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