softmax.ipynb

{

"cells": [

{

"cell_type": "code",

"execution_count": 15,

"metadata": {},

"outputs": [],

"source": [

"%matplotlib inline\n",

"%reload_ext autoreload\n",

"%autoreload 1\n",

"%aimport d2lzh_pytorch\n",

"import torch\n",

"import torchvision\n",

"import torchvision.transforms as transforms\n",

"import matplotlib.pyplot as plt\n",

"import numpy as np\n",

"import sys\n"

]

{

"cell_type": "code",

"execution_count": 14,

"metadata": {

"tags": [

"获取和读取数据"

]

"outputs": [

{

"name": "stdout",

"output_type": "stream",

"text": [

"60000 10000\n"

]

}

"source": [

"batch_size = 256\n",

"train_iter, test_iter = d2lzh_pytorch.load_data_fashion_mnist(batch_size)"

]

{

"cell_type": "code",

"execution_count": 17,

"metadata": {

"tags": [

"初始化模型参数"

]

"outputs": [],

"source": [

"num_inputs = 784\n",

"num_outputs = 10\n",

"\n",

"W = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_outputs)), dtype=torch.float)\n",

"b = torch.zeros(num_outputs, dtype=torch.float)"

]

{

"cell_type": "code",

"execution_count": 18,

"metadata": {

"tags": [

"开启计算梯度"

]

"outputs": [

{

"data": {

"text/plain": [

"tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], requires_grad=True)"

]

"execution_count": 18,

"metadata": {},

"output_type": "execute_result"

}

"source": [

"W.requires_grad_(requires_grad=True)\n",

"b.requires_grad_(requires_grad=True) "

]

{

"cell_type": "code",

"execution_count": 19,

"metadata": {

"tags": [

"实现softmax运算"

]

"outputs": [

{

"name": "stdout",

"output_type": "stream",

"text": [

"tensor([[5, 7, 9]])\n",

"tensor([[ 6],\n",

" [15]])\n"

]

}

"source": [

"X = torch.tensor([[1, 2, 3], [4, 5, 6]])\n",

"print(X.sum(dim=0, keepdim=True))\n",

"print(X.sum(dim=1, keepdim=True))"

]

{

"cell_type": "code",

"execution_count": 20,

"metadata": {},

"outputs": [],

"source": [

"def softmax(X):\n",

" X_exp = X.exp()\n",

" partition = X_exp.sum(dim=1, keepdim=True)\n",

" return X_exp / partition # 这里应用了广播机制"

]

{

"cell_type": "code",

"execution_count": 21,

"metadata": {},

"outputs": [

{

"name": "stdout",

"output_type": "stream",

"text": [

"tensor([[0.1301, 0.3243, 0.1301, 0.1509, 0.2646],\n",

" [0.1799, 0.2408, 0.2936, 0.1409, 0.1448]]) tensor([1.0000, 1.0000])\n"

]

}

"source": [

"X = torch.rand((2, 5))\n",

"X_prob = softmax(X)\n",

"print(X_prob, X_prob.sum(dim=1))"

]

{

"cell_type": "code",

"execution_count": 22,

"metadata": {

"tags": [

"定义模型"

]

"outputs": [],

"source": [

"def net(X):\n",

" return softmax(torch.mm(X.view((-1, num_inputs)), W) + b)"

]

{

"cell_type": "code",

"execution_count": 23,

"metadata": {

"tags": [

"定义损失函数"

]

"outputs": [

{

"data": {

"text/plain": [

"tensor([[0.1000],\n",

" [0.5000]])"

]

"execution_count": 23,

"metadata": {},

"output_type": "execute_result"

}

"source": [

"y_hat = torch.tensor([[0.1, 0.3, 0.6], [0.3, 0.2, 0.5]])\n",

"y = torch.LongTensor([0, 2])\n",

"y_hat.gather(1, y.view(-1, 1))"

]

{

"cell_type": "code",

"execution_count": 24,

"metadata": {

"tags": [

"交叉熵损失函数"

]

"outputs": [],

"source": [

"def cross_entropy(y_hat, y):\n",

" return - torch.log(y_hat.gather(1, y.view(-1, 1)))"

]

{

"cell_type": "code",

"execution_count": 25,

"metadata": {},

"outputs": [

{

"name": "stdout",

"output_type": "stream",

"text": [

"0.0682\n"

]

}

"source": [

"print(d2lzh_pytorch.evaluate_accuracy(test_iter, net))"

]

{

"cell_type": "code",

"execution_count": 29,

"metadata": {

"tags": [

"训练模型"

]

"outputs": [

{

"name": "stdout",

"output_type": "stream",

"text": [

"epoch 1, loss 0.7879, train acc 0.747, test acc 0.789\n",

"epoch 2, loss 0.5711, train acc 0.812, test acc 0.813\n",

"epoch 3, loss 0.5249, train acc 0.826, test acc 0.818\n",

"epoch 4, loss 0.5009, train acc 0.832, test acc 0.825\n",

"epoch 5, loss 0.4851, train acc 0.838, test acc 0.821\n"

]

}

"source": [

"num_epochs, lr = 5, 0.1\n",

"\n",

"d2lzh_pytorch.train_softmax(net, train_iter, test_iter, cross_entropy, num_epochs, batch_size, [W, b], lr)"

]

{

"cell_type": "code",

"execution_count": 32,

"metadata": {

"tags": [

"预测"

]

"outputs": [

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"text/plain": [

"<Figure size 864x864 with 9 Axes>"

]

"metadata": {

"needs_background": "light"

"output_type": "display_data"

}

"source": [

"X, y = iter(test_iter).next()\n",

"\n",

"true_labels = d2lzh_pytorch.get_fashion_mnist_labels(y.numpy())\n",

"pred_labels = d2lzh_pytorch.get_fashion_mnist_labels(net(X).argmax(dim=1).numpy())\n",

"titles = [true + '\\n' + pred for true, pred in zip(true_labels, pred_labels)]\n",

"\n",

"d2lzh_pytorch.show_fashion_mnist(X[0:9], titles[0:9])"

]

{

"cell_type": "code",

"execution_count": null,

"metadata": {},

"outputs": [],

"source": []

}

"metadata": {

"celltoolbar": "Tags",

"kernelspec": {

"display_name": "Python 3",

"language": "python",

"name": "python3"

"language_info": {

"codemirror_mode": {

"name": "ipython",

"version": 3

"file_extension": ".py",

"mimetype": "text/x-python",

"name": "python",

"nbconvert_exporter": "python",

"pygments_lexer": "ipython3",

"version": "3.6.10"

}

"nbformat": 4,

"nbformat_minor": 4

}

softmax.ipynb

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