diff --git a/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb b/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb index 840775597d81ff5e31db0e3bb77f029a5cb241b3..a6fac63b214b6a14dd0dfa1924f849001cd0ff2b 100644 --- a/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb +++ b/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb @@ -13,13 +13,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 环境\n", + "## 环境\n", "本教程基于paddle-develop编写，如果您的环境不是本版本，请先安装paddle-develop版本。" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -40,13 +40,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 加载数据集\n", + "## 加载数据集\n", "我们使用飞桨自带的paddle.dataset完成mnist数据集的加载。" ] }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -74,7 +74,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -111,13 +111,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 2.组网\n", + "## 组网\n", "用paddle.nn下的API，如`Conv2d`、`Pool2D`、`Linead`完成LeNet的构建。" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -155,39 +155,39 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 3.训练方式一\n", + "## 训练方式一\n", "组网后，开始对模型进行训练，先构建`train_loader`，加载训练数据，然后定义`train`函数，设置好损失函数后，按batch加载数据，完成模型的训练。" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0, batch_id: 0, loss is: [2.3064885], acc is: [0.109375]\n", - "epoch: 0, batch_id: 100, loss is: [1.5477252], acc is: [1.]\n", - "epoch: 0, batch_id: 200, loss is: [1.5201148], acc is: [1.]\n", - "epoch: 0, batch_id: 300, loss is: [1.525354], acc is: [0.953125]\n", - "epoch: 0, batch_id: 400, loss is: [1.5201038], acc is: [1.]\n", - "epoch: 0, batch_id: 500, loss is: [1.4901408], acc is: [1.]\n", - "epoch: 0, batch_id: 600, loss is: [1.4925538], acc is: [0.984375]\n", - "epoch: 0, batch_id: 700, loss is: [1.5247533], acc is: [0.96875]\n", - "epoch: 0, batch_id: 800, loss is: [1.5365943], acc is: [1.]\n", - "epoch: 0, batch_id: 900, loss is: [1.5154861], acc is: [0.984375]\n", - "epoch: 1, batch_id: 0, loss is: [1.4988302], acc is: [0.984375]\n", - "epoch: 1, batch_id: 100, loss is: [1.493154], acc is: [0.984375]\n", - "epoch: 1, batch_id: 200, loss is: [1.4974915], acc is: [1.]\n", - "epoch: 1, batch_id: 300, loss is: [1.5089471], acc is: [0.984375]\n", - "epoch: 1, batch_id: 400, loss is: [1.5041347], acc is: [1.]\n", - "epoch: 1, batch_id: 500, loss is: [1.5145375], acc is: [1.]\n", - "epoch: 1, batch_id: 600, loss is: [1.4904011], acc is: [0.984375]\n", - "epoch: 1, batch_id: 700, loss is: [1.5121607], acc is: [0.96875]\n", - "epoch: 1, batch_id: 800, loss is: [1.5078678], acc is: [1.]\n", - "epoch: 1, batch_id: 900, loss is: [1.500349], acc is: [0.984375]\n" + "epoch: 0, batch_id: 0, loss is: [2.3029077], acc is: [0.15625]\n", + "epoch: 0, batch_id: 100, loss is: [1.6757016], acc is: [0.84375]\n", + "epoch: 0, batch_id: 200, loss is: [1.5340967], acc is: [0.96875]\n", + "epoch: 0, batch_id: 300, loss is: [1.4943825], acc is: [0.984375]\n", + "epoch: 0, batch_id: 400, loss is: [1.5084226], acc is: [1.]\n", + "epoch: 0, batch_id: 500, loss is: [1.5035012], acc is: [0.984375]\n", + "epoch: 0, batch_id: 600, loss is: [1.4784969], acc is: [0.984375]\n", + "epoch: 0, batch_id: 700, loss is: [1.5656701], acc is: [0.96875]\n", + "epoch: 0, batch_id: 800, loss is: [1.5226105], acc is: [1.]\n", + "epoch: 0, batch_id: 900, loss is: [1.5094678], acc is: [1.]\n", + "epoch: 1, batch_id: 0, loss is: [1.4956206], acc is: [0.984375]\n", + "epoch: 1, batch_id: 100, loss is: [1.4908005], acc is: [1.]\n", + "epoch: 1, batch_id: 200, loss is: [1.485649], acc is: [0.984375]\n", + "epoch: 1, batch_id: 300, loss is: [1.5090752], acc is: [1.]\n", + "epoch: 1, batch_id: 400, loss is: [1.5163708], acc is: [1.]\n", + "epoch: 1, batch_id: 500, loss is: [1.4863018], acc is: [0.984375]\n", + "epoch: 1, batch_id: 600, loss is: [1.4764814], acc is: [0.984375]\n", + "epoch: 1, batch_id: 700, loss is: [1.5496588], acc is: [0.984375]\n", + "epoch: 1, batch_id: 800, loss is: [1.4998187], acc is: [1.]\n", + "epoch: 1, batch_id: 900, loss is: [1.5110929], acc is: [1.]\n" ] } ], @@ -229,23 +229,23 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "batch_id: 0, loss is: [1.4659549], acc is: [1.]\n", - "batch_id: 100, loss is: [1.4933192], acc is: [0.984375]\n", - "batch_id: 200, loss is: [1.4779761], acc is: [1.]\n", - "batch_id: 300, loss is: [1.4919193], acc is: [0.984375]\n", - "batch_id: 400, loss is: [1.5036212], acc is: [1.]\n", - "batch_id: 500, loss is: [1.4922347], acc is: [0.984375]\n", - "batch_id: 600, loss is: [1.4765416], acc is: [0.984375]\n", - "batch_id: 700, loss is: [1.4997746], acc is: [0.984375]\n", - "batch_id: 800, loss is: [1.4831288], acc is: [1.]\n", - "batch_id: 900, loss is: [1.498342], acc is: [0.984375]\n" + "batch_id: 0, loss is: [1.4929559], acc is: [0.984375]\n", + "batch_id: 100, loss is: [1.4921299], acc is: [0.984375]\n", + "batch_id: 200, loss is: [1.5021144], acc is: [1.]\n", + "batch_id: 300, loss is: [1.4809179], acc is: [0.984375]\n", + "batch_id: 400, loss is: [1.4768506], acc is: [1.]\n", + "batch_id: 500, loss is: [1.4768407], acc is: [1.]\n", + "batch_id: 600, loss is: [1.476671], acc is: [0.984375]\n", + "batch_id: 700, loss is: [1.5093586], acc is: [1.]\n", + "batch_id: 800, loss is: [1.5057312], acc is: [1.]\n", + "batch_id: 900, loss is: [1.4923737], acc is: [1.]\n" ] } ], @@ -289,7 +289,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -317,7 +317,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -325,202 +325,202 @@ "output_type": "stream", "text": [ "Epoch 1/2\n", - "step 10/938 - loss: 2.2434 - acc_top1: 0.1344 - acc_top2: 0.3719 - 14ms/step\n", - "step 20/938 - loss: 2.0292 - acc_top1: 0.2836 - acc_top2: 0.4633 - 14ms/step\n", - "step 30/938 - loss: 1.9341 - acc_top1: 0.3755 - acc_top2: 0.5214 - 14ms/step\n", - "step 40/938 - loss: 1.8009 - acc_top1: 0.4469 - acc_top2: 0.5727 - 14ms/step\n", - "step 50/938 - loss: 1.8000 - acc_top1: 0.4975 - acc_top2: 0.6125 - 13ms/step\n", - "step 60/938 - loss: 1.6335 - acc_top1: 0.5417 - acc_top2: 0.6438 - 14ms/step\n", - "step 70/938 - loss: 1.7931 - acc_top1: 0.5708 - acc_top2: 0.6643 - 13ms/step\n", - "step 80/938 - loss: 1.6699 - acc_top1: 0.5961 - acc_top2: 0.6846 - 13ms/step\n", - "step 90/938 - loss: 1.6832 - acc_top1: 0.6189 - acc_top2: 0.7069 - 13ms/step\n", - "step 100/938 - loss: 1.6336 - acc_top1: 0.6409 - acc_top2: 0.7245 - 14ms/step\n", - "step 110/938 - loss: 1.6598 - acc_top1: 0.6557 - acc_top2: 0.7376 - 13ms/step\n", - "step 120/938 - loss: 1.6348 - acc_top1: 0.6708 - acc_top2: 0.7488 - 13ms/step\n", - "step 130/938 - loss: 1.6223 - acc_top1: 0.6851 - acc_top2: 0.7601 - 13ms/step\n", - "step 140/938 - loss: 1.5622 - acc_top1: 0.6970 - acc_top2: 0.7694 - 13ms/step\n", - "step 150/938 - loss: 1.6455 - acc_top1: 0.7065 - acc_top2: 0.7767 - 14ms/step\n", - "step 160/938 - loss: 1.6127 - acc_top1: 0.7154 - acc_top2: 0.7837 - 14ms/step\n", - "step 170/938 - loss: 1.5963 - acc_top1: 0.7242 - acc_top2: 0.7898 - 14ms/step\n", - "step 180/938 - loss: 1.6485 - acc_top1: 0.7310 - acc_top2: 0.7948 - 14ms/step\n", - "step 190/938 - loss: 1.6666 - acc_top1: 0.7368 - acc_top2: 0.7992 - 14ms/step\n", - "step 200/938 - loss: 1.7862 - acc_top1: 0.7419 - acc_top2: 0.8030 - 14ms/step\n", - "step 210/938 - loss: 1.5479 - acc_top1: 0.7464 - acc_top2: 0.8064 - 14ms/step\n", - "step 220/938 - loss: 1.5650 - acc_top1: 0.7515 - acc_top2: 0.8106 - 14ms/step\n", - "step 230/938 - loss: 1.5822 - acc_top1: 0.7562 - acc_top2: 0.8141 - 14ms/step\n", - "step 240/938 - loss: 1.5966 - acc_top1: 0.7608 - acc_top2: 0.8179 - 14ms/step\n", - "step 250/938 - loss: 1.5551 - acc_top1: 0.7650 - acc_top2: 0.8213 - 14ms/step\n", - "step 260/938 - loss: 1.5584 - acc_top1: 0.7699 - acc_top2: 0.8249 - 14ms/step\n", - "step 270/938 - loss: 1.5933 - acc_top1: 0.7730 - acc_top2: 0.8273 - 14ms/step\n", - "step 280/938 - loss: 1.5589 - acc_top1: 0.7769 - acc_top2: 0.8301 - 14ms/step\n", - "step 290/938 - loss: 1.6513 - acc_top1: 0.7793 - acc_top2: 0.8315 - 14ms/step\n", - "step 300/938 - loss: 1.5929 - acc_top1: 0.7821 - acc_top2: 0.8337 - 14ms/step\n", - "step 310/938 - loss: 1.5672 - acc_top1: 0.7849 - acc_top2: 0.8360 - 14ms/step\n", - "step 320/938 - loss: 1.5147 - acc_top1: 0.7879 - acc_top2: 0.8381 - 14ms/step\n", - "step 330/938 - loss: 1.5697 - acc_top1: 0.7902 - acc_top2: 0.8397 - 14ms/step\n", - "step 340/938 - loss: 1.5697 - acc_top1: 0.7919 - acc_top2: 0.8406 - 14ms/step\n", - "step 350/938 - loss: 1.6122 - acc_top1: 0.7941 - acc_top2: 0.8423 - 14ms/step\n", - "step 360/938 - loss: 1.5934 - acc_top1: 0.7960 - acc_top2: 0.8435 - 14ms/step\n", - "step 370/938 - loss: 1.6258 - acc_top1: 0.7982 - acc_top2: 0.8451 - 14ms/step\n", - "step 380/938 - loss: 1.6805 - acc_top1: 0.7996 - acc_top2: 0.8463 - 14ms/step\n", - "step 390/938 - loss: 1.5997 - acc_top1: 0.8011 - acc_top2: 0.8475 - 14ms/step\n", - "step 400/938 - loss: 1.6151 - acc_top1: 0.8029 - acc_top2: 0.8488 - 14ms/step\n", - "step 410/938 - loss: 1.5800 - acc_top1: 0.8047 - acc_top2: 0.8499 - 14ms/step\n", - "step 420/938 - loss: 1.5950 - acc_top1: 0.8060 - acc_top2: 0.8508 - 14ms/step\n", - "step 430/938 - loss: 1.5533 - acc_top1: 0.8075 - acc_top2: 0.8517 - 14ms/step\n", - "step 440/938 - loss: 1.6171 - acc_top1: 0.8086 - acc_top2: 0.8521 - 14ms/step\n", - "step 450/938 - loss: 1.5756 - acc_top1: 0.8103 - acc_top2: 0.8533 - 14ms/step\n", - "step 460/938 - loss: 1.5655 - acc_top1: 0.8121 - acc_top2: 0.8544 - 14ms/step\n", - "step 470/938 - loss: 1.5816 - acc_top1: 0.8139 - acc_top2: 0.8555 - 14ms/step\n", - "step 480/938 - loss: 1.6202 - acc_top1: 0.8148 - acc_top2: 0.8562 - 14ms/step\n", - "step 490/938 - loss: 1.6223 - acc_top1: 0.8157 - acc_top2: 0.8567 - 14ms/step\n", - "step 500/938 - loss: 1.5198 - acc_top1: 0.8167 - acc_top2: 0.8574 - 14ms/step\n", - "step 510/938 - loss: 1.5853 - acc_top1: 0.8181 - acc_top2: 0.8583 - 14ms/step\n", - "step 520/938 - loss: 1.5252 - acc_top1: 0.8196 - acc_top2: 0.8593 - 14ms/step\n", - "step 530/938 - loss: 1.5265 - acc_top1: 0.8207 - acc_top2: 0.8601 - 14ms/step\n", - "step 540/938 - loss: 1.5297 - acc_top1: 0.8217 - acc_top2: 0.8608 - 14ms/step\n", - "step 550/938 - loss: 1.5743 - acc_top1: 0.8226 - acc_top2: 0.8613 - 13ms/step\n", - "step 560/938 - loss: 1.6419 - acc_top1: 0.8237 - acc_top2: 0.8622 - 13ms/step\n", - "step 570/938 - loss: 1.5556 - acc_top1: 0.8247 - acc_top2: 0.8630 - 13ms/step\n", - "step 580/938 - loss: 1.5349 - acc_top1: 0.8254 - acc_top2: 0.8635 - 13ms/step\n", - "step 590/938 - loss: 1.4915 - acc_top1: 0.8263 - acc_top2: 0.8640 - 13ms/step\n", - "step 600/938 - loss: 1.5672 - acc_top1: 0.8277 - acc_top2: 0.8651 - 13ms/step\n", - "step 610/938 - loss: 1.5464 - acc_top1: 0.8288 - acc_top2: 0.8659 - 13ms/step\n", - "step 620/938 - loss: 1.6329 - acc_top1: 0.8292 - acc_top2: 0.8661 - 13ms/step\n", - "step 630/938 - loss: 1.6121 - acc_top1: 0.8296 - acc_top2: 0.8662 - 13ms/step\n", - "step 640/938 - loss: 1.5636 - acc_top1: 0.8305 - acc_top2: 0.8668 - 13ms/step\n", - "step 650/938 - loss: 1.6227 - acc_top1: 0.8311 - acc_top2: 0.8672 - 13ms/step\n", - "step 660/938 - loss: 1.5646 - acc_top1: 0.8319 - acc_top2: 0.8678 - 13ms/step\n", - "step 670/938 - loss: 1.5620 - acc_top1: 0.8325 - acc_top2: 0.8681 - 13ms/step\n", - "step 680/938 - loss: 1.4908 - acc_top1: 0.8333 - acc_top2: 0.8688 - 13ms/step\n", - "step 690/938 - loss: 1.6010 - acc_top1: 0.8339 - acc_top2: 0.8691 - 13ms/step\n", - "step 700/938 - loss: 1.5592 - acc_top1: 0.8346 - acc_top2: 0.8695 - 13ms/step\n", - "step 710/938 - loss: 1.6226 - acc_top1: 0.8352 - acc_top2: 0.8699 - 13ms/step\n", - "step 720/938 - loss: 1.5642 - acc_top1: 0.8362 - acc_top2: 0.8705 - 13ms/step\n", - "step 730/938 - loss: 1.5807 - acc_top1: 0.8367 - acc_top2: 0.8707 - 13ms/step\n", - "step 740/938 - loss: 1.5721 - acc_top1: 0.8371 - acc_top2: 0.8708 - 13ms/step\n", - "step 750/938 - loss: 1.6542 - acc_top1: 0.8377 - acc_top2: 0.8711 - 13ms/step\n", - "step 760/938 - loss: 1.5128 - acc_top1: 0.8385 - acc_top2: 0.8716 - 13ms/step\n", - "step 770/938 - loss: 1.5711 - acc_top1: 0.8391 - acc_top2: 0.8721 - 14ms/step\n", - "step 780/938 - loss: 1.6095 - acc_top1: 0.8395 - acc_top2: 0.8725 - 14ms/step\n", - "step 790/938 - loss: 1.5348 - acc_top1: 0.8402 - acc_top2: 0.8730 - 14ms/step\n", - "step 800/938 - loss: 1.5715 - acc_top1: 0.8407 - acc_top2: 0.8732 - 14ms/step\n", - "step 810/938 - loss: 1.5880 - acc_top1: 0.8413 - acc_top2: 0.8737 - 14ms/step\n", - "step 820/938 - loss: 1.6160 - acc_top1: 0.8418 - acc_top2: 0.8740 - 14ms/step\n", - "step 830/938 - loss: 1.5585 - acc_top1: 0.8426 - acc_top2: 0.8746 - 14ms/step\n", - "step 840/938 - loss: 1.5829 - acc_top1: 0.8429 - acc_top2: 0.8748 - 14ms/step\n", - "step 850/938 - loss: 1.5348 - acc_top1: 0.8435 - acc_top2: 0.8753 - 14ms/step\n", - "step 860/938 - loss: 1.5448 - acc_top1: 0.8438 - acc_top2: 0.8754 - 14ms/step\n", - "step 870/938 - loss: 1.5463 - acc_top1: 0.8443 - acc_top2: 0.8759 - 14ms/step\n", - "step 880/938 - loss: 1.5763 - acc_top1: 0.8449 - acc_top2: 0.8762 - 14ms/step\n", - "step 890/938 - loss: 1.5699 - acc_top1: 0.8453 - acc_top2: 0.8764 - 14ms/step\n", - "step 900/938 - loss: 1.5616 - acc_top1: 0.8456 - acc_top2: 0.8766 - 14ms/step\n", - "step 910/938 - loss: 1.5026 - acc_top1: 0.8461 - acc_top2: 0.8771 - 14ms/step\n", - "step 920/938 - loss: 1.5380 - acc_top1: 0.8467 - acc_top2: 0.8774 - 14ms/step\n", - "step 930/938 - loss: 1.5993 - acc_top1: 0.8470 - acc_top2: 0.8777 - 14ms/step\n", - "step 938/938 - loss: 1.4942 - acc_top1: 0.8473 - acc_top2: 0.8778 - 14ms/step\n", + "step 10/938 - loss: 1.5564 - acc_top1: 0.7773 - acc_top2: 0.8103 - 17ms/step\n", + "step 20/938 - loss: 1.5538 - acc_top1: 0.7787 - acc_top2: 0.8115 - 16ms/step\n", + "step 30/938 - loss: 1.5591 - acc_top1: 0.7801 - acc_top2: 0.8128 - 16ms/step\n", + "step 40/938 - loss: 1.5234 - acc_top1: 0.7813 - acc_top2: 0.8138 - 16ms/step\n", + "step 50/938 - loss: 1.6375 - acc_top1: 0.7827 - acc_top2: 0.8150 - 16ms/step\n", + "step 60/938 - loss: 1.5435 - acc_top1: 0.7836 - acc_top2: 0.8159 - 16ms/step\n", + "step 70/938 - loss: 1.5900 - acc_top1: 0.7849 - acc_top2: 0.8170 - 15ms/step\n", + "step 80/938 - loss: 1.5130 - acc_top1: 0.7861 - acc_top2: 0.8180 - 15ms/step\n", + "step 90/938 - loss: 1.6275 - acc_top1: 0.7873 - acc_top2: 0.8190 - 15ms/step\n", + "step 100/938 - loss: 1.5574 - acc_top1: 0.7884 - acc_top2: 0.8200 - 15ms/step\n", + "step 110/938 - loss: 1.5883 - acc_top1: 0.7894 - acc_top2: 0.8208 - 15ms/step\n", + "step 120/938 - loss: 1.5808 - acc_top1: 0.7903 - acc_top2: 0.8216 - 15ms/step\n", + "step 130/938 - loss: 1.5924 - acc_top1: 0.7913 - acc_top2: 0.8226 - 15ms/step\n", + "step 140/938 - loss: 1.5238 - acc_top1: 0.7924 - acc_top2: 0.8234 - 15ms/step\n", + "step 150/938 - loss: 1.6007 - acc_top1: 0.7933 - acc_top2: 0.8242 - 15ms/step\n", + "step 160/938 - loss: 1.6028 - acc_top1: 0.7942 - acc_top2: 0.8250 - 15ms/step\n", + "step 170/938 - loss: 1.5838 - acc_top1: 0.7952 - acc_top2: 0.8258 - 14ms/step\n", + "step 180/938 - loss: 1.6334 - acc_top1: 0.7958 - acc_top2: 0.8265 - 14ms/step\n", + "step 190/938 - loss: 1.6356 - acc_top1: 0.7966 - acc_top2: 0.8272 - 14ms/step\n", + "step 200/938 - loss: 1.7113 - acc_top1: 0.7973 - acc_top2: 0.8279 - 14ms/step\n", + "step 210/938 - loss: 1.5355 - acc_top1: 0.7980 - acc_top2: 0.8285 - 14ms/step\n", + "step 220/938 - loss: 1.5510 - acc_top1: 0.7989 - acc_top2: 0.8293 - 14ms/step\n", + "step 230/938 - loss: 1.5542 - acc_top1: 0.7997 - acc_top2: 0.8300 - 14ms/step\n", + "step 240/938 - loss: 1.5730 - acc_top1: 0.8007 - acc_top2: 0.8309 - 14ms/step\n", + "step 250/938 - loss: 1.5378 - acc_top1: 0.8016 - acc_top2: 0.8317 - 14ms/step\n", + "step 260/938 - loss: 1.5517 - acc_top1: 0.8026 - acc_top2: 0.8326 - 14ms/step\n", + "step 270/938 - loss: 1.5762 - acc_top1: 0.8033 - acc_top2: 0.8332 - 14ms/step\n", + "step 280/938 - loss: 1.5611 - acc_top1: 0.8041 - acc_top2: 0.8339 - 14ms/step\n", + "step 290/938 - loss: 1.6558 - acc_top1: 0.8046 - acc_top2: 0.8343 - 14ms/step\n", + "step 300/938 - loss: 1.5836 - acc_top1: 0.8052 - acc_top2: 0.8349 - 14ms/step\n", + "step 310/938 - loss: 1.5332 - acc_top1: 0.8060 - acc_top2: 0.8356 - 14ms/step\n", + "step 320/938 - loss: 1.5105 - acc_top1: 0.8068 - acc_top2: 0.8362 - 14ms/step\n", + "step 330/938 - loss: 1.5325 - acc_top1: 0.8075 - acc_top2: 0.8368 - 14ms/step\n", + "step 340/938 - loss: 1.5779 - acc_top1: 0.8079 - acc_top2: 0.8371 - 14ms/step\n", + "step 350/938 - loss: 1.5903 - acc_top1: 0.8085 - acc_top2: 0.8377 - 14ms/step\n", + "step 360/938 - loss: 1.5884 - acc_top1: 0.8091 - acc_top2: 0.8382 - 14ms/step\n", + "step 370/938 - loss: 1.6248 - acc_top1: 0.8098 - acc_top2: 0.8388 - 14ms/step\n", + "step 380/938 - loss: 1.6995 - acc_top1: 0.8103 - acc_top2: 0.8392 - 14ms/step\n", + "step 390/938 - loss: 1.5695 - acc_top1: 0.8109 - acc_top2: 0.8397 - 14ms/step\n", + "step 400/938 - loss: 1.6015 - acc_top1: 0.8116 - acc_top2: 0.8403 - 14ms/step\n", + "step 410/938 - loss: 1.5643 - acc_top1: 0.8123 - acc_top2: 0.8409 - 14ms/step\n", + "step 420/938 - loss: 1.5745 - acc_top1: 0.8128 - acc_top2: 0.8413 - 14ms/step\n", + "step 430/938 - loss: 1.5517 - acc_top1: 0.8133 - acc_top2: 0.8417 - 14ms/step\n", + "step 440/938 - loss: 1.6097 - acc_top1: 0.8137 - acc_top2: 0.8419 - 14ms/step\n", + "step 450/938 - loss: 1.5700 - acc_top1: 0.8142 - acc_top2: 0.8424 - 14ms/step\n", + "step 460/938 - loss: 1.5696 - acc_top1: 0.8149 - acc_top2: 0.8430 - 14ms/step\n", + "step 470/938 - loss: 1.5713 - acc_top1: 0.8156 - acc_top2: 0.8436 - 14ms/step\n", + "step 480/938 - loss: 1.5909 - acc_top1: 0.8162 - acc_top2: 0.8440 - 14ms/step\n", + "step 490/938 - loss: 1.6004 - acc_top1: 0.8166 - acc_top2: 0.8443 - 14ms/step\n", + "step 500/938 - loss: 1.4905 - acc_top1: 0.8171 - acc_top2: 0.8448 - 14ms/step\n", + "step 510/938 - loss: 1.5842 - acc_top1: 0.8178 - acc_top2: 0.8453 - 14ms/step\n", + "step 520/938 - loss: 1.5237 - acc_top1: 0.8185 - acc_top2: 0.8459 - 14ms/step\n", + "step 530/938 - loss: 1.5130 - acc_top1: 0.8190 - acc_top2: 0.8464 - 14ms/step\n", + "step 540/938 - loss: 1.5345 - acc_top1: 0.8195 - acc_top2: 0.8468 - 14ms/step\n", + "step 550/938 - loss: 1.5513 - acc_top1: 0.8199 - acc_top2: 0.8472 - 14ms/step\n", + "step 560/938 - loss: 1.5704 - acc_top1: 0.8206 - acc_top2: 0.8480 - 14ms/step\n", + "step 570/938 - loss: 1.5481 - acc_top1: 0.8215 - acc_top2: 0.8490 - 14ms/step\n", + "step 580/938 - loss: 1.5087 - acc_top1: 0.8225 - acc_top2: 0.8500 - 14ms/step\n", + "step 590/938 - loss: 1.4844 - acc_top1: 0.8236 - acc_top2: 0.8510 - 14ms/step\n", + "step 600/938 - loss: 1.5162 - acc_top1: 0.8246 - acc_top2: 0.8520 - 14ms/step\n", + "step 610/938 - loss: 1.4730 - acc_top1: 0.8256 - acc_top2: 0.8530 - 14ms/step\n", + "step 620/938 - loss: 1.5118 - acc_top1: 0.8266 - acc_top2: 0.8540 - 14ms/step\n", + "step 630/938 - loss: 1.4760 - acc_top1: 0.8276 - acc_top2: 0.8550 - 14ms/step\n", + "step 640/938 - loss: 1.4942 - acc_top1: 0.8286 - acc_top2: 0.8559 - 14ms/step\n", + "step 650/938 - loss: 1.5077 - acc_top1: 0.8295 - acc_top2: 0.8568 - 14ms/step\n", + "step 660/938 - loss: 1.4973 - acc_top1: 0.8305 - acc_top2: 0.8577 - 14ms/step\n", + "step 670/938 - loss: 1.5091 - acc_top1: 0.8314 - acc_top2: 0.8586 - 14ms/step\n", + "step 680/938 - loss: 1.4692 - acc_top1: 0.8323 - acc_top2: 0.8595 - 14ms/step\n", + "step 690/938 - loss: 1.4746 - acc_top1: 0.8332 - acc_top2: 0.8604 - 14ms/step\n", + "step 700/938 - loss: 1.4619 - acc_top1: 0.8342 - acc_top2: 0.8613 - 14ms/step\n", + "step 710/938 - loss: 1.5545 - acc_top1: 0.8350 - acc_top2: 0.8621 - 14ms/step\n", + "step 720/938 - loss: 1.4629 - acc_top1: 0.8360 - acc_top2: 0.8630 - 14ms/step\n", + "step 730/938 - loss: 1.4738 - acc_top1: 0.8369 - acc_top2: 0.8638 - 14ms/step\n", + "step 740/938 - loss: 1.4804 - acc_top1: 0.8378 - acc_top2: 0.8647 - 14ms/step\n", + "step 750/938 - loss: 1.4808 - acc_top1: 0.8386 - acc_top2: 0.8655 - 14ms/step\n", + "step 760/938 - loss: 1.4695 - acc_top1: 0.8395 - acc_top2: 0.8664 - 14ms/step\n", + "step 770/938 - loss: 1.4899 - acc_top1: 0.8403 - acc_top2: 0.8671 - 14ms/step\n", + "step 780/938 - loss: 1.5244 - acc_top1: 0.8411 - acc_top2: 0.8679 - 14ms/step\n", + "step 790/938 - loss: 1.5005 - acc_top1: 0.8418 - acc_top2: 0.8686 - 14ms/step\n", + "step 800/938 - loss: 1.4614 - acc_top1: 0.8427 - acc_top2: 0.8694 - 14ms/step\n", + "step 810/938 - loss: 1.5227 - acc_top1: 0.8434 - acc_top2: 0.8702 - 14ms/step\n", + "step 820/938 - loss: 1.4852 - acc_top1: 0.8442 - acc_top2: 0.8709 - 14ms/step\n", + "step 830/938 - loss: 1.4978 - acc_top1: 0.8450 - acc_top2: 0.8717 - 14ms/step\n", + "step 840/938 - loss: 1.4686 - acc_top1: 0.8458 - acc_top2: 0.8724 - 14ms/step\n", + "step 850/938 - loss: 1.4729 - acc_top1: 0.8466 - acc_top2: 0.8732 - 14ms/step\n", + "step 860/938 - loss: 1.4715 - acc_top1: 0.8473 - acc_top2: 0.8739 - 14ms/step\n", + "step 870/938 - loss: 1.5115 - acc_top1: 0.8481 - acc_top2: 0.8746 - 14ms/step\n", + "step 880/938 - loss: 1.4826 - acc_top1: 0.8488 - acc_top2: 0.8753 - 14ms/step\n", + "step 890/938 - loss: 1.4619 - acc_top1: 0.8496 - acc_top2: 0.8760 - 14ms/step\n", + "step 900/938 - loss: 1.4775 - acc_top1: 0.8504 - acc_top2: 0.8767 - 14ms/step\n", + "step 910/938 - loss: 1.4614 - acc_top1: 0.8511 - acc_top2: 0.8773 - 14ms/step\n", + "step 920/938 - loss: 1.4762 - acc_top1: 0.8518 - acc_top2: 0.8780 - 14ms/step\n", + "step 930/938 - loss: 1.5233 - acc_top1: 0.8525 - acc_top2: 0.8787 - 14ms/step\n", + "step 938/938 - loss: 1.4616 - acc_top1: 0.8531 - acc_top2: 0.8792 - 14ms/step\n", "save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/0\n", "Epoch 2/2\n", - "step 10/938 - loss: 1.5919 - acc_top1: 0.8875 - acc_top2: 0.9047 - 14ms/step\n", - "step 20/938 - loss: 1.5900 - acc_top1: 0.8875 - acc_top2: 0.9062 - 14ms/step\n", - "step 30/938 - loss: 1.5929 - acc_top1: 0.8891 - acc_top2: 0.9036 - 13ms/step\n", - "step 40/938 - loss: 1.5855 - acc_top1: 0.8883 - acc_top2: 0.9027 - 13ms/step\n", - "step 50/938 - loss: 1.6197 - acc_top1: 0.8916 - acc_top2: 0.9072 - 13ms/step\n", - "step 60/938 - loss: 1.5084 - acc_top1: 0.8914 - acc_top2: 0.9078 - 13ms/step\n", - "step 70/938 - loss: 1.5552 - acc_top1: 0.8904 - acc_top2: 0.9067 - 13ms/step\n", - "step 80/938 - loss: 1.5700 - acc_top1: 0.8887 - acc_top2: 0.9049 - 13ms/step\n", - "step 90/938 - loss: 1.6073 - acc_top1: 0.8866 - acc_top2: 0.9030 - 13ms/step\n", - "step 100/938 - loss: 1.5754 - acc_top1: 0.8859 - acc_top2: 0.9022 - 13ms/step\n" + "step 10/938 - loss: 1.5034 - acc_top1: 0.9688 - acc_top2: 0.9891 - 16ms/step\n", + "step 20/938 - loss: 1.4879 - acc_top1: 0.9711 - acc_top2: 0.9898 - 16ms/step\n", + "step 30/938 - loss: 1.4661 - acc_top1: 0.9734 - acc_top2: 0.9906 - 15ms/step\n", + "step 40/938 - loss: 1.5272 - acc_top1: 0.9746 - acc_top2: 0.9914 - 15ms/step\n", + "step 50/938 - loss: 1.4768 - acc_top1: 0.9747 - acc_top2: 0.9919 - 15ms/step\n", + "step 60/938 - loss: 1.4924 - acc_top1: 0.9719 - acc_top2: 0.9898 - 15ms/step\n", + "step 70/938 - loss: 1.4800 - acc_top1: 0.9725 - acc_top2: 0.9906 - 15ms/step\n", + "step 80/938 - loss: 1.5056 - acc_top1: 0.9734 - acc_top2: 0.9914 - 15ms/step\n", + "step 90/938 - loss: 1.4628 - acc_top1: 0.9740 - acc_top2: 0.9915 - 15ms/step\n", + "step 100/938 - loss: 1.4794 - acc_top1: 0.9745 - acc_top2: 0.9919 - 15ms/step\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "step 110/938 - loss: 1.5484 - acc_top1: 0.8848 - acc_top2: 0.9017 - 14ms/step\n", - "step 120/938 - loss: 1.5904 - acc_top1: 0.8840 - acc_top2: 0.9020 - 14ms/step\n", - "step 130/938 - loss: 1.5108 - acc_top1: 0.8852 - acc_top2: 0.9025 - 14ms/step\n", - "step 140/938 - loss: 1.6199 - acc_top1: 0.8840 - acc_top2: 0.9016 - 14ms/step\n", - "step 150/938 - loss: 1.5337 - acc_top1: 0.8842 - acc_top2: 0.9019 - 13ms/step\n", - "step 160/938 - loss: 1.6094 - acc_top1: 0.8846 - acc_top2: 0.9023 - 13ms/step\n", - "step 170/938 - loss: 1.5653 - acc_top1: 0.8843 - acc_top2: 0.9019 - 13ms/step\n", - "step 180/938 - loss: 1.5978 - acc_top1: 0.8835 - acc_top2: 0.9011 - 13ms/step\n", - "step 190/938 - loss: 1.5950 - acc_top1: 0.8833 - acc_top2: 0.9012 - 13ms/step\n", - "step 200/938 - loss: 1.6422 - acc_top1: 0.8828 - acc_top2: 0.9002 - 13ms/step\n", - "step 210/938 - loss: 1.5752 - acc_top1: 0.8831 - acc_top2: 0.9004 - 13ms/step\n", - "step 220/938 - loss: 1.6635 - acc_top1: 0.8832 - acc_top2: 0.9001 - 13ms/step\n", - "step 230/938 - loss: 1.5726 - acc_top1: 0.8823 - acc_top2: 0.8991 - 13ms/step\n", - "step 240/938 - loss: 1.5702 - acc_top1: 0.8814 - acc_top2: 0.8981 - 13ms/step\n", - "step 250/938 - loss: 1.5748 - acc_top1: 0.8814 - acc_top2: 0.8981 - 14ms/step\n", - "step 260/938 - loss: 1.5589 - acc_top1: 0.8822 - acc_top2: 0.8988 - 14ms/step\n", - "step 270/938 - loss: 1.5902 - acc_top1: 0.8823 - acc_top2: 0.8988 - 14ms/step\n", - "step 280/938 - loss: 1.5646 - acc_top1: 0.8817 - acc_top2: 0.8982 - 14ms/step\n", - "step 290/938 - loss: 1.6280 - acc_top1: 0.8819 - acc_top2: 0.8985 - 14ms/step\n", - "step 300/938 - loss: 1.5697 - acc_top1: 0.8815 - acc_top2: 0.8982 - 14ms/step\n", - "step 310/938 - loss: 1.5540 - acc_top1: 0.8814 - acc_top2: 0.8981 - 14ms/step\n", - "step 320/938 - loss: 1.5598 - acc_top1: 0.8821 - acc_top2: 0.8988 - 14ms/step\n", - "step 330/938 - loss: 1.5498 - acc_top1: 0.8824 - acc_top2: 0.8991 - 14ms/step\n", - "step 340/938 - loss: 1.6276 - acc_top1: 0.8818 - acc_top2: 0.8984 - 14ms/step\n", - "step 350/938 - loss: 1.5129 - acc_top1: 0.8821 - acc_top2: 0.8988 - 14ms/step\n", - "step 360/938 - loss: 1.6158 - acc_top1: 0.8818 - acc_top2: 0.8984 - 14ms/step\n", - "step 370/938 - loss: 1.5300 - acc_top1: 0.8820 - acc_top2: 0.8986 - 14ms/step\n", - "step 380/938 - loss: 1.5718 - acc_top1: 0.8822 - acc_top2: 0.8988 - 14ms/step\n", - "step 390/938 - loss: 1.5898 - acc_top1: 0.8825 - acc_top2: 0.8990 - 14ms/step\n", - "step 400/938 - loss: 1.5177 - acc_top1: 0.8834 - acc_top2: 0.9000 - 14ms/step\n", - "step 410/938 - loss: 1.6493 - acc_top1: 0.8831 - acc_top2: 0.8997 - 14ms/step\n", - "step 420/938 - loss: 1.5071 - acc_top1: 0.8838 - acc_top2: 0.9002 - 14ms/step\n", - "step 430/938 - loss: 1.5982 - acc_top1: 0.8840 - acc_top2: 0.9002 - 14ms/step\n", - "step 440/938 - loss: 1.5649 - acc_top1: 0.8841 - acc_top2: 0.9003 - 14ms/step\n", - "step 450/938 - loss: 1.5555 - acc_top1: 0.8844 - acc_top2: 0.9005 - 14ms/step\n", - "step 460/938 - loss: 1.5536 - acc_top1: 0.8845 - acc_top2: 0.9005 - 14ms/step\n", - "step 470/938 - loss: 1.5401 - acc_top1: 0.8851 - acc_top2: 0.9011 - 14ms/step\n", - "step 480/938 - loss: 1.5549 - acc_top1: 0.8854 - acc_top2: 0.9013 - 14ms/step\n", - "step 490/938 - loss: 1.5596 - acc_top1: 0.8858 - acc_top2: 0.9017 - 14ms/step\n", - "step 500/938 - loss: 1.5059 - acc_top1: 0.8860 - acc_top2: 0.9018 - 14ms/step\n", - "step 510/938 - loss: 1.6073 - acc_top1: 0.8858 - acc_top2: 0.9017 - 14ms/step\n", - "step 520/938 - loss: 1.5588 - acc_top1: 0.8857 - acc_top2: 0.9016 - 14ms/step\n", - "step 530/938 - loss: 1.6165 - acc_top1: 0.8859 - acc_top2: 0.9019 - 14ms/step\n", - "step 540/938 - loss: 1.5884 - acc_top1: 0.8862 - acc_top2: 0.9023 - 14ms/step\n", - "step 550/938 - loss: 1.6552 - acc_top1: 0.8863 - acc_top2: 0.9027 - 14ms/step\n", - "step 560/938 - loss: 1.5529 - acc_top1: 0.8867 - acc_top2: 0.9030 - 14ms/step\n", - "step 570/938 - loss: 1.5441 - acc_top1: 0.8866 - acc_top2: 0.9029 - 14ms/step\n", - "step 580/938 - loss: 1.5438 - acc_top1: 0.8867 - acc_top2: 0.9029 - 14ms/step\n", - "step 590/938 - loss: 1.5761 - acc_top1: 0.8868 - acc_top2: 0.9029 - 14ms/step\n", - "step 600/938 - loss: 1.5384 - acc_top1: 0.8867 - acc_top2: 0.9029 - 14ms/step\n", - "step 610/938 - loss: 1.5858 - acc_top1: 0.8871 - acc_top2: 0.9032 - 14ms/step\n", - "step 620/938 - loss: 1.5524 - acc_top1: 0.8872 - acc_top2: 0.9034 - 14ms/step\n", - "step 630/938 - loss: 1.6182 - acc_top1: 0.8875 - acc_top2: 0.9035 - 14ms/step\n", - "step 640/938 - loss: 1.6326 - acc_top1: 0.8877 - acc_top2: 0.9037 - 14ms/step\n", - "step 650/938 - loss: 1.5871 - acc_top1: 0.8877 - acc_top2: 0.9035 - 14ms/step\n", - "step 660/938 - loss: 1.5403 - acc_top1: 0.8877 - acc_top2: 0.9034 - 14ms/step\n", - "step 670/938 - loss: 1.5539 - acc_top1: 0.8879 - acc_top2: 0.9035 - 14ms/step\n", - "step 680/938 - loss: 1.4918 - acc_top1: 0.8881 - acc_top2: 0.9036 - 14ms/step\n", - "step 690/938 - loss: 1.6007 - acc_top1: 0.8882 - acc_top2: 0.9036 - 14ms/step\n", - "step 700/938 - loss: 1.5539 - acc_top1: 0.8883 - acc_top2: 0.9037 - 14ms/step\n", - "step 710/938 - loss: 1.6036 - acc_top1: 0.8882 - acc_top2: 0.9035 - 14ms/step\n", - "step 720/938 - loss: 1.5943 - acc_top1: 0.8881 - acc_top2: 0.9035 - 14ms/step\n", - "step 730/938 - loss: 1.5714 - acc_top1: 0.8881 - acc_top2: 0.9035 - 14ms/step\n", - "step 740/938 - loss: 1.5095 - acc_top1: 0.8881 - acc_top2: 0.9035 - 14ms/step\n", - "step 750/938 - loss: 1.5069 - acc_top1: 0.8882 - acc_top2: 0.9035 - 14ms/step\n", - "step 760/938 - loss: 1.5816 - acc_top1: 0.8882 - acc_top2: 0.9035 - 14ms/step\n", - "step 770/938 - loss: 1.5855 - acc_top1: 0.8880 - acc_top2: 0.9033 - 14ms/step\n", - "step 780/938 - loss: 1.5599 - acc_top1: 0.8881 - acc_top2: 0.9034 - 14ms/step\n", - "step 790/938 - loss: 1.6029 - acc_top1: 0.8879 - acc_top2: 0.9032 - 14ms/step\n", - "step 800/938 - loss: 1.5839 - acc_top1: 0.8880 - acc_top2: 0.9033 - 14ms/step\n", - "step 810/938 - loss: 1.5545 - acc_top1: 0.8882 - acc_top2: 0.9035 - 14ms/step\n", - "step 820/938 - loss: 1.5458 - acc_top1: 0.8881 - acc_top2: 0.9036 - 14ms/step\n", - "step 830/938 - loss: 1.5911 - acc_top1: 0.8879 - acc_top2: 0.9033 - 14ms/step\n", - "step 840/938 - loss: 1.5845 - acc_top1: 0.8881 - acc_top2: 0.9035 - 14ms/step\n", - "step 850/938 - loss: 1.5628 - acc_top1: 0.8880 - acc_top2: 0.9035 - 14ms/step\n", - "step 860/938 - loss: 1.5596 - acc_top1: 0.8880 - acc_top2: 0.9035 - 14ms/step\n", - "step 870/938 - loss: 1.5843 - acc_top1: 0.8882 - acc_top2: 0.9036 - 14ms/step\n", - "step 880/938 - loss: 1.5393 - acc_top1: 0.8883 - acc_top2: 0.9036 - 14ms/step\n", - "step 890/938 - loss: 1.5382 - acc_top1: 0.8882 - acc_top2: 0.9035 - 14ms/step\n", - "step 900/938 - loss: 1.5910 - acc_top1: 0.8884 - acc_top2: 0.9036 - 14ms/step\n", - "step 910/938 - loss: 1.5682 - acc_top1: 0.8886 - acc_top2: 0.9038 - 14ms/step\n", - "step 920/938 - loss: 1.5736 - acc_top1: 0.8889 - acc_top2: 0.9039 - 14ms/step\n", - "step 930/938 - loss: 1.5283 - acc_top1: 0.8888 - acc_top2: 0.9038 - 14ms/step\n", - "step 938/938 - loss: 1.5582 - acc_top1: 0.8888 - acc_top2: 0.9038 - 14ms/step\n", + "step 110/938 - loss: 1.4665 - acc_top1: 0.9749 - acc_top2: 0.9918 - 15ms/step\n", + "step 120/938 - loss: 1.4769 - acc_top1: 0.9755 - acc_top2: 0.9917 - 15ms/step\n", + "step 130/938 - loss: 1.4883 - acc_top1: 0.9755 - acc_top2: 0.9918 - 15ms/step\n", + "step 140/938 - loss: 1.4779 - acc_top1: 0.9757 - acc_top2: 0.9920 - 15ms/step\n", + "step 150/938 - loss: 1.4926 - acc_top1: 0.9751 - acc_top2: 0.9922 - 15ms/step\n", + "step 160/938 - loss: 1.5458 - acc_top1: 0.9750 - acc_top2: 0.9924 - 15ms/step\n", + "step 170/938 - loss: 1.5166 - acc_top1: 0.9748 - acc_top2: 0.9924 - 15ms/step\n", + "step 180/938 - loss: 1.4676 - acc_top1: 0.9748 - acc_top2: 0.9923 - 14ms/step\n", + "step 190/938 - loss: 1.4773 - acc_top1: 0.9748 - acc_top2: 0.9924 - 15ms/step\n", + "step 200/938 - loss: 1.4893 - acc_top1: 0.9752 - acc_top2: 0.9928 - 15ms/step\n", + "step 210/938 - loss: 1.5408 - acc_top1: 0.9751 - acc_top2: 0.9926 - 15ms/step\n", + "step 220/938 - loss: 1.4934 - acc_top1: 0.9753 - acc_top2: 0.9925 - 15ms/step\n", + "step 230/938 - loss: 1.5162 - acc_top1: 0.9753 - acc_top2: 0.9925 - 15ms/step\n", + "step 240/938 - loss: 1.5097 - acc_top1: 0.9752 - acc_top2: 0.9926 - 14ms/step\n", + "step 250/938 - loss: 1.5264 - acc_top1: 0.9752 - acc_top2: 0.9927 - 14ms/step\n", + "step 260/938 - loss: 1.4843 - acc_top1: 0.9752 - acc_top2: 0.9926 - 14ms/step\n", + "step 270/938 - loss: 1.4818 - acc_top1: 0.9753 - acc_top2: 0.9927 - 14ms/step\n", + "step 280/938 - loss: 1.4627 - acc_top1: 0.9755 - acc_top2: 0.9925 - 14ms/step\n", + "step 290/938 - loss: 1.4932 - acc_top1: 0.9755 - acc_top2: 0.9923 - 14ms/step\n", + "step 300/938 - loss: 1.4641 - acc_top1: 0.9754 - acc_top2: 0.9924 - 14ms/step\n", + "step 310/938 - loss: 1.4908 - acc_top1: 0.9757 - acc_top2: 0.9925 - 14ms/step\n", + "step 320/938 - loss: 1.4910 - acc_top1: 0.9756 - acc_top2: 0.9926 - 14ms/step\n", + "step 330/938 - loss: 1.4693 - acc_top1: 0.9754 - acc_top2: 0.9925 - 14ms/step\n", + "step 340/938 - loss: 1.4968 - acc_top1: 0.9755 - acc_top2: 0.9925 - 14ms/step\n", + "step 350/938 - loss: 1.4963 - acc_top1: 0.9754 - acc_top2: 0.9923 - 14ms/step\n", + "step 360/938 - loss: 1.5005 - acc_top1: 0.9755 - acc_top2: 0.9925 - 14ms/step\n", + "step 370/938 - loss: 1.4683 - acc_top1: 0.9756 - acc_top2: 0.9925 - 14ms/step\n", + "step 380/938 - loss: 1.5069 - acc_top1: 0.9757 - acc_top2: 