01，kerasmodel_example.ipynb

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   "source": [
    "# KerasModel Example"
   ]
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   "source": [
    "You can install torchkeras using pip:\n",
    "`pip install torchkeras`\n",
    "\n",
    "Here is the mnist example using torchkeras.KerasModel. \n"
   ]
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   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "from matplotlib import pyplot as plt\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import Dataset,DataLoader\n",
    "import torchkeras \n"
   ]
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   "source": [
    "### 1, prepare data "
   ]
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   "source": [
    "import torchvision \n",
    "from torchvision import transforms\n",
    "\n",
    "transform = transforms.Compose([transforms.ToTensor()])\n",
    "ds_train = torchvision.datasets.MNIST(root=\"mnist/\",train=True,download=True,transform=transform)\n",
    "ds_train = torch.utils.data.Subset(ds_train,range(0,len(ds_train),20))\n",
    "ds_val = torchvision.datasets.MNIST(root=\"mnist/\",train=False,download=True,transform=transform)\n",
    "ds_val = torch.utils.data.Subset(ds_val,range(0,len(ds_val),20))\n",
    "dl_train =  torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=2)\n",
    "dl_val =  torch.utils.data.DataLoader(ds_val, batch_size=128, shuffle=False, num_workers=2)\n"
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     "text": [
      "torch.Size([128, 1, 28, 28])\n",
      "torch.Size([128])\n"
     ]
    }
   ],
   "source": [
    "for features,labels in dl_train:\n",
    "    break\n",
    "print(features.shape)\n",
    "print(labels.shape)\n"
   ]
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      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'svg'\n",
    "\n",
    "#查看部分样本\n",
    "from matplotlib import pyplot as plt \n",
    "\n",
    "fig = plt.figure(figsize=(8,8)) \n",
    "for i in range(9):\n",
    "    img,label = ds_train[i]\n",
    "    img = img.permute(1,2,0)\n",
    "    ax=plt.subplot(3,3,i+1)\n",
    "    ax.imshow(img.numpy())\n",
    "    ax.set_title(\"label = %d\"%label)\n",
    "    ax.set_xticks([])\n",
    "    ax.set_yticks([]) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88ac1eef-3dcb-4388-92ca-2200e5e05852",
   "metadata": {
    "papermill": {
     "duration": 0.005087,
     "end_time": "2024-01-08T03:46:17.780315",
     "exception": false,
     "start_time": "2024-01-08T03:46:17.775228",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "485da125",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.003635,
     "end_time": "2024-01-08T03:46:17.788930",
     "exception": false,
     "start_time": "2024-01-08T03:46:17.785295",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 2, create the  model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6174ae8a",
   "metadata": {
    "editable": true,
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    "slideshow": {
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   },
   "outputs": [],
   "source": [
    "def create_net():\n",
    "    net = nn.Sequential()\n",
    "    net.add_module(\"conv1\",nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3))\n",
    "    net.add_module(\"pool1\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"conv2\",nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5))\n",
    "    net.add_module(\"pool2\",nn.MaxPool2d(kernel_size = 2,stride = 2))\n",
    "    net.add_module(\"dropout\",nn.Dropout2d(p = 0.1))\n",
    "    net.add_module(\"adaptive_pool\",nn.AdaptiveMaxPool2d((1,1)))\n",
    "    net.add_module(\"flatten\",nn.Flatten())\n",
    "    net.add_module(\"linear1\",nn.Linear(64,32))\n",
    "    net.add_module(\"relu\",nn.ReLU())\n",
    "    net.add_module(\"linear2\",nn.Linear(32,10))\n",
    "    return net\n",
    "\n",
    "\n",
    "class Accuracy(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "\n",
    "        self.correct = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "        self.total = nn.Parameter(torch.tensor(0.0),requires_grad=False)\n",
    "\n",
    "    def forward(self, preds: torch.Tensor, targets: torch.Tensor):\n",
    "        preds = preds.argmax(dim=-1)\n",
    "        m = (preds == targets).sum()\n",
    "        n = targets.shape[0] \n",
    "        self.correct += m \n",
    "        self.total += n\n",
    "        \n",
    "        return m/n\n",
    "\n",
    "    def compute(self):\n",
    "        return self.correct.float() / self.total \n",
    "    \n",
    "    def reset(self):\n",
    "        self.correct -= self.correct\n",
    "        self.total -= self.total\n",
    "        "
   ]
  },
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   "cell_type": "code",
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   "id": "ad17716f",
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    {
     "name": "stdout",
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     "text": [
      "--------------------------------------------------------------------------\n",
      "Layer (type)                            Output Shape              Param #\n",
      "==========================================================================\n",
      "Conv2d-1                            [-1, 32, 26, 26]                  320\n",
      "MaxPool2d-2                         [-1, 32, 13, 13]                    0\n",
      "Conv2d-3                              [-1, 64, 9, 9]               51,264\n",
      "MaxPool2d-4                           [-1, 64, 4, 4]                    0\n",
      "Dropout2d-5                           [-1, 64, 4, 4]                    0\n",
      "AdaptiveMaxPool2d-6                   [-1, 64, 1, 1]                    0\n",
      "Flatten-7                                   [-1, 64]                    0\n",
      "Linear-8                                    [-1, 32]                2,080\n",
      "ReLU-9                                      [-1, 32]                    0\n",
      "Linear-10                                   [-1, 10]                  330\n",
