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lab6_train_tfLenet5_mnist.py
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#-*- coding: utf-8 -*-
#! /usr/bin/env python
'''
filename: lab6_train_tfLenet5_mnist.py
description: simple end-to-end LetNet5 implementation
- For the purpose of EverybodyTensorFlow tutorial
-
- training with Mnist data set from Yann's website.
- the benchmark test error rate is 0.95% which is given by LeCun 1998
- references:
- https://github.com/tensorflow/models/blob/master/tutorials/image/mnist/convolutional.py
- https://github.com/sujaybabruwad/LeNet-in-Tensorflow/blob/master/LeNet-Lab.ipynb
author: Jaewook Kang
date : 2018 Feb.
'''
# Anybody know why we should include "__future__" code conventionally?
# anyway I include the below:
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import sys
import time
from datetime import datetime
import argparse
from os import getcwd
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
sys.path.insert(0, getcwd()+'/tf_cnn_model')
from tfmodel_lenet5 import Lenet5
from mnist_data_loader import DataFilename
from mnist_data_loader import MnistLoader
# configure training parameters =====================================
class TrainConfig(object):
def __init__(self):
self.learning_rate = 0.01
self.is_learning_rate_decay = True
self.learning_rate_decay_rate =0.99
self.opt_type='Adam'
self.training_epochs = 100
self.minibatch_size = 1000
# the number of step between evaluation
self.display_step = 5
self.total_batch = int(TRAININGSET_SIZE / self.minibatch_size)
# batch norm config
self.batch_norm_epsilon = 1E-5
self.batch_norm_decay = 0.99
self.FLAGS = None
# FC layer config
self.dropout_keeprate = 0.8
self.fc_layer_l2loss_epsilon = 5E-5
self.tf_data_type = tf.float32
# tensorboard config
now = datetime.utcnow().strftime("%Y%m%d%H%M%S")
root_logdir = 'tf_logs'
self.logdir = "{}/run-{}/".format(root_logdir, now)
# data size config
# TRAININGSET_SIZE = 50000
# VALIDATIONSET_SIZE = 10000
# TESTSET_SIZE = 10000
TRAININGSET_SIZE = 5000
VALIDATIONSET_SIZE = 1000
TESTSET_SIZE = 1000
# worker instance declaration
datafilename_worker = DataFilename()
mnist_data_loader = MnistLoader()
trainconfig_worker = TrainConfig()
# Download the data
train_data_filepathname = mnist_data_loader.download_mnist_dataset(filename=datafilename_worker.trainingimages_filename)
train_labels_filepathname = mnist_data_loader.download_mnist_dataset(filename=datafilename_worker.traininglabels_filename)
test_data_filepathname = mnist_data_loader.download_mnist_dataset(filename=datafilename_worker.testimages_filename)
test_labels_filepathname = mnist_data_loader.download_mnist_dataset(filename=datafilename_worker.testlabels_filename)
# extract data from gzip files into numpy arrays
train_data = mnist_data_loader.extract_data(filename=train_data_filepathname,
num_images=TRAININGSET_SIZE + VALIDATIONSET_SIZE)
train_labels = mnist_data_loader.extract_label(filename=train_labels_filepathname,
num_images=TRAININGSET_SIZE + VALIDATIONSET_SIZE)
test_data = mnist_data_loader.extract_data(filename=test_data_filepathname,
num_images=TESTSET_SIZE)
test_labels = mnist_data_loader.extract_label(filename=test_labels_filepathname,
num_images=TESTSET_SIZE)
# prepare validation by spliting training set
validation_data = train_data[:VALIDATIONSET_SIZE, ...]
validation_labels = train_labels[:VALIDATIONSET_SIZE]
train_data = train_data[VALIDATIONSET_SIZE:, ...]
