xunge

import time
import shutil
import os

import torch
import torch.nn as nn
import torchvision.datasets as datasets
import torchvision.transforms as transforms
import torchvision.models as models
import torch.backends.cudnn as cudnn

xunge

#!/usr/bin/env python3
import os
import json
import hashlib
import tempfile
import shutil
import logging
import subprocess as sp
from pathlib import Path
from email.utils import parsedate_to_datetime

xunge

import tensorflow as tf

# 输入训练数据，这里是python的list, 也可以定义为numpy的ndarray
x_data = [[1., 0.], [0., 1.], [0., 0.], [1., 1.]]
x = tf.placeholder(tf.float32, shape=[None, 2])  # 定义占位符，占位符在运行图的时候必须feed数据
y_data = [[1], [1], [0], [0]]  # 训练数据的标签，注意维度
y = tf.placeholder(tf.float32, shape=[None, 1])
# 定义variables，在运行图的过程中会被按照优化目标改变和保存
weights = {'w1': tf.Variable(tf.random_normal([2, 16])),
           'w2': tf.Variable(tf.random_normal([16, 1]))}

xunge

with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    for i in range(1000):
        for j in range(4):
            sess.run(train, feed_dict={x: np.expand_dims(X[j], 0), y: np.expand_dims(Y[j], 0)})
        loss_ = sess.run(loss, feed_dict={x: X, y: Y})
        print("step: %d, loss: %.3f" % (i, loss_))
    print("X: %r" % X)
    print("pred: %r" % sess.run(out, feed_dict={x: X}))

xunge

import tensorflow as tf
import numpy as np

X = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
Y = np.array([[0], [1], [1], [0]])

x = tf.placeholder(tf.float32, [None, 2])
y = tf.placeholder(tf.float32, [None, 1])

w = tf.Variable(tf.random_normal([2, 1]))