cifar10_monitor.py

import matplotlib
matplotlib.use("Agg")

from pyimagesearch.callbacks.trainingmonitor import TrainingMonitor
from sklearn.preprocessing import LabelBinarizer
from pyimagesearch.nn.conv.minivggnet import MiniVGGNet
from keras.optimizers import SGD
from keras.datasets import cifar10
import argparse
import os


# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True,
    help="path to the output directory")
args = vars(ap.parse_args())

#show information on the process ID
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

lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)

labelNames = ["airplane", "automobile", "birds", "cat", "deer", "dog", "frog",
    "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=["accuracy"])

# construct the set of callbacks
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=jsonPath)]

# train the network
print("[INFO] training network...")

model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=64,
    epochs=100, callbacks=callbacks, verbose=1)