tflearn实vgg16模型

Posted 2020-08-03 徐长卿学数据分析

 

vgg16构造模型图：http://ethereon.github.io/netscope/#/gist/dc5003de6943ea5a6b8b

一下为代码：注释会不断添加。

 

# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
"""
Created on Sat Jul  2 14:58:30 2016

@author: ubuntu
"""

# -*- coding: utf-8 -*-

""" Very Deep Convolutional Networks for Large-Scale Visual Recognition.
Applying VGG 16-layers convolutional network to Oxford\'s 17 Category Flower
Dataset classification task.
References:
    Very Deep Convolutional Networks for Large-Scale Image Recognition.
    K. Simonyan, A. Zisserman. arXiv technical report, 2014.
Links:
    http://arxiv.org/pdf/1409.1556
"""

#在Ubuntu的terminal中运行是偶尔会报错关于PIL的，但是如此使用就不会报错了
from PIL import Image
a=Image.open(\'/home/ubuntu/pythonproject/tflearnproject/17flowers/jpg/0/image_0001.jpg\')

import tflearn
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.estimator import regression
import numpy as np

def load_image(in_image):
    """ Load an image, returns PIL.Image. """
    img = Image.open(in_image)
    return img
img_path=\'/home/ubuntu/pythonproject/tflearnproject/17flowers/jpg/0/image_0001.jpg\'
img=load_image(img_path)
def resize_image(in_image, new_width, new_height, out_image=None,
                 resize_mode=Image.ANTIALIAS):
    """ Resize an image.

    Arguments:
        in_image: `PIL.Image`. The image to resize.
        new_width: `int`. The image new width.
        new_height: `int`. The image new height.
        out_image: `str`. If specified, save the image to the given path.
        resize_mode: `PIL.Image.mode`. The resizing mode.

    Returns:
        `PIL.Image`. The resize image.

    """
    img = in_image.resize((new_width, new_height), resize_mode)
    if out_image:
        img.save(out_image)
    return img
img=resize_image(img, 224, 224)
def pil_to_nparray(pil_image):
    """ Convert a PIL.Image to numpy array. """
    pil_image.load()
    return np.asarray(pil_image, dtype="float32")
    
img=pil_to_nparray(img)
print(u\'用于测试的图片加载完成！\')
# Data loading and preprocessing
import tflearn.datasets.oxflower17 as oxflower17
print(\'------\')
print(\'666666666666\')
X, Y = oxflower17.load_data(one_hot=True)

# Building \'VGG Network\'以下为模型的加载，其中3是卷积核的大小即3*3.64/128/256/512是卷积核的个数
network = input_data(shape=[None, 224, 224, 3])

network = conv_2d(network, 64, 3, activation=\'relu\')
network = conv_2d(network, 64, 3, activation=\'relu\')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 128, 3, activation=\'relu\')
network = conv_2d(network, 128, 3, activation=\'relu\')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 256, 3, activation=\'relu\')
network = conv_2d(network, 256, 3, activation=\'relu\')
network = conv_2d(network, 256, 3, activation=\'relu\')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 512, 3, activation=\'relu\')
network = conv_2d(network, 512, 3, activation=\'relu\')
network = conv_2d(network, 512, 3, activation=\'relu\')
network = max_pool_2d(network, 2, strides=2)

network = conv_2d(network, 512, 3, activation=\'relu\')
network = conv_2d(network, 512, 3, activation=\'relu\')
network = conv_2d(network, 512, 3, activation=\'relu\')
network = max_pool_2d(network, 2, strides=2)

network = fully_connected(network, 4096, activation=\'relu\')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation=\'relu\')
network = dropout(network, 0.5)
network = fully_connected(network, 17, activation=\'softmax\')

network = regression(network, optimizer=\'rmsprop\',
                     loss=\'categorical_crossentropy\',
                     learning_rate=0.001)

# Training
#max_checkpoints是存储checkpoint文件的个数，如果超过个数，应该是自动删除
model = tflearn.DNN(network, checkpoint_path=\'model_vgg\',
                    max_checkpoints=1, tensorboard_verbose=0)
#snapshot_step表示执行多少步后保存checkpoint文件,n_epoch是执行循环的次数，batch_size每次读取图片的个数,如果内存不足可以通过这个进行调节。
print(u\'开始加载模型\')
#model.load(\'/home/ubuntu/pythonproject/tflearnproject/model_vgg-20\')
#model.load(\'model_vgg-30\')
model.fit(X, Y, n_epoch=1, shuffle=True,
          show_metric=True, batch_size=8, snapshot_step=10,
          snapshot_epoch=False, run_id=\'vgg_oxflowers17\')
model.save(\'vgg16.tflearn\')
#model.predit(X[0])
print(u\'开始预测\')
model.predict(img)
#model.load(\'vgg16.tflearn\')

图片版模型图：