From ffb9d886b7b73a44aed0a90637ae96719e62346f Mon Sep 17 00:00:00 2001
From: lyhue1991 <lyhue1991@163.com>
Date: Fri, 10 Feb 2023 01:06:00 +0800
Subject: [PATCH] 3.7.2

---
 "1\357\274\214kerasmodel_example.ipynb" | 4472 +----------------------
 push2github.md                          |   18 +-
 setup.py                                |    4 +-
 torchkeras/data.py                      |   14 +
 4 files changed, 59 insertions(+), 4449 deletions(-)
 create mode 100644 torchkeras/data.py

diff --git "a/1\357\274\214kerasmodel_example.ipynb" "b/1\357\274\214kerasmodel_example.ipynb"
index 69693dd..659ea9b 100644
--- "a/1\357\274\214kerasmodel_example.ipynb"
+++ "b/1\357\274\214kerasmodel_example.ipynb"
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "id": "3e915b9b",
    "metadata": {},
    "outputs": [],
@@ -32,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 2,
    "id": "a1920bc7",
    "metadata": {},
    "outputs": [],
@@ -47,14 +47,6 @@
     "import torchkeras #Attention this line \n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "101cd9c3-5061-450e-a022-df09afed935e",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "e73ca619",
@@ -65,113 +57,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": null,
    "id": "25ab4939",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to mnist/MNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "383bed3915db4058b97803e6ce7dfb56",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/9912422 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting mnist/MNIST/raw/train-images-idx3-ubyte.gz to mnist/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "70c709ebd2a240d48925fcda12d69f90",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/28881 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting mnist/MNIST/raw/train-labels-idx1-ubyte.gz to mnist/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8d3113535c5140faa9429a5610fd9d6d",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/1648877 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to mnist/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "0a09d8adfcaf4c029409f779e3231d3f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/4542 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to mnist/MNIST/raw\n",
-      "\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import torchvision \n",
     "from torchvision import transforms\n",
@@ -185,19 +74,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "id": "85a45abd",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([128, 1, 28, 28])\n",
-      "torch.Size([128])\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "for features,labels in dl_train:\n",
     "    break\n",
@@ -207,817 +87,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": null,
    "id": "c484c0b4",
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    "source": [
     "%matplotlib inline\n",
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-   "execution_count": 13,
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-     "name": "stdout",
-     "output_type": "stream",
-     "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): 0.000069\n",
-      "Forward/backward pass size (MB): 0.263016\n",
-      "Params size (MB): 0.205971\n",
-      "Estimated Total Size (MB): 0.469055\n",
-      "--------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "model = torchkeras.KerasModel(net,\n",
     "      loss_fn = nn.CrossEntropyLoss(),\n",
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-   "execution_count": 15,
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    "id": "54bcc553",
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-   "outputs": [
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-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:34:30\n",
-      "Epoch 1 / 15\n",
-      "\n",
-      "100%|████████████████| 469/469 [00:18<00:00, 24.99it/s, lr=0.001, train_acc=0.845, train_loss=0.527]\n",
-      "100%|████████████████████████████████| 79/79 [00:01<00:00, 46.42it/s, val_acc=0.954, val_loss=0.149]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9544000029563904 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:34:50\n",
-      "Epoch 2 / 15\n",
-      "\n",
-      "100%|█████████████████| 469/469 [00:18<00:00, 25.71it/s, lr=0.001, train_acc=0.96, train_loss=0.134]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 48.45it/s, val_acc=0.973, val_loss=0.0844]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9728000164031982 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:35:10\n",
-      "Epoch 3 / 15\n",
-      "\n",
-      "100%|████████████████| 469/469 [00:18<00:00, 25.63it/s, lr=0.001, train_acc=0.97, train_loss=0.0977]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 47.24it/s, val_acc=0.974, val_loss=0.0777]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.974399983882904 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:35:30\n",
