A Dense layer, also known as a fully connected layer, is a type of layer in a neural network where each neuron is connected to every neuron in the previous layer. It’s like a classroom where every student (neuron) is friends with all students in the previous grade (layer).
In the context of a Recurrent Neural Network (RNN), a Dense layer is often used as the final layer. The RNN processes the input sequence one element at a time, and then the final output is passed through a Dense layer to produce the predictions.
The Softmax function is often used in the Dense layer when we’re dealing with classification tasks. It takes a bunch of numbers and turns them into probabilities that sum up to one. So, it’s like taking the votes of each neuron in the Dense layer and turning them into a percentage-like score.
Let’s say we’re building a model to predict the next word in a sentence (a common task for RNNs). The RNN processes the sentence one word at a time, and for each word, it tries to predict the next one. The Dense layer takes the RNN’s output and calculates a score for each possible next word. The Softmax function then turns these scores into probabilities.
Here’s a simple example in Python using TensorFlow and Keras:
from tensorflow.keras.models import Sequential from tensorflow.keras.layers import SimpleRNN, Dense # Let's say we have some sentences, and we've converted the words to integers sentences = np.array([ [0, 1, 2, 3], [1, 2, 3, 4], [2, 3, 4, 5], # ... more sentences ... ]) # The labels are the same sentences but shifted by one word labels = np.array([ [1, 2, 3, 4], [2, 3, 4, 5], [3, 4, 5, 6], # ... more labels ... ]) # Create a simple RNN model model = Sequential([ SimpleRNN(10, return_sequences=True, input_shape=(None, 1)), Dense(100, activation='softmax'), # Let's say we have 100 possible words ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy') # Train the model with teacher forcing model.fit(sentences[:, :, np.newaxis], labels[:, :, np.newaxis], epochs=10)
In this example, the RNN processes the input sentences one word at a time. For each word, it tries to predict the next one. The Dense layer takes the RNN’s output and calculates a score for each of the 100 possible next words. The Softmax function then turns these scores into probabilities. The model is trained to increase the probability of the correct next word and decrease the probabilities of all the other words.