小编Su *_* JK的帖子

在以下代码中，我从tensorflow教程（官方）中修改了Deep MNIST示例。

修改-将重量衰减添加到损失函数中，并且还修改了重量。（如果它不正确，请告诉我）。

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data

import tensorflow as tf

from hyperopt import STATUS_OK, STATUS_FAIL

Flags2=None

def build_and_optimize(hp_space):
    global Flags2
    Flags2 = {}
    Flags2['dp'] = hp_space['dropout_global']
    Flags2['wd'] = hp_space['wd']

    res = main(Flags2)

    results = {
        'loss': res,
        'status': STATUS_OK
    }
    return results

def deepnn(x):
    """deepnn builds the graph for a deep net for classifying digits.
        args:
            x: an input tensor with the dimensions …

以下是评估训练高斯过程 (GP) 并用于对来自 MNIST 数据集的图像进行分类的代码。

import numpy as np


from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.gaussian_process.kernels import RBF
from sklearn import datasets
from sklearn.datasets import fetch_mldata

import random

SAMPLE_SIZE = 2000

def build_and_optimize(hp_space):
    build_train()
    return

def build_train(hp_space):
    l_scale = hp_space['l_scale']
    bias = hp_space['bias']
    gp_fix = GaussianProcessClassifier(kernel=bias * RBF(length_scale=l_scale), optimizer=None, multi_class='one_vs_rest')

    X_train, X_test, y_train, y_test = prepare_data()


    gp_fix.fit(X_train, y_train)

    print("Log Marginal Likelihood (initial): %.3f"
        % gp_fix.log_marginal_likelihood(gp_fix.kernel_.theta))


    y_ = gp_fix.predict(X_test[0:500])
    print(y_)
    print(y_test[0:500])
    print("Accuracy: %.3f (initial)"
        % (accuracy_score(y_test[0:500], y_))) …