shallownet_cifar10.py

# USAGE
# python shallownet_cifar10.py

# import the necessary packages
from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.core import Activation
from keras.layers.core import Flatten
from keras.layers.core import Dense
from keras import backend as K


# load the training and testing data, then scale it into the
# range [0, 1]
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"]

# initialize the optimizer and model
print("[INFO] compiling model...")
opt = SGD(lr=0.01)
model = Sequential()
inputShape = (32, 32, 3)

# if we are using "channels first", update the input shape
if K.image_data_format() == "channels_first":
	inputShape = (depth, height, width)

# define the first (and only) CONV => RELU layer
model.add(Conv2D(32, (3, 3), padding="same",
	input_shape=inputShape))
model.add(Activation("relu"))

# softmax classifier
model.add(Flatten())
model.add(Dense(10))
model.add(Activation("softmax"))

model.compile(loss="categorical_crossentropy", optimizer=opt,
metrics=["accuracy"])

print("[INFO] training network...")
H = model.fit(trainX, trainY, validation_data=(testX, testY),
batch_size=32, epochs=100, verbose=1)
# 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))

model.save('shalownet.h5')
# plot the training loss and accuracy
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 40), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 40), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 40), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 40), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.show()