CNN 回归 model 预测值远远超出预期范围
[英]CNN Regression model predicting values far outside the expected range
问题描述
I am training a multi-output CNN regression model which predicts x and y coordinate values given a single image input.
我正在训练一个多输出 CNN 回归 model，它在给定单个图像输入的情况下预测 x 和 y 坐标值。 Both the image data and associated x and y target labels have been normalized to be within the range of 0 and 1. However when using the model to predict the x and y values of a given image, the model is predicting values far outside this range, for example:图像数据和相关的 x 和 y 目标标签都已标准化为 0 和 1 的范围内。但是，当使用 model 预测给定图像的 x 和 y 值时，model 预测的值远远超出此范围， 例如：
[114, -17] as opposed to [0.83, 0.16] [114, -17] 而不是 [0.83, 0.16]
I am unable to figure out the reason for this, and would appreciate if someone would be able to give some insight.我无法找出原因，如果有人能够提供一些见解，我将不胜感激。 Find the code used below:找到下面使用的代码：
IMAGESTRAIN_PATH = os.path.sep.join([BASE_PATH, "RegressionDatasetGrey"])
ANNOTSTRAIN_PATH = os.path.sep.join([BASE_PATH, "RegressionDatasetGrey.csv"])
IMAGESTEST_PATH = os.path.sep.join([BASE_PATH, "RegressionDSGreyTesting"])
ANNOTSTEST_PATH = os.path.sep.join([BASE_PATH, "RegressionDSGreyTesting.csv"])
print("[INFO] loading training dataset...")
rows = open(ANNOTSTRAIN_PATH).read().strip().split("\n")
data = []
targets = []
filenames = []
for row in rows:
row = row.split(",")
(filename, coordX, coordY) = row
coordX = float(int(coordX)/1920)
coordY = float(int(coordY)/1080)
imagePath = os.path.sep.join([IMAGESTRAIN_PATH, filename])
img = load_img(imagePath, color_mode="grayscale", target_size=(64, 80))
# img = img.size(1, 64, 80, 1)
img = img_to_array(img)
data.append(img)
targets.append((coordX, coordY))
filenames.append(filename)
trainImages = np.array(data, dtype="float32") / 255.0
trainCoords = np.array(targets, dtype="float32")
print("[INFO] loading testing dataset...")
rows1 = open(ANNOTSTEST_PATH).read().strip().split("\n")
data1 = []
targets1 = []
filenames1 = []
for row1 in rows1:
row1 = row1.split(",")
(filename1, coordX1, coordY1) = row1
coordX1 = float(int(coordX1)/1920)
coordY1 = float(int(coordY1)/1080)
imagePath1 = os.path.sep.join([IMAGESTEST_PATH, filename1])
image1 = load_img(imagePath1, color_mode="grayscale", target_size=(64, 80))
image1 = img_to_array(image1)
data1.append(image1)
targets1.append((coordX1, coordY1))
filenames1.append(filename1)
testImages = np.array(data1, dtype="float32") / 255.0
testCoords = np.array(targets1, dtype="float32")
inputs = Input(shape=(64, 80, 1))
x = inputs
x = Conv2D(16, (3, 3), padding='same')(x)
x = Activation('relu')(x)
# x = BatchNormalization()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(32, (3, 3), padding='same')(x)
# x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), padding='same')(x)
# x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(128)(x)
x = Activation('relu')(x)
x = Dropout(0.3)(x)
x = Dense(64)(x)
x = Activation('relu')(x)
x = Dropout(0.3)(x)
x = Dense(2)(x)
x = Activation('linear')(x)
model = Model(inputs, x)
opt = Adam(lr=0.001)
model.compile(loss="mse", optimizer=opt)
# print(model.summary())
print("[INFO] training Regressor...")
