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LeNet Implemention using MXNet on Fashion Mnist Dataset
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "LeNet.ipynb",
"provenance": [],
"authorship_tag": "ABX9TyNWhFwxIj8Xk7wbw17SFosn",
"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/997ecb8a354eed1a5b5f6f3a752adf3e/lenet.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "code",
"metadata": {
"id": "nZ_I8Udk1tlk"
},
"source": [
"# !pip install -U mxnet-cu101==1.7.0"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "98o48krN17Ze"
},
"source": [
"import mxnet\n",
"from mxnet import np,npx,autograd,gluon,init\n",
"from mxnet.gluon import nn\n",
"from d2l import mxnet as d2l\n",
"npx.set_np()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "CCDBnZ3L2VtW"
},
"source": [
"net = nn.Sequential()\n",
"net.add(\n",
" nn.Conv2D(channels=6,kernel_size=5,padding=2,activation='sigmoid'),\n",
" nn.AvgPool2D(pool_size=2,strides=2),\n",
" nn.Conv2D(channels=16,kernel_size=5,activation='sigmoid'),\n",
" nn.AvgPool2D(pool_size=2,strides=2),\n",
" nn.Dense(120,activation='sigmoid'),\n",
" nn.Dense(84,activation='sigmoid'),\n",
" nn.Dense(10)\n",
")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i1P5pp1m3tCZ",
"outputId": "c4f05df4-3f11-4113-dc63-1d81265cf50d"
},
"source": [
"net"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Sequential(\n",
" (0): Conv2D(-1 -> 6, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), Activation(sigmoid))\n",
" (1): AvgPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=avg, layout=NCHW)\n",
" (2): Conv2D(-1 -> 16, kernel_size=(5, 5), stride=(1, 1), Activation(sigmoid))\n",
" (3): AvgPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=avg, layout=NCHW)\n",
" (4): Dense(-1 -> 120, Activation(sigmoid))\n",
" (5): Dense(-1 -> 84, Activation(sigmoid))\n",
" (6): Dense(-1 -> 10, linear)\n",
")"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "ENaj5fdL34ZH",
"outputId": "72500818-2ab6-4113-c1a2-373b3d96d899"
},
"source": [
"X=np.random.uniform(size=(1,1,28,28))\n",
"net.initialize()\n",
"for layer in net:\n",
" X=layer(X)\n",
" print(layer.name,'Output shape:\\t',X.shape)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"conv0 Output shape:\t (1, 6, 28, 28)\n",
"pool0 Output shape:\t (1, 6, 14, 14)\n",
"conv1 Output shape:\t (1, 16, 10, 10)\n",
"pool1 Output shape:\t (1, 16, 5, 5)\n",
"dense0 Output shape:\t (1, 120)\n",
"dense1 Output shape:\t (1, 84)\n",
"dense2 Output shape:\t (1, 10)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QqEx6RqE4Z8z"
},
"source": [
"batch_size = 256\n",
"train_iter,test_iter = d2l.load_data_fashion_mnist(batch_size=batch_size)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mwPWpqbl4nfL"
},
"source": [
"def evaluate_accuracy_gpu(net,data_iter,device=None):\n",
" \n",
" if not device:\n",
" device = d2l.try_gpu()\n",
" metric = d2l.Accumulator(2)\n",
" for X,y in data_iter:\n",
" X,y = X.as_in_ctx(device),y.as_in_ctx(device)\n",
" metric.add(d2l.accuracy(net(X),y),y.size)\n",
" return metric[0]/metric[1]"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "e_qy4-Js6Bul"
},
"source": [
"def train(net,train_iter,test_iter,num_epochs,lr,device=None):\n",
" # if not device:\n",
" # device = list(net.collect_params().values())[0].list_ctx()[0]\n",
" net.initialize(force_reinit=True,ctx=device,init=init.Xavier())\n",
" loss = gluon.loss.SoftmaxCrossEntropyLoss()\n",
" trainer = gluon.Trainer(net.collect_params(),'sgd',\n",
" {'learning_rate':lr})\n",
" for epoch in range(num_epochs):\n",
" metric = d2l.Accumulator(3)\n",
" for i, (X,y) in enumerate(train_iter):\n",
" X,y = X.as_in_ctx(device),y.as_in_ctx(device)\n",
" with autograd.record():\n",
" y_hat = net(X)\n",
" l=loss(y_hat,y)\n",
" l.backward()\n",
" trainer.step(X.shape[0])\n",
" metric.add(float(l.sum()),d2l.accuracy(y_hat,y),X.shape[0])\n",
" train_l = metric[0]/metric[2]\n",
" train_acc = metric[1]/metric[2]\n",
" test_acc = evaluate_accuracy_gpu(net,test_iter)\n",
