VGG-16 tflearn实现
tflearn 官方github上有给出基于tflearn下的VGG-16的实现
from future import division, print_function, absolute_import
- 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
- # Data loading and preprocessing
- import tflearn.datasets.oxflower17 as oxflower17
- X, Y = oxflower17.load_data(one_hot=True)
- # Building 'VGG Network'
- 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
- model = tflearn.DNN(network, checkpoint_path='model_vgg',
- max_checkpoints=1, tensorboard_verbose=0)
- model.fit(X, Y, n_epoch=500, shuffle=True,
- show_metric=True, batch_size=32, snapshot_step=500,
- snapshot_epoch=False, run_id='vgg_oxflowers17')
VGG-16 graph如下：
对VGG，我个人觉得他的亮点不多，pre-trained的model我们可以很好的使用，但是不如GoogLeNet那样让我有眼前一亮的感觉。
