diff --git a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb index 5868aa5e137..35c7bbe5854 100644 --- a/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb +++ b/docs/guides/02_paddle2.0_develop/05_train_eval_predict_cn.ipynb @@ -62,20 +62,20 @@ "\n", "模型训练时，需要用到 CPU、 GPU 等计算处理器资源，由于飞桨框架的安装包是区分处理器类型的，默认情况下飞桨框架会根据所安装的版本自动选择对应硬件，比如安装的 GPU 版本的飞桨，则自动使用 GPU 训练模型，无需手动指定。因此一般情况下，无需执行此步骤。\n", "\n", - "但是如果安装的 GPU 版本的飞桨框架，想切换到 CPU 上训练，则可通过 [paddle.device.set_device](../../api/paddle/device/set_device_cn.html#set-device) 修改，如果本机有多个 GPU 卡，也可以通过该 API 选择指定的卡进行训练。" + "但是如果安装的 GPU 版本的飞桨框架，想切换到 CPU 上训练，则可通过 [paddle.device.set_device](../../api/paddle/device/set_device_cn.html#set-device) 修改，如果本机有多个 GPU 卡，也可以通过该 API 选择指定的卡进行训练。多卡情况下如果不手动指定GPU，默认使用GPU:0。" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "0da73e3c-adb6-49bb-9da2-637ab920f558", "metadata": { "execution": { - "iopub.execute_input": "2022-02-15T05:48:36.733300Z", - "iopub.status.busy": "2022-02-15T05:48:36.732654Z", - "iopub.status.idle": "2022-02-15T05:48:38.199626Z", - "shell.execute_reply": "2022-02-15T05:48:38.199024Z", - "shell.execute_reply.started": "2022-02-15T05:48:36.733265Z" + "iopub.execute_input": "2022-02-23T03:53:25.061855Z", + "iopub.status.busy": "2022-02-23T03:53:25.061069Z", + "iopub.status.idle": "2022-02-23T03:53:26.486095Z", + "shell.execute_reply": "2022-02-23T03:53:26.485309Z", + "shell.execute_reply.started": "2022-02-23T03:53:25.061819Z" }, "scrolled": true }, @@ -86,7 +86,7 @@ "CPUPlace" ] }, - "execution_count": 2, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -126,15 +126,15 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "id": "c20487eb-ae05-4a35-a459-a00484f02f1b", "metadata": { "execution": { - "iopub.execute_input": "2022-02-15T05:48:38.201208Z", - "iopub.status.busy": "2022-02-15T05:48:38.200733Z", - "iopub.status.idle": "2022-02-15T05:48:42.416167Z", - "shell.execute_reply": "2022-02-15T05:48:42.415347Z", - "shell.execute_reply.started": "2022-02-15T05:48:38.201177Z" + "iopub.execute_input": "2022-02-23T04:04:26.920997Z", + "iopub.status.busy": "2022-02-23T04:04:26.920406Z", + "iopub.status.idle": "2022-02-23T04:04:30.892862Z", + "shell.execute_reply": "2022-02-23T04:04:30.892026Z", + "shell.execute_reply.started": "2022-02-23T04:04:26.920963Z" }, "scrolled": true }, @@ -282,14 +282,7 @@ "text": [ "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", "Epoch 1/5\n", - "step 10/938 [..............................] - loss: 0.9679 - acc: 0.4109 - ETA: 13s - 14ms/step" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "step 938/938 [==============================] - loss: 0.1158 - acc: 0.9020 - 10ms/step \n", + "step 10/938 [..............................] - loss: 0.9679 - acc: 0.4109 - ETA: 13s - 14ms/stepstep 938/938 [==============================] - loss: 0.1158 - acc: 0.9020 - 10ms/step \n", "Epoch 2/5\n", "step 938/938 [==============================] - loss: 0.0981 - acc: 0.9504 - 10ms/step \n", "Epoch 3/5\n", @@ -744,9 +737,88 @@ "source": [ "## 四、总结\n", "\n", - "本节中介绍了在飞桨框架中使用高层 API 进行模型训练、评估和推理的方法，并拆解出对应的基础 API 实现方法。