" print(f\"device:{str(device)}epoch:{epoch+1} loss:{train_l:.2f} train_acc:{train_acc:.2f} test_acc:{test_acc:.2f}\")\n"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "yB5S0O068uAv",
"outputId": "64aa6c70-d41c-4884-d9e7-f9d9ac254e13"
},
"source": [
"lr,num_epochs = 0.9,10\n",
"train(net,train_iter,test_iter,num_epochs,lr,d2l.try_gpu())"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"device:gpu(0)epoch:1 loss:2.32 train_acc:0.10 test_acc:0.10\n",
"device:gpu(0)epoch:2 loss:1.69 train_acc:0.34 test_acc:0.53\n",
"device:gpu(0)epoch:3 loss:0.93 train_acc:0.63 test_acc:0.67\n",
"device:gpu(0)epoch:4 loss:0.75 train_acc:0.71 test_acc:0.69\n",
"device:gpu(0)epoch:5 loss:0.66 train_acc:0.74 test_acc:0.75\n",
"device:gpu(0)epoch:6 loss:0.60 train_acc:0.76 test_acc:0.76\n",
"device:gpu(0)epoch:7 loss:0.56 train_acc:0.78 test_acc:0.76\n",
"device:gpu(0)epoch:8 loss:0.52 train_acc:0.80 test_acc:0.79\n",
"device:gpu(0)epoch:9 loss:0.50 train_acc:0.81 test_acc:0.81\n",
"device:gpu(0)epoch:10 loss:0.48 train_acc:0.82 test_acc:0.79\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "22OHAqY4Fcnu"
},
"source": [
"def predict(model,test_iter,n=3):\n",
"\n",
" device=list(net.collect_params().values())[0].list_ctx()[0]\n",
" for X,y in test_iter:\n",
" X,y = X.as_in_ctx(device),y.as_in_ctx(device)\n",
" break\n",
" \n",
" trues=d2l.get_fashion_mnist_labels(y)\n",
" preds=d2l.get_fashion_mnist_labels(model(X).argmax(axis=1))\n",
" titles = ['true_label:\\n'+true + '\\n' + \"predicted:\\n\"+pred for true,pred in zip(trues,preds)]\n",
" d2l.show_images(X[0:n].reshape((n,28,28)),1,n,titles[:n])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ofCJ6sJ2Mspm"
},
"source": [
""
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 167
},
"id": "oTwQCy7RGqEf",
"outputId": "8e8dd6f6-5fd1-4be1-abef-22f7b7179e0a"
},
"source": [
"predict(net,test_iter,n=6)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 648x108 with 6 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "8VLkLg3CA44B",
"outputId": "2ebb160c-7533-4e5f-8423-3ff3bd73639f"
},
"source": [
"evaluate_accuracy_gpu(net,test_iter)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.7876"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7V4Nl18wKA_Q"
},
"source": [
"net.save_parameters('model.params')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "E5fWuZB7LZdC"
},
"source": [
"net.load_parameters('model.params',ctx=d2l.try_gpu())"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 102
},
"id": "glM0Wz9mL0aj",
"outputId": "efd78713-2447-4070-920e-faf5918e62dc"
},
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"def verify_loaded_model(net):\n",
" \"\"\"Run inference using ten random images.\n",
" Print both input and output of the model\"\"\"\n",
"\n",
" def transform(data, label):\n",
" return data.astype(np.float32)/255, label.astype(np.float32)\n",
"\n",
" # Load ten random images from the test dataset\n",
" sample_data = mxnet.gluon.data.DataLoader(mx.gluon.data.vision.FashionMNIST(train=False, transform=transform),\n",
" 10, shuffle=True)\n",
"\n",
" for data, label in sample_data:\n",
"\n",
" # Display the images\n",
" img = np.transpose(data, (1,0,2,3))\n",
" img = np.reshape(img, (28,10*28,1))\n",
" imtiles = np.tile(img, (1,1,3))\n",
" plt.imshow(imtiles.asnumpy())\n",
" plt.show()\n",
"\n",
" # Display the predictions\n",
" data = np.transpose(data, (0, 3, 1, 2))\n",
" out = net(data.as_in_context(d2l.try_gpu()))\n",
" predictions = np.argmax(out, axis=1)\n",
" print('Model predictions: ', predictions.asnumpy())\n",
"\n",
" break\n",
"\n",
"verify_loaded_model(net)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "stream",
"text": [
"Model predictions: [2 8 0 5 5 8 9 2 1 8]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "OvFrYTZFMtzM",
"outputId": "723ea66e-d0bf-48d0-882b-25b100acd54c"
},
"source": [
"d2l.get_fashion_mnist_labels(np.array([5]))"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['sandal']"
]
},
"metadata": {
"tags": []
},
"execution_count": 71
}
]
}
]
}
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