我有几千个音频文件,我想用Keras和Theano对它们进行分类.到目前为止,我生成了每个音频文件的28x28频谱图(更大可能更好,但我只是想让算法工作),并将图像读入矩阵.所以最后我将这个大图像矩阵输入网络进行图像分类.
在教程中,我发现了这个mnist分类代码:
import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers.core import Dense
from keras.utils import np_utils
batch_size = 128
nb_classes = 10
nb_epochs = 2
(X_train, y_train), (X_test, y_test) = mnist.load_data()
X_train = X_train.reshape(60000, 784)
X_test = X_test.reshape(10000, 784)
X_train = X_train.astype("float32")
X_test = X_test.astype("float32")
X_train /= 255
X_test /= 255
print(X_train.shape[0], "train samples")
print(X_test.shape[0], "test samples")
y_train = np_utils.to_categorical(y_train, nb_classes)
y_test = np_utils.to_categorical(y_test, nb_classes)
model = Sequential()
model.add(Dense(output_dim = 100, input_dim = 784, …Run Code Online (Sandbox Code Playgroud) 我只是在尝试 Three.js,所以我可以将它实现到我的项目中,但是当我运行文档中的第一个示例时:
<html>
<head>
<title>My first Three.js app</title>
<style>
body { margin: 0; }
canvas { width: 100%; height: 100% }
</style>
</head>
<body>
<script src="three.js"></script>
<script>
var scene = new THREE.Scene();
var camera = new THREE.PerspectiveCamera( 75, window.innerWidth/window.innerHeight, 0.1, 1000 );
var renderer = new THREE.WebGLRenderer();
renderer.setSize( window.innerWidth, window.innerHeight );
document.body.appendChild( renderer.domElement );
var geometry = new THREE.BoxGeometry( 1, 1, 1 );
var material = new THREE.MeshBasicMaterial( { color: 0x00ff00 } );
var cube = new THREE.Mesh( geometry, material …Run Code Online (Sandbox Code Playgroud)