cjsutton77

cjsutton77

#include <stdio.h>
#include <stdlib.h>

typedef struct vector {
  int xhat;
  int yhat;
  int zhat;
} vector;

vector* scalar(int s, vector* S);

cjsutton77

cnt = 0
print(f'Number of test samples is: {num-size}')
for i in range(num-size):
    t = y_test[i].reshape(output_layer,1)
    # set nodes
    x0 = X_test[i].reshape(input_layer,1)
    x1 = sigmoid(w1.dot(x0)+b1)
    x2 = sigmoid(w2.dot(x1)+b2)
    cnt += np.argmax(x2) == np.argmax(t)
print(f'Testing accuracy is {100 * cnt/(num-size):.2f}%')

cjsutton77

lr = .001

epochs = [x for x in range(50)]
Error = 0
for epoch in epochs:
    error = 0
    cnt = 0
    for i in range(size):
        # get the true value
        t = y_train[i].reshape(output_layer,1)

cjsutton77

input_layer = 784
hidden_layer = 64
output_layer = 10

w1 = np.random.normal(0,
                      1/np.sqrt(input_layer),
                      size = (hidden_layer,input_layer))
w2 = np.random.normal(0,
                      1/np.sqrt(hidden_layer),
                      size = (output_layer,hidden_layer))

cjsutton77

mnist = fetch_openml('mnist_784', version=1, cache=True)
list(mnist)
mnist.data.shape
mnist.target.shape
plt.imshow(mnist.data[100].reshape(28,28),cmap='gray')

images = (0.99 * mnist.data + 0.01)/ 255

def sigmoid(x):
    return 1/(1+np.exp(-x))

cjsutton77

import numpy as np
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split

import matplotlib.pyplot as plt