python - 从 VGG16 模型到 Mobilenet 再到 android studio
问题描述
所以这是我从本教程https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html得到的图像分类器,当前模型是 VGG16 I想把它改成移动网络并实现到android studio中
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
from PIL import ImageFile, Image
print(Image.__file__)
import numpy
import matplotlib.pyplot as plt
# dimensions of our images.
img_width, img_height = 150, 150
train_data_dir = r'C:\Users\Acer\imagerec\Brain\TRAIN'
validation_data_dir = r'C:\Users\Acer\imagerec\Brain\VAL'
nb_train_samples = 140
nb_validation_samples = 40
epochs = 50
batch_size = 5
if K.image_data_format() == 'channels_first':
input_shape = (3, img_width, img_height)
else:
input_shape = (img_width, img_height, 3)
from keras.applications.vgg16 import VGG16
from keras.models import Model
from keras.layers import Dense
vgg = VGG16(include_top=False, weights='imagenet', input_shape=(), pooling='avg')
x = vgg.output
x = Dense(1, activation='sigmoid')(x)
model = Model(vgg.input, x)
model.summary()
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1. / 255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
validation_generator = test_datagen.flow_from_directory(
validation_data_dir,
target_size=(img_width, img_height),
batch_size=batch_size,
class_mode='binary')
model.fit_generator(
train_generator,
steps_per_epoch=nb_train_samples // batch_size,
epochs=epochs,
validation_data=validation_generator,
validation_steps=nb_validation_samples // batch_size)
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
import seaborn as sns
test_steps_per_epoch = numpy.math.ceil(validation_generator.samples / validation_generator.batch_size)
predictions = model.predict_generator(validation_generator, steps=test_steps_per_epoch)
# Get most likely class
predicted_classes = numpy.argmax(predictions, axis=1)
true_classes = validation_generator.classes
class_labels = list(validation_generator.class_indices.keys())
report = classification_report(true_classes, predicted_classes, target_names=class_labels)
print(report)
cm=confusion_matrix(true_classes,predicted_classes)
sns.heatmap(cm, annot=True)
print(cm)
plt.show()
会改变
from keras.applications.vgg16 import VGG16
from keras.models import Model
from keras.layers import Dense
vgg = VGG16(include_top=False, weights='imagenet', input_shape=(), pooling='avg')
x = vgg.output
x = Dense(1, activation='sigmoid')(x)
model = Model(vgg.input, x)
model.summary()
到预训练的移动网络模型中,我可以通过简单地将 .py 文件更改为 .pb 文件并将其导入到 android studio 中来将这些代码实现到 android studio 中吗?
解决方案
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