create a neural network with two neurons in the hidden layer in python

To create a neural network with two neurons in the hidden layer using Python, you can use a deep learning framework such as TensorFlow or Keras. Here's an example using Keras:

main.py
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense

# Create the model
model = Sequential()

# Add the input layer
model.add(Dense(units=2, activation='relu', input_dim=2))

# Add the hidden layer with two neurons
model.add(Dense(units=2, activation='relu'))

# Add the output layer
model.add(Dense(units=1, activation='sigmoid'))

# Compile the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

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

# Evaluate the model
loss, accuracy = model.evaluate(X_test, y_test)
print('Accuracy:', accuracy)
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In this example, model.add(Dense(units=2, activation='relu')) adds a hidden layer with two neurons and the ReLU (Rectified Linear Unit) activation function. The input layer has 2 units to match the number of input features, and the output layer has 1 unit with a sigmoid activation function for binary classification.

Remember to replace X_train, y_train, X_test, and y_test with your actual training and testing data. Additionally, you may need to preprocess your data and make any other necessary adjustments based on your specific problem.

Please make sure to install the required libraries (tensorflow and keras) before running the code.