change api name

paddle2.0_docs/save_model/save_model.ipynb

@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -36,7 +36,7 @@
     "from paddle.nn import Layer\n",
     "from paddle.vision.datasets import MNIST\n",
     "from paddle.metric import Accuracy\n",
-    "from paddle.nn import Conv2d,Pool2D,Linear\n",
+    "from paddle.nn import Conv2d,MaxPool2d,Linear\n",
     "from paddle.static import InputSpec\n",
     "\n",
     "print(paddle.__version__)\n",
@@ -54,7 +54,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -79,9 +79,9 @@
     "    def __init__(self):\n",
     "        super(MyModel, self).__init__()\n",
     "        self.conv1 = paddle.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)\n",
-    "        self.max_pool1 = Pool2D(pool_size=2, pool_type='max', pool_stride=2)\n",
+    "        self.max_pool1 = MaxPool2d(kernel_size=2, stride=2)\n",
     "        self.conv2 = Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)\n",
-    "        self.max_pool2 = Pool2D(pool_size=2, pool_type='max', pool_stride=2)\n",
+    "        self.max_pool2 = MaxPool2d(kernel_size=2, stride=2)\n",
     "        self.linear1 = Linear(in_features=16*5*5, out_features=120)\n",
     "        self.linear2 = Linear(in_features=120, out_features=84)\n",
     "        self.linear3 = Linear(in_features=84, out_features=10)\n",
@@ -93,7 +93,7 @@
     "        x = F.relu(x)\n",
     "        x = self.conv2(x)\n",
     "        x = self.max_pool2(x)\n",
-    "        x = paddle.reshape(x, shape=[-1, 16*5*5])\n",
+    "        x = paddle.flatten(x, start_axis=1, stop_axis=-1)\n",
     "        x = self.linear1(x)\n",
     "        x = F.relu(x)\n",
     "        x = self.linear2(x)\n",
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -121,22 +121,22 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/1\n",
-      "step 100/938 - loss: 1.6444 - acc_top1: 0.5708 - acc_top2: 0.6325 - 16ms/step\n",
-      "step 200/938 - loss: 1.7200 - acc_top1: 0.6946 - acc_top2: 0.7496 - 16ms/step\n",
-      "step 300/938 - loss: 1.5864 - acc_top1: 0.7472 - acc_top2: 0.7947 - 16ms/step\n",
-      "step 400/938 - loss: 1.5369 - acc_top1: 0.7743 - acc_top2: 0.8161 - 16ms/step\n",
-      "step 500/938 - loss: 1.6392 - acc_top1: 0.7935 - acc_top2: 0.8309 - 16ms/step\n",
-      "step 600/938 - loss: 1.5316 - acc_top1: 0.8066 - acc_top2: 0.8411 - 16ms/step\n",
-      "step 700/938 - loss: 1.5870 - acc_top1: 0.8155 - acc_top2: 0.8478 - 16ms/step\n",
-      "step 800/938 - loss: 1.6136 - acc_top1: 0.8230 - acc_top2: 0.8532 - 16ms/step\n",
-      "step 900/938 - loss: 1.5605 - acc_top1: 0.8290 - acc_top2: 0.8574 - 16ms/step\n",
-      "step 938/938 - loss: 1.4618 - acc_top1: 0.8312 - acc_top2: 0.8591 - 16ms/step\n",
-      "save checkpoint at /Users/dingjiawei/Desktop/教程/mnist_checkpoint/0\n",
+      "step 100/938 - loss: 1.6177 - acc_top1: 0.6119 - acc_top2: 0.6813 - 15ms/step\n",
+      "step 200/938 - loss: 1.7720 - acc_top1: 0.7230 - acc_top2: 0.7788 - 15ms/step\n",
+      "step 300/938 - loss: 1.6114 - acc_top1: 0.7666 - acc_top2: 0.8164 - 15ms/step\n",
+      "step 400/938 - loss: 1.6537 - acc_top1: 0.7890 - acc_top2: 0.8350 - 15ms/step\n",
+      "step 500/938 - loss: 1.5229 - acc_top1: 0.8170 - acc_top2: 0.8619 - 15ms/step\n",
