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lijin-THUlijin-THU
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Theano 模块完成
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‎09. theano/09.10 softmax on mnist.ipynb

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"Using gpu device 1: Tesla K10.G2.8GB (CNMeM is disabled)\n"
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"Using gpu device 1: Tesla C2075 (CNMeM is disabled)\n"
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‎09. theano/09.14 convolutional net on mnist.ipynb

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"source": [
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"`conv2d` 函数接受两个输入：\n",
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"\n",
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"- 对应参数矩阵的 `4D` 张量，其形状如下：\n",
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"- 对应输入的 `4D` 张量，其形状如下：\n",
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" \n",
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" `[mini-batch size, number of feature maps at layer m-1, image height, image width]`\n",
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"outputs": [],
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"source": [
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"def model(X, w, w2, w3, w4, p_drop_conv, p_drop_hidden):\n",
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" \n",
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" # X: 128 * 1 * 28 * 28\n",
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" # w: 32 * 1 * 3 * 3\n",
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" # full mode\n",
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" # l1a: 128 * 32 * (28 + 3 - 1) * (28 + 3 - 1)\n",
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" l1a = rectify(conv2d(X, w, border_mode='full'))\n",
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" # l1a: 128 * 32 * 30 * 30\n",
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" # ignore_border False\n",
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" # l1: 128 * 32 * (30 / 2) * (30 / 2)\n",
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" l1 = max_pool_2d(l1a, (2, 2), ignore_border=False)\n",
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" l1 = dropout(l1, p_drop_conv)\n",
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" l2a = rectify(conv2d(l1, w2))\n",
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" # l1: 128 * 32 * 15 * 15\n",
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" # w2: 64 * 32 * 3 * 3\n",
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" # valid mode\n",
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" # l2a: 128 * 64 * (15 - 3 + 1) * (15 - 3 + 1)\n",
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" l2a = rectify(conv2d(l1, w2)) \n",
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" # l2a: 128 * 64 * 13 * 13\n",
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" # l2: 128 * 64 * (13 / 2 + 1) * (13 / 2 + 1)\n",
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" l2 = max_pool_2d(l2a, (2, 2), ignore_border=False)\n",
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" l2 = dropout(l2, p_drop_conv)\n",
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" # l2: 128 * 64 * 7 * 7\n",
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" # w3: 128 * 64 * 3 * 3\n",
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" # l3a: 128 * 128 * (7 - 3 + 1) * (7 - 3 + 1)\n",
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" l3a = rectify(conv2d(l2, w3))\n",
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" l3b = max_pool_2d(l3a, (2, 2), ignore_border=False)\n",
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" # l3a: 128 * 128 * 5 * 5\n",
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" # l3b: 128 * 128 * (5 / 2 + 1) * (5 / 2 + 1)\n",
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" l3b = max_pool_2d(l3a, (2, 2), ignore_border=False) \n",
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" # l3b: 128 * 128 * 3 * 3\n",
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" # l3: 128 * (128 * 3 * 3)\n",
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" l3 = T.flatten(l3b, outdim=2)\n",
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" # l3: 128 * (128 * 3 * 3)\n",
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" # w4: (128 * 3 * 3) * 625\n",
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" # l4: 128 * 625\n",
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" l4 = rectify(T.dot(l3, w4))\n",
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" # l5: 128 * 625\n",
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" # w5: 625 * 10\n",
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" # pyx: 128 * 10\n",
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" return l1, l2, l3, l4, pyx"
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]

‎09. theano/09.15 tensor module basics.ipynb renamed to ‎09. theano/09.15 tensor basics.ipynb

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‎09. theano/09.16 tensor module indexing.ipynb renamed to ‎09. theano/09.16 tensor indexing.ipynb

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‎09. theano/09.17 operator and elementwise operations.ipynb

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