Dense In Deep Learning

📜Dense Layers With Keras📜

🎯Syntax🎯

keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)

Now we will look at each of the hyperparameters quickly.

💡Keras Dense Layer Parameters💡

Further, Keras also provides some of the primary methods discussed below:

Keras Dense Layer Methods💡

Also see, Introduction to Machine Learning

Basic Operations With Dense Layer

The three main parameters for a dense layer are an activation function, the weight matrix, and a bias vector. Using these parameters, a dense layer operation can be represented as:

👉output = activation(dot(input, kernel) + bias)

In the above equation, the activation function is used for performing element-wise activation, and the kernel is the weights matrix generated by the layer. Bias is a bias vector formed by the dense layer. Keras dense layer performs dot product of input tensor and weight kernel matrix.

Implementation

🎯Sequential Model With Single Dense Layer

We use the Sequential model, an in-built Keras model. First, we fed the input layer to the model and then added a dense layer along with ReLU activation.

🧑‍💻Code🧑‍💻

import tensorflow as tf
model_1 = tf.keras.models.Sequential()
model_1.add(tf.keras.Input(shape=(16,)))
model_1.add(tf.keras.layers.Dense(32, activation='relu'))
model_1.output_shape

👉Output

(None, 32)

🎯Sequential Model With Multiple Dense Layers

We look at a model where multiple hidden layers are used in deep neural networks. Here we are using the ReLu activation function in the neurons of the hidden dense layer.

🧑‍💻Code🧑‍💻
import tensorflow as tf
model1 = tf.keras.models.Sequential()
model1.add(tf.keras.Input(shape=(16,)))
model1.add(tf.keras.layers.Dense(32, activation='relu'))
model1.add(tf.keras.layers.Dense(32))
print(model1.output_shape)

👉Output

Check out this article - Padding In Convolutional Neural Network, and Difference between argument and parameter.

Frequently Asked Questions

Why do we use dense layers?

A Dense layer feeds all outputs from the previous layer to all its neurons, each providing one output to the next layer. It's the most basic layer in neural networks.

Are dense layers fully connected?

The dense layer, also called the fully-connected layer, refers to the layer whose inside neurons connect to every neuron in the preceding layer.

What is the use of dense Layer in CNN?

The dense layer is a simple Layer of neurons in which each neuron receives input from all the neurons of the previous layer, thus called as dense. The dense layer is used to classify images based on output from convolutional layers.

What does dense do?

Dense is the only actual network layer in that model. A Dense layer feeds all outputs from the previous layer to all its neurons, each providing one output to the next layer.

Is a dense layer a hidden layer?

The first Dense object is the first hidden layer. The input layer is specified as a parameter to the first Dense object's constructor.

Conclusion

Let us brief the article. Firstly, we saw the intuition behind the dense layer. Later, we saw how it could be implemented using Keras and saw different parameters and methods associated with it. Since it is the primary part of any neural network, we should know about the various primary and dense layers.  

You can also refer to Stochastic Gradient Descent, Feature Selection, Logistic Regression, Sigmoid neuron and many more to enhance your knowledge.

I hope you all like this article. 

Happy Learning, Ninjas!