python - 我应该使用评估生成器还是评估来评估我的 CNN 模型

问题描述

我正在使用 keras 实现 CNN 来执行图像分类，并且我使用 .fit_generator() 方法来训练模型，直到验证停止条件我使用了下一个代码：

history_3conv = cnn3.fit_generator(train_data,steps_per_epoch = train_data.n // 98, callbacks = [es,ckpt_3Conv], 
    validation_data = valid_data, validation_steps = valid_data.n // 98,epochs=50)

停止前的最后两个时期是下一个：

如图所示，最后一次训练的准确率为 0.91。然而，当我使用model.evaluate()方法来评估训练、测试和验证集时，我得到了下一个结果：

所以，我的问题是：为什么我得到两个不同的值？

我应该使用evaluate_generator()吗？还是我应该知道要执行数据扩充我使用了下一个代码seed：flow_from_directory()

trdata = ImageDataGenerator(rotation_range=90,horizontal_flip=True)
vldata = ImageDataGenerator()
train_data = trdata.flow(x_train,y_train,batch_size=98)
valid_data = vldata.flow(x_valid,y_valid,batch_size=98)

此外，我知道use_multiprocessing=False在 fit_generator 中进行设置会使我显着减慢训练速度。那么你认为最好的解决方案是什么

标签： pythontensorflowkerasmodel-validationdata-augmentation

解决方案

model.fit()并且model.evaluate()是不推荐使用的model.fit_generator方式model.evaluate_generator。

training和validation数据是生成器生成的增强数据。因此，您的准确性会有所不同。如果您在of和 for or中使用了非增强validation或test数据，那么准确性不会有任何变化。validation_datafit_generatormodel.evaluate()model.evaluate_generator

以下是我运行了一个时代的简单猫狗分类程序-

验证数据生成器只有重新缩放转换，没有其他增强技术。
验证准确度在 epoch 结束后显示。
使用重置验证数据生成器val_data_gen.reset()。虽然我们没有做任何增强，但应该没有必要。
model.evaluate使用和以及评估验证数据的准确性model.evaluate_generator。

在 epoch 结束后计算的验证准确度和使用model.evaluate和model.evaluate_generatoris 匹配计算的准确度。

代码：

import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Conv2D, Flatten, Dropout, MaxPooling2D
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.optimizers import Adam

import os
import numpy as np
import matplotlib.pyplot as plt

_URL = 'https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip'

path_to_zip = tf.keras.utils.get_file('cats_and_dogs.zip', origin=_URL, extract=True)

PATH = os.path.join(os.path.dirname(path_to_zip), 'cats_and_dogs_filtered')

train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')

train_cats_dir = os.path.join(train_dir, 'cats')  # directory with our training cat pictures
train_dogs_dir = os.path.join(train_dir, 'dogs')  # directory with our training dog pictures
validation_cats_dir = os.path.join(validation_dir, 'cats')  # directory with our validation cat pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')  # directory with our validation dog pictures

num_cats_tr = len(os.listdir(train_cats_dir))
num_dogs_tr = len(os.listdir(train_dogs_dir))

num_cats_val = len(os.listdir(validation_cats_dir))
num_dogs_val = len(os.listdir(validation_dogs_dir))

total_train = num_cats_tr + num_dogs_tr
total_val = num_cats_val + num_dogs_val

batch_size = 1
epochs = 1
IMG_HEIGHT = 150
IMG_WIDTH = 150

train_image_generator = ImageDataGenerator(rescale=1./255,brightness_range=[0.5,1.5]) # Generator for our training data
validation_image_generator = ImageDataGenerator(rescale=1./255) # Generator for our validation data

train_data_gen = train_image_generator.flow_from_directory(batch_size=batch_size,
                                                           directory=train_dir,
                                                           shuffle=True,
                                                           target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                           class_mode='binary')

val_data_gen = validation_image_generator.flow_from_directory(batch_size=batch_size,
                                                              directory=validation_dir,
                                                              target_size=(IMG_HEIGHT, IMG_WIDTH),
                                                              class_mode='binary')

model = Sequential([
    Conv2D(16, 3, padding='same', activation='relu', input_shape=(IMG_HEIGHT, IMG_WIDTH ,3)),
    MaxPooling2D(),
    Conv2D(32, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Conv2D(64, 3, padding='same', activation='relu'),
    MaxPooling2D(),
    Flatten(),
    Dense(512, activation='relu'),
    Dense(1)
])

optimizer = 'SGD'

model.compile(optimizer=optimizer, 
          loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
          metrics=['accuracy'])

history = model.fit_generator(
          train_data_gen,
          steps_per_epoch=total_train // batch_size,
          epochs=epochs,
          validation_data=val_data_gen,
          validation_steps=total_val // batch_size)


from sklearn.metrics import confusion_matrix

# Reset 
val_data_gen.reset()

# Evaluate on Validation data
scores = model.evaluate(val_data_gen)
print("%s%s: %.2f%%" % ("evaluate ",model.metrics_names[1], scores[1]*100))

scores = model.evaluate_generator(val_data_gen)
print("%s%s: %.2f%%" % ("evaluate_generator ",model.metrics_names[1], scores[1]*100))

输出：

Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
2000/2000 [==============================] - 74s 37ms/step - loss: 0.6932 - accuracy: 0.5025 - val_loss: 0.6815 - val_accuracy: 0.5000
1000/1000 [==============================] - 11s 11ms/step - loss: 0.6815 - accuracy: 0.5000
evaluate accuracy: 50.00%
evaluate_generator accuracy: 50.00%

python - 我应该使用评估生成器还是评估来评估我的 CNN 模型

问题描述

解决方案

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