title, dtype='int32') news_key = np.array(news_key, dtype='int32') print("load.py文件执行完毕")
u = NNUtil.neural_net(x, self.weights, self.biases) f = self.source_term(x) integrals = 0.5 * self.C * tf.matmul(self.sW_tf, u + f) first = integrals[0:1, 0:1] hats = integrals[0:1, 1:] + integrals[1:2, 0:-1] last = integrals[1:2, -1:] return tf.transpose(tf.concat([first, hats, last], axis=1)) def net_pred(self, x): u = NNUtil.neural_net(x, self.weights, self.biases) return u if __name__ == "__main__": working_dir = "./Test_Collection/CB/" paras = util.load_parameter(working_dir + "paras.txt") N = int(paras["N"]) width = int(paras["width"]) depth = int(paras["depth"]) max_epoch = int(paras["max_epoch"]) num_tested_per_element = int(paras["num_tested_per_element"]) pivot = util.to_list(paras["pivot"]) upper_bound = pivot[-1] lower_bound = pivot[0] num_element = len(pivot) - 1 hidden_layers = [width] * depth new_dir = "{}_{}_{}/N={}/{}_{}/".format(lower_bound, upper_bound, num_element, N, depth, width) dump_dir = working_dir + new_dir
import keras # import keras.models as kmod import keras.layers as klay import keras.backend as K import keras.callbacks as kclbk import util import polylib import skbasis as skb _epsilon = 1.0e-14 ## ========================================= ## root_dir = "./Test_Collection/" result_dir = root_dir + "CB_keras/" paras = util.load_parameter(root_dir + "paras.txt") # domain contains coordinates of element boundaries domain = util.to_list(paras["pivot"]) depth = int(sys.argv[2]) width = int(sys.argv[3]) max_epoch = int(sys.argv[4]) # domain parameters N_elem = len(domain) - 1 # number of elements upper_bound = domain[-1] lower_bound = domain[0] # C_k continuity CK = 0 # C^k continuity