-      "Epoch 4 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.35it/s, lr=0.001, train_acc=0.975, train_loss=0.0808]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 46.35it/s, val_acc=0.977, val_loss=0.0656]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.977400004863739 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:35:51\n",
-      "Epoch 5 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.23it/s, lr=0.001, train_acc=0.978, train_loss=0.0702]\n",
-      "100%|████████████████████████████████| 79/79 [00:01<00:00, 46.67it/s, val_acc=0.98, val_loss=0.0643]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9797000288963318 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:36:11\n",
-      "Epoch 6 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.24it/s, lr=0.001, train_acc=0.981, train_loss=0.0611]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 47.87it/s, val_acc=0.982, val_loss=0.0554]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9815000295639038 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:36:31\n",
-      "Epoch 7 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.14it/s, lr=0.001, train_acc=0.983, train_loss=0.0528]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 48.14it/s, val_acc=0.982, val_loss=0.0566]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9815999865531921 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:36:51\n",
-      "Epoch 8 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.29it/s, lr=0.001, train_acc=0.984, train_loss=0.0482]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 46.61it/s, val_acc=0.983, val_loss=0.0568]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9825000166893005 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:37:12\n",
-      "Epoch 9 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 24.91it/s, lr=0.001, train_acc=0.986, train_loss=0.0432]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 46.04it/s, val_acc=0.985, val_loss=0.0444]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9850999712944031 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:37:32\n",
-      "Epoch 10 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.09it/s, lr=0.001, train_acc=0.987, train_loss=0.0409]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 47.21it/s, val_acc=0.984, val_loss=0.0473]\n",
-      "\n",
-      "================================================================================2023-02-09 19:37:52\n",
-      "Epoch 11 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.14it/s, lr=0.001, train_acc=0.988, train_loss=0.0372]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 47.82it/s, val_acc=0.987, val_loss=0.0445]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9868000149726868 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:38:13\n",
-      "Epoch 12 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 24.91it/s, lr=0.001, train_acc=0.988, train_loss=0.0343]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 46.46it/s, val_acc=0.985, val_loss=0.0479]\n",
-      "\n",
-      "================================================================================2023-02-09 19:38:33\n",
-      "Epoch 13 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.03it/s, lr=0.001, train_acc=0.989, train_loss=0.0335]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 47.64it/s, val_acc=0.988, val_loss=0.0418]\n",
-      "\u001b[0;31m<<<<<< reach best val_acc : 0.9876000285148621 >>>>>>\u001b[0m\n",
-      "\n",
-      "================================================================================2023-02-09 19:38:54\n",
-      "Epoch 14 / 15\n",
-      "\n",
-      "100%|████████████████| 469/469 [00:18<00:00, 24.98it/s, lr=0.001, train_acc=0.99, train_loss=0.0287]\n",
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 46.02it/s, val_acc=0.985, val_loss=0.0487]\n",
-      "\n",
-      "================================================================================2023-02-09 19:39:14\n",
-      "Epoch 15 / 15\n",
-      "\n",
-      "100%|███████████████| 469/469 [00:18<00:00, 25.03it/s, lr=0.001, train_acc=0.991, train_loss=0.0273]\n",
-      "100%|████████████████████████████████| 79/79 [00:01<00:00, 46.63it/s, val_acc=0.987, val_loss=0.041]\n",
-      "    epoch  train_loss  train_acc     lr  val_loss  val_acc\n",
-      "0       1    0.526868   0.845133  0.001  0.148538   0.9544\n",
-      "1       2    0.134293   0.959750  0.001  0.084383   0.9728\n",
-      "2       3    0.097699   0.970250  0.001  0.077715   0.9744\n",
-      "3       4    0.080804   0.974733  0.001  0.065645   0.9774\n",
