NumPy-neural-net-5.py

class layer:
    def __init__(self, layer_index, is_output, input_dim, output_dim, activation):
        self.layer_index = layer_index # zero indicates input layer
        self.is_output = is_output # true indicates output layer, false otherwise
        self.input_dim = input_dim
        self.output_dim = output_dim
        self.activation = activation
        
        # the multiplication constant is sorta arbitrary
        if layer_index != 0:
            self.W = np.random.randn(output_dim, input_dim) * np.sqrt(2/input_dim) 
            self.b = np.random.randn(output_dim, 1) * np.sqrt(2/input_dim)
            
            
# Change layers_dim to configure your own neural net!            
layers_dim = [X_num_row, 4, 4, y_num_row] # input layer --- hidden layers --- output layers
neural_net = []

# Construct the net layer by layer
for layer_index in range(len(layers_dim)):
    if layer_index == 0: # if input layer
        neural_net.append(layer(layer_index, False, 0, layers_dim[layer_index], 'irrelevant'))
    elif layer_index+1 == len(layers_dim): # if output layer
        neural_net.append(layer(layer_index, True, layers_dim[layer_index-1], layers_dim[layer_index], activation='linear'))
    else: 
        neural_net.append(layer(layer_index, False, layers_dim[layer_index-1], layers_dim[layer_index], activation='relu'))
        
        
# Simple check on overfitting

pred_n_param = sum([(layers_dim[layer_index]+1)*layers_dim[layer_index+1] for layer_index in range(len(layers_dim)-1)])
act_n_param = sum([neural_net[layer_index].W.size + neural_net[layer_index].b.size for layer_index in range(1,len(layers_dim))])
print(f'Predicted number of hyperparameters: {pred_n_param}')
print(f'Actual number of hyperparameters: {act_n_param}')
print(f'Number of data: {X_num_col}')

if act_n_param >= X_num_col:
    raise Exception('It will overfit.')