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VGG16.py
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#coding=utf-8
'''
Very Deep Convolutional Networks for Large-Scale Visual Recognition
论文地址 http://arxiv.org/pdf/1409.1556
'''
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 tflearn.datasets.oxflower17 as oxflower17
X , Y = oxflower17.load_data(one_hot = True)
def net():
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 , loss = 'categorical_crossentropy' , optimizer = 'rmsprop' , learning_rate = 0.001)
vggnet = net()
model = tflearn.DNN(vggnet , 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')
model.save('vgg_model.tflearn')