How to use TensorFlow and Keras in Sigmoid

TensorFlow and Keras are popular open-source machine learning libraries used for building and training neural networks. Sigmoid is a commonly used activation function in neural networks, which maps input values to a range between 0 and 1. In this article, we will discuss how to use TensorFlow and Keras in Sigmoid activation function.

Using TensorFlow in Sigmoid

To use Sigmoid activation function in a neural network built with TensorFlow, you can simply add a Sigmoid layer to your model using the following code:


import tensorflow as tf
model = tf.keras.models.Sequential([
tf.keras.layers.Dense(128, activation='sigmoid'),
tf.keras.layers.Dense(10, activation='softmax')
])


In the above example, we have added a Sigmoid activation function to the first layer of the neural network. The ‘Dense’ layer is a fully connected layer, and we have specified the number of neurons (128) and the activation function (‘sigmoid’).

Using Keras in Sigmoid

If you’re using Keras as a high-level neural networks API that runs on top of TensorFlow, you can easily add a Sigmoid activation function to your model using the following code:


from keras.models import Sequential
from keras.layers import Dense
model = Sequential()
model.add(Dense(128, activation='sigmoid', input_shape=(input_dim,)))


In the above code, we have added a Sigmoid activation function to the first layer of the model. The ‘Dense’ layer is a fully connected layer, and we have specified the number of neurons (128) and the activation function (‘sigmoid’).

Conclusion

Sigmoid activation function is commonly used in neural networks for binary classification problems. We have discussed how to use TensorFlow and Keras to add a Sigmoid activation function to a neural network model. By following the examples provided, you can easily incorporate Sigmoid activation function into your own machine learning projects.