import tensorflow as tf import numpy as np from get_v3 import get_test_csi import os import time random_point = list(np.array([1, 2, 4, 5, 6, 7, 8, 9, 10, 12, 14, 15, 16, 17])) param = 14 test_loc = random_point[param - 1] print(test_loc) csi = get_test_csi(test_loc) # 10*90 csi = np.squeeze(csi) n_hidden_1 = 30 # 1st layer num features n_hidden_2 = 20 # 2nd layer num features n_hidden_3 = 10 n_hidden_4 = 5 n_input = 90 ers = [] times = [] # Applying encode and decode over test set for k in range(1, 14): # give to all trained network for each point ---> DeepFi X = tf.placeholder("float", [None, n_input]) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) saver1 = tf.train.import_meta_graph('model/Test' + str(param) + '/' + str(k) + '/trained_variables' + str(param) + str(k) + '.ckpt.meta')
import tensorflow as tf import numpy as np from get_v3 import get_test_csi import os import multiprocessing from Detemine_error import compute_error_metric import time random_points = list( np.array([1, 2, 4, 5, 6, 7, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19])) param = 16 test_loc = random_points[param - 1] csi = get_test_csi(test_loc) csi = np.squeeze(csi) print(csi.shape) n_hidden_1 = 45 # 1st layer num features n_hidden_2 = 20 # 2nd layer num features n_hidden_3 = 10 n_hidden_4 = 5 n_input = 90 n_labels = 15 X = tf.placeholder("float", [None, n_input]) new_input = csi[1:6, :] # for example give some packets input #print(new_input.shape) errors = [] times = []