keras用vgg16做图像分类

最新推荐文章于 2024-07-23 10:06:30 发布

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最新推荐文章于 2024-07-23 10:06:30 发布

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文章标签：人工智能 python

本文介绍如何使用VGG16预训练模型进行迁移学习，实现图像分类任务。通过加载训练集和测试集，对图像进行预处理，并利用Keras搭建深度学习模型，最终完成模型训练与预测。

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实际上我只是提供一个模版而已，代码应该很容易看得懂，label是存在一个csv里面的，图片是在一个文件夹里面的

没GPU的就不用尝试了，训练一次要很久很久。。。

## import libaries
import pandas as pd
import numpy as np
from skimage import io
import os, sys
from tqdm import tqdm

## load data
train = pd.read_csv('./data/data/train.csv')
test = pd.read_csv('./data/data/test.csv')

def read_img(img_path):
    img = io.imread(img_path)
    return img

## set path for images
TRAIN_PATH = './data/data/train_img/'
TEST_PATH = './data/data/test_img/'


# load data
train_img, test_img = [],[]
for img_path in tqdm(train['image_id'].values):
    train_img.append(read_img(TRAIN_PATH + img_path + '.png'))

for img_path in tqdm(test['image_id'].values):
    test_img.append(read_img(TEST_PATH + img_path + '.png'))

# normalize images
x_train = np.array(train_img, np.float32) / 255.
x_test = np.array(test_img, np.float32) / 255.

# target variable - encoding numeric value
label_list = train['label'].tolist()
Y_train = {k:v+1 for v,k in enumerate(set(label_list))}
y_train = [Y_train[k] for k in label_list]   
y_train = np.array(y_train)


from keras import applications
from keras.models import Model
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.metrics import categorical_accuracy
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import EarlyStopping
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint

y_train = to_categorical(y_train)

#Transfer learning with Inception V3 
base_model = applications.VGG16(weights='imagenet', include_top=False, input_shape=(256, 256, 3))

## set model architechture 
add_model = Sequential()
add_model.add(Flatten(input_shape=base_model.output_shape[1:]))
add_model.add(Dense(256, activation='relu'))
add_model.add(Dense(y_train.shape[1], activation='softmax'))

model = Model(inputs=base_model.input, outputs=add_model(base_model.output))
model.compile(loss='categorical_crossentropy', optimizer=optimizers.SGD(lr=1e-4, momentum=0.9),
            metrics=['accuracy'])

model.summary()

batch_size = 128 # tune it
epochs = 30 # increase it
print ("Hello")
train_datagen = ImageDataGenerator(
        shear_range=0.2,
        zoom_range=0.2,
        rotation_range=30, 
        width_shift_range=0.1,
        height_shift_range=0.1, 
        horizontal_flip=True)
train_datagen.fit(x_train)

history = model.fit_generator(
    train_datagen.flow(x_train, y_train, batch_size=batch_size),
    steps_per_epoch=x_train.shape[0] // batch_size,
    epochs=epochs,
    callbacks=[ModelCheckpoint('VGG16-transferlearning2.model', monitor='val_acc', save_best_only=True)]
)

## predict test data
predictions = model.predict(x_test)


# get labels
predictions = np.argmax(predictions, axis=1)
rev_y = {v:k for k,v in Y_train.items()}
pred_labels = [rev_y[k] for k in predictions]

## make submission
sub = pd.DataFrame({'image_id':test.image_id, 'label':pred_labels})
sub.to_csv('sub_vgg2.csv', index=False) ## ~0.59