letnet_mnist.py

from pyimagesearch.nn.conv.lenet import LeNet
from keras.optimizers import SGD
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from sklearn import datasets
from keras import backend as K
import matplotlib.pyplot as plt
import numpy as np


print("[INFO] accessing MNIST...")
dataset = datasets.fetch_mldata("MNIST Original")
data = dataset.data

if K.image_data_format() == "channels_first":
    data = data.reshape(data.shape[0], 1, 28, 28)
else:
    data = data.reshape(data.shape[0], 28, 28, 1)

(trainX, testX, trainY, testY) = train_test_split(data/255, dataset.target.astype("int"), test_size=0.25,
    random_state=42)

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

print ("[INFO] compiling model...")

optimizer = SGD(lr=0.01)
model = LeNet.build(width=28, height=28, depth=1, classes=10)
model.compile(loss="categorical_crossentropy", optimizer=optimizer,
    metrics=["accuracy"])

print("[INFO] training network...")

H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=128,
    epochs=20, verbose=1)

print("[INFO] evaluating network...")

predictions = model.predict(testX, batch_size=128, verbose=1)

print(classification_report(
    testY.argmax(axis=1),
    predictions.argmax(axis=1),
    target_names=[str(x) for x in lb.classes_]
))

plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, 20), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, 20), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, 20), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, 20), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend()
plt.show()