AlexNet in TFLearn

for Oxford's 17 Category Flower Dataset Classification

Based on https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py



In [1]:

    
from __future__ import division, print_function, absolute_import



In [2]:

    
import tflearn



In [3]:

    
from tflearn.layers.core import input_data, dropout, fully_connected
from tflearn.layers.conv import conv_2d, max_pool_2d
from tflearn.layers.normalization import local_response_normalization
from tflearn.layers.estimator import regression

Import Data



In [4]:

    
import tflearn.datasets.oxflower17 as oxflower17



In [5]:

    
X, Y = oxflower17.load_data(one_hot=True, resize_pics=(227, 227))









    



Downloading Oxford 17 category Flower Dataset, Please wait...






    



100.0% 60276736 / 60270631






    



('Succesfully downloaded', '17flowers.tgz', 60270631, 'bytes.')
File Extracted
Starting to parse images...
Parsing Done!

Build 'AlexNet'



In [6]:

    
network = input_data(shape=[None, 227, 227, 3])

network = conv_2d(network, 96, 11, strides=4, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)

network = conv_2d(network, 256, 5, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)

network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 384, 3, activation='relu')
network = conv_2d(network, 256, 3, activation='relu')
network = max_pool_2d(network, 3, strides=2)
network = local_response_normalization(network)

network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)
network = fully_connected(network, 4096, activation='tanh')
network = dropout(network, 0.5)

network = fully_connected(network, 17, activation='softmax')

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









    



WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)
WARNING:tensorflow:tf.variable_op_scope(values, name, default_name) is deprecated, use tf.variable_scope(name, default_name, values)

Training



In [7]:

    
model = tflearn.DNN(network, checkpoint_path='model_alexnet',
                    max_checkpoints=1, tensorboard_verbose=2)



In [9]:

    
# n_epoch=1000 is recommended:
model.fit(X, Y, n_epoch=10, validation_set=0.1, shuffle=True,
          show_metric=True, batch_size=64, snapshot_step=200,
          snapshot_epoch=False, run_id='alexnet_oxflowers17')









    



Training Step: 220  | total loss: 1.83088
| Momentum | epoch: 011 | loss: 1.83088 - acc: 0.3729 -- iter: 1224/1224



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