History = model.fit(
trainImages, trainCoords,
validation_data=(testImages, testCoords),
batch_size=32,
epochs=50,
verbose=1, shuffle=True)
print("[INFO] Saving Model...")
model.save('ModelV12.h5')
N = 50
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), History.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), History.history["val_loss"], label="val_loss")
plt.title("Loss on Training Set")
plt.xlabel("Epoch #")
plt.ylabel("Loss")
plt.legend(loc="lower left")
plt.savefig('RegressionPlot.png')
1 个解决方案
解决方案1
0 2021-05-03 07:14:16
I am training a multi-output CNN regression model which predicts x and y coordinate values given a single image input.我正在训练一个多输出 CNN 回归 model，它在给定单个图像输入的情况下预测 x 和 y 坐标值。 Both the image data and associated x and y target labels have been normalized to be within the range of 0 and 1. However when using the model to predict the x and y values of a given image, the model is predicting values far outside this range, for example:图像数据和相关的 x 和 y 目标标签都已标准化为 0 和 1 的范围内。但是，当使用 model 预测给定图像的 x 和 y 值时，model 预测的值远远超出此范围， 例如：
[114, -17] as opposed to [0.83, 0.16] [114, -17] 而不是 [0.83, 0.16]
I am unable to figure out the reason for this, and would appreciate if someone would be able to give some insight.我无法找出原因，如果有人能够提供一些见解，我将不胜感激。 Find the code used below:找到下面使用的代码：
IMAGESTRAIN_PATH = os.path.sep.join([BASE_PATH, "RegressionDatasetGrey"])
ANNOTSTRAIN_PATH = os.path.sep.join([BASE_PATH, "RegressionDatasetGrey.csv"])
IMAGESTEST_PATH = os.path.sep.join([BASE_PATH, "RegressionDSGreyTesting"])
ANNOTSTEST_PATH = os.path.sep.join([BASE_PATH, "RegressionDSGreyTesting.csv"])
print("[INFO] loading training dataset...")
rows = open(ANNOTSTRAIN_PATH).read().strip().split("\n")
data = []
targets = []
filenames = []
for row in rows:
row = row.split(",")
(filename, coordX, coordY) = row
coordX = float(int(coordX)/1920)
coordY = float(int(coordY)/1080)
imagePath = os.path.sep.join([IMAGESTRAIN_PATH, filename])
img = load_img(imagePath, color_mode="grayscale", target_size=(64, 80))
# img = img.size(1, 64, 80, 1)
img = img_to_array(img)
data.append(img)
targets.append((coordX, coordY))
filenames.append(filename)
trainImages = np.array(data, dtype="float32") / 255.0
trainCoords = np.array(targets, dtype="float32")
print("[INFO] loading testing dataset...")
rows1 = open(ANNOTSTEST_PATH).read().strip().split("\n")
data1 = []
targets1 = []
filenames1 = []
for row1 in rows1:
row1 = row1.split(",")
(filename1, coordX1, coordY1) = row1
coordX1 = float(int(coordX1)/1920)
coordY1 = float(int(coordY1)/1080)
imagePath1 = os.path.sep.join([IMAGESTEST_PATH, filename1])
image1 = load_img(imagePath1, color_mode="grayscale", target_size=(64, 80))
image1 = img_to_array(image1)
data1.append(image1)
targets1.append((coordX1, coordY1))
filenames1.append(filename1)
testImages = np.array(data1, dtype="float32") / 255.0
testCoords = np.array(targets1, dtype="float32")
inputs = Input(shape=(64, 80, 1))
x = inputs
x = Conv2D(16, (3, 3), padding='same')(x)
x = Activation('relu')(x)
# x = BatchNormalization()(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(32, (3, 3), padding='same')(x)
# x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Conv2D(64, (3, 3), padding='same')(x)
# x = BatchNormalization()(x)
x = Activation('relu')(x)
x = MaxPooling2D(pool_size=(2, 2))(x)
x = Flatten()(x)
x = Dense(128)(x)
x = Activation('relu')(x)
x = Dropout(0.3)(x)
x = Dense(64)(x)
x = Activation('relu')(x)
x = Dropout(0.3)(x)
x = Dense(2)(x)
x = Activation('linear')(x)
model = Model(inputs, x)
opt = Adam(lr=0.001)
model.compile(loss="mse", optimizer=opt)
# print(model.summary())
print("[INFO] training Regressor...")
History = model.fit(
trainImages, trainCoords,
validation_data=(testImages, testCoords),
batch_size=32,
epochs=50,
verbose=1, shuffle=True)
print("[INFO] Saving Model...")
model.save('ModelV12.h5')
N = 50
plt.style.use("ggplot")
plt.figure()
plt.plot(np.arange(0, N), History.history["loss"], label="train_loss")
plt.plot(np.arange(0, N), History.history["val_loss"], label="val_loss")
plt.title("Loss on Training Set")
plt.xlabel("Epoch #")
plt.ylabel("Loss")
plt.legend(loc="lower left")
plt.savefig('RegressionPlot.png')
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