0.9925 - 14ms/step\n", + "step 390/938 - loss: 1.4619 - acc_top1: 0.9759 - acc_top2: 0.9926 - 14ms/step\n", + "step 400/938 - loss: 1.4627 - acc_top1: 0.9761 - acc_top2: 0.9928 - 14ms/step\n", + "step 410/938 - loss: 1.5207 - acc_top1: 0.9760 - acc_top2: 0.9928 - 14ms/step\n", + "step 420/938 - loss: 1.5234 - acc_top1: 0.9758 - acc_top2: 0.9928 - 14ms/step\n", + "step 430/938 - loss: 1.4797 - acc_top1: 0.9759 - acc_top2: 0.9930 - 14ms/step\n", + "step 440/938 - loss: 1.4618 - acc_top1: 0.9759 - acc_top2: 0.9929 - 14ms/step\n", + "step 450/938 - loss: 1.4760 - acc_top1: 0.9759 - acc_top2: 0.9930 - 14ms/step\n", + "step 460/938 - loss: 1.4612 - acc_top1: 0.9760 - acc_top2: 0.9929 - 14ms/step\n", + "step 470/938 - loss: 1.4922 - acc_top1: 0.9759 - acc_top2: 0.9929 - 14ms/step\n", + "step 480/938 - loss: 1.4831 - acc_top1: 0.9761 - acc_top2: 0.9930 - 14ms/step\n", + "step 490/938 - loss: 1.4914 - acc_top1: 0.9762 - acc_top2: 0.9931 - 14ms/step\n", + "step 500/938 - loss: 1.4861 - acc_top1: 0.9760 - acc_top2: 0.9931 - 14ms/step\n", + "step 510/938 - loss: 1.4703 - acc_top1: 0.9760 - acc_top2: 0.9931 - 14ms/step\n", + "step 520/938 - loss: 1.5457 - acc_top1: 0.9755 - acc_top2: 0.9930 - 14ms/step\n", + "step 530/938 - loss: 1.4808 - acc_top1: 0.9754 - acc_top2: 0.9929 - 14ms/step\n", + "step 540/938 - loss: 1.4831 - acc_top1: 0.9753 - acc_top2: 0.9929 - 14ms/step\n", + "step 550/938 - loss: 1.5072 - acc_top1: 0.9753 - acc_top2: 0.9929 - 14ms/step\n", + "step 560/938 - loss: 1.4628 - acc_top1: 0.9753 - acc_top2: 0.9929 - 14ms/step\n", + "step 570/938 - loss: 1.4721 - acc_top1: 0.9753 - acc_top2: 0.9929 - 14ms/step\n", + "step 580/938 - loss: 1.4768 - acc_top1: 0.9755 - acc_top2: 0.9929 - 14ms/step\n", + "step 590/938 - loss: 1.4793 - acc_top1: 0.9755 - acc_top2: 0.9930 - 14ms/step\n", + "step 600/938 - loss: 1.4916 - acc_top1: 0.9754 - acc_top2: 0.9929 - 14ms/step\n", + "step 610/938 - loss: 1.4612 - acc_top1: 0.9755 - acc_top2: 0.9930 - 14ms/step\n", + "step 620/938 - loss: 1.5034 - acc_top1: 0.9753 - acc_top2: 0.9930 - 14ms/step\n", + "step 630/938 - loss: 1.4640 - acc_top1: 0.9754 - acc_top2: 0.9930 - 14ms/step\n", + "step 640/938 - loss: 1.4822 - acc_top1: 0.9756 - acc_top2: 0.9931 - 14ms/step\n", + "step 650/938 - loss: 1.4955 - acc_top1: 0.9756 - acc_top2: 0.9932 - 14ms/step\n", + "step 660/938 - loss: 1.4615 - acc_top1: 0.9757 - acc_top2: 0.9933 - 14ms/step\n", + "step 670/938 - loss: 1.5060 - acc_top1: 0.9757 - acc_top2: 0.9932 - 14ms/step\n", + "step 680/938 - loss: 1.4640 - acc_top1: 0.9758 - acc_top2: 0.9933 - 14ms/step\n", + "step 690/938 - loss: 1.5070 - acc_top1: 0.9759 - acc_top2: 0.9933 - 14ms/step\n", + "step 700/938 - loss: 1.4615 - acc_top1: 0.9761 - acc_top2: 0.9933 - 14ms/step\n", + "step 710/938 - loss: 1.5210 - acc_top1: 0.9760 - acc_top2: 0.9933 - 14ms/step\n", + "step 720/938 - loss: 1.5154 - acc_top1: 0.9761 - acc_top2: 0.9933 - 14ms/step\n", + "step 730/938 - loss: 1.4965 - acc_top1: 0.9760 - acc_top2: 0.9933 - 14ms/step\n", + "step 740/938 - loss: 1.4612 - acc_top1: 0.9761 - acc_top2: 0.9933 - 14ms/step\n", + "step 750/938 - loss: 1.4878 - acc_top1: 0.9761 - acc_top2: 0.9934 - 14ms/step\n", + "step 760/938 - loss: 1.4775 - acc_top1: 0.9761 - acc_top2: 0.9933 - 14ms/step\n", + "step 770/938 - loss: 1.4834 - acc_top1: 0.9762 - acc_top2: 0.9933 - 14ms/step\n", + "step 780/938 - loss: 1.4661 - acc_top1: 0.9763 - acc_top2: 0.9934 - 14ms/step\n", + "step 790/938 - loss: 1.4895 - acc_top1: 0.9764 - acc_top2: 0.9934 - 14ms/step\n", + "step 800/938 - loss: 1.4767 - acc_top1: 0.9765 - acc_top2: 0.9934 - 14ms/step\n", + "step 810/938 - loss: 1.4779 - acc_top1: 0.9767 - acc_top2: 0.9935 - 14ms/step\n", + "step 820/938 - loss: 1.4768 - acc_top1: 0.9766 - acc_top2: 0.9935 - 14ms/step\n", + "step 830/938 - loss: 1.4630 - acc_top1: 0.9767 - acc_top2: 0.9935 - 14ms/step\n", + "step 840/938 - loss: 1.4612 - acc_top1: 0.9767 - acc_top2: 0.9936 - 14ms/step\n", + "step 850/938 - loss: 1.4993 - acc_top1: 0.9766 - acc_top2: 0.9935 - 14ms/step\n", + "step 860/938 - loss: 1.4817 - acc_top1: 0.9766 - acc_top2: 0.9936 - 14ms/step\n", + "step 870/938 - loss: 1.4786 - acc_top1: 0.9766 - acc_top2: 0.9935 - 14ms/step\n", + "step 880/938 - loss: 1.4772 - acc_top1: 0.9765 - acc_top2: 0.9935 - 14ms/step\n", + "step 890/938 - loss: 1.4646 - acc_top1: 0.9766 - acc_top2: 0.9936 - 14ms/step\n", + "step 900/938 - loss: 1.4813 - acc_top1: 0.9766 - acc_top2: 0.9936 - 14ms/step\n", + "step 910/938 - loss: 1.4815 - acc_top1: 0.9767 - acc_top2: 0.9936 - 14ms/step\n", + "step 920/938 - loss: 1.4854 - acc_top1: 0.9767 - acc_top2: 0.9936 - 14ms/step\n", + "step 930/938 - loss: 1.4890 - acc_top1: 0.9769 - acc_top2: 0.9936 - 14ms/step\n", + "step 938/938 - loss: 1.4617 - acc_top1: 0.9769 - acc_top2: 0.9937 - 14ms/step\n", "save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/1\n", "save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final\n" ] @@ -542,7 +542,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -550,32 +550,32 @@ "output_type": "stream", "text": [ "Eval begin...\n", - "step 10/157 - loss: 1.5447 - acc_top1: 0.8953 - acc_top2: 0.9078 - 5ms/step\n", - "step 20/157 - loss: 1.6185 - acc_top1: 0.8930 - acc_top2: 0.9078 - 5ms/step\n", - "step 30/157 - loss: 1.6497 - acc_top1: 0.8917 - acc_top2: 0.9057 - 5ms/step\n", - "step 40/157 - loss: 1.6318 - acc_top1: 0.8902 - acc_top2: 0.9055 - 5ms/step\n", - "step 50/157 - loss: 1.5533 - acc_top1: 0.8856 - acc_top2: 0.9012 - 5ms/step\n", - "step 60/157 - loss: 1.6212 - acc_top1: 0.8878 - acc_top2: 0.9036 - 5ms/step\n", - "step 70/157 - loss: 1.5674 - acc_top1: 0.8839 - acc_top2: 0.9002 - 5ms/step\n", - "step 80/157 - loss: 1.5409 - acc_top1: 0.8891 - acc_top2: 0.9043 - 5ms/step\n", - "step 90/157 - loss: 1.6133 - acc_top1: 0.8903 - acc_top2: 0.9045 - 5ms/step\n", - "step 100/157 - loss: 1.5535 - acc_top1: 0.8909 - acc_top2: 0.9044 - 5ms/step\n", - "step 110/157 - loss: 1.5690 - acc_top1: 0.8916 - acc_top2: 0.9054 - 5ms/step\n", - "step 120/157 - loss: 1.6147 - acc_top1: 0.8926 - acc_top2: 0.9055 - 5ms/step\n", - "step 130/157 - loss: 1.5203 - acc_top1: 0.8944 - acc_top2: 0.9066 - 5ms/step\n", - "step 140/157 - loss: 1.5066 - acc_top1: 0.8952 - acc_top2: 0.9068 - 5ms/step\n", - "step 150/157 - loss: 1.5536 - acc_top1: 0.8958 - acc_top2: 0.9072 - 5ms/step\n", - "step 157/157 - loss: 1.5855 - acc_top1: 0.8956 - acc_top2: 0.9076 - 5ms/step\n", + "step 10/157 - loss: 1.5023 - acc_top1: 0.9781 - acc_top2: 0.9969 - 7ms/step\n", + "step 20/157 - loss: 1.5326 - acc_top1: 0.9750 - acc_top2: 0.9953 - 