-      "4       5    0.070243   0.977833  0.001  0.064307   0.9797\n",
-      "5       6    0.061092   0.981017  0.001  0.055448   0.9815\n",
-      "6       7    0.052803   0.982733  0.001  0.056596   0.9816\n",
-      "7       8    0.048157   0.984267  0.001  0.056754   0.9825\n",
-      "8       9    0.043176   0.986050  0.001  0.044400   0.9851\n",
-      "9      10    0.040888   0.986500  0.001  0.047307   0.9843\n",
-      "10     11    0.037161   0.987867  0.001  0.044459   0.9868\n",
-      "11     12    0.034350   0.988250  0.001  0.047916   0.9849\n",
-      "12     13    0.033464   0.989000  0.001  0.041772   0.9876\n",
-      "13     14    0.028671   0.990467  0.001  0.048730   0.9850\n",
-      "14     15    0.027258   0.991050  0.001  0.041001   0.9870\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# if gpu/mps is available, will auto use it, otherwise cpu will be used.\n",
     "dfhistory=model.fit(train_data=dl_train, \n",
@@ -1300,853 +218,10 @@
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   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "id": "1ddc3317",
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-   "outputs": [
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-       "<Figure size 800x800 with 9 Axes>"
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+   "outputs": [],
    "source": [
     "# visual the  prediction\n",
     "device = None\n",
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   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": null,
    "id": "520c6a1b",
    "metadata": {},
    "outputs": [],
@@ -2204,2146 +279,30 @@
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    "source": [
     "plot_metric(dfhistory,'loss')"
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+   "outputs": [],
    "source": [
     "plot_metric(dfhistory,\"acc\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": null,
    "id": "62f8cf7b",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 46.50it/s, val_acc=0.988, val_loss=0.0418]\n"
-     ]
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-     "execution_count": 52,
-     "metadata": {},
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-   ],
+   "outputs": [],
    "source": [
     "model.evaluate(dl_val)"
    ]
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-   "outputs": [
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-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "net = model.net\n",
     "net.eval();\n",
@@ -4758,28 +356,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": null,
    "id": "45089d19",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "100%|███████████████████████████████| 79/79 [00:01<00:00, 48.51it/s, val_acc=0.988, val_loss=0.0418]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "{'val_loss': 0.041772333724491295, 'val_acc': 0.9876000285148621}"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "#  used the saved model parameters \n",
     "net_clone = create_net() \n",
diff --git a/push2github.md b/push2github.md
index 64957d6..5bbaa8b 100644
--- a/push2github.md
+++ b/push2github.md
@@ -2,6 +2,7 @@
 
 ```python
 %run setup.py sdist bdist_wheel
+
 ```
 
 ```python
@@ -58,7 +59,7 @@
 ```
 
 ```python
-!git push -f origin master 
+!git push  origin master 
 ```
 
 ```python
@@ -68,3 +69,18 @@
 ```python
 
 ```
+
+## gitignore
+
+```python
+# %load .gitignore
+.DS_store
+.ipynb_checkpoints
+.ipynb_checkpoints/* 
+torchkeras.egg-info/*
+dist/*
+build/*
+torchkeras/__pycache__/
+.idea
+
+```
diff --git a/setup.py b/setup.py
index 6f51607..95bcd08 100644
--- a/setup.py
+++ b/setup.py
@@ -1,7 +1,7 @@
 # -*- coding:utf-8 -*-
 import re
 from pathlib import Path
-from setuptools import find_packages, setup
+from setuptools import find_packages, setup, find_namespace_packages
 
 # Settings
 FILE = Path(__file__).resolve()
@@ -32,7 +32,7 @@ def get_version():
        ],
     long_description_content_type="text/markdown",
     url="https://github.com/lyhue1991/torchkeras",
-    packages=find_packages(),
+    packages=find_namespace_packages(exclude=['torchkeras.assets','data']),
     include_package_data=True,
     classifiers=[
         "Programming Language :: Python :: 3",
diff --git a/torchkeras/data.py b/torchkeras/data.py
new file mode 100644
index 0000000..7c0628d
--- /dev/null
+++ b/torchkeras/data.py
@@ -0,0 +1,14 @@
+from pathlib import Path 
+from PIL import Image 
+import os
+
+path = Path(__file__)
+
+def get_example_image(img_name='park.jpg'):
+    'name can be bus.jpg / park.jpg / zidane.jpg'
+    img_path = str(path.parent/f"assets/{img_name}")
+    assert os.path.exists(img_path), 'img_name can only be bus.jpg / park.jpg / zidane.jpg'
+    return Image.open(img_path)
+
+   
+    
\ No newline at end of file