main.py

from keras.datasets import cifar10
import matplotlib.pyplot as plt

(train_X, train_Y), (test_X, test_Y) = cifar10.load_data()

n=6
plt.figure(figsize=(20,10))
for i in range(n):
 plt.subplot(330+1+i)
 plt.imshow(train_X[i])
plt.show()

from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.constraints import maxnorm
from tensorflow.keras.optimizers import SGD
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.utils import np_utils

# Converting pixels into float and normalizing them
train_x = train_X.astype('float32')
test_X = test_X.astype('float32')

train_X = train_X / 255.0
test_X = test_X / 255.0
num_classes=10
model=Sequential()
model.add(Conv2D(32,(3,3),input_shape=(32,32,3),
    padding='same',activation='relu',
    kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(32,(3,3),activation='relu',padding='same',kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
model.add(Dense(512,activation='relu',kernel_constraint=maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))

sgd = SGD(lr=0.01, momentum=0.9, decay=(0.01 / 25), nesterov=False)

model.compile(loss='categorical_crossentropy',
              optimizer=sgd,
              metrics=['accuracy'])
model.summary()

model.fit(train_X,train_Y,
    validation_data=(test_X,test_Y),
    epochs=10,batch_size=32)

_,acc=model.evaluate(test_X,test_Y)
print(acc*100)

model.save("model1_cifar_10epoch.h5")

results = {
    0: 'aeroplane',
    1: 'automobile',
    2: 'bird',
    3: 'cat',
    4: 'deer',
    5: 'dog',
    6: 'frog',
    7: 'horse',
    8: 'ship',
    9: 'truck'
}
from PIL import Image
import numpy as np

im = Image.open("__image_path__")
# the input image is required to be in the shape of dataset, i.e (32,32,3)

im = im.resize((32, 32))
im = np.expand_dims(im, axis=0)
im = np.array(im)
pred = model.predict_classes([im])[0]
print(pred, results[pred])