实际应用中，飞桨的高层 API 和基础 API 可以组合使用，并不是完全割裂开的，这样有助于开发者更便捷地完成算法迭代。\n", + "本节中介绍了在飞桨框架中使用高层 API 进行模型训练、评估和推理的方法，并拆解出对应的基础 API 实现方法。需要注意的是，这里的推理仅用于实验阶段的模型效果验证，实际生产应用中，部署上线时会使用到部署工具。同时，实际应用中，飞桨的高层 API 和基础 API 可以组合使用，并不是完全割裂开的，这样有助于开发者更便捷地完成算法迭代。例如：\n", "\n" ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "50b8953f-9df0-4fa3-b420-f5987ad0cf3b", + "metadata": { + "execution": { + "iopub.execute_input": "2022-02-23T04:10:14.866170Z", + "iopub.status.busy": "2022-02-23T04:10:14.865665Z", + "iopub.status.idle": "2022-02-23T04:11:59.489596Z", + "shell.execute_reply": "2022-02-23T04:11:59.488958Z", + "shell.execute_reply.started": "2022-02-23T04:10:14.866136Z" + }, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The loss value printed in the log is the current step, and the metric is the average value of previous steps.\n", + "Epoch 1/5\n", + "step 938/938 [==============================] - loss: 0.0863 - acc: 0.9369 - 22ms/step \n", + "Eval begin...\n", + "step 157/157 [==============================] - loss: 0.1034 - acc: 0.9741 - 8ms/step \n", + "Eval samples: 10000\n", + "Epoch 2/5\n", + "step 938/938 [==============================] - loss: 0.0366 - acc: 0.9769 - 21ms/step \n", + "Eval begin...\n", + "step 157/157 [==============================] - loss: 0.0047 - acc: 0.9835 - 8ms/step \n", + "Eval samples: 10000\n", + "Epoch 3/5\n", + "step 938/938 [==============================] - loss: 0.0073 - acc: 0.9813 - 21ms/step \n", + "Eval begin...\n", + "step 157/157 [==============================] - loss: 0.0345 - acc: 0.9818 - 9ms/step \n", + "Eval samples: 10000\n", + "Epoch 4/5\n", + "step 938/938 [==============================] - loss: 0.0025 - acc: 0.9842 - 21ms/step \n", + "Eval begin...\n", + "step 157/157 [==============================] - loss: 0.0033 - acc: 0.9834 - 8ms/step \n", + "Eval samples: 10000\n", + "Epoch 5/5\n", + "step 938/938 [==============================] - loss: 0.0294 - acc: 0.9859 - 20ms/step \n", + "Eval begin...\n", + "step 157/157 [==============================] - loss: 0.0039 - acc: 0.9834 - 7ms/step \n", + "Eval samples: 10000\n" + ] + } + ], + "source": [ + "from paddle.vision.models import LeNet\n", + "\n", + "class FaceNet(paddle.nn.Layer):\n", + " def __init__(self):\n", + " super().__init__()\n", + " # 使用高层API组网\n", + " self.backbone = LeNet()\n", + " # 使用基础API组网\n", + " self.outLayer1 = paddle.nn.Sequential(\n", + " paddle.nn.Linear(10, 512),\n", + " paddle.nn.ReLU(),\n", + " paddle.nn.Dropout(0.2)\n", + " )\n", + " self.outLayer2 = paddle.nn.Linear(512, 10)\n", + " \n", + " def forward(self, inputs):\n", + " out = self.backbone(inputs)\n", + " out = self.outLayer1(out)\n", + " out = self.outLayer2(out)\n", + " return out\n", + "# 使用高层API封装网络\n", + "model = paddle.Model(FaceNet())\n", + "# 使用基础API定义优化器\n", + "optim = paddle.optimizer.Adam(learning_rate=1e-3, parameters=model.parameters())\n", + "# 使用高层API封装优化器和损失函数\n", + "model.prepare(optim, paddle.nn.CrossEntropyLoss(), metrics=paddle.metric.Accuracy())\n", + "# 使用高层API训练和评估网络\n", + "model.fit(train_dataset, test_dataset, epochs=5, batch_size=64, verbose=1)" + ] } ], "metadata": {