      "==========================================================================\n",
      "Total params: 53,994\n",
      "Trainable params: 53,994\n",
      "Non-trainable params: 0\n",
      "--------------------------------------------------------------------------\n",
      "Input size (MB): 0.000076\n",
      "Forward/backward pass size (MB): 0.263016\n",
      "Params size (MB): 0.205971\n",
      "Estimated Total Size (MB): 0.469063\n",
      "--------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "net = create_net()\n",
    "\n",
    "model = torchkeras.KerasModel(net,\n",
    "      loss_fn = nn.CrossEntropyLoss(),\n",
    "      optimizer= torch.optim.Adam(net.parameters(),lr=5e-3),\n",
    "      metrics_dict = {\"acc\":Accuracy()}\n",
    "    )\n",
    "\n",
    "from torchkeras import summary\n",
    "summary(model,input_data=features);\n",
    "\n"
   ]
  },
  {
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   "id": "e98c79f5",
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   "source": [
    "### 3, train the model"
   ]
  },
  {
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   "execution_count": 11,
   "id": "54bcc553",
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     "slide_type": ""
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n"
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    "# if gpu/mps is available, will auto use it, otherwise cpu will be used.\n",
    "ckpt_path='checkpoint'\n",
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     "exception": false,
     "start_time": "2024-01-08T03:48:06.428237",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 4, evaluate the model"
   ]
  },
  {
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   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_metric(dfhistory, metric):\n",
    "    train_metrics = dfhistory[\"train_\"+metric]\n",
    "    val_metrics = dfhistory['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics, 'bo--')\n",
    "    plt.plot(epochs, val_metrics, 'ro-')\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a179179f-65ff-434e-b317-17ca56355e7e",
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     "status": "completed"
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    "slideshow": {
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    "tags": []
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   "outputs": [],
   "source": [
    "plot_metric(dfhistory,'loss')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "960119f4",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.114503,
     "end_time": "2024-01-08T03:48:06.663299",
     "exception": false,
     "start_time": "2024-01-08T03:48:06.548796",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
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   "outputs": [],
   "source": [
    "plot_metric(dfhistory,\"acc\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62f8cf7b",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 1.413179,
     "end_time": "2024-01-08T03:48:08.087468",
     "exception": false,
     "start_time": "2024-01-08T03:48:06.674289",
     "status": "completed"
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     "slide_type": ""
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   "outputs": [],
   "source": [
    "model.evaluate(dl_val,quiet=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0405a48f-000b-40aa-8869-409d571f512e",
   "metadata": {
    "papermill": {
     "duration": 0.004456,
     "end_time": "2024-01-08T03:48:08.096576",
     "exception": false,
     "start_time": "2024-01-08T03:48:08.092120",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "96911b58",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.004136,
     "end_time": "2024-01-08T03:48:08.105115",
     "exception": false,
     "start_time": "2024-01-08T03:48:08.100979",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 5, use the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91b21a4e-be1c-4fba-ae99-7fe4962c67b6",
   "metadata": {
    "editable": true,
    "papermill": {
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     "end_time": "2024-01-08T03:48:08.963572",
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     "start_time": "2024-01-08T03:48:08.109379",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "net = model.net\n",
    "net.eval();\n",
    "\n",
    "device = torch.device('mps')\n",
    "img,label = ds_train[1]\n",
    "tensor = img.to(device)\n",
    "\n",
    "y_pred = torch.argmax(net(tensor[None,...])) \n",
    "y_prob = torch.softmax(net(tensor[None,...]),dim=-1).max() \n",
    "\n",
    "img = img.permute(1,2,0)\n",
    "plt.imshow(img)\n",
    "print('y_pred = ', y_pred.item())\n",
    "print('y_prob = ', y_prob.item())\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81da8622",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.005361,
     "end_time": "2024-01-08T03:48:08.983532",
     "exception": false,
     "start_time": "2024-01-08T03:48:08.978171",
     "status": "completed"
    },
    "slideshow": {
     "slide_type": "slide"
    },
    "tags": []
   },
   "source": [
    "### 6, save the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45089d19",
   "metadata": {
    "editable": true,
    "papermill": {
     "duration": 0.022714,
     "end_time": "2024-01-08T03:48:09.017030",
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     "start_time": "2024-01-08T03:48:08.994316",
     "status": "completed"
    },
    "slideshow": {
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    },
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   },
   "outputs": [],
   "source": [
    "#  used the saved model parameters \n",
    "net_clone = create_net() \n",
    "\n",
    "model_clone = torchkeras.KerasModel(net_clone,loss_fn = nn.CrossEntropyLoss(),\n",
    "             optimizer= torch.optim.Adam(net_clone.parameters(),lr = 0.001),\n",
    "             metrics_dict={\"acc\":Accuracy()})\n",
    "\n",
    "model_clone.load_ckpt(\"checkpoint\")\n",
    "\n"
   ]
  }
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