Python Forum

Hello!

I created a neural network for time-series forecasting and I would like to run it on GPU.
So I need to make all my opertations tensor.
First of all - splitting of data.
Here is my code:

import tensorflow as tf

import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd

TRAIN_SPLIT = 300000
future_target = 36
past_history = 720
STEP = 1

df = pd.read_csv('data/USDT_BTC.csv')
df.columns = ['date','high','low','open','close','volume','quoteVolume','weightedAverage']
df['ema'] = df1.close.ewm(span=14).mean()

features_considered = ['open', 'close', 'ema']
features = df[features_considered]
features.index = df['date']
dataset = features.values

def multivariate_data(dataset, target, start_index, end_index, history_size,
                      target_size, step, single_step=False):
  data = []
  labels = []
  start_index = start_index + history_size
  if end_index is None:
    end_index = len(dataset) - target_size
  #print(history_size)
  for i in range(start_index, end_index):
    indices = range(i-history_size, i, step)
    data.append(dataset[indices])
    if single_step:
      labels.append(target[i+target_size])
    else:
      labels.append(target[i:i+target_size])
  return np.array(data), np.array(labels)

x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0,
                                                 TRAIN_SPLIT, past_history,
                                                 future_target, STEP)
x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1],
                                             TRAIN_SPLIT, None, past_history,
                                             future_target, STEP)

The main problem is nested loops of tf.while_loop, especially shape_invariants. I need to take care of the shape of my result data and one more thing that I understood: the shape of the result data must be the same as the shape of the loop indexes.
The question is how to write this code, using tf.while_loop