diff --git a/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb b/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb index f7012ee9713176f6a42b360478b42ead149632f1..77e62760f255c339a01b2fe9dfd497c33b11a61f 100644 --- a/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb +++ b/paddle2.0_docs/image_classification/mnist_lenet_classification.ipynb @@ -19,7 +19,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -47,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -75,7 +75,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -118,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -162,33 +162,23 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "epoch: 0, batch_id: 0, loss is: [2.3037894], acc is: [0.140625]\n", - "epoch: 0, batch_id: 100, loss is: [1.6175328], acc is: [0.9375]\n", - "epoch: 0, batch_id: 200, loss is: [1.5388051], acc is: [0.96875]\n", - "epoch: 0, batch_id: 300, loss is: [1.5251061], acc is: [0.96875]\n", - "epoch: 0, batch_id: 400, loss is: [1.4678856], acc is: [1.]\n", - "epoch: 0, batch_id: 500, loss is: [1.4944503], acc is: [0.984375]\n", - "epoch: 0, batch_id: 600, loss is: [1.5365536], acc is: [0.96875]\n", - "epoch: 0, batch_id: 700, loss is: [1.4885054], acc is: [0.984375]\n", - "epoch: 0, batch_id: 800, loss is: [1.4872254], acc is: [0.984375]\n", - "epoch: 0, batch_id: 900, loss is: [1.4884174], acc is: [0.984375]\n", - "epoch: 1, batch_id: 0, loss is: [1.4776722], acc is: [1.]\n", - "epoch: 1, batch_id: 100, loss is: [1.4751343], acc is: [1.]\n", - "epoch: 1, batch_id: 200, loss is: [1.4772581], acc is: [1.]\n", - "epoch: 1, batch_id: 300, loss is: [1.4918218], acc is: [0.984375]\n", - "epoch: 1, batch_id: 400, loss is: [1.5038397], acc is: [0.96875]\n", - "epoch: 1, batch_id: 500, loss is: [1.5088196], acc is: [0.96875]\n", - "epoch: 1, batch_id: 600, loss is: [1.4961376], acc is: [0.984375]\n", - "epoch: 1, batch_id: 700, loss is: [1.4755756], acc is: [1.]\n", - "epoch: 1, batch_id: 800, loss is: [1.4921497], acc is: [0.984375]\n", - "epoch: 1, batch_id: 900, loss is: [1.4944404], acc is: [1.]\n" + "epoch: 0, batch_id: 0, loss is: [2.3017962], acc is: [0.28125]\n", + "epoch: 0, batch_id: 200, loss is: [1.5294291], acc is: [0.96875]\n", + "epoch: 0, batch_id: 400, loss is: [1.4693298], acc is: [1.]\n", + "epoch: 0, batch_id: 600, loss is: [1.5237448], acc is: [0.984375]\n", + "epoch: 0, batch_id: 800, loss is: [1.4795951], acc is: [0.984375]\n", + "epoch: 1, batch_id: 0, loss is: [1.5161536], acc is: [0.96875]\n", + "epoch: 1, batch_id: 200, loss is: [1.4763479], acc is: [1.]\n", + "epoch: 1, batch_id: 400, loss is: [1.4929678], acc is: [1.]\n", + "epoch: 1, batch_id: 600, loss is: [1.4999642], acc is: [1.]