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Ki Hyun KimKi Hyun Kim
Ki Hyun Kim
authored and
Ki Hyun Kim
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fix minor things
1 parent 0759d4b commit 48ff901

6 files changed

+47
-25
lines changed

‎lab-01_tensor_manipulation.ipynb

+16-5
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@@ -209,7 +209,9 @@
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"metadata": {
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"scrolled": true
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},
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"outputs": [
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{
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"name": "stdout",
@@ -257,7 +259,11 @@
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}
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],
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"source": [
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"t = torch.FloatTensor([[1., 2., 3.], [4., 5., 6.], [7., 8., 9.], [10., 11., 12.]])\n",
262+
"t = torch.FloatTensor([[1., 2., 3.],\n",
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" [4., 5., 6.],\n",
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" [7., 8., 9.],\n",
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" [10., 11., 12.]\n",
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" ])\n",
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"print(t)"
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]
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},
@@ -302,8 +308,13 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"t = torch.FloatTensor([[[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]],\n",
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" [[13, 14, 15, 16], [17, 18, 19, 20], [21, 22, 23, 24]]]])"
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"t = torch.FloatTensor([[[[1, 2, 3, 4],\n",
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" [5, 6, 7, 8],\n",
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" [9, 10, 11, 12]],\n",
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" [[13, 14, 15, 16],\n",
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" [17, 18, 19, 20],\n",
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" [21, 22, 23, 24]]\n",
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" ]])"
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]
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},
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{
@@ -1340,7 +1351,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,

‎lab-03_minimizing_cost.ipynb

+1-1
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@@ -428,7 +428,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,

‎lab-05_logistic_classification.ipynb

+11-8
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@@ -114,7 +114,7 @@
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{
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"data": {
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"text/plain": [
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"<torch._C.Generator at 0x7f247d342fb0>"
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"<torch._C.Generator at 0x106951ed0>"
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]
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},
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"execution_count": 2,
@@ -265,7 +265,7 @@
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" [0.5000],\n",
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" [0.5000],\n",
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" [0.5000],\n",
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" [0.5000]], grad_fn=<MulBackward0>)\n",
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" [0.5000]], grad_fn=<MulBackward>)\n",
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"torch.Size([6, 1])\n"
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]
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}
@@ -413,7 +413,8 @@
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}
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],
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"source": [
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"-(y_train[0] * torch.log(hypothesis[0]) + (1 - y_train[0]) * torch.log(1 - hypothesis[0]))"
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"-(y_train[0] * torch.log(hypothesis[0]) + \n",
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" (1 - y_train[0]) * torch.log(1 - hypothesis[0]))"
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]
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},
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{
@@ -442,7 +443,8 @@
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}
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],
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"source": [
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"losses = -(y_train * torch.log(hypothesis) + (1 - y_train) * torch.log(1 - hypothesis))\n",
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"losses = -(y_train * torch.log(hypothesis) + \n",
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" (1 - y_train) * torch.log(1 - hypothesis))\n",
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"print(losses)"
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]
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},
@@ -559,7 +561,8 @@
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"\n",
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" # Cost 계산\n",
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" hypothesis = torch.sigmoid(x_train.matmul(W) + b) # or .mm or @\n",
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" cost = -(y_train * torch.log(hypothesis) + (1 - y_train) * torch.log(1 - hypothesis)).mean()\n",
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" cost = -(y_train * torch.log(hypothesis) + \n",
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" (1 - y_train) * torch.log(1 - hypothesis)).mean()\n",
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"\n",
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" # cost로 H(x) 개선\n",
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" optimizer.zero_grad()\n",
@@ -595,7 +598,7 @@
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"Epoch 300/1000 Cost: 0.057900\n",
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"Epoch 400/1000 Cost: 0.045300\n",
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"Epoch 500/1000 Cost: 0.037261\n",
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"Epoch 600/1000 Cost: 0.031673\n",
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"Epoch 600/1000 Cost: 0.031672\n",
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"Epoch 700/1000 Cost: 0.027556\n",
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"Epoch 800/1000 Cost: 0.024394\n",
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"Epoch 900/1000 Cost: 0.021888\n",
@@ -708,7 +711,7 @@
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"Epoch 40/100 Cost: 0.507066\n",
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"Epoch 50/100 Cost: 0.498539\n",
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"Epoch 60/100 Cost: 0.492549\n",
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"Epoch 70/100 Cost: 0.488208\n",
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"Epoch 70/100 Cost: 0.488209\n",
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"Epoch 80/100 Cost: 0.484985\n",
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"Epoch 90/100 Cost: 0.482543\n",
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"Epoch 100/100 Cost: 0.480661\n"
@@ -1043,7 +1046,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,

‎lab-06_1_softmax_classification.ipynb

+7-7
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@@ -50,7 +50,7 @@
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{
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"data": {
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"text/plain": [
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"<torch._C.Generator at 0x7f8740076fb0>"
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"<torch._C.Generator at 0x105eccfb0>"
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]
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},
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"execution_count": 2,
@@ -210,7 +210,7 @@
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}
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],
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"source": [
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"y = torch.randint(5, (3,))\n",
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"y = torch.randint(5, (3,)).long()\n",
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"print(y)"
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]
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},
@@ -560,7 +560,7 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"이제 새로운 데이터로 코드를 돌려 피팅시켜보겠습니다."
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"Let's try another new dataset."
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]
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},
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{
@@ -573,16 +573,16 @@
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"output_type": "stream",
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"text": [
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"Epoch 0/1000 Cost: 1.849513\n",
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"Epoch 100/1000 Cost: 0.689895\n",
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"Epoch 100/1000 Cost: 0.689894\n",
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"Epoch 200/1000 Cost: 0.609259\n",
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"Epoch 300/1000 Cost: 0.551218\n",
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"Epoch 400/1000 Cost: 0.500141\n",
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"Epoch 500/1000 Cost: 0.451947\n",
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"Epoch 600/1000 Cost: 0.405051\n",
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"Epoch 700/1000 Cost: 0.358733\n",
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"Epoch 800/1000 Cost: 0.312912\n",
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"Epoch 900/1000 Cost: 0.269522\n",
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"Epoch 1000/1000 Cost: 0.241921\n"
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"Epoch 900/1000 Cost: 0.269521\n",
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"Epoch 1000/1000 Cost: 0.241922\n"
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]
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}
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],
@@ -628,7 +628,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,

‎lab-06_2_fancy_softmax_classification.ipynb

+1-1
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@@ -461,7 +461,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,

‎lab-07_1_tips.ipynb

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@@ -60,7 +60,15 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"x_train = torch.FloatTensor([[1, 2, 1], [1, 3, 2], [1, 3, 4], [1, 5, 5], [1, 7, 5], [1, 2, 5], [1, 6, 6], [1, 7, 7]])\n",
63+
"x_train = torch.FloatTensor([[1, 2, 1],\n",
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" [1, 3, 2],\n",
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" [1, 3, 4],\n",
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" [1, 5, 5],\n",
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" [1, 7, 5],\n",
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" [1, 2, 5],\n",
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" [1, 6, 6],\n",
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" [1, 7, 7]\n",
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" ])\n",
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"y_train = torch.LongTensor([2, 2, 2, 1, 1, 1, 0, 0])"
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]
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},
@@ -628,7 +636,7 @@
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"\n",
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"1. 더 많은 학습 데이터\n",
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"2. 더 적은 양의 feature\n",
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"3. **Regularization** (일반화)"
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"3. **Regularization**"
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]
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},
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.7.0"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,

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