python - 具有来自目录的流的多输入模型
问题描述
我正在尝试学习如何通过 flow_from_directory 使用多模型输入,但有些东西我不知道。谢谢你的帮助。
我理解的方式是我们为 fit_generator 提供 two_image_generator 和 fit 方法将推断标签......我错过了什么......
def two_image_generator(generator,
directory,
batch_size,
shuffle = False,
img_size1 = (224,224),
img_size2 = (299,299)):
gen1 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size1,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 1)
gen2 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size2,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 1)
while True:
X1i = gen1.next()
X2i = gen2.next()
if y_col:
yield [X1i[0], X2i[0]], X1i[1] #X1i[1] is the label
else:
yield [X1i, X2i]
#add data_augmentation
train_aug_datagen = ImageDataGenerator(
rotation_range = 20,
shear_range = 0.1,
zoom_range = 0.2,
width_shift_range = 0.1,
height_shift_range = 0.1,
horizontal_flip = True
)
train_generator = two_image_generator(train_aug_datagen,
train_dir,
batch_size = batch_size,
shuffle = True)
validation_datagen = ImageDataGenerator()
validation_generator = two_image_generator(validation_datagen,
validation_dir,
batch_size = batch_size,
shuffle = True)
def create_base_model(MODEL, img_size, lambda_fun = None):
inp = Input(shape = (img_size[0], img_size[1], 3))
x = inp
if lambda_fun:
x = Lambda(lambda_fun)(x)
base_model = MODEL(input_tensor = x, weights = 'imagenet',
include_top = False, pooling = 'avg')
model = Model(inp, base_model.output)
return model
#define vgg + resnet50 + densenet
model1 = create_base_model(vgg16.VGG16, (224, 224), vgg16.preprocess_input)
model2 = create_base_model(resnet50.ResNet50, (224, 224), resnet50.preprocess_input)
model3 = create_base_model(inception_v3.InceptionV3, (299, 299), inception_v3.preprocess_input)
model1.trainable = False
model2.trainable = False
model3.trainable = False
inpA = Input(shape = (224, 224, 3))
inpB = Input(shape = (299, 299, 3))
out1 = model1(inpA)
out2 = model2(inpA)
out3 = model3(inpB)
x = Concatenate()([out1, out2, out3])
x = Dropout(0.2)(x)
x = Dense(2, activation='softmax')(x)
model = Model([inpA, inpB], x)
############################################################################
trained_models_path = './models/VggFace_best_model'
model_names = trained_models_path + '_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.hdf5'
checkpoint = ModelCheckpoint(model_names, 'val_accuracy', verbose=1, save_best_only=True)
############################################################################
early = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto')
callbacks = [checkpoint,early]
history = model.fit_generator(train_generator,
steps_per_epoch= NUM_TRAIN //batch_size,
epochs=100,
validation_data=validation_generator,
validation_steps= NUM_TEST //batch_size,
verbose=1,
use_multiprocessing=True,
workers=14,
callbacks=callbacks )
NameError:名称“y_col”未定义
解决方案
#add data_augmentation
train_aug_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range = 20,
shear_range = 0.1,
zoom_range = 0.2,
width_shift_range = 0.1,
height_shift_range = 0.1,
horizontal_flip = True
)
validation_datagen = ImageDataGenerator(rescale = 1./255)
def two_image_generator(generator,
directory,
batch_size,
shuffle = False,
img_size1 = (224,224),
img_size2 = (299,299)):
gen1 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size1,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 7)
gen2 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size2,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 7)
while True:
X1i = gen1.next()
X2i = gen2.next()
yield [X1i[0], X2i[0]], X2i[1] #Yield both images and their mutual label
train_generator = two_image_generator(train_aug_datagen,
train_dir,
batch_size = batch_size,
shuffle = True)
validation_generator = two_image_generator(validation_datagen,
validation_dir,
batch_size = batch_size,
shuffle = True)
############################################################################
trained_models_path = './models/VggFace_best_model'
model_names = trained_models_path + '_epoch_{epoch:02d}_val_acc_{val_accuracy:.4f}.hdf5'
checkpoint = ModelCheckpoint(model_names, 'val_accuracy', verbose=1, save_best_only=True)
############################################################################
early = EarlyStopping(monitor='val_loss', min_delta=0, patience=3, verbose=1, mode='auto')
callbacks = [checkpoint,early]
history = model.fit_generator(train_generator,
steps_per_epoch= NUM_TRAIN //batch_size,
epochs=100,
validation_data=validation_generator,
validation_steps= NUM_TEST //batch_size,
verbose=1,
use_multiprocessing=True,
# workers=14,
callbacks=callbacks )
Epoch 1/100
Found 5000 images belonging to 2 classes.
Found 5000 images belonging to 2 classes.
Found 52700 images belonging to 2 classes.
Found 52700 images belonging to 2 classes.
340/625 [===============>..............] - ETA: 4:37 - loss: 7.7634 - acc: 0.4926
import pandas as pd
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