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shallownet_animals.py
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shallownet_animals.py
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from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from pyimagesearch.preprocessing import ImageToArrayPreprocessor
from pyimagesearch.preprocessing import SimplePreprocessor
from pyimagesearch.datasets import SimpleDatasetLoader
from pyimagesearch.nn.conv.shallownet import ShallowNet
from keras.optimizers import SGD
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
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 we'll be describing
print("[INFO] loading images...")
imagePaths = list(paths.list_images(args["dataset"]))
sp = SimplePreprocessor(32, 32)
iap = ImageToArrayPreprocessor()
sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
# convert values to between 0-1
data = data.astype("float") / 255.0
# partition our data into training and test sets
(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
print("[INFO] compiling model...")
# initialize stochastic gradient descent with learning rate of 0.005
opt = SGD(lr=0.005)
model = ShallowNet.build(width=32, height=32, depth=3, classes=3)
model.compile(loss="categorical_crossentropy", optimizer=opt,
metrics=["accuracy"])
# train the network
print("[INFO] training network...")
H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32,
epochs=100, verbose=1)
print("[INFO] evaluating network...")
predictions = model.predict(testX, batch_size=32)
print(classification_report(
testY.argmax(axis=1),
predictions.argmax(axis=1),
target_names=["cat", "dog", "panda"]
))
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 100), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 100), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 100), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 100), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.show()