Help Python vs Jupyter

[futureofAI]

Hi to everyone,

In a first step I want to admit that I´m a very very beginner so please dont judge me on this question - I sure it is very simple but I don´t get it by myself.

I was reading the book Make Your Own Neural Network from Rashid, Tariq and he is using Ipython with jupyter notebook when he was explaining the code.
So I decided to use also Jupyter Notebook and the following code worked out there - I had an output generated - when I pasted the same code to my python visual basic code to run it there it just throwed nothing out. (no Error no exception nothing ..)

just follwing output from termin:

PS C:\Users\Admin\Desktop\NN> c:; cd 'c:\Users\Admin\Desktop\NN'; & 'C:\Users\Admin\AppData\Local\Programs\Python\Python39\python.exe' 'c:\Users\Admin\.vscode\extensions\ms-python.python-2021.10.1336267007\pythonFiles\lib\python\debugpy\launcher' '62379' '--' 'c:\Users\Admin\Desktop\NN\#Neuronales Netzwerk.py'

And I can´t find the problem :( can someone help and explain it to me that I can also understand it.

Code as follows:

#Neuronales Netzwerk
import numpy
# scipy.special for the sigmoid function expit() 
import scipy.special


# neural network class definition 
class neuralNetwork: 
    

    # initialise the neural network 
    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
        # set number of nodes in each input, hidden, output layer 
        self.inodes = inputnodes 
        self.hnodes = hiddennodes 
        self.onodes = outputnodes 

        # link weight matrices, wih and who 
        # weights inside the arrays are w_i_j, where link is from node i to node j in the next layer
        # w11 w21 
        # # w12 w22 etc 
        self.wih = numpy.random.normal(0.0, pow(self.inodes, -0.5), (self.hnodes, self.inodes))
        self.who = numpy.random.normal(0.0, pow(self.hnodes, -0.5), (self.onodes, self.hnodes))

        # learning rate 
        self.lr = learningrate

        # activation function is the sigmoid function 
        self.activation_function = lambda x: scipy.special.expit(x)

        pass


    # train the neural network 
    def train(self, inputs_list, targets_list):
        # convert inputs list to 2d array
        inputs = numpy.array(inputs_list, ndmin=2).T
        targets = numpy.array(targets_list, ndmin=2).T

        # calculate signals into hidden layer 
        hidden_inputs = numpy.dot(self.wih, inputs) 
        # calculate the signals emerging from hidden layer 
        hidden_outputs = self.activation_function(hidden_inputs)

        # calculate signals into final output layer 
        final_inputs = numpy.dot(self.who, hidden_outputs) 
        # calculate the signals emerging from final output layer 
        final_outputs = self.activation_function(final_inputs)

        # output layer error is the (target - actual)
        output_errors = targets - final_outputs
        # hidden layer error is the output_errors, split by weights, recombined at hidden nodes
        hidden_errors = numpy.dot(self.who.T, output_errors) 

        # update the weights for the links between the hidden and output layers
        self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)), numpy.transpose(hidden_outputs))
        
        # update the weights for the links between the input and hidden layers
        self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)), numpy.transpose(inputs))
        
        pass


        # query the neural network 
    def query(self, inputs_list):
        # convert inputs list to 2d array
        inputs = numpy.array(inputs_list, ndmin=2).T
        
        # calculate signals into hidden layer
        hidden_inputs = numpy.dot(self.wih, inputs)
        # calculate the signals emerging from hidden layer
        hidden_outputs = self.activation_function(hidden_inputs)
        
        # calculate signals into final output layer
        final_inputs = numpy.dot(self.who, hidden_outputs)
        # calculate the signals emerging from final output layer
        final_outputs = self.activation_function(final_inputs)
        
        return final_outputs


# number of input, hidden and output nodes 
input_nodes = 3
hidden_nodes = 3 
output_nodes = 3

# learning rate is 0.3 
learning_rate = 0.3

# create instance of neural network 
n = neuralNetwork(input_nodes,hidden_nodes,output_nodes,learning_rate)

n.query([1.0,0.5,1.5])

Input is the n.query ([1.0,0.5,1.5])
The ouput should be an array with the weighted values.

[Yoriz]

In Jupyter the results of a cell are automatically output.
To see the output in VS code you would use print

print(n.query([1.0,0.5,1.5]))

[futureofAI]

Thank you very much for your help. :)
I also tried it with print but not like you did - learned something.