""" completely connected layer""def __init__(selfnnnum_outputslimit=nonealpha= epsilon - vel = )""" completely connected linear layer nn is neural network that the inputs come from num_outputs is the number of outputs max_init is the maximum value for random initialization of parameters vel is the initial velocity for each parameter ""linear_complete_layer __init__(selfnnnum_outputslimit=limitself weights[ ][iis the weight between input and output self velocity [[vel for inf in range(self num_inputs+ )for outf in range(self num_outputs)self alpha alpha self epsilon epsilon def update(self)"""updates parameters after batch""batch_step_size self nn learning_rate self nn batch_size for out in range(self num_outputs)for inp in range(self num_inputs+ )self velocity[out][inpself alpha*self velocity[out][inpbatch_step_size self delta[out][inpself weights[out][inp+self velocity[out][inpself delta[out][inp rms-prop learnnn py -(continued class linear_complete_layer_rms_prop(linear_complete_layer)""" completely connected layer""def __init__(selfnnnum_outputslimit=nonerho= epsilon - )""" completely connected linear layer nn is neural network that the inputs come from num_outputs is the number of outputs max_init is the maximum value for random initialization of parameters ""linear_complete_layer __init__(selfnnnum_outputslimit=limitself weights[ ][iis the weight between input and output self ms [[ for inf in range(self num_inputs+ )for outf in range(self num_outputs)self rho rho self epsilon epsilon def update(self)"""updates parameters after batch""for out in range(self num_outputs)version june