def deep_neural_network(deep_params, x):
    # N_hidden is the number of hidden layers  
    N_hidden = np.size(deep_params) - 1 # -1 since params consist of parameters to all the hidden layers AND the output layer
        
    # Assumes input x being an one-dimensional array
    num_values = np.size(x)
    x = x.reshape(-1, num_values)
    
    # Assume that the input layer does nothing to the input x
    x_input = x
    
    # Due to multiple hidden layers, define a variable referencing to the
    # output of the previous layer:
    x_prev = x_input 
    
    ## Hidden layers:
    
    for l in range(N_hidden):
        # From the list of parameters P; find the correct weigths and bias for this layer
        w_hidden = deep_params[l]
        
        # Add a row of ones to include bias
        x_prev = np.concatenate((np.ones((1,num_values)), x_prev ), axis = 0)

        z_hidden = np.matmul(w_hidden, x_prev)
        x_hidden = sigmoid(z_hidden)

        # Update x_prev such that next layer can use the output from this layer
        x_prev = x_hidden 

    ## Output layer:
    
    # Get the weights and bias for this layer
    w_output = deep_params[-1]
    
    # Include bias:
    x_prev = np.concatenate((np.ones((1,num_values)), x_prev), axis = 0)

    z_output = np.matmul(w_output, x_prev)
    x_output = z_output

    return x_output