+      "step 600/938 - loss: 1.5269 - acc_top1: 0.8391 - acc_top2: 0.8821 - 15ms/step\n",
+      "step 700/938 - loss: 1.4821 - acc_top1: 0.8561 - acc_top2: 0.8970 - 15ms/step\n",
+      "step 800/938 - loss: 1.4860 - acc_top1: 0.8689 - acc_top2: 0.9081 - 15ms/step\n",
+      "step 900/938 - loss: 1.5032 - acc_top1: 0.8799 - acc_top2: 0.9174 - 15ms/step\n",
+      "step 938/938 - loss: 1.4617 - acc_top1: 0.8835 - acc_top2: 0.9203 - 15ms/step\n",
+      "save checkpoint at /Users/dingjiawei/online_repo/book/paddle2.0_docs/save_model/mnist_checkpoint/0\n",
       "Eval begin...\n",
-      "step 100/157 - loss: 1.5209 - acc_top1: 0.8700 - acc_top2: 0.8912 - 5ms/step\n",
-      "step 157/157 - loss: 1.5226 - acc_top1: 0.8769 - acc_top2: 0.8939 - 5ms/step\n",
+      "step 100/157 - loss: 1.4765 - acc_top1: 0.9636 - acc_top2: 0.9891 - 6ms/step\n",
+      "step 157/157 - loss: 1.4612 - acc_top1: 0.9705 - acc_top2: 0.9910 - 6ms/step\n",
       "Eval samples: 10000\n",
-      "save checkpoint at /Users/dingjiawei/Desktop/教程/mnist_checkpoint/final\n"
+      "save checkpoint at /Users/dingjiawei/online_repo/book/paddle2.0_docs/save_model/mnist_checkpoint/final\n"
      ]
     }
    ],
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -280,34 +280,34 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/2\n",
-      "step 100/938 - loss: 1.4777 - acc_top1: 0.9806 - acc_top2: 0.9962 - 16ms/step\n",
-      "step 200/938 - loss: 1.5163 - acc_top1: 0.9795 - acc_top2: 0.9962 - 16ms/step\n",
-      "step 300/938 - loss: 1.4872 - acc_top1: 0.9796 - acc_top2: 0.9957 - 16ms/step\n",
-      "step 400/938 - loss: 1.4717 - acc_top1: 0.9795 - acc_top2: 0.9955 - 16ms/step\n",
-      "step 500/938 - loss: 1.4778 - acc_top1: 0.9794 - acc_top2: 0.9955 - 16ms/step\n",
-      "step 600/938 - loss: 1.4653 - acc_top1: 0.9798 - acc_top2: 0.9955 - 16ms/step\n",
-      "step 700/938 - loss: 1.4768 - acc_top1: 0.9799 - acc_top2: 0.9954 - 16ms/step\n",
-      "step 800/938 - loss: 1.4771 - acc_top1: 0.9804 - acc_top2: 0.9954 - 16ms/step\n",
-      "step 900/938 - loss: 1.4864 - acc_top1: 0.9807 - acc_top2: 0.9954 - 16ms/step\n",
-      "step 938/938 - loss: 1.4612 - acc_top1: 0.9807 - acc_top2: 0.9955 - 16ms/step\n",
+      "step 100/938 - loss: 1.4635 - acc_top1: 0.9650 - acc_top2: 0.9898 - 15ms/step\n",
+      "step 200/938 - loss: 1.5459 - acc_top1: 0.9659 - acc_top2: 0.9897 - 15ms/step\n",
+      "step 300/938 - loss: 1.5109 - acc_top1: 0.9658 - acc_top2: 0.9893 - 15ms/step\n",
+      "step 400/938 - loss: 1.4797 - acc_top1: 0.9664 - acc_top2: 0.9899 - 15ms/step\n",
+      "step 500/938 - loss: 1.4786 - acc_top1: 0.9673 - acc_top2: 0.9902 - 15ms/step\n",
+      "step 600/938 - loss: 1.5082 - acc_top1: 0.9679 - acc_top2: 0.9906 - 15ms/step\n",
+      "step 700/938 - loss: 1.4768 - acc_top1: 0.9687 - acc_top2: 0.9909 - 15ms/step\n",
+      "step 800/938 - loss: 1.4638 - acc_top1: 0.9696 - acc_top2: 0.9913 - 15ms/step\n",
+      "step 900/938 - loss: 1.5058 - acc_top1: 0.9704 - acc_top2: 0.9916 - 15ms/step\n",
+      "step 938/938 - loss: 1.4702 - acc_top1: 0.9708 - acc_top2: 0.9917 - 15ms/step\n",
       "Eval begin...\n",
