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update several notebooks (#894)
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@jzhang533
jzhang533 authored Sep 10, 2020
1 parent 5ab525d commit 3166204
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94 changes: 46 additions & 48 deletions paddle2.0_docs/convnet_image_classification/convnet_image_classification.ipynb
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100 changes: 49 additions & 51 deletions paddle2.0_docs/dynamic_graph/dynamic_graph.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -29,8 +29,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0.0.0\n",
"89af2088b6e74bdfeef2d4d78e08461ed2aafee5\n"
"2.0.0-beta0\n"
]
}
],
Expand All @@ -40,8 +39,7 @@
"import numpy as np\n",
"\n",
"paddle.disable_static()\n",
"print(paddle.__version__)\n",
"print(paddle.__git_commit__)\n"
"print(paddle.__version__)"
]
},
{
Expand All @@ -62,16 +60,16 @@
"name": "stdout",
"output_type": "stream",
"text": [
"[[-0.49341336 -0.8112665 ]\n",
" [ 0.8929015 0.24661176]\n",
" [-0.64440054 -0.7945008 ]\n",
" [-0.07345356 1.3641853 ]]\n",
"[[ 1.5645729 -0.74514765]\n",
" [-0.01248 0.68240154]\n",
" [ 0.11316949 -1.6579045 ]\n",
" [-0.1425675 -1.0153968 ]]\n",
"[1. 2.]\n",
"[[0.5065867 1.1887336 ]\n",
" [1.8929014 2.2466118 ]\n",
" [0.35559946 1.2054992 ]\n",
" [0.92654645 3.3641853 ]]\n",
"[-2.1159463 1.386125 -2.2334023 2.654917 ]\n"
"[[2.5645728 1.2548523 ]\n",
" [0.98752 2.6824017 ]\n",
" [1.1131694 0.3420955 ]\n",
" [0.8574325 0.98460317]]\n",
"[ 0.07427764 1.352323 -3.2026396 -2.173361 ]\n"
]
}
],
Expand Down Expand Up @@ -100,20 +98,20 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 +> [5 6 7]\n",
"1 +> [5 7 9]\n",
"1 -> [-3 -3 -3]\n",
"2 +> [ 5 9 15]\n",
"3 -> [-3 3 21]\n",
"4 -> [-3 11 75]\n",
"5 +> [ 5 37 249]\n",
"6 +> [ 5 69 735]\n",
"4 +> [ 5 21 87]\n",
"5 -> [ -3 27 237]\n",
"6 -> [ -3 59 723]\n",
"7 -> [ -3 123 2181]\n",
"8 +> [ 5 261 6567]\n",
"9 +> [ 5 517 19689]\n"
Expand Down Expand Up @@ -146,7 +144,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -172,28 +170,28 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 [2.0915627]\n",
"200 [0.67530334]\n",
"400 [0.52042854]\n",
"600 [0.28010666]\n",
"800 [0.09739777]\n",
"1000 [0.09307177]\n",
"1200 [0.04252927]\n",
"1400 [0.03095707]\n",
"1600 [0.03022156]\n",
"1800 [0.01616007]\n",
"2000 [0.01069116]\n",
"2200 [0.0055158]\n",
"2400 [0.00195092]\n",
"2600 [0.00101116]\n",
"2800 [0.00192219]\n"
"0 [1.3384138]\n",
"200 [0.7855983]\n",
"400 [0.59084535]\n",
"600 [0.30849028]\n",
"800 [0.26992702]\n",
"1000 [0.03990713]\n",
"1200 [0.07111286]\n",
"1400 [0.01177792]\n",
"1600 [0.03160322]\n",
"1800 [0.02757282]\n",
"2000 [0.00916022]\n",
"2200 [0.00217024]\n",
"2400 [0.00186833]\n",
"2600 [0.00101926]\n",
"2800 [0.0009654]\n"
]
}
],
Expand All @@ -220,8 +218,8 @@
" print(t, loss.numpy())\n",
"\n",
" loss.backward()\n",
" optimizer.minimize(loss)\n",
" model.clear_gradients()"
" optimizer.step()\n",
" optimizer.clear_grad()"
]
},
{
Expand All @@ -230,29 +228,29 @@
"source": [
"# 构建更加灵活的网络：共享权重\n",
"\n",
"- 使用动态图还可以更加方便的创建共享权重的网络，下面的示例展示了一个共享了权重的简单的AutoEncoder的示例。\n",
"- 使用动态图还可以更加方便的创建共享权重的网络，下面的示例展示了一个共享了权重的简单的AutoEncoder。\n",
"- 你也可以参考图像搜索的示例看到共享参数权重的更实际的使用。"
]
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"step: 0, loss: [0.37666085]\n",
"step: 1, loss: [0.3063845]\n",
