hadware

from scipy.io.wavfile import read, write
import io

## This may look a bit intricate/useless, considering the fact that scipy's read() and write() function already return a
## numpy ndarray, but the BytesIO "hack" may be useful in case you get the wav not through a file, but trough some websocket or
## HTTP Post request. This should obviously work with any other sound format, as long as you have the proper decoding function

with open("input_wav.wav", "rb") as wavfile:
    input_wav = wavfile.read()

akors

import tensorflow as tf
import numpy as np

FC_SIZE = 1024
DTYPE = tf.float32


def _weight_variable(name, shape):
    return tf.get_variable(name, shape, DTYPE, tf.truncated_normal_initializer(stddev=0.1))

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats