help="path to pre-trained model")
args = vars(ap.parse_args())
# initialize the class labels
classLabels = ["cat", "dog"]
print("[INFO] sampling images...")
#grab the list of images in the dataset then randomly sample
imagePaths = np.array(list(paths.list_images(args['dataset'])))
idxs = np.random.randint(0, len(imagePaths), size=(10, ))
imagePaths = imagePaths[idxs]
sp = SimplePreProcessor(32, 32)
ima = ImageToArrayPreprocessor()
sdl = SimpleDatasetLoader([sp, ima])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype(dtype='float32') / 255.0
#TODO *********************************
# load the pre-trained network
print("[INFO] loading pre-trained network...")
model = load_model(args['model'])
# **************************************
print("[INFO] predicting...")
preds = model.predict(data, batch_size=32).argmax(axis=1)
for (i, imagePath) in enumerate(imagePaths):
image = cv2.imread(imagePath)
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
args = vars(ap.parse_args())
# grab the list of images that we'll be describing
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
# initialize the image preprocessors
sp = SimplePreprocessor(32, 32)
iap = ImageToArrayPreprocessor()
# load the dataset from disk then scale the raw pixel intensities
# to the range [0, 1]
sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
labels,
test_size=0.25,
random_state=42)
# convert the labels from integers to vectors
trainY = LabelBinarizer().fit_transform(trainY)
testY = LabelBinarizer().fit_transform(testY)
# initialize the optimizer and model
classLabels = ["cat", "dog", "panda"]
# grab the list of images in the dataset then randomly sample
# indexes into the image paths list
print("[INFO] sampling images...")
imagePaths = np.array(list(paths.list_images(args["dataset"])))
idxs = np.random.randint(0, len(imagePaths), size=(10, ))
imagesPaths = imagePaths[idxs]
# initialize the image preprocessors
sp = SimplePreprocessor(32, 32)
iap = ImageToArrayPreprocessor()
# load the dataset from disk then scale the raw pixel intensities
# to the range [0, 1]
sdl = SimpleDatasetLoader(preprocessor=[sp, iap])
(data, labels) = sdl.load(imagePaths)
data = data.astype("float") / 255.0
################################################
# load the pre-trained network
print("[INFO] loading pre-trained network...")
model = load_model(args["model"])
################################################
# make prediction on the images
print("[INFO] predicting...")
preds = model.predict(data, batch_size=32).argmax(axis=1)
# loop over the sample images
for (i, imagePath) in enumerate(imagePaths):