def __init__(self):
reader = DataReader(187, 6)
self.x_train, self.y_train = reader.read_train_human('./train/')
self.x_test, self.y_test = reader.read_test_human("./test/")
self.x_input, self.y_input = reader.temp_read_test_human("./test/")
def get_libsvm_data(self):
reader = DataReader()
#self.x_train, self.y_train = reader.temp_read_train_data('../UCR_TS_Archive_2015/ECG5000/ECG5000_TEST')
#self.x_test, self.y_test = reader.temp_read_test_data('../UCR_TS_Archive_2015/ECG5000/ECG5000_TRAIN')
self.x_train, self.y_train = reader.temp_read_train_human(
'../human/train/')
self.x_test, self.y_test = reader.temp_read_test_human(
"../human/test/")
import tensorflow as tf
from tensorflow.python.ops import rnn, rnn_cell
import pandas as pd
import numpy as np
import sys
sys.path.append("../script")
from convert_input import Convert
from validation import Validation
from read_data import DataReader
reader = DataReader(1,6)
x_train, y_train = reader.read_train_human('../human/train/')
x_test, y_test = reader.read_test_human("../human/test/")
x_input, y_input = reader.temp_read_test_human("../human/test/")
x_train = np.transpose(x_train, [1,0,2])
x_test = np.transpose(x_test, [1,0,2])
'''
To classify images using a recurrent neural network, we consider every image
row as a sequence of pixels. Because MNIST image shape is 28*28px, we will then
handle 28 sequences of 28 steps for every sample.
'''
# Parameters
learning_rate = 0.99