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[Keras] Build a CNN model to classify Cifar-10

Last Updated on 2021-06-02 by Clay

Cifar-10 is dataset like “MNIST”, it have 60000 pictures, 10 types. One of types have 6000 pictures. 50000 training data and 10000 test data.

But the pictures in Cifar-10 are 32 x 32 “RGB” mode, the process of training the classifier is more complicated. We can see the world record is 96.53%, significantify lower than MNIST.

But don’t worry. This is not the hardest toy data set. For example, Cifar-100 even more difficult.

Digression, let’s look at the code now!

Prepare

If you have no any GPU can accelerate training your model, maybe you can use the “Google Colab”. More information you can read this article: How to use the free GPU from Google Colab

Program

import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

from keras.models import Sequential, load_model
from keras.datasets import cifar10
from keras.utils import np_utils,plot_model
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D



First, import all the packages we need.

(X_train, Y_train), (X_test, Y_test) = cifar10.load_data()
x_train = X_train.astype('float32')/255
x_test = X_test.astype('float32')/255
y_train = np_utils.to_categorical(Y_train)
y_test = np_utils.to_categorical(Y_test)



Prepare the Cifar-10 dataset. We can use the dataset collected by Keras.

The pre-processing is similar to MNIST and we needs to split our data to “training data” and “test data”. In addition, we have to divide it into “picture data” and “labels”.

(“x” is picture data, and “y” is picture label)

model = Sequential()
model.add(Conv2D(filters=64, kernel_size=3, input_shape=(32, 32, 3), activation='relu', padding='same'))
model.add(Conv2D(filters=64, kernel_size=3, input_shape=(32, 32, 3), activation='relu', padding='same'))
model.add(MaxPool2D(pool_size=2))

model.add(Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'))
model.add(Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPool2D(pool_size=2))

model.add(Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'))
model.add(Conv2D(filters=128, kernel_size=3, activation='relu', padding='same'))
model.add(MaxPool2D(pool_size=2))

model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(Dropout(rate=0.25))
model.add(Dense(10, activation='softmax'))



We used a multi-layer CNN when we building the model. You can try different model architectures, and you can pay attention to check the “Accuracy” result have improved or not.

print(model.summary())



We can print the overview of the model. It look like this:

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x_train, y_train, epochs=10, batch_size=64, verbose=1)



And then we start to train our model.

loss, accuracy = model.evaluate(x_test, y_test)
print('Test:')
print('Loss:', loss)
print('Accuracy:', accuracy)



Show the model accuracy:

10000/10000 [==============================] - 3s 259us/step
Test:
Loss: 0.8153500760555268
Accuracy: 0.7908

It is normal that my accuracy is different from yours. Different start point will find different local optimal.

References

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