def __init__(self):
reader = DataReader(1,3)
name ="StarLightCurves" + "/" + "StarLightCurves"
self.x_train, self.y_train = reader.read_train_data('../UCR_TS_Archive_2015/'+name+"_TRAIN")
self.x_test, self.y_test = reader.read_test_data("../UCR_TS_Archive_2015/"+name+"_TEST")
self.x_input, self.y_input = reader.temp_read_test_data("../UCR_TS_Archive_2015/"+name+"_TEST")
def __init__(self):
reader = DataReader(1,5)
name ="ECG5000" + "/" + "ECG5000"
self.x_train, self.y_train = reader.read_train_data('../UCR_TS_Archive_2015/'+name+"_TEST")
self.x_test, self.y_test = reader.read_test_data("../UCR_TS_Archive_2015/"+name+"_TRAIN")
self.x_input, self.y_input = reader.temp_read_test_data("../UCR_TS_Archive_2015/"+name+"_TRAIN")
def __init__(self):
reader = DataReader(152, 2)
name = "wafer" + "/" + "wafer"
self.x_train, self.y_train = reader.read_train_data(
'../UCR_TS_Archive_2015/' + name + "_TRAIN2")
self.x_test, self.y_test = reader.read_test_data(
"../UCR_TS_Archive_2015/" + name + "_TEST2")
self.x_input, self.y_input = reader.temp_read_test_data(
"../UCR_TS_Archive_2015/" + name + "_TEST2")
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, 2)
name = "wafer"
x_train, y_train = reader.read_train_data('../UCR_TS_Archive_2015/' + name +
"/" + name + "_TRAIN2")
x_test, y_test = reader.read_test_data("../UCR_TS_Archive_2015/" + name + "/" +
name + "_TEST2")
x_input, y_input = reader.temp_read_test_data("../UCR_TS_Archive_2015/" +
name + "/" + name + "_TEST2")
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 = 0.5
training_iters = 100