Classification of cifar-10 based on Kears

I. Introduction to CIFAR-10 data set

CIFAR-10 data set contains 60000 32 * 32 color images in 10 categories, 6000 images in each category, all of which have been labeled. There are 50000 training pictures and 10000 test pictures, and 10 categories are respectively:

  • airplane
  • automobile
  • Bird bird
  • Cat cat
  • Deer deer
  • Dog dog
  • Frog frog
  • Horse horse
  • Ship ship
  • Truck truck

II. The code is as follows

# Import package
from __future__ import print_function
import keras
from keras.models import Sequential
from keras.layers import Conv2D, MaxPool2D, Flatten
from keras.layers import Dense, Dropout
from keras.preprocessing.image import ImageDataGenerator
import os
# Initialization parameters
batch_size = 32
epochs = 100  # Iteration times
num_classes = 10  # Classification number
data_augmentation = False  # Data enhancement
model_dir = os.path.join(os.getcwd(), 'saved_models')  # Save path
model_name = 'keras_cifar10_trained_model.h5'  # file name
# Load and check datasets
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10()
print('x_train.shape = ' + str(x_train.shape))
print('y_train.shape = ' + str(x_test.shape))
print('train samples = ' + str(x_train.shape[0]))
print('test samples = ' + str(x_test.shape[0]))
# Data preprocessing
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
x_train = x_train.astype('float32')
x_train /= 255.
x_test = x_test.astype('float32')
x_test /= 255.
# Build Sequential model
model = keras.Sequential()

# conv -> conv -> maxpool -> dropout -> conv -> conv -> maxpool -> dropout -> flatten -> fc -> dropout -> fc -> softmax
model.add(Conv2D(32, (3, 3), padding='same', activation='relu', input_shape=x_train.shape[1:]))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPool2D(pool_size=(2, 2)))

model.add(Dense(512, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
# Model compilation
opt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)  # Optimizer, choose to use
model.compile(optimizer=opt, loss='categorical_crossentropy', metrics=['accuracy'])
# Training model
if data_augmentation:
    print("Not using data augementation"), y_train, batch_size=batch_size, epochs=epochs, validation_data=(x_test, y_test), shuffle=True)
    print("Using real-time data augmentation")  # Use real-time data increase
    data_generate = ImageDataGenerator(
        featurewise_center=False,  # Set the mean value of the input data to 0
        samplewise_center=False,  # Set the mean value of each sample to 0
        featurewise_std_normalization=False,  # Divide the input by the standard deviation of data, and carry out feature by feature
        samplewise_std_normalization=False,  # Divide each output by its standard deviation
        zca_epsilon=1e-6,  # epsilon value of ZCA whitening, default is 1e-6
        zca_whitening=False,  # Whether ZCA whitening is applied
        rotation_range=0,  # Degree range of random rotation, input as integer
        width_shift_range=0.1,  # Translation left and right, input as floating point number, output as pixel value when greater than 1
        height_shift_range=0.1,  # Translation up and down, input as floating point number, output as pixel value when greater than 1
        shear_range=0.,  # Shear strength, input as floating point number
        zoom_range=0.,  # Random scaling, input as floating point number
        channel_shift_range=0.,  # Random channel conversion range, input as floating point number
        fill_mode='nearest',  # There are three filling methods: constant, reflect and wrap
        cval=0.,  # The value used for filling, which takes effect when fill mode = constant '
        horizontal_flip=True,  # Random horizontal flip
        vertical_flip=False,  # Random vertical flip
        rescale=None,  # Replay factor, no scaling when None or 0
        preprocessing_function=None,  # Functions applied to each input
        data_format=None,  # Image data format, the default is channels? Last
    # Using real-time data enhanced batch to fit the model:
    model.fit_generator(data_generate.flow(x_train, y_train, batch_size),
                        steps_per_epoch=len(x_train)/32, epochs=epochs)
# Preservation model
if not os.path.isdir(model_dir):  # Check whether the vehicle directory exists, if not, create the corresponding directory
model_path = os.path.join(model_dir, model_name)
print("Saved trained model at: " + str(model_path))
# model prediction
scores = model.evaluate(x_test, y_test, batch_sizeb=batch_size, verbose=1)  # Show iterations as progress bars
print("Test loss is: " + str(scores[0]))
print("Test accuracy is: " + str(scores[1]))

III. operation results

Generally speaking, when running on the CPU, it takes several hours to load the data set and run the results. A total of 100 iterations are needed. The intermediate results are as follows:

It should be noted that when using the fit ﹣ generator iterator for data fitting, steps ﹣ per ﹣ epoch needs to be set, otherwise the result is likely to report an error, as shown below:

Please specify `steps_per_epoch` or use the `keras.utils.Sequence` class.

The final experimental results are as follows:

The loss value of the final test set is 0.748, and the prediction accuracy is only 0.7543

The saved model is as shown above, which can be loaded and used directly next time

Posted on Wed, 04 Dec 2019 11:07:57 -0500 by DamianTV