Lay4U

var page_no = getCurrentPageNumber();
var macro_flag = false;

function wait(msecs) {
	var start = new Date().getTime();
	var cur = start;
	while (cur - start < msecs) {
		cur = new Date().getTime();
	}
}

Lay4U

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Conv2D, Reshape, TimeDistributed, Flatten, Conv1D,ConvLSTM2D, MaxPooling1D
from keras.layers.core import Dense, Activation, Dropout
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
import matplotlib.pyplot as plt

Lay4U

Lay4U

https://drive.google.com/open?id=1tTsEtlIJl69OiYKqwYxp0aviE69hJq32

Lay4U

Installing, this may take a few minutes...
Please create a default UNIX user account. The username does not need to match your Windows username.
For more information visit: https://aka.ms/wslusers
Enter new UNIX username: layy
Enter new UNIX password:
Retype new UNIX password:
passwd: password updated successfully
Installation successful!
To run a command as administrator (user "root"), use "sudo <command>".
See "man sudo_root" for details.

Lay4U

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Conv2D, Reshape, TimeDistributed, Flatten, Conv1D,ConvLSTM2D, MaxPooling1D
from keras.layers.core import Dense, Activation, Dropout
from sklearn.preprocessing import MinMaxScaler, StandardScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
import matplotlib.pyplot as plt

Lay4U

def create_dataset(signal_data, look_back=1):
    dataX, dataY = [], []
    for i in range(len(signal_data) - look_back):
        dataX.append(signal_data[i:(i + look_back), :])
        dataY.append(signal_data[i + look_back, -1])
    return np.array(dataX), np.array(dataY)



look_back = 20

Lay4U

Lay4U

Lay4U

import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout, Conv2D, Reshape, TimeDistributed, Flatten, Conv1D,ConvLSTM2D, MaxPooling1D
from keras.layers.core import Dense, Activation, Dropout
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
import tensorflow as tf
import matplotlib.pyplot as plt