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test tensors

This commit is contained in:
John 2023-06-13 22:12:02 +02:00
parent 2e2f0e1b17
commit e8a08a8a93
1 changed files with 65 additions and 27 deletions
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92
Courses/GANs_Specialization/1_Build_Basic_GANs/Week_1:Intro_to_GANs/C1W1_Your_First_GAN.ipynb
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@ -42,7 +42,7 @@
},
{
"cell_type": "code",
"execution_count": 52,
"execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
@ -61,6 +61,8 @@
"from torch.utils.data import DataLoader\n",
"import matplotlib.pyplot as plt\n",
"\n",
"from collections import OrderedDict\n",
"\n",
"torch.manual_seed(0) # Set for testing purposes, please do not change!\n",
"\n",
"def show_tensor_images(image_tensor, num_images=25, size=(1, 28, 28)):\n",
@ -116,7 +118,7 @@
},
{
"cell_type": "code",
"execution_count": 23,
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
@ -150,6 +152,57 @@
" )"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"tensor([[-1.0690],\n",
" [-0.2673],\n",
" [ 1.3363]], grad_fn=<NativeBatchNormBackward0>)\n",
"tensor([[-1.0000, -1.0000],\n",
" [ 1.0000, 1.0000]], grad_fn=<NativeBatchNormBackward0>)\n",
"tensor([[-1.0000, -1.0000, -1.0000],\n",
" [ 1.0000, 1.0000, 1.0000]], grad_fn=<NativeBatchNormBackward0>)\n"
]
}
],
"source": [
"x1 = nn.BatchNorm1d(1)\n",
"x2 = nn.BatchNorm1d(2)\n",
"x3 = nn.BatchNorm1d(3)\n",
"a1 = torch.tensor([[5.],[6.],[8.]]) # torch.Size([3, 1])\n",
"a2 = torch.tensor([[2.,3.],[5.,4.]]) # torch.Size([2, 2])\n",
"a3 = torch.tensor([[2.,3.,6.],[5.,4.,9.]]) # torch.Size([2, 3])\n",
"print(x1(a1))\n",
"print(x2(a2))\n",
"print(x3(a3))"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"torch.Size([3, 1])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a1.size()"
]
},
{
"cell_type": "code",
"execution_count": 4,
@ -224,34 +277,19 @@
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Sequential(\n",
" (0): Sequential(\n",
" (0): Linear(in_features=10, out_features=128, bias=True)\n",
" (1): BatchNorm1d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU(inplace=True)\n",
" )\n",
" (1): Sequential(\n",
" (0): Linear(in_features=128, out_features=256, bias=True)\n",
" (1): BatchNorm1d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU(inplace=True)\n",
" )\n",
" (2): Sequential(\n",
" (0): Linear(in_features=256, out_features=512, bias=True)\n",
" (1): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (2): ReLU(inplace=True)\n",
" )\n",
")"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
"ename": "NameError",
"evalue": "name 'nn' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[1], line 4\u001b[0m\n\u001b[1;32m 2\u001b[0m im_dim \u001b[39m=\u001b[39m \u001b[39m784\u001b[39m\n\u001b[1;32m 3\u001b[0m hidden_dim \u001b[39m=\u001b[39m \u001b[39m128\u001b[39m\n\u001b[0;32m----> 4\u001b[0m nn\u001b[39m.\u001b[39mSequential(\n\u001b[1;32m 5\u001b[0m get_generator_block(z_dim, hidden_dim),\n\u001b[1;32m 6\u001b[0m get_generator_block(hidden_dim, hidden_dim \u001b[39m*\u001b[39m \u001b[39m2\u001b[39m),\n\u001b[1;32m 7\u001b[0m get_generator_block(hidden_dim \u001b[39m*\u001b[39m \u001b[39m2\u001b[39m, hidden_dim \u001b[39m*\u001b[39m \u001b[39m4\u001b[39m),\n\u001b[1;32m 8\u001b[0m )\n",
"\u001b[0;31mNameError\u001b[0m: name 'nn' is not defined"
]
}
],
"source": [