Kindly I am beginner in neural network Kindly support me for send clarification and comment for each line for the code and kindly clarify the output & input . Code --------- import pandas as pd import numpy as np import matplotlib.pyplot as plt """Import data and organize it in numpy arrays""" data = pd.read_csv('mnist_train.csv') labels = np.array(data.iloc[:, 0]) x_train = np.array(data.iloc[:, 1:]) / 255 encoded_labels = [] for i in range(len(labels)): naked = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] naked[labels[i]] = 1 encoded_labels.append(naked) """Take a look at what the images look like""" random_index = np.random.randint(0, 40000) img = x_train[random_index].reshape(28, 28) plt.imshow(img, cmap="gray") class DNN(): def __init__(self, layers): self.layers = layers self.weights = [] for i in range(len(layers) - 1): layers_weights = np.random.rand(layers[i + 1], layers[i] + 1) self.weights.append(layers_weights) def sigmoid(self, x): return 1 / (1 + np.exp(-.01 * x)) def predict(self, data): x_s = [data] for i in range(len(self.layers) - 1): """add bias""" x_s[-1] = np.concatenate((x_s[-1], [1])) z = np.dot(self.weights[i], x_s[i]) x_s.append(self.sigmoid(z)) return x_s[-1] def train(self, data, y_true): x_s = [data] for i in range(len(self.layers) - 1): """add bias""" x_s[-1] = np.concatenate((x_s[-1], [1])) z = np.dot(self.weights[i], x_s[i]) x_s.append(self.sigmoid(z)) psi = [] for i in range(len(y_true)): output = x_s[-1][i] psi.append(-2 * (y_true[i] - output) * (output * (1 - output))) psi = np.array(psi) psi = np.reshape(psi, (psi.shape[0], 1)) gradients = [] gradients.append(psi * x_s[-2]) for i in range(len(self.layers) - 2, 0, -1): w = self.weights[i][:, :-1] x = x_s[i][:-1] term = w * x * (1 - x) term = np.transpose(term) psi = np.dot(term, psi) psi = np.reshape(psi, (psi.shape[0], 1)) gradients.append(psi * x_s[i - 1]) for i in range(len(gradients)): self.weights[i] -= .1 * gradients[-(i + 1)] return sum((y_true - x_s[-1]) ** 2) model = DNN([784, 1250, 10]) from collections import deque error = deque(maxlen=1000) for n in range(30000): index = np.random.randint(0, 59998) error.append(model.train(x_train[index], encoded_labels[index])) if n % 1000 == 0: print(" Step: ", n) print("Average Error: ", sum(error) / 1000) plt.imshow(x_train[index].reshape(28, 28), cmap="gray") plt.show() print("Prediction: ", np.argmax(model.predict(x_train[index]))) test_data = pd.read_csv('mnist_test.csv') test_labels = np.array(test_data.iloc[:, 0]) x_test = np.array(test_data.iloc[:, 1:]) / 255 correct = 0 for i in range(len(test_data)): prediction = np.argmax(model.predict(x_test[i])) if prediction == test_labels[i]: correct += 1 percent_correct = correct / len(test_data) * 100 print(percent_correct, '%')

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Kindly clarify the output

Please clarify the input

I request to do project in nuerual network and i got this code from internet could you send explanation for this code

Step: 11000 Average Error: 0.6673313010068169 Prediction: 0 Step: 12000 Average Error: 0.5971398864092368