def main(): signal_data, signal_labels = readFile(backward.filepath) signal_re, labels_re = data_reshape_test(signal_data, signal_labels) print("signal_re.shape:") print(signal_re.shape) print("labels_re.shape:") print(labels_re.shape) re_data = pre_data_reshape(signal_re) print("re_data.shape:") print(re_data.shape) z_score_data = more_norm_dataset(re_data) data_1Dto2D = more_dataset_1Dto2D(z_score_data) print("data_1Dto2D.shape:") print(data_1Dto2D.shape) test(data_1Dto2D, labels_re)
def main(): signal_data, signal_labels = readFile(backward.filepath) signal_re, labels_re = data_reshape_test(signal_data, signal_labels) print("signal_re.shape:") print(signal_re.shape) print("labels_re.shape:") print(labels_re.shape) re_data = pre_data_reshape(signal_re) print("re_data.shape:") print(re_data.shape) z_score_data = more_norm_dataset(re_data) data_1Dto2D = more_dataset_1Dto2D(z_score_data) print("data_1Dto2D.shape:") print(data_1Dto2D.shape) dict_data = {"data": data_1Dto2D, "labels": labels_re} with open('CNN_test.pkl', 'wb') as f: pickle.dump(dict_data, f, pickle.HIGHEST_PROTOCOL) print("okkkkkkkkkkkkk")
def main(): signal_data, signal_labels = readFile('F:/情感计算/数据集/DEAP/s02.mat') signal_re, labels_re = data_reshape_test(signal_data, signal_labels) test(signal_re, labels_re)
import numpy as np from input_data import readFile, data_reshape, data_reshape_test, PEOPEL_NUM n_steps = 40 # X的数量 n_inputs = 8064 # 一个X有n_inputs个数 n_neurons = 128 # RNN神经元数目 n_outputs = 2 # 输出 n_layers = 4 # n_layer层神经元 BATCH_SIZE_ALL = PEOPEL_NUM * 40 // 4 * 3 BATCH_SIZE = 10 n_epochs = 1000 learning_rate_base = 0.001 signal_data, signal_labels = readFile('F:/情感计算/数据集/DEAP/') signal_re, labels_re = data_reshape(signal_data, signal_labels) signal_test_re, labels_test_re = data_reshape_test(signal_data, signal_labels) X = tf.placeholder(tf.float32, [None, n_steps, n_inputs]) # 初始化x y = tf.placeholder(tf.int32, [None]) # 初始化y lstm_cells = [ tf.contrib.rnn.BasicLSTMCell(num_units=n_neurons) for layer in range(n_layers) ] # 生成n_layers层,每层包括n_neurons个神经元的神经元列表 multi_cell = tf.contrib.rnn.MultiRNNCell(lstm_cells) # 根据神经元列表 构建多层循环神经网络 outputs, states = tf.nn.dynamic_rnn(multi_cell, X, dtype=tf.float32) # outputs(tensor):[ batch_size, max_time, cell.output_size ] # states:state是一个tensor。state是最终的状态,也就是序列中最后一个cell输出的状态。一般情况下state的形状为 [batch_size, # cell.output_size ],但当输入的cell为BasicLSTMCell时,state的形状为[2,batch_size, cell.output_size ],其中2也对应着 # LSTM中的cell state和hidden state top_layer_h_state = states[-1][0] + states[-1][1]