hccho2

import torch
import numpy as np
from torch import nn
import torch.nn.functional as F

H=4;W=3
pad = 1
stride=2
kernel_size = 3 

hccho2

import numpy as np
import tensorflow as tf
import torch
print(f'Tensorflow Version: {tf.__version__}')

HCCHO = True  # cn231n code에서 im2col의 2-dim의 행벡터가 batch data가 섞여서 나오는 방식이라, 이를 batch data간에 섞이지 않는 방식으로 변환.

def get_im2col_indices(x_shape, field_height, field_width, padding=1, stride=1):
    # First figure out what the size of the output should be

hccho2

lr = 0.1
model = nn.Linear(10,1)

optimizer = torch.optim.Adam(model.parameters(), lr=lr)
lambda1 = lambda epoch: epoch/10   # lr * lambda1(epoch+1) 
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer,lambda1)


print(optimizer.state_dict())

hccho2

from PIL import ImageTk, Image, ImageDraw
import PIL
from tkinter import *
import os
width = 200
height = 200
center = height//2
white = (255, 255, 255)
black = (0,0,0)
green = (0,128,0)

hccho2

import torch
import numpy as np

x = torch.Tensor([[100],[200]])
y = init.uniform_(x,-5,5)  # x의 값을 변경 --> return 되는 것은 alias
w = x.detach()
print(x,y,w)  # x,y는 같은 객체
a = np.array([[2],[1.]])   
case = 2
if case==1:

hccho2

# waveform = torch.from_numpy(waveform)   ----> numpy array를 torch tensor로 변환하는 것은 속도에 영향이 거의 없다.

# sr = 22050이면 --> torchaudio가 많이 느리다.
# sr = 16000  ---> origin sample_rate과 일치하면 모두 다 빠르다.
# sr = 8000

n_samples = 100
sr = 22050
s_time=time.time()
for i in range(n_samples):

hccho2

\documentclass[]{article}
\usepackage[margin=1cm]{geometry}
\usepackage{tikz,pgfplots,pgf}
\usepackage{neuralnetwork}
\usepackage{sidecap}
\usepackage{amsmath}

\usetikzlibrary{matrix,shapes,arrows,positioning}
\usetikzlibrary{chains,decorations.pathreplacing}
\usetikzlibrary{backgrounds}

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# remove last step
def strip(var, nenvs, nsteps, flat = False):
    # var: [ nenvs*(nsteps+1), last_dim]
    last_dim = var.get_shape()[-1].value
    vars = batch_to_seq(var, nenvs, nsteps + 1, flat)  # var: (nenvs,last_dim)  ---> list of length nsteps+1
    # vars: [(nenvs,last_dim), (nenvs,last_dim), .... ]  <----- nsteps+1 길이
    return seq_to_batch(vars[:-1],last_dim, flat)

def batch_to_seq(h, nbatch, nsteps, flat=False):
    if flat:

hccho2

'''
ddpg
'''


import tensorflow as tf
import numpy as np
import gym

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x = np.array([[0.49593696, 0.504063  ],
              [0.4912244 , 0.50877565],
              [0.48871803, 0.51128197],
              [0.48469874, 0.5153013 ],
              [0.4801116 , 0.5198884 ]])
a = np.array([0,0,1,1,0]).astype(np.int32)

# numpy array
Z = x[range(5),a]