softmax回归的简洁实现.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.core.display import display, HTML\n",
    "# display(HTML(\"<style>.container { width:80% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "print(tf.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = '0, 1'\n",
    "# 获取物理gpu, cpu对象\n",
    "gpus = tf.config.experimental.list_physical_devices(device_type='GPU')\n",
    "cpus = tf.config.experimental.list_physical_devices(device_type='CPU')\n",
    "\n",
    "# 设置当前程序的　物理可见设备范围\n",
    "tf.config.experimental.set_visible_devices(devices=gpus[0], device_type='GPU')\n",
    "\n",
    "#设置仅在需要时申请：\n",
    "# for gpu in gpus:\n",
    "#     tf.config.experimental.set_memory_growth(gpu, True)\n",
    "\n",
    "# 设置在物理gpu上设置虚拟gpu，并用来限制gpu内存使用\n",
    "tf.config.experimental.set_virtual_device_configuration(\n",
    "    gpus[0], [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024), tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)])\n",
    "\n",
    "# 获取当前虚拟gpu对象\n",
    "logical_gpus = tf.config.experimental.list_logical_devices('GPU')\n",
    "\n",
    "print('物理gpu个数: ', len(gpus))\n",
    "print('总的gpu个数，其中包括括虚拟Logical gpus: ', len(logical_gpus))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 加载模型\n",
    "fashion_mnist = keras.datasets.fashion_mnist\n",
    "(x_train, y_train), (x_test, y_test) = fashion_mnist.load_data()\n",
    "\n",
    "x_train = x_train / 255.0\n",
    "x_test = x_test / 255.0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 创建模型\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Flatten(input_shape=(28, 28)),\n",
    "    keras.layers.Dense(10, activation=tf.nn.softmax)\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "loss = 'sparse_categorical_crossentropy'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = tf.keras.optimizers.SGD(lr=0.1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 训练模型\n",
    "model.compile(optimizer=optimizer,\n",
    "              loss = loss,\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "model.fit(x_train,y_train,epochs=5,batch_size=256)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试集测试\n",
    "test_loss, test_acc = model.evaluate(x_test, y_test)\n",
    "print('Test Acc:',test_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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