{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import torch\n",
"from sklearn.metrics import confusion_matrix\n",
"\n",
"from mnist_ten.data import test_loader\n",
"from mnist_ten.models import classifier, weights_path\n",
"\n",
"from tqdm.auto import tqdm\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (0): Sequential(\n",
" (0): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" (2): SqueezeLayer(\n",
" (short): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
" (long): Sequential(\n",
" (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (3): LeakyReLU(negative_slope=0.01)\n",
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" )\n",
" (3): SqueezeLayer(\n",
" (short): AvgPool2d(kernel_size=2, stride=2, padding=0)\n",
" (long): Sequential(\n",
" (0): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" (2): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
" (3): LeakyReLU(negative_slope=0.01)\n",
" (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
" )\n",
" )\n",
" (4): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))\n",
" (5): LeakyReLU(negative_slope=0.01)\n",
" (6): Flatten()\n",
" )\n",
" (1): Sequential(\n",
" (0): Linear(in_features=3136, out_features=64, bias=True)\n",
" (1): LeakyReLU(negative_slope=0.01)\n",
" (2): Linear(in_features=64, out_features=10, bias=True)\n",
" )\n",
")"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"classifier.load_state_dict(torch.load(weights_path))\n",
"classifier.eval()\n",
"classifier"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "dba786832e944c45a1edc876fc827e41",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(FloatProgress(value=0.0, max=625.0), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"real_labels = []\n",
"predicted_labels = []\n",
"\n",
"for images, labels in tqdm(test_loader):\n",
" real_labels += list(labels)\n",
" predictions = classifier(images).detach().numpy()\n",
" predicted_labels += np.argmax(predictions, axis=1).tolist()\n",
"\n",
"real_labels = np.array(real_labels)\n",
"predicted_labels = np.array(predicted_labels)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.5263\n"
]
}
],
"source": [
"print(f'Accuracy: {np.mean(predicted_labels == real_labels)}')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"confusion = confusion_matrix(real_labels, predicted_labels)\n",
"plt.xlabel('Predicted label')\n",
"plt.ylabel('Real label')\n",
"plt.imshow(confusion)\n",
"plt.colorbar()\n",
"plt.show()"
]
}
],
"metadata": {
"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": "3.8.3"
},
"widgets": {
"application/vnd.jupyter.widget-state+json": {
"state": {
"01400dc6a9044330ade5d63b63a80cf6": {
"model_module": "@jupyter-widgets/base",
"model_module_version": "1.2.0",
"model_name": "LayoutModel",
"state": {}
},
"19f5da64f00242a7a1076d226f4d4d45": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "ProgressStyleModel",
"state": {
"description_width": "initial"
}
},
"1f7fef16e7f14160862181cc580675f3": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "DescriptionStyleModel",
"state": {
"description_width": ""
}
},
"20317563edd147bdb52187237be276d6": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "FloatProgressModel",
"state": {
"bar_style": "success",
"description": "100%",
"layout": "IPY_MODEL_01400dc6a9044330ade5d63b63a80cf6",
"max": 625,
"style": "IPY_MODEL_bbf98c3b755341ffa133e5c03953d3ba",
"value": 625
}
},
"2132ccdf1f53408092b52c82744adb28": {
"model_module": "@jupyter-widgets/controls",
"model_module_version": "1.5.0",
"model_name": "HTMLModel",
"state": {
"layout": "IPY_MODEL_3d8c97d3be7c4a1388cd14d9d21b1f06",
"style": "IPY_MODEL_7282afea5f974f90b64946cd88c0a204",
"value": " 625/625 [00:10<00:00, 60.66it/s]"
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