jkotra

#include <iostream>

using namespace std;

void gen_prime(int limit);

int main() {

    int limit;
    cout << "generate prime numbers upto:";

jkotra

jkotra

#include <iostream>
#include <fstream>
#include <string>
#include <array>

using namespace std;

class Cycling{
private:
    float place_finished;

jkotra

import mxnet as mx
from mxnet import nd, gluon, autograd
from mxnet.gluon import nn
import mxnet.ndarray as F

class Net(gluon.Block):
    def __init__(self, **kwargs):
        super(Net, self).__init__(**kwargs)
        with self.name_scope():
            

jkotra

EPOCHS = 30

trn_loss = []
val_loss = []

for epoch in range(EPOCHS):
    
    train_iter = mx.io.NDArrayIter(trn_x, trn_y, 1000, shuffle=True)
    val_iter = mx.io.NDArrayIter(val_x, val_y, 1000, shuffle=True)
    

jkotra

(charm) [jagadeesh@maxfunc Downloads]$ conda install anaconda-clean
WARNING: The conda.compat module is deprecated and will be removed in a future release.
Collecting package metadata: done
Solving environment: done


==> WARNING: A newer version of conda exists. <==
  current version: 4.6.11
  latest version: 4.6.14

jkotra

today = total_x[-1].reshape(-1).tolist()
last_price = scaler.transform(infered['Open_Predicted'][-1].reshape(-1, 1))
today.append(last_price[0])
today = today[1:]
today = np.array(today).reshape(-1,60,1)
tomorrow = model.predict(today)
tomorrow = scaler.inverse_transform(tomorrow)[0]
print(tomorrow)

jkotra

jkotra

#include <iostream>
#include <conio>

using namespace std;

int main()
{
    long a = 0;
    int b = 0;
    

jkotra

# calculate size during Conv opr.

F = 5  # kernel
P = 0  # padding
S = 1  # Strides

shape = [38, 18]
new_shape = []

for i in range(2):