create neural net for interpolation with relu in python

To create a neural network for interpolation using ReLU activation function in Python, you can use a deep learning framework such as Keras or PyTorch. Here's an example using Keras:


main.py
import numpy as np
from keras.models import Sequential
from keras.layers import Dense

# Generate some sample data for interpolation
x_train = np.linspace(-1, 1, 1000)
y_train = np.sin(x_train * np.pi)

# Create the neural network model
model = Sequential()
model.add(Dense(64, input_shape=(1,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(1))

# Compile the model
model.compile(optimizer='adam', loss='mse')

# Train the model
model.fit(x_train, y_train, epochs=10, batch_size=32)

# Generate some test data for interpolation
x_test = np.linspace(-1, 1, 100)
y_test = model.predict(x_test)

# Print the predicted values
print(y_test)
667 chars
27 lines

In this example, we create a neural network model with two hidden layers, each consisting of 64 neurons with the ReLU activation function. The model is compiled with the Adam optimizer and mean squared error (MSE) loss function. We then train the model using our sample data and finally generate some test data to obtain the interpolated values.

Note that this is just a basic example and you can modify the architecture and parameters of the neural network based on your specific requirements.

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