aidiary

Promise Tutorial

• https://www.youtube.com/watch?v=PoRJizFvM7s
• Promiseオブジェクトを返す
• resolveとrejectの二つの引数を取る
• resolve() を返したら呼び出し元で then() で受けられる
• reject() を返したら呼び出し元で catch() で受けられる

const posts = [

aidiary

Docker and Kubernetes: The Complete Guide (vol.1)

https://www.udemy.com/course/docker-and-kubernetes-the-complete-guide/

2. Manipulating Containers with the Docker Client

Hello World

docker run hello-world

aidiary

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model


def load_dataset():
    mnist = tf.keras.datasets.mnist

    (x_train, y_train), (x_test, y_test) = mnist.load_data()
    x_train, x_test = x_train / 255.0, x_test / 255.0

aidiary

import tensorflow as tf
import matplotlib.pyplot as plt
from IPython.display import display, Audio

wav_file = 'test.wav'
wav_raw = tf.io.read_file(wav_file)
wav_tensor = tf.audio.decode_wav(wav_raw)

print('sample_rate:', wav_tensor.sample_rate)

aidiary

func_args = []
for wav_file in sorted(os.listdir(wav_dir)):
    prefix = os.path.splitext(wav_file)[0]
    wav_path = os.path.join(wav_dir, wav_file)
    mulaw_path = os.path.join(mulaw_dir, '{}.npy'.format(prefix))
    melspec_path = os.path.join(melspec_dir, '{}.npy'.format(prefix))
    func_args.append((extract_features, wav_path, mulaw_path, melspec_path, config))

with Pool(os.cpu_count()) as p:
    for result in tqdm.tqdm(p.imap_unordered(argwrapper, func_args), total=len(func_args)):

aidiary

import os
import glob
from scipy.io import wavfile
import matplotlib.pyplot as plt
import IPython.display
%matplotlib inline

target_dir = '/path/to/*.wav'
for f in sorted(glob.glob(os.path.join(target_dir)))[:10]:
    print(f)

aidiary

for layer in net.children():
    if isinstance(layer, nn.Conv2d):
        do something with the layer

aidiary

load_weights = torch.load(load_path, map_location={'cuda:0': 'cpu'})
net.load_state_dict(load_weights)

aidiary

torch.backends.cudnn.benchmark = True

aidiary

device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')