Keras實現CIFAR-10分類

  僅僅為了學習Keras的使用，使用一個四層的全連接網絡對MNIST數據集進行分類，網絡模型各層結點數為：3072: : 1024 : 512:10；

  使用50000張圖片進行訓練，10000張測試：

              precision    recall  f1-score   support

    airplane       0.61      0.69      0.65      1000
  automobile       0.69      0.67      0.68      1000
        bird       0.43      0.49      0.45      1000
         cat       0.40      0.32      0.36      1000
        dear       0.49      0.50      0.50      1000
         dog       0.45      0.48      0.47      1000
        frog       0.58      0.65      0.61      1000
       horse       0.63      0.60      0.62      1000
        ship       0.72      0.66      0.69      1000
       truck       0.63      0.58      0.60      1000

   micro avg       0.56      0.56      0.56     10000
   macro avg       0.56      0.56      0.56     10000
weighted avg       0.56      0.56      0.56     10000

訓練過程中，損失和正確率曲線：

  可以看到，訓練集的損失在一直降低，而測試集的損失出現大范圍波動，並趨於上升，說明在一些epoch之后，出現過擬合；

  訓練集的正確率也在一直上升，並接近100%；而測試集的正確率達到50%就趨於平穩了；

代碼：

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 19-5-9

"""
implement classification for CIFAR-10 with Keras
"""

__author__ = 'Zhen Chen'

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import argparse

# construct the argument parse and parse the arguments
parser = argparse.ArgumentParser()
parser.add_argument("-o", "--output", default="./Training Loss and Accuracy_CIFAR10.png")
args = parser.parse_args()

# load the training and testing data, scale it into the range [0, 1],
# then reshape the design matrix
print("[INFO] loading CIFAR-10 data...")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0
trainX = trainX.reshape((trainX.shape[0], 3072))
testX = testX.reshape((testX.shape[0], 3072))

# convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.fit_transform(testY)

# initialize the label names for the CIFAR-10 dataset
labelNames = ["airplane", "automobile", "bird", "cat", "dear", "dog", "frog", "horse", "ship", "truck"]

# define the 2072-1024-512-10 architecture Keras
model = Sequential()
model.add(Dense(1024, input_shape=(3072,), activation="relu"))
model.add(Dense(512, activation="relu"))
model.add(Dense(10, activation="softmax"))

# train the model using SGD
print("[INFO] training network...")
sgd = SGD(0.01)
model.compile(loss="categorical_crossentropy", optimizer=sgd, metrics=["accuracy"])
H = model.fit(trainX, trainY, validation_data=(testX, testY), epochs=100, batch_size=32)
model.save_weights('SGD_100_32.h5')


# evaluate the network
print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32)
print(classification_report(testY.argmax(axis=1), predictions.argmax(axis=1), target_names=labelNames))

# plot the training losss and accuracy
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 100), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 100), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 100), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 100), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy on CIRFAR-10")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.savefig(args.output)