7ms/step\n", + "step 30/157 - loss: 1.4881 - acc_top1: 0.9745 - acc_top2: 0.9943 - 7ms/step\n", + "step 40/157 - loss: 1.4703 - acc_top1: 0.9715 - acc_top2: 0.9934 - 6ms/step\n", + "step 50/157 - loss: 1.4793 - acc_top1: 0.9728 - acc_top2: 0.9934 - 6ms/step\n", + "step 60/157 - loss: 1.5338 - acc_top1: 0.9721 - acc_top2: 0.9924 - 6ms/step\n", + "step 70/157 - loss: 1.4801 - acc_top1: 0.9721 - acc_top2: 0.9922 - 6ms/step\n", + "step 80/157 - loss: 1.4763 - acc_top1: 0.9725 - acc_top2: 0.9928 - 6ms/step\n", + "step 90/157 - loss: 1.4682 - acc_top1: 0.9747 - acc_top2: 0.9936 - 6ms/step\n", + "step 100/157 - loss: 1.4780 - acc_top1: 0.9758 - acc_top2: 0.9939 - 6ms/step\n", + "step 110/157 - loss: 1.4686 - acc_top1: 0.9763 - acc_top2: 0.9942 - 6ms/step\n", + "step 120/157 - loss: 1.4624 - acc_top1: 0.9780 - acc_top2: 0.9947 - 6ms/step\n", + "step 130/157 - loss: 1.4968 - acc_top1: 0.9787 - acc_top2: 0.9948 - 6ms/step\n", + "step 140/157 - loss: 1.4612 - acc_top1: 0.9798 - acc_top2: 0.9952 - 6ms/step\n", + "step 150/157 - loss: 1.4613 - acc_top1: 0.9806 - acc_top2: 0.9955 - 6ms/step\n", + "step 157/157 - loss: 1.4612 - acc_top1: 0.9803 - acc_top2: 0.9955 - 6ms/step\n", "Eval samples: 10000\n" ] }, { "data": { "text/plain": [ - "{'loss': [1.585474], 'acc_top1': 0.8956, 'acc_top2': 0.9076}" + "{'loss': [1.4611506], 'acc_top1': 0.9803, 'acc_top2': 0.9955}" ] }, - "execution_count": 43, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -605,13 +605,6 @@ "source": [ "以上就是用LeNet对手写数字数据及MNIST进行分类。本示例提供了两种训练模型的方式，一种可以快速完成模型的组建与预测，非常适合新手用户上手。另一种则需要多个步骤来完成模型的训练，适合进阶用户使用。" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/paddle2.0_docs/n_gram_model/n_gram_model.ipynb b/paddle2.0_docs/n_gram_model/n_gram_model.ipynb index 30bb2126ed232dde3be662933008cd9f98dd2956..9ff3a94f1fdd74aab20f90048ee5a03372f71b3c 100644 --- a/paddle2.0_docs/n_gram_model/n_gram_model.ipynb +++ b/paddle2.0_docs/n_gram_model/n_gram_model.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "\n", - "## 用N-Gram模型在莎士比亚文集中训练word embedding\n", + "# 用N-Gram模型在莎士比亚文集中训练word embedding\n", "N-gram 是计算机语言学和概率论范畴内的概念，是指给定的一段文本中N个项目的序列。\n", "N=1 时 N-gram 又称为 unigram，N=2 称为 bigram，N=3 称为 trigram，以此类推。实际应用通常采用 bigram 和 trigram 进行计算。\n", "本示例在莎士比亚文集上实现了trigram。" @@ -15,13 +15,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 环境\n", + "## 环境\n", "本教程基于paddle-develop编写，如果您的环境不是本版本，请先安装paddle-develop。" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -30,7 +30,7 @@ "'0.0.0'" ] }, - "execution_count": 17, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -51,23 +51,22 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "--2020-09-03 08:41:10-- https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt\n", - "正在解析主机 ocw.mit.edu (ocw.mit.edu)... 151.101.230.133\n", - "正在连接 ocw.mit.edu (ocw.mit.edu)|151.101.230.133|:443... 已连接。\n", - "已发出 HTTP 请求，正在等待回应... 200 OK\n", + "--2020-09-08 19:07:26-- https://ocw.mit.edu/ans7870/6/6.006/s08/lecturenotes/files/t8.shakespeare.txt\n", + "正在连接 172.19.57.45:3128... 已连接。\n", + "已发出 Proxy 请求，正在等待回应... 200 OK\n", "长度：5458199 (5.2M) [text/plain]\n", - "正在保存至: “t8.shakespeare.txt.1”\n", + "正在保存至: “t8.shakespeare.txt”\n", "\n", - "t8.shakespeare.txt. 100%[===================>] 5.21M 26.1KB/s 用时 4m 14s \n", + "t8.shakespeare.txt 100%[===================>] 5.21M 862KB/s 用时 7.1s \n", "\n", - "2020-09-03 08:45:25 (21.0 KB/s) - 已保存 “t8.shakespeare.txt.1” [5458199/5458199])\n", + "2020-09-08 19:07:34 (755 KB/s) - 已保存 “t8.shakespeare.txt” [5458199/5458199])\n", "\n" ] } @@ -78,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -113,12 +112,12 @@ "metadata": {}, "source": [ "## 去除标点符号\n", - "用`string`库中的punctuation，完成英文符号的替换。" + "因为标点符号本身无实际意义，用`string`库中的punctuation，完成英文符号的替换。" ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -137,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -166,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -197,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -233,7 +232,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -243,7 +242,7 @@ "class NGramModel(paddle.nn.Layer):\n", " def __init__(self, vocab_size, embedding_dim, context_size):\n", " super(NGramModel, self).__init__()\n", - " self.embedding = paddle.nn.Embedding(size=[vocab_size, embedding_dim])\n", + " self.embedding = paddle.nn.Embedding(num_embeddings=vocab_size, embedding_dim=embedding_dim)\n", " self.linear1 = paddle.nn.Linear(context_size * embedding_dim, hidden_size)\n", " self.linear2 = paddle.nn.Linear(hidden_size, len(vocab))\n", "\n", @@ -260,90 +259,90 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### 定义`train()`函数，对模型进行训练。" + "## 定义`train()`函数，对模型进行训练。" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0, batch_id: 0, loss is: [10.252256]\n", - "epoch: 0, batch_id: 100, loss is: [7.0485706]\n", - "epoch: 0, batch_id: 200, loss is: [7.282592]\n", - "epoch: 0, batch_id: 300, loss is: [6.9604626]\n", - "epoch: 0, batch_id: 400, loss is: [6.7308316]\n", - "epoch: 0, batch_id: 500, loss is: [6.7940483]\n", - "epoch: 0, batch_id: 600, loss is: [6.6574802]\n", - "epoch: 0, batch_id: 700, loss is: [6.862562]\n", - "epoch: 0, batch_id: 800, loss is: [7.2091002]\n", - "epoch: 0, batch_id: 900, loss is: [7.0172606]\n", - "epoch: 0, batch_id: 1000, loss is: [6.9888105]\n", - "epoch: 0, batch_id: 1100, loss is: [6.9609995]\n", - "epoch: 0, batch_id: 1200, loss is: [6.550024]\n", - "epoch: 0, batch_id: 1300, loss is: [6.714109]\n", - "epoch: 0, batch_id: 1400, loss is: [6.995716]\n", - "epoch: 0, batch_id: 1500, loss is: [6.939434]\n", - "epoch: 0, batch_id: 1600, loss is: [6.5966253]\n", - "epoch: 0, batch_id: 1700, loss is: [6.9880104]\n", - "epoch: 0, batch_id: 1800, loss is: [6.6459093]\n", - "epoch: 0, batch_id: 1900, loss is: [6.8095036]\n", - "epoch: 0, batch_id: 2000, loss is: [6.8447037]\n", - "epoch: 0, batch_id: 2100, loss is: [6.8313]\n", - "epoch: 0, batch_id: 2200, loss is: [6.808483]\n", - "epoch: 0, batch_id: 2300, loss is: [6.502908]\n", - "epoch: 0, batch_id: 2400, loss is: [6.561283]\n", - "epoch: 0, batch_id: 2500, loss is: [7.0093765]\n", - "epoch: 0, batch_id: 2600, loss is: [6.512396]\n", - "epoch: 0, batch_id: 2700, loss is: [6.809763]\n", - "epoch: 0, batch_id: 2800, loss is: [6.806659]\n", - "epoch: 0, batch_id: 2900, loss is: [6.95402]\n", - "epoch: 0, batch_id: 3000, loss is: [6.634927]\n", - "epoch: 0, batch_id: 3100, loss is: [6.644098]\n", - "epoch: 0, batch_id: 3200, loss is: [6.705504]\n", - "epoch: 0, batch_id: 3300, loss is: [6.2121572]\n", - "epoch: 0, batch_id: 3400, loss is: [6.638401]\n", - "epoch: 