Explain the architecture of the neural network used in the example, including the activation functions and number of units in each layer.
The architecture of the neural network used in the example is a feedforward neural network with three layers: an input layer, a hidden layer, and an output layer. The input layer consists of 784 units, which corresponds to the number of pixels in the input image. Each unit in the input layer represents the intensity value of a pixel in the image.
The hidden layer consists of 128 units, which are fully connected to the input layer. Each unit in the hidden layer calculates a weighted sum of the inputs from the input layer and applies an activation function to produce an output. In this example, the activation function used in the hidden layer is the rectified linear unit (ReLU) function. The ReLU function is defined as f(x) = max(0, x), where x is the weighted sum of the inputs to the unit. The ReLU function introduces non-linearity to the network, allowing it to learn complex patterns and relationships in the data.
The output layer consists of 10 units, each representing one of the possible classes in the classification problem. The units in the output layer are also fully connected to the units in the hidden layer. Similar to the hidden layer, each unit in the output layer calculates a weighted sum of the inputs from the hidden layer and applies an activation function. In this example, the activation function used in the output layer is the softmax function. The softmax function converts the weighted sum of inputs into a probability distribution over the classes, where the sum of the probabilities is equal to 1. The unit with the highest probability represents the predicted class of the input image.
To summarize, the neural network architecture used in the example consists of an input layer with 784 units, a hidden layer with 128 units using the ReLU activation function, and an output layer with 10 units using the softmax activation function.
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