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AlexNet implementation using MXNet
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alexnet.ipynb
{
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"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "AlexNet.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyOQvIXXifvlGA9chzzw87e0",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/devil-cyber/5284c822a34d77207f981b5e91a68d43/alexnet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "0H3BD8CENT0k"
},
"source": [
"# !pip install -U mxnet-cu101==1.7.0\n",
"# !pip install mxnet d2l"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "92jxjKO6OKYg"
},
"source": [
"from mxnet import np,npx,init\n",
"from mxnet.gluon import nn\n",
"from d2l import mxnet as d2l\n",
"npx.set_np()"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Rm7bmqVLOK3e"
},
"source": [
"net = nn.Sequential()"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "g1Ft6TF9OLNn"
},
"source": [
"net.add(\n",
" # Here, we use a larger 11 x 11 window to capture objects. At the same time,\n",
" # we use a stride of 4 to greatly reduce the height and width of the output.\n",
" nn.Conv2D(96,kernel_size=11,strides=4,activation='relu'),\n",
" nn.MaxPool2D(pool_size=3,strides=2),\n",
" # Make the convolution window smaller, set padding to 2 for consistent\n",
" # height and width across the input and output, and increase the\n",
" # number of output channels\n",
" nn.Conv2D(256,kernel_size=5,padding=2,activation='relu'),\n",
" nn.MaxPool2D(pool_size=3,strides=2),\n",
" # Use three successive convolutional layers and a smaller convolution\n",
" # window. Except for the final convolutional layer, the number of\n",
" # output channels is further increased. Pooling layers are not used to\n",
" # reduce the height and width of input after the first two\n",
" # convolutional layers\n",
" nn.Conv2D(384, kernel_size=3,padding=1,activation='relu'),\n",
" nn.Conv2D(384, kernel_size=3,padding=1,activation='relu'),\n",
" nn.Conv2D(256, kernel_size=3,padding=1,activation='relu'),\n",
" nn.MaxPool2D(pool_size=3, strides=2),\n",
" nn.Dense(4096,activation='relu'),\n",
" nn.Dropout(.5),\n",
" nn.Dense(4096,activation='relu'),\n",
" nn.Dropout(0.5),\n",
" nn.Dense(10)\n",
"\n",
")"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TWkFMOrbOLYf",
"outputId": "1fb84cb4-baa2-4e5c-b584-015b7d223d65"
},
"source": [
"X = np.random.uniform(size=(1,1,224,224))\n",
"net.initialize()\n",
"for layer in net:\n",
" X=layer(X)\n",
" print(layer.name,'output shape:\\t',X.shape)"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"conv2 output shape:\t (1, 96, 54, 54)\n",
"pool1 output shape:\t (1, 96, 26, 26)\n",
"conv3 output shape:\t (1, 256, 26, 26)\n",
"pool2 output shape:\t (1, 256, 12, 12)\n",
"conv4 output shape:\t (1, 384, 12, 12)\n",
"conv5 output shape:\t (1, 384, 12, 12)\n",
"conv6 output shape:\t (1, 256, 12, 12)\n",
"pool3 output shape:\t (1, 256, 5, 5)\n",
"dense0 output shape:\t (1, 4096)\n",
"dropout0 output shape:\t (1, 4096)\n",
"dense1 output shape:\t (1, 4096)\n",
"dropout1 output shape:\t (1, 4096)\n",
"dense2 output shape:\t (1, 10)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "5aw8vz4_OLiL",
"outputId": "55735940-439a-448d-e688-d3d717394f10"
},
"source": [
"batch_size = 128\n",
"train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)"
],
"execution_count": 18,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading /root/.mxnet/datasets/fashion-mnist/train-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-images-idx3-ubyte.gz...\n",
"Downloading /root/.mxnet/datasets/fashion-mnist/train-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/train-labels-idx1-ubyte.gz...\n",
"Downloading /root/.mxnet/datasets/fashion-mnist/t10k-images-idx3-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-images-idx3-ubyte.gz...\n",
"Downloading /root/.mxnet/datasets/fashion-mnist/t10k-labels-idx1-ubyte.gz from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/dataset/fashion-mnist/t10k-labels-idx1-ubyte.gz...\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 296
},
"id": "2--mLBBvRh0e",
"outputId": "091c0f99-e8bb-4f20-d9ee-c6946c9c498c"
},
"source": [
"lr, num_epochs = 0.01,10\n",
"d2l.train_ch6(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu())"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"loss 0.331, train acc 0.879, test acc 0.890\n",
"1494.6 examples/sec on gpu(0)\n"
],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 252x180 with 1 Axes>"
],
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},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "4oCbbFh0T1PG"
},
"source": [
"# Data for prediction\n",
"for X,y in test_iter:\n",
" X=X\n",
" y=y\n",
" break"
],
"execution_count": 118,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "cmWr6faWYa1o"
},
"source": [
"def get_fashion_mnist_labels(labels):\n",
" text_labels=['t-shirt','trouser','pullover','dress','coat','sandal','shirt','sneaker','bag','ankle boot']\n",
" return text_labels[labels]"
],
"execution_count": 65,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "aX3XebRnZllw"
},
"source": [
"import pandas as pd"
],
"execution_count": 87,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CmqvhH00aBYj"
},
"source": [
"def predict(X,y):\n",
" trl=[]\n",
" prel=[]\n",
" count = 0\n",
" X=X.as_in_ctx(d2l.try_gpu())\n",
" z=net(X).argmax(axis=1)\n",
" n=len(z)\n",
" for i,j in zip(z,y):\n",
" pre=get_fashion_mnist_labels(int(i))\n",
" tru = get_fashion_mnist_labels(int(j))\n",
" if pre==tru:\n",
" count+=1\n",
" trl.append(tru)\n",
" prel.append(pre)\n",
" label = {'true_label':trl,'predicted_label':prel}\n",
" df = pd.DataFrame(label)\n",
" print(df)\n",
" print('The accuracy:',(count/n)*100)"
],
"execution_count": 115,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "3042_7U1a66W",
"outputId": "825dad27-ab0e-48ab-af0b-f8bcc8ee6273"
},
"source": [
"predict(X[:10],y)"
],
"execution_count": 123,
"outputs": [
{
"output_type": "stream",
"text": [
" true_label predicted_label\n",
"0 t-shirt t-shirt\n",
"1 trouser trouser\n",
"2 pullover pullover\n",
"3 pullover shirt\n",
"4 dress dress\n",
"5 pullover shirt\n",
"6 bag bag\n",
"7 shirt pullover\n",
"8 sandal sandal\n",
"9 t-shirt t-shirt\n",
"The accuracy: 70.0\n"
],
"name": "stdout"
}
]
}
]
}
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