"step: 2, loss: [0.2647248]\n",
"step: 3, loss: [0.23831272]\n",
"step: 4, loss: [0.21714918]\n",
"step: 5, loss: [0.1955545]\n",
"step: 6, loss: [0.17261818]\n",
"step: 7, loss: [0.15009595]\n",
"step: 8, loss: [0.13051331]\n",
"step: 9, loss: [0.11537809]\n"
"step: 0, loss: [0.33474904]\n",
"step: 1, loss: [0.31669515]\n",
"step: 2, loss: [0.29729688]\n",
"step: 3, loss: [0.27288628]\n",
"step: 4, loss: [0.24694422]\n",
"step: 5, loss: [0.2203041]\n",
"step: 6, loss: [0.19171436]\n",
"step: 7, loss: [0.16213782]\n",
"step: 8, loss: [0.13443354]\n",
"step: 9, loss: [0.11170781]\n"
]
}
],
Expand All @@ -270,8 +268,8 @@
" loss = loss_fn(outputs, inputs)\n",
" loss.backward()\n",
" print(\"step: {}, loss: {}\".format(i, loss.numpy()))\n",
" optimizer.minimize(loss)\n",
" linear.clear_gradients()"
" optimizer.step()\n",
" optimizer.clear_grad()"
]
},
{
Expand Down
46 changes: 23 additions & 23 deletions paddle2.0_docs/hello_paddle/hello_paddle.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@
},
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 22,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -90,21 +90,21 @@
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"paddle version 0.0.0\n"
"paddle 2.0.0-beta0\n"
]
}
],
"source": [
"import paddle\n",
"paddle.disable_static()\n",
"print(\"paddle version \" + paddle.__version__)"
"print(\"paddle \" + paddle.__version__)"
]
},
{
Expand All @@ -121,7 +121,7 @@
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -150,7 +150,7 @@
},
{
"cell_type": "code",
"execution_count": 27,
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -168,14 +168,14 @@
},
{
"cell_type": "code",
"execution_count": 28,
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w before optimize: -1.7107375860214233\n",
"w before optimize: -1.696260690689087\n",
"b before optimize: 0.0\n"
]
}
Expand Down Expand Up @@ -205,7 +205,7 @@
},
{
"cell_type": "code",
"execution_count": 29,
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -224,19 +224,19 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"epoch 0 loss [2107.3943]\n",
"epoch 1000 loss [7.8432994]\n",
"epoch 2000 loss [1.7537074]\n",
"epoch 3000 loss [0.39211753]\n",
"epoch 4000 loss [0.08767726]\n",
"finished training， loss [0.01963376]\n"
"epoch 0 loss [2094.069]\n",
"epoch 1000 loss [7.8451133]\n",
"epoch 2000 loss [1.7541145]\n",
"epoch 3000 loss [0.39221546]\n",
"epoch 4000 loss [0.08769739]\n",
"finished training， loss [0.0196382]\n"
]
}
],
Expand All @@ -246,8 +246,8 @@
" y_predict = linear(x_data)\n",
" loss = mse_loss(y_predict, y_data)\n",
" loss.backward()\n",
" sgd_optimizer.minimize(loss)\n",
" linear.clear_gradients()\n",
" sgd_optimizer.step()\n",
" sgd_optimizer.clear_grad()\n",
" \n",
" if i%1000 == 0:\n",
" print(\"epoch {} loss {}\".format(i, loss.numpy()))\n",
Expand All @@ -266,15 +266,15 @@
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"w after optimize: 2.017843246459961\n",
"b after optimize: 9.771851539611816\n"
"w after optimize: 2.0178451538085938\n",
"b after optimize: 9.771825790405273\n"
]
}
],
Expand All @@ -297,7 +297,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": 10,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -339,5 +339,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 1
"nbformat_minor": 4
}
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