def predict_ch3(model, t_i, n=6): # @save
for X, y in t_i:
break
trues = d2l.get_fashion_mnist_labels(y)
preds = d2l.get_fashion_mnist_labels(model(X).argmax(axis=1))
titles = [true + '\n' + pred for true, pred in zip(trues, preds)]
d2l.show_images(X[0:n].reshape(n, 28, 28), 1, n, titles=titles[0:n])
plt.show()
def show_samples():
image, label = mnist_train[2]
print(image.shape, label, type(image))
print(label, type(label), label.dtype)
images, labels = mnist_train[0:4:1]
print(images.shape, labels.shape)
d2l.show_images(images, 2, 9)
plt.show()
def train_model(images, labels):
l_c = label_counts(labels)
# label probabilities
P_y = l_c / l_c.sum()
for label, probability in enumerate(P_y):
print('label {} : {}'.format(label, probability))
print('Samples', nd.size_array(images))
P_xy = label_pixel_probabilities(images, labels, l_c)
# test the prediction
image, label = mnist_test[1]
predict_one(image, label, P_y, P_xy)
X, y = mnist_test[:18]
d2l.show_images(X, 2, 9, titles=predict(X, P_y, P_xy))
plt.show()
ac = accuracy(P_y, P_xy)
print('accuracy : {}'.format(ac))
import os
import cv2
from mxnet import gluon, image, nd
from mxnet.gluon import data as gdata, utils as gutils
import d2l
import numpy as np
import tensorflow as tf
import sklearn
import mxnet
voc_dir = "C:\\Users\\bhatt\\Desktop\\Jupyter Notebook\\VOCdevkit\\VOC2012"
def read_voc_images(root=voc_dir, is_train=True):
txt_fname = '%s/ImageSets/Segmentation/%s' % (root, 'train.txt'
if is_train else 'val.txt')
with open(txt_fname, 'r') as f:
images = f.read().split()
features, labels = [None] * len(images), [None] * len(images)
for i, fname in enumerate(images):
features[i] = image.imread('%s/JPEGImages/%s.jpg' % (root, fname))
labels[i] = image.imread('%s/SegmentationClass/%s.png' % (root, fname))
return features, labels
train_features, train_labels = read_voc_images()
n = 5
imgs = train_features[0:n] + train_labels[0:n]
d2l.show_images(imgs, 2, n)