shallownet_cifar10.py

from sklearn.preprocessing import LabelBinarizer
from sklearn.metrics import classification_report
from pyimagesearch.nn.conv import ShallowNet
from keras.optimizers import SGD
from keras.datasets import cifar10
import matplotlib.pyplot as plt
import numpy as np

print("[info] loading CIFAR10.... ")
((train_x,train_y),(test_x,test_y))=cifar10.load_data()
test_x=test_x.astype("float")/255.0
train_x=train_x.astype("float")/255.0
lb=LabelBinarizer()
train_y=lb.fit_transform(train_y)
test_y=lb.fit_transform(test_y)

labelNames = ["airplane", "automobile", "bird", "cat", "deer","dog", "frog", "horse", "ship", "truck"]
print("[INFO]: Compiling model....")
optimizer = SGD(lr=0.01)
model = ShallowNet.build(width=32, height=32, depth=3, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=optimizer, metrics=["acc"])

# Train the network
print("[INFO]: Training....")
H = model.fit(train_x, train_y, validation_data=(test_x, test_y), batch_size=32, epochs=40, verbose=1)

# Evaluate the network
print("[INFO]: Evaluating....")
predictions = model.predict(test_x, batch_size=32)
print(classification_report(test_y.argmax(axis=1), predictions.argmax(axis=1), target_names=labelNames))

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()