keras分類之多分類（dress and color）

1. 數據準備

      建立數據文件夾dataset，然後按照目錄分別建立對應顏色的服裝，目錄名爲分類名，幷包含了顏色關鍵字，如black_jeans、red_dress、blue_shirt，在每個目錄下放入已經存儲的圖像，圖像大小可以不同。

2. 程序中需要引入的庫

# set the matplotlib backend so figures can be saved in the background
import matplotlib
matplotlib.use("Agg")

# import the necessary packages
from keras.optimizers import Adam
from keras.preprocessing.image import img_to_array
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from pyimagesearch.fashionnet import FashionNet
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import random
import pickle
import cv2
import os

3. 引入數據常量

base_dir = 'dataset/'

# initialize the number of epochs to train for, initial learning rate,
# batch size, and image dimensions
EPOCHS = 50
INIT_LR = 1e-3
BS = 32
IMAGE_DIMS = (96, 96, 3)

4.讀取數據

# grab the image paths and randomly shuffle them
print("[INFO] loading images...")
imagePaths = sorted(list(paths.list_images(base_dir)))
random.seed(42)
random.shuffle(imagePaths)

# print(imagePaths)

# initialize the data, clothing category labels (i.e., shirts, jeans,
# dresses, etc.) along with the color labels (i.e., red, blue, etc.)
data = []
categoryLabels = []
colorLabels = []

# loop over the input images
for imagePath in imagePaths:
	# load the image, pre-process it, and store it in the data list
	image = cv2.imread(imagePath)
	image = cv2.resize(image, (IMAGE_DIMS[1], IMAGE_DIMS[0]))
	image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
	image = img_to_array(image)
	data.append(image)

	# extract the clothing color and category from the path and
	# update the respective lists
	(color, cat) = imagePath.split(os.path.sep)[-2].split("_")
	categoryLabels.append(cat)
	colorLabels.append(color)
    
    
# scale the raw pixel intensities to the range [0, 1] and convert to
# a NumPy array
data = np.array(data, dtype="float") / 255.0

print("[INFO] data matrix: {} images ({:.2f}MB)".format(
	len(imagePaths), data.nbytes / (1024 * 1000.0)))

# convert the label lists to NumPy arrays prior to binarization
categoryLabels = np.array(categoryLabels)
colorLabels = np.array(colorLabels)

  借用OpenCV，依次讀取圖像，並將圖像調整爲統一的大小，轉換爲array後，添加到data列表中，同時得到該圖像對應的Label屬性，分別添加到color和cat列表中。

5. 建立模型

# partition the data into training and testing splits using 80% of
# the data for training and the remaining 20% for testing
split = train_test_split(data, categoryLabels, colorLabels,
	test_size=0.2, random_state=42)
(trainX, testX, trainCategoryY, testCategoryY,
	trainColorY, testColorY) = split

# initialize our FashionNet multi-output network
model = FashionNet.build(96, 96,
	numCategories=len(categoryLB.classes_),
	numColors=len(colorLB.classes_),
	finalAct="softmax")

# define two dictionaries: one that specifies the loss method for
# each output of the network along with a second dictionary that
# specifies the weight per loss
losses = {
	"category_output": "categorical_crossentropy",
	"color_output": "categorical_crossentropy",
}
lossWeights = {"category_output": 1.0, "color_output": 1.0}

# initialize the optimizer and compile the model
print("[INFO] compiling model...")
opt = Adam(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(optimizer=opt, loss=losses, loss_weights=lossWeights,
	metrics=["accuracy"])

6. 訓練模型並存儲

# train the network to perform multi-output classification
H = model.fit(trainX,
	{"category_output": trainCategoryY, "color_output": trainColorY},
	validation_data=(testX,
		{"category_output": testCategoryY, "color_output": testColorY}),
	epochs=EPOCHS,
	verbose=1)

# save the model to disk
print("[INFO] serializing network...")
model.save('cats_and_dogs_small_1.h5')

存儲兩個標籤數據

# save the category binarizer to disk
print("[INFO] serializing category label binarizer...")
f = open('cateBin', "wb")
f.write(pickle.dumps(categoryLB))
f.close()

# save the color binarizer to disk
print("[INFO] serializing color label binarizer...")
f = open('colorBin', "wb")
f.write(pickle.dumps(colorLB))
f.close()

7. 評價

# plot the total loss, category loss, and color loss
lossNames = ["loss", "category_output_loss", "color_output_loss"]
plt.style.use("ggplot")
(fig, ax) = plt.subplots(3, 1, figsize=(13, 13))

# loop over the loss names
for (i, l) in enumerate(lossNames):
	# plot the loss for both the training and validation data
	title = "Loss for {}".format(l) if l != "loss" else "Total loss"
	ax[i].set_title(title)
	ax[i].set_xlabel("Epoch #")
	ax[i].set_ylabel("Loss")
	ax[i].plot(np.arange(0, EPOCHS), H.history[l], label=l)
	ax[i].plot(np.arange(0, EPOCHS), H.history["val_" + l],
		label="val_" + l)
	ax[i].legend()

# save the losses figure
plt.tight_layout()
plt.savefig("{}_losses.png".format('losses'))
plt.close()

8. 預測

預測可以寫成單獨的文件

# USAGE
# python classify.py --model output/fashion.model \
#	--categorybin output/category_lb.pickle --colorbin output/color_lb.pickle \
#	--image examples/black_dress.jpg

# import the necessary packages
from keras.preprocessing.image import img_to_array
from keras.models import load_model
import tensorflow as tf
import numpy as np
import argparse
import imutils
import pickle
import cv2
base_dir = 'dataset/'

imagename = 'E:\\python learn\\multi-output-classification\\dataset\\blue_jeans\\00000030.jpg'

# load the image
image = cv2.imread(imagename)
output = imutils.resize(image, width=400)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# pre-process the image for classification
image = cv2.resize(image, (96, 96))
image = image.astype("float") / 255.0
image = img_to_array(image)
image = np.expand_dims(image, axis=0)

# load the trained convolutional neural network from disk, followed
# by the category and color label binarizers, respectively
print("[INFO] loading network...")
model = load_model('cats_and_dogs_small_1.h5', custom_objects={"tf": tf})
categoryLB = pickle.loads(open('cateBin', "rb").read())
colorLB = pickle.loads(open('colorBin', "rb").read())

# classify the input image using Keras' multi-output functionality
print("[INFO] classifying image...")
(categoryProba, colorProba) = model.predict(image)

# find indexes of both the category and color outputs with the
# largest probabilities, then determine the corresponding class
# labels
categoryIdx = categoryProba[0].argmax()
colorIdx = colorProba[0].argmax()
categoryLabel = categoryLB.classes_[categoryIdx]
colorLabel = colorLB.classes_[colorIdx]

# draw the category label and color label on the image
categoryText = "category: {} ({:.2f}%)".format(categoryLabel,
	categoryProba[0][categoryIdx] * 100)
colorText = "color: {} ({:.2f}%)".format(colorLabel,
	colorProba[0][colorIdx] * 100)
cv2.putText(output, categoryText, (10, 25), cv2.FONT_HERSHEY_SIMPLEX,
	0.7, (0, 255, 0), 2)
cv2.putText(output, colorText, (10, 55), cv2.FONT_HERSHEY_SIMPLEX,
	0.7, (0, 255, 0), 2)

# display the predictions to the terminal as well
print("[INFO] {}".format(categoryText))
print("[INFO] {}".format(colorText))

# show the output image
cv2.imshow("Output", output)

程序在Spyder4下運行正常