-      "step 100/157 - loss: 1.4612 - acc_top1: 0.9762 - acc_top2: 0.9952 - 6ms/step\n",
-      "step 157/157 - loss: 1.4612 - acc_top1: 0.9807 - acc_top2: 0.9959 - 6ms/step\n",
+      "step 100/157 - loss: 1.4613 - acc_top1: 0.9755 - acc_top2: 0.9944 - 5ms/step\n",
+      "step 157/157 - loss: 1.4612 - acc_top1: 0.9805 - acc_top2: 0.9956 - 5ms/step\n",
       "Eval samples: 10000\n",
       "Epoch 2/2\n",
-      "step 100/938 - loss: 1.4696 - acc_top1: 0.9812 - acc_top2: 0.9942 - 16ms/step\n",
-      "step 200/938 - loss: 1.4619 - acc_top1: 0.9827 - acc_top2: 0.9956 - 16ms/step\n",
-      "step 300/938 - loss: 1.4616 - acc_top1: 0.9826 - acc_top2: 0.9955 - 16ms/step\n",
-      "step 400/938 - loss: 1.4766 - acc_top1: 0.9824 - acc_top2: 0.9954 - 16ms/step\n",
-      "step 500/938 - loss: 1.4770 - acc_top1: 0.9830 - acc_top2: 0.9953 - 16ms/step\n",
-      "step 600/938 - loss: 1.4924 - acc_top1: 0.9831 - acc_top2: 0.9955 - 16ms/step\n",
-      "step 700/938 - loss: 1.4623 - acc_top1: 0.9837 - acc_top2: 0.9959 - 16ms/step\n",
-      "step 800/938 - loss: 1.4768 - acc_top1: 0.9839 - acc_top2: 0.9960 - 16ms/step\n",
-      "step 900/938 - loss: 1.4768 - acc_top1: 0.9838 - acc_top2: 0.9960 - 16ms/step\n",
-      "step 938/938 - loss: 1.4879 - acc_top1: 0.9838 - acc_top2: 0.9960 - 16ms/step\n",
+      "step 100/938 - loss: 1.4832 - acc_top1: 0.9789 - acc_top2: 0.9927 - 15ms/step\n",
+      "step 200/938 - loss: 1.4618 - acc_top1: 0.9779 - acc_top2: 0.9932 - 14ms/step\n",
+      "step 300/938 - loss: 1.4613 - acc_top1: 0.9779 - acc_top2: 0.9929 - 15ms/step\n",
+      "step 400/938 - loss: 1.4765 - acc_top1: 0.9772 - acc_top2: 0.9932 - 15ms/step\n",
+      "step 500/938 - loss: 1.4932 - acc_top1: 0.9775 - acc_top2: 0.9934 - 15ms/step\n",
+      "step 600/938 - loss: 1.4773 - acc_top1: 0.9773 - acc_top2: 0.9936 - 15ms/step\n",
+      "step 700/938 - loss: 1.4612 - acc_top1: 0.9783 - acc_top2: 0.9939 - 15ms/step\n",
+      "step 800/938 - loss: 1.4653 - acc_top1: 0.9779 - acc_top2: 0.9939 - 15ms/step\n",
+      "step 900/938 - loss: 1.4639 - acc_top1: 0.9780 - acc_top2: 0.9939 - 15ms/step\n",
+      "step 938/938 - loss: 1.4678 - acc_top1: 0.9779 - acc_top2: 0.9937 - 15ms/step\n",
       "Eval begin...\n",
-      "step 100/157 - loss: 1.4612 - acc_top1: 0.9825 - acc_top2: 0.9956 - 6ms/step\n",
-      "step 157/157 - loss: 1.4701 - acc_top1: 0.9854 - acc_top2: 0.9965 - 6ms/step\n",
+      "step 100/157 - loss: 1.4612 - acc_top1: 0.9733 - acc_top2: 0.9945 - 6ms/step\n",
+      "step 157/157 - loss: 1.4612 - acc_top1: 0.9778 - acc_top2: 0.9952 - 6ms/step\n",
       "Eval samples: 10000\n"
      ]
     }
@@ -323,13 +323,12 @@
     "test_dataset = MNIST(mode='test')\n",
     "\n",
     "paddle.disable_static()\n",
-    "params_path = \"mnist_checkpoint/test\"\n",
     "\n",
     "inputs = InputSpec([None, 784], 'float32', 'x')\n",
     "labels = InputSpec([None, 10], 'float32', 'x')\n",
     "model = paddle.Model(MyModel(), inputs, labels)\n",
     "optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())\n",
-    "model.load(\"../教程/mnist_checkpoint/final\")\n",
+    "model.load(\"./mnist_checkpoint/final\")\n",
     "model.prepare( \n",
     "      optim,\n",
     "      paddle.nn.loss.CrossEntropyLoss(),\n",