如何使用分割图像数据集从classification_report输出中执行predict_generator和confusion_matrix？( Keras Tensorflow)
如何使用分割图像数据集从classification_report输出中执行predict_generator和confusion_matrix？( Keras Tensorflow)
提问于 2019-05-06 15:16:10
我为ImageDataGenerator创建了一个分类器，它使用flow_from_directory来加载数据集，然后执行模型和预测的训练。
我的问题是如何从classifier.predict_generator?的输出中获取度量(即acc、recall、FPR等)。
如果我没有错的话，使用confusion_matrix和classification_report方法会有很大帮助。图像(.tif文件)在
/data/test/image ---> RGB images
/data/test/label ---> Binary mask images
/data/train/image ---> RGB images
/data/train/label ---> Binary mask images这些图像如下所示：RGB图像 掩模图像。predict_generator方法返回如下所示的图像：预测图像
我已经尝试过像下面这样的代码来生成混淆矩阵，但没有正常工作：
predicted_classes_indices = np.argmax(results,axis=1)
labels = (image_generator.class_indices)
labels = dict((v, k) for k, v in labels.items())
predictions = [labels[k] for k in predicted_classes_indices]
cm = confusion_matrix(labels, predicted_classes_indices)所有代码：
from redeUnet import get_unet
import matplotlib.pyplot as plt
import numpy as np
import os
from tensorflow.python.keras.preprocessing.image import ImageDataGenerator
from tensorflow.python.keras.models import model_from_json
from tensorflow.python.keras.callbacks import EarlyStopping
from tensorflow.python.keras.callbacks import ModelCheckpoint
PATH_TRAIN = "..\\data\\train\\"
btSize = 4
alt = 256 # image row
larg = 256 # image col
image_folder = 'image'
mask_folder = 'label'
image_color_mode = 'rgb'
mask_color_mode = 'grayscale'
clMode = 'None' #'binary'
epocas = 5
qtdPatience = 60
'''
Data augmentation
'''
data_gen_args = dict(featurewise_center=False,
samplewise_center=False, # set each sample mean to 0
featurewise_std_normalization=False, # divide inputs by std of the dataset
samplewise_std_normalization=False, # divide each input by its std
zca_whitening=False, # apply ZCA whitening
rotation_range=40, # randomly rotate images in the range (degrees, 0 to 180)
zoom_range = 0.2, # Randomly zoom image
width_shift_range=0.2, # randomly shift images horizontally (fraction of total width)
height_shift_range=0.2, # randomly shift images vertically (fraction of total height)
horizontal_flip=True, # randomly flip images
vertical_flip=False,
rescale=1./255,
validation_split = 0.2) # randomly flip images
train_image_datagen = ImageDataGenerator(**data_gen_args)
train_mask_datagen = ImageDataGenerator(**data_gen_args)
'''
DATASET prepare and load (20% Validation)
'''
# Load RGB images TRAINING
image_generator = train_image_datagen.flow_from_directory(PATH_TRAIN,
classes = [image_folder],
class_mode = None,
color_mode = image_color_mode,
target_size = (larg, alt),
batch_size = btSize,
save_to_dir = None,
shuffle = False,
subset = 'training',
seed = 1)
# Load BINARY (Mask) images TRAINING
mask_generator = train_mask_datagen.flow_from_directory(PATH_TRAIN,
classes = [mask_folder],
class_mode = None,
color_mode = mask_color_mode,
target_size = (larg, alt),
batch_size = btSize,
save_to_dir = None,
shuffle = False,
subset = 'training',
seed = 1)
train_generator = zip(image_generator, mask_generator)
#-------------------------------------------------
# VALIDATION images RGB
valid_image_generator = train_image_datagen.flow_from_directory(PATH_TRAIN,
classes = [image_folder],
class_mode = None,
color_mode = image_color_mode,
target_size = (larg, alt),
batch_size = btSize,
save_to_dir = None,
shuffle = False,
subset = 'validation',
seed = 1)
# VALIDATION images BINARY (Mask)
valid_mask_generator = train_mask_datagen.flow_from_directory(PATH_TRAIN,
classes = [mask_folder],
class_mode = None,
color_mode = mask_color_mode,
target_size = (larg, alt),
batch_size = btSize,
save_to_dir = None,
shuffle = False,
subset = 'validation',
seed = 1)
valid_generator = zip(valid_image_generator, valid_mask_generator)
#-------------------------------------------------
'''
RUN TRAINING
'''
# Get UNET
classificador = get_unet(larg, alt, 3)
classificador.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy']) #metrics = ['accuracy', minhaMetrica])
# Salvando o Modelo e Pesos (Best Model, StopEarly)
es = EarlyStopping(monitor = 'val_loss', mode = 'min', verbose = 1, patience = qtdPatience)
mc = ModelCheckpoint('best_polyp_unet_model.h5', monitor = 'val_loss', verbose = 1, save_best_only = True)
history = classificador.fit_generator(train_generator,
steps_per_epoch = image_generator.n // btSize,
validation_data = valid_generator,
validation_steps = valid_image_generator.n // btSize,
epochs = epocas, callbacks=[es, mc])
resultados = classificador.predict_generator(valid_generator,
steps = valid_image_generator.n,
verbose = 1)
#-------------------------------------------------
#HOW TO GET THE METRICS?
predicted_classes_indices = np.argmax(resultados,axis=1)
labels = (image_generator.class_indices)
labels = dict((v, k) for k, v in labels.items())
predictions = [labels[k] for k in predicted_classes_indices]
cm = confusion_matrix(ground_truth, predicted_classes)
#-------------------------------------------------在这一行中我收到了一条错误消息：predictions = [labels[k] for k in predicted_classes_indices]
错误:无法使用的类型：“numpy.ndarray”
当我通过运行以下命令检查预测的输出变量("resultados")时：resultados.shape.Shows：
(480, 256, 256, 1)有480张来自U网预测的图像。
但是，我如何将这些信息转换为与"confusion_matrix“或"classification_report”匹配的信息呢？我认为这更困难，因为这是一个分割问题。
如有任何建议，将不胜感激。
回答 1
Stack Overflow用户
发布于 2021-01-24 19:25:10
你需要平平你的预言和基本真理(y)：
import numpy as np
predictions_flat = predictions.flatten()
y_flat = y.flatten()然后，可以在扁平矩阵上运行分类报告和混淆矩阵。
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
print('Train report', classification_report(y_flat, predictions_flat))
print('Train conf matrix', confusion_matrix(y_flat, predictions_flat))页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/56008022
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