Need help with Neural network code

[AsRycka]

Hello, I am new in python. Need help with neural network code.

I have data in .xlxs file. Dimensions 7027x65.
This is data file
Data file

First of all I calculate 20% of my data for training my neural network, and set output training array

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import pandas as pd import numpy as np   dataset = pd.read_excel("C:\\Users\\tigra\\Desktop\\1year.xlsx") # nuskaitymas exel failo   # Calculate 20% dydis=dataset.shape print('Masyvo dimension: ',dydis) row=round((dydis[0]*(dydis[1]-1))*0.2/(dydis[0])) Training_array = dataset.iloc[:,:row]   Training_array_dimensions=Training_array.shape print('Masyvo dimension: ',Training_array_dimensions)   # input data inputs = Training_array outputs = np.array([100])

For prediction I am using other 80% of data

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# create two new examples to predict                                   example = dataset.iloc[:,row:] example_dimension= example.shape print('Masyvo dimension: ',example_dimension)

This all the code:

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# create NeuralNetwork class class NeuralNetwork:       # intialize variables in class     def __init__(self, inputs, outputs):         self.inputs  = inputs         self.outputs = outputs         # initialize weights as .50 for simplicity         self.weights = np.full([7027, 1], .50)         self.error_history = []         self.epoch_list = []       #activation function ==> S(x) = 1/1+e^(-x)     def sigmoid(self, x, deriv=False):         if deriv == True:             return x * (1 - x)         return 1 / (1 + np.exp(-x))       # data will flow through the neural network.     def feed_forward(self):         self.hidden = self.sigmoid(np.dot(self.inputs, self.weights))       # going backwards through the network to update weights     def backpropagation(self):         self.error  = self.outputs - self.hidden         delta = self.error * self.sigmoid(self.hidden, deriv=True)         self.weights += np.dot(self.inputs.T, delta)       # train the neural net for 25,000 iterations     def train(self, epochs=25000):         for epoch in range(epochs):             # flow forward and produce an output             self.feed_forward()             # go back though the network to make corrections based on the output             self.backpropagation()                # keep track of the error history over each epoch             self.error_history.append(np.average(np.abs(self.error)))             self.epoch_list.append(epoch)       # function to predict output on new and unseen input data                                   def predict(self, new_input):         prediction = self.sigmoid(np.dot(new_input, self.weights))         return prediction   # create neural network   NN = NeuralNetwork(inputs, outputs) # train neural network NN.train()   # print the predictions for both examples                                   print(NN.predict(example), ' - Correct: ')

By running the code I'm get this error:


How to fix that? Thanks for your help.

[Yoriz]

Please could you post errors as text using error tags, not as an image of an error.
Thank you

[AsRycka]

LOAD_GLOBAL(dispatcher), LOAD_FAST(args), LOAD_FAST(kwargs), CALL_FUNCTION_EX{1}, STORE_FAST(relevant_args)
return implement_array_function(

Error:
Exception has occurred: ValueError
shapes (7027,13) and (7027,1) not aligned: 13 (dim 1) != 7027 (dim 0)
  File "<__array_function__ internals>", line 5, in dot
  File "C:\Users\tigra\Desktop\Untitled-1.py", line 49, in feed_forward
    self.hidden = self.sigmoid(np.dot(self.inputs, self.weights))
  File "C:\Users\tigra\Desktop\Untitled-1.py", line 61, in train
    self.feed_forward()
  File "C:\Users\tigra\Desktop\Untitled-1.py", line 80, in <module>
    NN.train()

implementation, dot, relevant_args, args, kwargs)

[jefsummers]

Don't take this wrong - are you supposed to be doing it the hard way, coding each piece when there are fully vetted functions out there to do it? I know sometimes assignments are done that way to try to teach you something special, but for example:

1 2	trainval_dataset = df.sample(frac=0.8,random_state=42) test_dataset = df.drop(trainval_dataset.index)

does your training and testing split, in the usual 80% training and 20% test.

Current problem is with the shapes of your data. Look at the shapes being fed into feedforward.