# -*- coding: utf-8 -*-
""" Finetuning Example. Using weights from model trained in
convnet_cifar10.py to retrain network for a new task (your own dataset).
All weights are restored except last layer (softmax) that will be retrained
to match the new task (finetuning).
"""
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
# Note: You input here any dataset you would like to finetune
X, Y = your_dataset()
num_classes = 10
# Redefinition of convnet_cifar10 network
network = input_data(shape=[None, 32, 32, 3])
network = conv_2d(network, 32, 3, activation='relu')
network = max_pool_2d(network, 2)
network = dropout(network, 0.75)
network = conv_2d(network, 64, 3, activation='relu')
network = conv_2d(network, 64, 3, activation='relu')
network = max_pool_2d(network, 2)
network = dropout(network, 0.5)
network = fully_connected(network, 512, activation='relu')
network = dropout(network, 0.5)
# Finetuning Softmax layer (Setting restore=False to not restore its weights)
softmax = fully_connected(network, num_classes, activation='softmax', restore=False)
regression = regression(softmax, optimizer='adam',
loss='categorical_crossentropy',
learning_rate=0.001)
model = tflearn.DNN(regression, checkpoint_path='model_finetuning',
max_checkpoints=3, tensorboard_verbose=0)
# Load pre-existing model, restoring all weights, except softmax layer ones
model.load('cifar10_cnn')
# Start finetuning
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='model_finetuning')
model.save('model_finetuning')
