cifar10_monitor.py

import matplotlib
matplotlib.use("Agg")
# import the necessary packages
from pyimagesearch.callbacks import TrainingMonitor
from sklearn.preprocessing import LabelBinarizer
from pyimagesearch.nn.conv import MiniVGGNet
from keras.optimizers import SGD
from keras.datasets import cifar10
import argparse
import os

ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True,
help="path to the output directory")
args = vars(ap.parse_args())
print("[INFO process ID: {}".format(os.getpid()))

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

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

# initialize the label names for the CIFAR-10 dataset
labelNames = ["airplane", "automobile", "bird", "cat", "deer","dog", "frog", "horse", "ship", "truck"]
print("[INFO] compiling model...")
opt = SGD(lr=0.01, momentum=0.9, nesterov=True)
model = MiniVGGNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=opt,metrics=["acc"])


figPath=os.path.sep.join([args["output"],"{}.png".format(os.getpid())])

jsonPath=os.path.sep.join([args["output"], "{}.json".format(os.getpid())])
callbacks=[TrainingMonitor(figPath,jsonPath)]
print("[INFO] training network...")
model.fit(trainX, trainY, validation_data=(testX, testY),batch_size=64, epochs=100, callbacks=callbacks, verbose=1)