Use this data set when submitting code offline for competitions otherwise just use !pip install tabpfn for online use. Usage for offline code submissions within Kaggle notebooks is as follows:

1**.First add the dataset by selecting "add data" and searching for this dataset and adding it to your input. **

2.**Next add the following code to a code block in your notebook ** python !pip install tabpfn --no-index --find-links=file:///kaggle/input/tabpfn !mkdir -p /opt/conda/lib/python3.10/site-packages/tabpfn/models_diff !cp /kaggle/input/tabpfn/prior_diff_real_checkpoint_n_0_epoch_100.cpkt /opt/conda/lib/python3.10/site-packages/tabpfn/models_diff/ 3.** Import** :
from tabpfn import TabPFNClassifier

4.**Now you are all set you can create a classifier and run it offline for submission in offline kaggle code competitions:** python classifier = TabPFNClassifier(device='cpu',N_ensemble_configurations=64) classifier.fit(X_train, Y_train) y_eval, p_eval = classifier.predict(X_cv, return_winning_probability=True)

If you want to use TabPFN with GPU use the following code when you make the model: classifier = TabPFNClassifier(device='cuda',N_ensemble_configurations=32)

You can find documentation for this package on GitHub: https://github.com/automl/TabPFN.git Original paper on TabPFN can be found at: https://arxiv.org/abs/2207.01848 License Copyright 2022 Noah Hollmann, Samuel Müller, Katharina Eggensperger, Frank Hutter

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

Starter Notebook

ABOUT (from project's README)

Keras style model.summary() in PyTorch

Keras has a neat API to view the visualization of the model which is very helpful while debugging your network. Here is a barebone code to try and mimic the same in PyTorch. The aim is to provide information complementary to, what is not provided by print(your_model) in PyTorch.

Usage

• pip install torchsummary or
• git clone https://github.com/sksq96/pytorch-summary

from torchsummary import summary
summary(your_model, input_size=(channels, H, W))

• Note that the input_size is required to make a forward pass through the network.

Examples

CNN for MNIST

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary

class Net(nn.Module):
  def _init_(self):
    super(Net, self)._init_()
    self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
    self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
    self.conv2_drop = nn.Dropout2d()
    self.fc1 = nn.Linear(320, 50)
    self.fc2 = nn.Linear(50, 10)

  def forward(self, x):
    x = F.relu(F.max_pool2d(self.conv1(x), 2))
    x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
    x = x.view(-1, 320)
    x = F.relu(self.fc1(x))
    x = F.dropout(x, training=self.training)
    x = self.fc2(x)
    return F.log_softmax(x, dim=1)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # PyTorch v0.4.0
model = Net().to(device)

summary(model, (1, 28, 28))

----------------------------------------------------------------
    Layer (type)        Output Shape     Param #
================================================================
      Conv2d-1      [-1, 10, 24, 24]       260
      Conv2d-2       [-1, 20, 8, 8]      5,020
     Dropout2d-3       [-1, 20, 8, 8]        0
      Linear-4          [-1, 50]     16,050
      Linear-5          [-1, 10]       510
================================================================
Total params: 21,840
Trainable params: 21,840
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.06
Params size (MB): 0.08
Estimated Total Size (MB): 0.15
----------------------------------------------------------------

VGG16

import torch
from torchvision import models
from torchsummary import summary

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
vgg = models.vgg16().to(device)

summary(vgg, (3, 224, 224))

----------------------------------------------------------------
    Layer (type)        Output Shape     Param #
================================================================
      Conv2d-1     [-1, 64, 224, 224]      1,792
       ReLU-2     [-1, 64, 224, 224]        0
      Conv2d-3     [-1, 64, 224, 224]     36,928
       ReLU-4     [-1, 64, 224, 224]        0
     MaxPool2d-5     [-1, 64, 112, 112]        0
      Conv2d-6    [-1, 128, 112, 112]     73,856
       ReLU-7    [-1, 128, 112, 112]        0
      Conv2d-8    [-1, 128, 112, 112]     147,584
       ReLU-9    [-1, 128, 112, 112]        0
    MaxPool2d-10     [-1, 128, 56, 56]        0
      Conv2d-11     [-1, 256, 56, 56]     295,168
       ReLU-12     [-1, 256, 56, 56]        0
      Conv2d-13     [-1, 256, 56, 56]     590,080
       ReLU-14     [-1, 256, 56, 56]        0
      Conv2d-15     [-1, 256, 56, 56]     590,080
       ReLU-16     [-1, 256, 56, 56]        0
    MaxPool2d-17     [-1, 256, 28, 28]        0
      Conv2d-18     [-1, 512, 28, 28]    1,180,160
       ReLU-19     [-1, 512, 28, 28]        0
      Conv2d-20     [-1, 512, 28, 28]    2,359,808
       ReLU-21     [-1, 512, 28, 28]        0
      Conv2d-22     [-1, 512, 28, 28]    2,359,808
       ReLU-23     [-1, 512, 28, 28]        0
    MaxPool2d-24     [-1, 512, 14, 14]        0
      Conv2d-25     [-1, 512, 14, 14]    2,359,808
       ReLU-26     [-1, 512, 14, 14]        0
      Conv2d-27     [-1, 512, 14, 14]    2,359,808
       ReLU...