How many weights does a neural network have if it consists of an input layer with 2 neurons, three hidden layers each with 4 neurons, and an output layer with 2 neurons? Assume there are no bias terms in the network.

A multilayer neural network is simply a neural network with at least one hidden layer, such as the one in the diagram below.Figure 15. Neural Network with hidden layerIn the example above each neuron in the output layer is connected to all neurons in the hidden layer, and each neuron in the hidden layer is connected to both inputs. How many weights does this network have in total?

Suppose we have a convolutional neural network. The first layer is a convolutional layer, and it has an output volume with dimensions 8x8x8. Suppose the second layer is a fully connected layer with 5 neurons. How many weights does the second layer have?

Suppose we have a convolutional neural network with a 5x5x1 input volume, followed by one convolutional layer with 5 filters that have a 2x2x1 receptive field, followed by one fully connected output layer with 5 neurons.  How many weights does the network have in total?

In a feed-forward neural network with the following specifications:The input layer has 4 neurons, the hidden layer has 3 neurons, and the output layer has 2 neurons using the sigmoid activation function for given input values [0.5, 0.8, 0.2, 0.6] as well as the initial weights for the connections.W1: [0.1, 0.3, 0.5, 0.2]W2: [0.2, 0.4, 0.6, 0.2] Input layer to hidden layer weightsW3: [0.3, 0.5, 0.7, 0.2]W4: [0.4, 0.1, 0.3]W5: [0.5, 0.2, 0.4] Hidden layer to output layer weightsWhat is the output of the output layer when the given input values are passed through the neural network? Round the answer to two decimal places:Question 29Answera.[0.72, 0.78]b.[0.62, 0.68]c.[0.82, 0.88]d.[0.92, 0.98]

Figure QA1 is a small convolutional neural network that converts a 15x15 image into4 output values. The network has the following layers and operations from input tooutput: convolution with 3 filters, max pooling, ReLu, and finally a fully-connectedlayer. For this network we will not be using any bias to set parameters (b).Fig. QA1(a) How many weights in the convolutional layer do we need to learn