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pytorch model

Hurt Hippopotamus answered on November 19, 2020 Popularity 10/10 Helpfulness 4/10

answer pytorch model

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class Model(nn.Module):
    
    
    def __init__(self, in_features=4, h1=8, h2=9, out_features=3):
        # how many layers?
        super().__init__()
        self.fc1 = nn.Linear(in_features, h1)
        self.fc2 = nn.Linear(h1, h2)
        self.out = nn.Linear(h2, out_features)
    
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.out(x)
        
        return x

Popularity 10/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: model python pytorch

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Contributed on Nov 19 2020

Hurt Hippopotamus

0 Answers Avg Quality 2/10

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# Column names based on the CSV preview provided earlier
columns = [
    'RowNumber', 'CustomerId', 'Surname', 'CreditScore', 'Geography', 'Gender', 'Age', 'Tenure',
    'Balance', 'NumOfProducts', 'HasCrCard', 'IsActiveMember', 'EstimatedSalary', 'Exited'
]

# Template for PyTorch based on the column names and the steps discussed

# Assuming you'll start with importing necessary libraries:
code_template = """
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
from sklearn.model_selection import train_test_split

# 1. Data Preparation
ds = pd.read_csv('<path_to_your_file>')  # Replace with the path to your CSV file
X = ds.drop("Exited", axis=1).values  # Assuming "Exited" is the target column
y = ds["Exited"].values

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32).view(-1, 1)
X_test_tensor = torch.tensor(X_test, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test, dtype=torch.float32).view(-1, 1)

batch_size = 64
train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# 2. Model Setup
class ChurnNN(nn.Module):
    def __init__(self, input_dim):
        super(ChurnNN, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.dropout = nn.Dropout(0.5)
        self.fc2 = nn.Linear(128, 1)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.dropout(x)
        x = torch.sigmoid(self.fc2(x))
        return x

model = ChurnNN(input_dim=X_train.shape[1])
criterion = nn.BCELoss()  # Binary Cross-Entropy Loss
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 3. Training Loop
num_epochs = 10
for epoch in range(num_epochs):
    for batch_idx, (data, target) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(data)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

# 4. Evaluation (placeholder)
# Here you can add code to evaluate the model's performance on the test set.

print("Training complete!")
"""

code_template

Popularity 9/10 Helpfulness 1/10 Language python

Source: chat.openai.com

Tags: model mode

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Contributed on Oct 26 2023

Tired Toucan

0 Answers Avg Quality 2/10

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