dr_domi/udlbook
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Fix inor typos in chap 8 notebooks

Browse Source
This commit is contained in:
Youcef Rahal
2024-04-05 08:42:10 -04:00
parent 4652f90f09
commit a5413d6a15
4 changed files with 11 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

3
Notebooks/Chap08/8_1_MNIST_1D_Performance.ipynb
View File

@@ -83,6 +83,8 @@
{ {
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"!mkdir ./sample_data\n",
"\n",
"args = mnist1d.data.get_dataset_args()\n", "args = mnist1d.data.get_dataset_args()\n",
"data = mnist1d.data.get_dataset(args, path='./sample_data/mnist1d_data.pkl', download=False, regenerate=False)\n", "data = mnist1d.data.get_dataset(args, path='./sample_data/mnist1d_data.pkl', download=False, regenerate=False)\n",
"\n", "\n",
@@ -136,7 +138,6 @@
"optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n", "optimizer = torch.optim.SGD(model.parameters(), lr = 0.05, momentum=0.9)\n",
"# object that decreases learning rate by half every 10 epochs\n", "# object that decreases learning rate by half every 10 epochs\n",
"scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n", "scheduler = StepLR(optimizer, step_size=10, gamma=0.5)\n",
"# create 100 dummy data points and store in data loader class\n",
"x_train = torch.tensor(data['x'].astype('float32'))\n", "x_train = torch.tensor(data['x'].astype('float32'))\n",
"y_train = torch.tensor(data['y'].transpose().astype('long'))\n", "y_train = torch.tensor(data['y'].transpose().astype('long'))\n",
"x_test= torch.tensor(data['x_test'].astype('float32'))\n", "x_test= torch.tensor(data['x_test'].astype('float32'))\n",

6
Notebooks/Chap08/8_2_Bias_Variance_Trade_Off.ipynb
View File

@@ -92,7 +92,7 @@
{ {
"cell_type": "code", "cell_type": "code",
"source": [ "source": [
"# Draw the fitted function, together win uncertainty used to generate points\n", "# Draw the fitted function, together with uncertainty used to generate points\n",
"def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n", "def plot_function(x_func, y_func, x_data=None,y_data=None, x_model = None, y_model =None, sigma_func = None, sigma_model=None):\n",
"\n", "\n",
" fig,ax = plt.subplots()\n", " fig,ax = plt.subplots()\n",
@@ -203,7 +203,7 @@
"# Closed form solution\n", "# Closed form solution\n",
"beta, omega = fit_model_closed_form(x_data,y_data,n_hidden=3)\n", "beta, omega = fit_model_closed_form(x_data,y_data,n_hidden=3)\n",
"\n", "\n",
"# Get prediction for model across graph grange\n", "# Get prediction for model across graph range\n",
"x_model = np.linspace(0,1,100);\n", "x_model = np.linspace(0,1,100);\n",
"y_model = network(x_model, beta, omega)\n", "y_model = network(x_model, beta, omega)\n",
"\n", "\n",
@@ -302,7 +302,7 @@
"sigma_func = 0.3\n", "sigma_func = 0.3\n",
"n_hidden = 5\n", "n_hidden = 5\n",
"\n", "\n",
"# Set random seed so that get same result every time\n", "# Set random seed so that we get the same result every time\n",
"np.random.seed(1)\n", "np.random.seed(1)\n",
"\n", "\n",
"for c_hidden in range(len(hidden_variables)):\n", "for c_hidden in range(len(hidden_variables)):\n",

3
Notebooks/Chap08/8_3_Double_Descent.ipynb
View File

@@ -124,7 +124,7 @@
" D_k = n_hidden # Hidden dimensions\n", " D_k = n_hidden # Hidden dimensions\n",
" D_o = 10 # Output dimensions\n", " D_o = 10 # Output dimensions\n",
"\n", "\n",
" # Define a model with two hidden layers of size 100\n", " # Define a model with two hidden layers\n",
" # And ReLU activations between them\n", " # And ReLU activations between them\n",
" model = nn.Sequential(\n", " model = nn.Sequential(\n",
" nn.Linear(D_i, D_k),\n", " nn.Linear(D_i, D_k),\n",
@@ -157,7 +157,6 @@
" optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum=0.9)\n", " optimizer = torch.optim.SGD(model.parameters(), lr = 0.01, momentum=0.9)\n",
"\n", "\n",
"\n", "\n",
" # create 100 dummy data points and store in data loader class\n",
" x_train = torch.tensor(data['x'].astype('float32'))\n", " x_train = torch.tensor(data['x'].astype('float32'))\n",
" y_train = torch.tensor(data['y'].transpose().astype('long'))\n", " y_train = torch.tensor(data['y'].transpose().astype('long'))\n",
" x_test= torch.tensor(data['x_test'].astype('float32'))\n", " x_test= torch.tensor(data['x_test'].astype('float32'))\n",

2
Notebooks/Chap08/8_4_High_Dimensional_Spaces.ipynb
View File

@@ -224,7 +224,7 @@
{ {
"cell_type": "markdown", "cell_type": "markdown",
"source": [ "source": [
"You should see see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent. <br><br>\n", "You should see that by the time we get to 300 dimensions most of the volume is in the outer 1 percent. <br><br>\n",
"\n", "\n",
"The conclusion of all of this is that in high dimensions you should be sceptical of your intuitions about how things work. I have tried to visualize many things in one or two dimensions in the book, but you should also be sceptical about these visualizations!" "The conclusion of all of this is that in high dimensions you should be sceptical of your intuitions about how things work. I have tried to visualize many things in one or two dimensions in the book, but you should also be sceptical about these visualizations!"
], ],