train_model.py

import matplotlib
matplotlib.use("Agg")

# import the necessary packages
from keras.preprocessing.image import ImageDataGenerator
from keras.optimizers import SGD
from pyimagesearch.resnet import ResNet
from sklearn.metrics import classification_report
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import os

# initialize the number of training epochs and batch size
DATASET = 'streetart'
NUM_EPOCHS = 50
BS = 24
IMAGE_NAME = 'streetart_plot.png'
MODEL_NAME = 'new_streetart.model'

# derive the path to the directories containing the training,
# validation, and testing splits, respectively
TRAIN_PATH = os.path.sep.join([DATASET, "training"])
VAL_PATH = os.path.sep.join([DATASET, "validation"])
TEST_PATH = os.path.sep.join([DATASET, "testing"])

# determine the total number of image paths in training, validation,
# and testing directories
totalTrain = len(list(paths.list_images(TRAIN_PATH)))
totalVal = len(list(paths.list_images(VAL_PATH)))
totalTest = len(list(paths.list_images(TEST_PATH)))

# initialize the training training data augmentation object
trainAug = ImageDataGenerator(
	rescale=1 / 255.0,
	rotation_range=20,
	zoom_range=0.05,
	width_shift_range=0.05,
	height_shift_range=0.05,
	shear_range=0.05,
	horizontal_flip=True,
	fill_mode="nearest")

# initialize the validation (and testing) data augmentation object
valAug = ImageDataGenerator(rescale=1 / 255.0)

# initialize the training generator
trainGen = trainAug.flow_from_directory(
	TRAIN_PATH,
	class_mode="categorical",
	target_size=(64, 64),
	color_mode="rgb",
	shuffle=True,
	batch_size=32)

# initialize the validation generator
valGen = valAug.flow_from_directory(
	VAL_PATH,
	class_mode="categorical",
	target_size=(64, 64),
	color_mode="rgb",
	shuffle=False,
	batch_size=BS)

# initialize the testing generator
testGen = valAug.flow_from_directory(
	TEST_PATH,
	class_mode="categorical",
	target_size=(64, 64),
	color_mode="rgb",
	shuffle=False,
	batch_size=BS)

# initialize our Keras implementation of ResNet model and compile it
model = ResNet.build(64, 64, 3, 2, (2, 2, 3),
	(32, 64, 128, 256), reg=0.0005)
opt = SGD(lr=1e-1, momentum=0.9, decay=1e-1 / NUM_EPOCHS)
model.compile(loss="binary_crossentropy", optimizer=opt,
	metrics=["accuracy"])

# train our Keras model
H = model.fit_generator(
	trainGen,
	steps_per_epoch=totalTrain // BS,
	validation_data=valGen,
	validation_steps=totalVal // BS,
	epochs=NUM_EPOCHS)

# reset the testing generator and then use our trained model to
# make predictions on the data
print("[INFO] evaluating network...")
testGen.reset()
predIdxs = model.predict_generator(testGen,
	steps=(totalTest // BS) + 1)

# for each image in the testing set we need to find the index of the
# label with corresponding largest predicted probability
predIdxs = np.argmax(predIdxs, axis=1)

# show a nicely formatted classification report
print(classification_report(testGen.classes, predIdxs,
	target_names=testGen.class_indices.keys()))

# save the network to disk
print("[INFO] serializing network to '{}'...".format(MODEL_NAME))
model.save(MODEL_NAME)

# plot the training loss and accuracy
N = NUM_EPOCHS
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), H.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), H.history["val_loss"], label="val_loss")
plt.plot(np.arange(0, N), H.history["acc"], label="train_acc")
plt.plot(np.arange(0, N), H.history["val_acc"], label="val_acc")
plt.title("Training Loss and Accuracy on Dataset")
plt.xlabel("Epoch #")
plt.ylabel("Loss/Accuracy")
plt.legend(loc="lower left")
plt.savefig(IMAGE_NAME)