keras_cifar10.py

from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np
import argparse


# Parse command line arguments
ap = argparse.ArgumentParser()
ap.add_argument("-o", "--output", required=True,
    help="path to the output loss/accuracy plot")
args = vars(ap.parse_args())

print("[INFO] Loading CIFAR-10 dataset...")
((trainX, trainY), (testX, testY)) = cifar10.load_data()
trainX = trainX.astype("float") / 255.0
testX = testX.astype("float") / 255.0
# flatten
trainX = trainX.reshape((trainX.shape[0], 3072))
testX = testX.reshape((testX.shape[0], 3072))


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

print("testY", trainY.shape, trainY)
print("trainY", testY.shape)

labelNames = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog",
    "horse", "ship", "truck"]

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

# train it
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)

# evaluate
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))