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import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
# Set up Streamlit layout
st.title("PyTorch vs Keras Comparison")
# Define PyTorch model
class PyTorchModel(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(PyTorchModel, self).__init__()
self.fc1 = nn.Linear(input_size, hidden_size)
self.fc2 = nn.Linear(hidden_size, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = self.fc2(x)
return x
# Define Keras model
def KerasModel(input_size, hidden_size, output_size):
model = Sequential()
model.add(Dense(hidden_size, activation='relu', input_shape=(input_size,)))
model.add(Dense(output_size))
return model
# Define example NLP tasks
nlp_tasks = {
'Task 1: Sentiment Analysis': {
'PyTorch': {
'model': PyTorchModel(100, 64, 2),
'optimizer': optim.Adam,
},
'Keras': {
'model': KerasModel(100, 64, 2),
'optimizer': Adam,
}
},
'Task 2: Text Classification': {
'PyTorch': {
'model': PyTorchModel(200, 128, 5),
'optimizer': optim.SGD,
},
'Keras': {
'model': KerasModel(200, 128, 5),
'optimizer': Adam,
}
}
}
# Select NLP task
task = st.sidebar.selectbox("Select NLP Task", list(nlp_tasks.keys()))
# Select framework
framework = st.sidebar.selectbox("Select Framework", ['PyTorch', 'Keras'])
# Get model and optimizer for selected task and framework
model = nlp_tasks[task][framework]['model']
optimizer = nlp_tasks[task][framework]['optimizer']
# Display model summary
st.subheader(f"{framework} Model Summary")
st.text(model)
# Display optimizer details
st.subheader(f"{framework} Optimizer Details")
st.text(optimizer)
# Perform example computations
if st.button("Perform Computation"):
# Perform forward pass
input_data = torch.randn(1, model.fc1.in_features)
output = model(input_data)
st.write(f"Output: {output}")
# Perform backward pass
loss = output.mean()
optimizer = optimizer(model.parameters(), lr=0.01)
optimizer.zero_grad()
loss.backward()
optimizer.step()
st.write("Backward pass completed.") |