neelriyer

neelriyer

neelriyer

# https://www.cointribune.com/en/columns/the-defi-column/has-pancake-bunny-fallen-victim-to-a-1-billion-hack/

#
#  Panoramix 17 Feb 2020
#
#
#  I failed with these:
#  - unknownee872558(?)
#  All the rest is below.
#

neelriyer

# adapted from: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/test.py

for epoch in range(opt.epoch_count, opt.n_epochs + opt.n_epochs_decay + 1):    # outer loop for different epochs; we save the model by <epoch_count>, <epoch_count>+<save_latest_freq>
    epoch_start_time = time.time()  # timer for entire epoch
    iter_data_time = time.time()    # timer for data loading per iteration
    epoch_iter = 0                  # the number of training iterations in current epoch, reset to 0 every epoch
    visualizer.reset()              # reset the visualizer: make sure it saves the results to HTML at least once every epoch
    model.update_learning_rate()    # update learning rates in the beginning of every epoch.
    
    for i, data in enumerate(dataset):  # inner loop within one epoch

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from sklearn.ensemble import RandomForestRegressor
import xgboost as xgb
import lightgbm as lgb

def rmspe_calc(y_true, y_pred):
  # Compute Root Mean Square Percentage Error between two arrays.
  return np.sqrt(np.mean(np.square(((y_true - y_pred) / y_true)), axis=0))


models = [

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from itertools import product
from tqdm.notebook import tqdm


def get_learner(emb_szs=emb_szs, layers=[1000,500], ps=[0.02,0.04], emb_drop=0.08):
	return (tabular_learner(data,
	 layers=layers,
	 ps=ps,
	 emb_drop=emb_drop,
	 y_range=y_range,

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# Fill Missing values
# Encode categorical variables
# Normalize continous variables

procs=[FillMissing, Categorify, Normalize]
cont_vars = [i for i in [‘checkout_price’, 
 ‘base_price’, 
 ‘Elapsed’,
 ‘week_sin’, 
 ‘week_cos’, 

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# taken from: https://github.com/alishdipani/Neural-Style-Transfer-Audio/blob/master/NeuralStyleTransfer.py

if torch.cuda.is_available():
	output = output.cpu()

output = output.squeeze(0)
output = output.numpy()

N_FFT=2048
a = np.zeros_like(output)

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import torch
import torch.nn as nn
from torch.nn import Conv2d, ReLU, AvgPool1d, MaxPool2d, Linear, Conv1d
from torch.autograd import Variable
import torch.optim as optim
import numpy as np 
import os
import torchvision.transforms as transforms

import gc; gc.collect()

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import torch
import torch.nn as nn
from torch.nn import ReLU, Conv1d
import torch.optim as optim
import numpy as np 
import copy

class CNNModel(nn.Module):
	def __init__(self):
			super(CNNModel, self).__init__()