0, batch_id: 3500, loss is: [6.986831]\n", - "epoch: 1, batch_id: 0, loss is: [6.795429]\n", - "epoch: 1, batch_id: 100, loss is: [6.582568]\n", - "epoch: 1, batch_id: 200, loss is: [6.527663]\n", - "epoch: 1, batch_id: 300, loss is: [6.714637]\n", - "epoch: 1, batch_id: 400, loss is: [6.574902]\n", - "epoch: 1, batch_id: 500, loss is: [6.305031]\n", - "epoch: 1, batch_id: 600, loss is: [6.803609]\n", - "epoch: 1, batch_id: 700, loss is: [6.2429113]\n", - "epoch: 1, batch_id: 800, loss is: [6.7452283]\n", - "epoch: 1, batch_id: 900, loss is: [6.383783]\n", - "epoch: 1, batch_id: 1000, loss is: [6.4906135]\n", - "epoch: 1, batch_id: 1100, loss is: [6.6007314]\n", - "epoch: 1, batch_id: 1200, loss is: [6.63466]\n", - "epoch: 1, batch_id: 1300, loss is: [6.540749]\n", - "epoch: 1, batch_id: 1400, loss is: [6.7752547]\n", - "epoch: 1, batch_id: 1500, loss is: [6.2411666]\n", - "epoch: 1, batch_id: 1600, loss is: [6.540929]\n", - "epoch: 1, batch_id: 1700, loss is: [6.6563463]\n", - "epoch: 1, batch_id: 1800, loss is: [6.4592104]\n", - "epoch: 1, batch_id: 1900, loss is: [7.0268345]\n", - "epoch: 1, batch_id: 2000, loss is: [6.803793]\n", - "epoch: 1, batch_id: 2100, loss is: [6.8454733]\n", - "epoch: 1, batch_id: 2200, loss is: [6.651756]\n", - "epoch: 1, batch_id: 2300, loss is: [6.5876465]\n", - "epoch: 1, batch_id: 2400, loss is: [6.258934]\n", - "epoch: 1, batch_id: 2500, loss is: [6.5422425]\n", - "epoch: 1, batch_id: 2600, loss is: [6.184501]\n", - "epoch: 1, batch_id: 2700, loss is: [6.6847773]\n", - "epoch: 1, batch_id: 2800, loss is: [6.684101]\n", - "epoch: 1, batch_id: 2900, loss is: [6.374978]\n", - "epoch: 1, batch_id: 3000, loss is: [6.8277273]\n", - "epoch: 1, batch_id: 3100, loss is: [6.5195084]\n", - "epoch: 1, batch_id: 3200, loss is: [6.311832]\n", - "epoch: 1, batch_id: 3300, loss is: [6.4282994]\n", - "epoch: 1, batch_id: 3400, loss is: [6.603338]\n", - "epoch: 1, batch_id: 3500, loss is: [6.4541807]\n" + "epoch: 0, batch_id: 0, loss is: [10.252116]\n", + "epoch: 0, batch_id: 100, loss is: [7.078615]\n", + "epoch: 0, batch_id: 200, loss is: [7.0399227]\n", + "epoch: 0, batch_id: 300, loss is: [6.981158]\n", + "epoch: 0, batch_id: 400, loss is: [7.3663793]\n", + "epoch: 0, batch_id: 500, loss is: [6.535556]\n", + "epoch: 0, batch_id: 600, loss is: [6.872655]\n", + "epoch: 0, batch_id: 700, loss is: [6.6887097]\n", + "epoch: 0, batch_id: 800, loss is: [7.1285286]\n", + "epoch: 0, batch_id: 900, loss is: [6.8373947]\n", + "epoch: 0, batch_id: 1000, loss is: [6.35812]\n", + "epoch: 0, batch_id: 1100, loss is: [6.46247]\n", + "epoch: 0, batch_id: 1200, loss is: [6.6280813]\n", + "epoch: 0, batch_id: 1300, loss is: [6.8213453]\n", + "epoch: 0, batch_id: 1400, loss is: [6.546687]\n", + "epoch: 0, batch_id: 1500, loss is: [6.5811205]\n", + "epoch: 0, batch_id: 1600, loss is: [6.8401494]\n", + "epoch: 0, batch_id: 1700, loss is: [6.552598]\n", + "epoch: 0, batch_id: 1800, loss is: [6.9257517]\n", + "epoch: 0, batch_id: 1900, loss is: [6.449529]\n", + "epoch: 0, batch_id: 2000, loss is: [6.781369]\n", + "epoch: 0, batch_id: 2100, loss is: [6.56577]\n", + "epoch: 0, batch_id: 2200, loss is: [7.2461166]\n", + "epoch: 0, batch_id: 2300, loss is: [6.9522705]\n", + "epoch: 0, batch_id: 2400, loss is: [6.9158196]\n", + "epoch: 0, batch_id: 2500, loss is: [6.70108]\n", + "epoch: 0, batch_id: 2600, loss is: [6.6069937]\n", + "epoch: 0, batch_id: 2700, loss is: [6.3994207]\n", + "epoch: 0, batch_id: 2800, loss is: [6.838238]\n", + "epoch: 0, batch_id: 2900, loss is: [6.9941645]\n", + "epoch: 0, batch_id: 3000, loss is: [6.467593]\n", + "epoch: 0, batch_id: 3100, loss is: [6.910493]\n", + "epoch: 0, batch_id: 3200, loss is: [6.582421]\n", + "epoch: 0, batch_id: 3300, loss is: [6.5058546]\n", + "epoch: 0, batch_id: 3400, loss is: [6.704367]\n", + "epoch: 0, batch_id: 3500, loss is: [6.464218]\n", + "epoch: 1, batch_id: 0, loss is: [6.7665234]\n", + "epoch: 1, batch_id: 100, loss is: [6.588025]\n", + "epoch: 1, batch_id: 200, loss is: [6.4301405]\n", + "epoch: 1, batch_id: 300, loss is: [7.1541805]\n", + "epoch: 1, batch_id: 400, loss is: [6.553849]\n", + "epoch: 1, batch_id: 500, loss is: [6.21858]\n", + "epoch: 1, batch_id: 600, loss is: [6.330143]\n", + "epoch: 1, batch_id: 700, loss is: [6.1063113]\n", + "epoch: 1, batch_id: 800, loss is: [6.71904]\n", + "epoch: 1, batch_id: 900, loss is: [6.7976933]\n", + "epoch: 1, batch_id: 1000, loss is: [6.4078493]\n", + "epoch: 1, batch_id: 1100, loss is: [6.5992503]\n", + "epoch: 1, batch_id: 1200, loss is: [6.2867823]\n", + "epoch: 1, batch_id: 1300, loss is: [6.1241736]\n", + "epoch: 1, batch_id: 1400, loss is: [6.903452]\n", + "epoch: 1, batch_id: 1500, loss is: [6.8167877]\n", + "epoch: 1, batch_id: 1600, loss is: [6.785468]\n", + "epoch: 1, batch_id: 1700, loss is: [6.72624]\n", + "epoch: 1, batch_id: 1800, loss is: [6.668326]\n", + "epoch: 1, batch_id: 1900, loss is: [6.592691]\n", + "epoch: 1, batch_id: 2000, loss is: [6.542628]\n", + "epoch: 1, batch_id: 2100, loss is: [6.616316]\n", + "epoch: 1, batch_id: 2200, loss is: [6.786495]\n", + "epoch: 1, batch_id: 2300, loss is: [6.4466743]\n", + "epoch: 1, batch_id: 2400, loss is: [6.931132]\n", + "epoch: 1, batch_id: 2500, loss is: [6.3207083]\n", + "epoch: 1, batch_id: 2600, loss is: [6.697523]\n", + "epoch: 1, batch_id: 2700, loss is: [6.8533525]\n", + "epoch: 1, batch_id: 2800, loss is: [6.375583]\n", + "epoch: 1, batch_id: 2900, loss is: [6.7229414]\n", + "epoch: 1, batch_id: 3000, loss is: [6.7564845]\n", + "epoch: 1, batch_id: 3100, loss is: [6.9129057]\n", + "epoch: 1, batch_id: 3200, loss is: [6.732751]\n", + "epoch: 1, batch_id: 3300, loss is: [6.6692004]\n", + "epoch: 1, batch_id: 3400, loss is: [6.4342775]\n", + "epoch: 1, batch_id: 3500, loss is: [6.594665]\n" ] } ], @@ -383,22 +382,22 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 29, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -428,24 +427,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "the input words is: of, william\n", - "the predict words is: shakespeare\n", - "the true words is: shakespeare\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Library/Python/3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" + "the input words is: complete, works\n", + "the predict words is: of\n", + "the true words is: of\n" ] } ], @@ -466,13 +457,6 @@ " print('the true words is: ' + y_data)\n", "test(model)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -480,6 +464,18 @@ "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": "3.7.3" } }, "nbformat": 4,