import pandas as pd
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df = pd.read_csv('/home/helong/share/ML/MobiAct_Dataset_v2.0/Annotated Data/FOL/FOL_1_1_annotated.csv')
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df.head()
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x = df[['acc_x','acc_y','acc_z']]
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x.head()
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data_move=20
data_scale=6 def transform_rgb(x): return (x + data_move) * data_scale x = df[['acc_x','acc_y','acc_z']].apply(transform_rgb)
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x.head()
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x = x.stack().to_frame().T
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x.head()
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x.head()
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import os
import tensorflow as tf import numpy as np import pandas as pd
convert the data
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df_list = []
sum_df = pd.DataFrame() #sum_df = df_.fillna(0) # with 0s rather than NaNs PATH = '/home/helong/share/ML/MobiAct_Dataset_v2.0/Annotated Data/STU' for file in os.listdir(PATH): # print(file) df = pd.read_csv(os.path.join(PATH,file)) if not df.empty: df_list.append(df) for df in df_list: x = df[['acc_x','acc_y','acc_z']].apply(transform_rgb) x = x.stack().to_frame().T # print(x.head()) sum_df = sum_df.append(x) #sum_df.insert(idx, col_name, value) sum_df.insert(loc=0, column='A', value=0) print(sum_df.head()) #print(sum_df.info()) sum_df.to_csv('/home/helong/share/ML/MobiAct_Dataset_v2.0/tran_data_transform/STU.csv',index=False) #final_df = df.append(df for df in df_list) #final_df[0].count() print("done")
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def get_all_data():
PATH = '/home/helong/share/ML/MobiAct_Dataset_v2.0/train_data_transform' fs = os.listdir(PATH) all_data = pd.DataFrame() for f in fs: file_path = os.path.join(PATH, f) print(file_path) if 'csv' in f: data = pd.read_csv(file_path, index_col=False, nrows=5, low_memory=False) data = data.iloc[1:,0:1201] #print(data.head()) #break all_data = all_data.append(data) #for fast test #break #count_row = all_data.shape[0] #print(count_row) np.random.shuffle(all_data.values) return all_data
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def get_test_data():
PATH = '/home/helong/share/ML/MobiAct_Dataset_v2.0/train_data_transform' fs = os.listdir(PATH) all_data = pd.DataFrame() for f in fs: file_path = os.path.join(PATH, f) print(file_path) if 'csv' in f: data = pd.read_csv(file_path, index_col=False, low_memory=False) data = data.iloc[1:,0:1201] #print(data.head()) #break all_data = all_data.append(data) #for fast test #break #count_row = all_data.shape[0] #print(count_row) np.random.shuffle(all_data.values) return all_data
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data = get_all_data()
print(data.shape[0]) #all_data.to_csv('/home/helong/share/ML/MobiAct_Dataset_v2.0/all_data_transform.csv',nrows=10, index=False) #all_data.to_csv('/home/helong/share/ML/MobiAct_Dataset_v2.0/all_data_transform.csv',index=False)
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CLASS_NUM = 1
LEARNING_RATE = 0.001 TRAIN_STEP = 10000 BATCH_SIZE = 50 _index_in_epoch = 0 _epochs_completed = 0 _num_examples = 0 MODEL_SEVE_PATH = '../model/model.ckpt'
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def wights_variable(shape):
''' 权重变量tensor :param shape: :return: ''' wights = tf.truncated_normal(shape=shape,stddev=0.1) return tf.Variable(wights,dtype=tf.float32) def biases_variable(shape): ''' 偏置变量tensor :param shape: :return: ''' bias = tf.constant(0.1,shape=shape) return tf.Variable(bias,dtype=tf.float32) def conv2d(x,kernel): ''' 网络卷积层 :param x: 输入x :param kernel: 卷积核 :return: 返回卷积后的结果 ''' return tf.nn.conv2d(x,kernel,strides=[1,1,1,1],padding='SAME') def max_pooling_2x2(x): ''' 最大赤化层 :param x: 输入x :return: 返回池化后数据 ''' return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1],padding='SAME') def lrn(x): ''' local response normalization 局部响应归一化,可以提高准确率 :param x: 输入x :return: ''' return tf.nn.lrn(x,4,1.0,0.001,0.75) def fall_net(x): ''' 跌到检测网络 :param x: 输入tensor,shape=[None,] :return: ''' with tf.name_scope('reshape'): x = tf.reshape(x,[-1,20,20,3]) #x = x / 255.0 * 2 - 1 with tf.name_scope('conv1'): # value shape:[-1,18,18,32] conv1_kernel = wights_variable([5,5,3,32]) conv1_bias = biases_variable