\n", + "epoch: 1, batch_id: 800, loss is: [1.5029153], acc is: [0.984375]\n" ] } ], @@ -209,11 +199,9 @@ " loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n", " # 计算损失\n", " acc = paddle.metric.accuracy(predicts, y_data, k=2)\n", - " avg_loss = paddle.mean(loss)\n", - " avg_acc = paddle.mean(acc)\n", - " avg_loss.backward()\n", - " if batch_id % 100 == 0:\n", - " print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, avg_loss.numpy(), avg_acc.numpy()))\n", + " loss.backward()\n", + " if batch_id % 200 == 0:\n", + " print(\"epoch: {}, batch_id: {}, loss is: {}, acc is: {}\".format(epoch, batch_id, loss.numpy(), acc.numpy()))\n", " optim.step()\n", " optim.clear_grad()\n", "model = LeNet()\n", @@ -230,21 +218,21 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "batch_id: 0, loss is: [1.4915928], acc is: [1.]\n", - "batch_id: 20, loss is: [1.4818308], acc is: [1.]\n", - "batch_id: 40, loss is: [1.5006062], acc is: [0.984375]\n", - "batch_id: 60, loss is: [1.521233], acc is: [1.]\n", - "batch_id: 80, loss is: [1.4772738], acc is: [1.]\n", - "batch_id: 100, loss is: [1.4755945], acc is: [1.]\n", - "batch_id: 120, loss is: [1.4746133], acc is: [1.]\n", - "batch_id: 140, loss is: [1.4786345], acc is: [1.]\n" + "batch_id: 0, loss is: [1.4616354], acc is: [1.]\n", + "batch_id: 20, loss is: [1.4927294], acc is: [0.984375]\n", + "batch_id: 40, loss is: [1.4990321], acc is: [1.]\n", + "batch_id: 60, loss is: [1.4892884], acc is: [1.]\n", + "batch_id: 80, loss is: [1.4767071], acc is: [1.]\n", + "batch_id: 100, loss is: [1.4611524], acc is: [1.]\n", + "batch_id: 120, loss is: [1.4613531], acc is: [1.]\n", + "batch_id: 140, loss is: [1.4928315], acc is: [1.]\n" ] } ], @@ -262,11 +250,9 @@ " # 获取预测结果\n", " loss = paddle.nn.functional.cross_entropy(predicts, y_data)\n", " acc = paddle.metric.accuracy(predicts, y_data, k=2)\n", - " avg_loss = paddle.mean(loss)\n", - " avg_acc = paddle.mean(acc)\n", - " avg_loss.backward()\n", + " loss.backward()\n", " if batch_id % 20 == 0:\n", - " print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id, avg_loss.numpy(), avg_acc.numpy()))\n", + " print(\"batch_id: {}, loss is: {}, acc is: {}\".format(batch_id, loss.numpy(), acc.numpy()))\n", "test(model)" ] }, @@ -288,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -316,7 +302,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -324,17 +310,17 @@ "output_type": "stream", "text": [ "Epoch 1/2\n", - "step 200/938 - loss: 1.5219 - acc_top1: 0.9829 - acc_top2: 0.9965 - 14ms/step\n", - "step 400/938 - loss: 1.4765 - acc_top1: 0.9825 - acc_top2: 0.9958 - 13ms/step\n", - "step 600/938 - loss: 1.4624 - acc_top1: 0.9823 - acc_top2: 0.9953 - 13ms/step\n", - "step 800/938 - loss: 1.4768 - acc_top1: 0.9829 - acc_top2: 0.9955 - 13ms/step\n", - "step 938/938 - loss: 1.4612 - acc_top1: 0.9836 - acc_top2: 0.9956 - 13ms/step\n", + "step 200/938 - loss: 1.4868 - acc_top1: 0.9805 - acc_top2: 0.9951 - 14ms/step\n", + "step 400/938 - loss: 1.4643 - acc_top1: 0.9802 - acc_top2: 0.9944 - 14ms/step\n", + "step 600/938 - loss: 1.4638 - acc_top1: 0.9799 - acc_top2: 0.9942 - 13ms/step\n", + "step 800/938 - loss: 1.4767 - acc_top1: 0.9801 - acc_top2: 0.9944 - 13ms/step\n", + "step 938/938 - loss: 1.4614 - acc_top1: 0.9804 - acc_top2: 0.9945 - 13ms/step\n", "Epoch 2/2\n", - "step 200/938 - loss: 1.4705 - acc_top1: 0.9834 - acc_top2: 0.9959 - 13ms/step\n", - "step 400/938 - loss: 1.4620 - acc_top1: 0.9833 - acc_top2: 0.9960 - 13ms/step\n", - "step 600/938 - loss: 1.4613 - acc_top1: 0.9830 - acc_top2: 0.9960 - 13ms/step\n", - "step 800/938 - loss: 1.4763 - acc_top1: 0.9831 - acc_top2: 0.9960 - 13ms/step\n", - "step 938/938 - loss: 1.4924 - acc_top1: 0.9834 - acc_top2: 0.9959 - 13ms/step\n" + "step 200/938 - loss: 1.4618 - acc_top1: 0.9812 - acc_top2: 0.9956 - 13ms/step\n", + "step 400/938 - loss: 1.4778 - acc_top1: 0.9804 - acc_top2: 0.9952 - 13ms/step\n", + "step 600/938 - loss: 1.4698 - acc_top1: 0.9810 - acc_top2: 0.9954 - 13ms/step\n", + "step 800/938 - loss: 1.4621 - acc_top1: 0.9815 - acc_top2: 0.9957 - 13ms/step\n", + "step 938/938 - loss: 1.4847 - acc_top1: 0.9814 - acc_top2: 0.9958 - 13ms/step\n" ] } ], @@ -355,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -363,24 +349,24 @@ "output_type": "stream", "text": [ "Eval begin...\n", - "step 20/157 - loss: 1.5246 - acc_top1: 0.9773 - acc_top2: 0.9969 - 6ms/step\n", - "step 40/157 - loss: 1.4622 - acc_top1: 0.9758 - acc_top2: 0.9961 - 6ms/step\n", - "step 60/157 - loss: 1.5241 - acc_top1: 0.9763 - acc_top2: 0.9951 - 6ms/step\n", - "step 80/157 - loss: 1.4612 - acc_top1: 0.9787 - acc_top2: 0.9959 - 6ms/step\n", - "step 100/157 - loss: 1.4612 - acc_top1: 0.9823 - acc_top2: 0.9967 - 5ms/step\n", - "step 120/157 - loss: 1.4612 - acc_top1: 0.9835 - acc_top2: 0.9966 - 5ms/step\n", - "step 140/157 - loss: 1.4612 - acc_top1: 0.9844 - acc_top2: 0.9969 - 5ms/step\n", - "step 157/157 - loss: 1.4612 - acc_top1: 0.9838 - acc_top2: 0.9966 - 5ms/step\n", + "step 20/157 - loss: 1.5160 - acc_top1: 0.9805 - acc_top2: 0.9930 - 7ms/step\n", + "step 40/157 - loss: 1.4612 - acc_top1: 0.9793 - acc_top2: 0.9949 - 6ms/step\n", + "step 60/157 - loss: 1.5095 - acc_top1: 0.9792 - acc_top2: 0.9943 - 6ms/step\n", + "step 80/157 - loss: 1.4612 - acc_top1: 0.9785 - acc_top2: 0.9941 - 6ms/step\n", + "step 100/157 - loss: 1.4612 - acc_top1: 0.9816 - acc_top2: 0.9950 - 6ms/step\n", + "step 120/157 - loss: 1.4763 - acc_top1: 0.9832 - acc_top2: 0.9954 - 6ms/step\n", + "step 140/157 - loss: 1.4612 - acc_top1: 0.9849 - acc_top2: 0.9959 - 6ms/step\n", + "step 157/157 - loss: 1.4612 - acc_top1: 0.9844 - acc_top2: 0.9959 - 6ms/step\n", "Eval samples: 10000\n" ] }, { "data": { "text/plain": [ - "{'loss': [1.4611504], 'acc_top1': 0.9838, 'acc_top2': 0.9966}" + "{'loss': [1.4611504], 'acc_top1': 0.9844, 'acc_top2': 0.9959}" ] }, - "execution_count": 17, + "execution_count": 19, "metadata": {}, "output_type": "execute_result" }