train_labels = train_labels[VALIDATIONSET_SIZE:]
# [data set should be zipped here]
# network model construction ======================
# TF computational graph construction
lenet5_tf_graph = tf.Graph()
with lenet5_tf_graph.as_default():
# training nodes (data,label) placeholders
data_node = tf.placeholder(dtype=trainconfig_worker.tf_data_type,
shape=[None, mnist_data_loader.IMAGE_SIZE,
mnist_data_loader.IMAGE_SIZE,
mnist_data_loader.NUM_CHANNELS])
labels_node = tf.placeholder(dtype=tf.int64,
shape=[None, ])
dropout_keeprate_node = tf.placeholder(dtype=trainconfig_worker.tf_data_type)
lenet5_model_builder = Lenet5(dropout_keeprate_for_fc=dropout_keeprate_node,
dtype=trainconfig_worker.tf_data_type)
lenet5_model_builder.get_tf_model(input_nodes=data_node)
with tf.name_scope("cost_func"):
lenet5_model_builder.get_tf_cost_fuction(train_labels_node = labels_node,
is_l2_loss=True,
epsilon=trainconfig_worker.fc_layer_l2loss_epsilon)
with tf.name_scope('optimizer'):
lenet5_model_builder.get_tf_optimizer(opt_type=trainconfig_worker.opt_type,
learning_rate=trainconfig_worker.learning_rate,
total_batch_size=TRAININGSET_SIZE,
minibatch_size=trainconfig_worker.minibatch_size,
is_exp_decay=trainconfig_worker.is_learning_rate_decay,
decay_rate=trainconfig_worker.learning_rate_decay_rate)
with tf.name_scope('model_out'):
model_pred = tf.nn.softmax(lenet5_model_builder.out_layer_out)
with tf.name_scope('eval_performance'):
error = tf.equal(tf.argmax(model_pred,1),labels_node)
tf_pred_accuracy = tf.reduce_mean(tf.cast(error,tf.float32))
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# file writing for Tensorboard
#file_writer = tf.summary.FileWriter(logdir=trainconfig_worker.logdir)
#file_writer.add_graph(lenet5_tf_graph)
# Summary for Tensorboard visualization
#tb_summary_accuracy = tf.summary.scalar('accuracy', tf_pred_accuracy)
#tb_summary_cost = tf.summary.scalar('loss', lenet5_model_builder.tf_cost)
# network model training ==============================
train_error_rate = np.zeros(shape=np.ceil(trainconfig_worker.training_epochs/trainconfig_worker.display_step).astype(np.int16),
dtype=np.float32)
validation_error_rate = np.zeros(shape=np.ceil(trainconfig_worker.training_epochs/trainconfig_worker.display_step).astype(np.int16),
dtype=np.float32)
test_error_rate = np.zeros(shape=np.ceil(trainconfig_worker.training_epochs/trainconfig_worker.display_step).astype(np.int16),
dtype=np.float32)
with tf.Session(graph=lenet5_tf_graph) as sess:
# Run the variable initializer
sess.run(init)
print("-------------------------------------------")
rate_record_index = 0
for epoch in range(trainconfig_worker.training_epochs):
avg_cost = 0.
avg_minibatch_error_rate = 0.
start_time = time.time()
# [data shuffling here]
for i in range(trainconfig_worker.total_batch):
data_start_index = i * trainconfig_worker.minibatch_size
data_end_index = (i + 1) * trainconfig_worker.minibatch_size
minibatch_data = train_data [data_start_index:data_end_index, ...]
minibatch_label = train_labels[data_start_index:data_end_index]
_, minibatch_cost = sess.run([lenet5_model_builder.tf_optimizer,lenet5_model_builder.tf_cost],
feed_dict={data_node: minibatch_data,
labels_node: minibatch_label,
dropout_keeprate_node: trainconfig_worker.dropout_keeprate})
# compute average cost and error rate
avg_cost += minibatch_cost
avg_cost = avg_cost / trainconfig_worker.total_batch
if trainconfig_worker.display_step == 0:
continue
elif (epoch + 1) % trainconfig_worker.display_step == 0:
elapsed_time = time.time() - start_time
train_error_rate[rate_record_index] = (1.0 - tf_pred_accuracy.eval(feed_dict={data_node: train_data,
labels_node: train_labels,
dropout_keeprate_node: 1.0})) *100.0
validation_error_rate[rate_record_index] = (1.0 - tf_pred_accuracy.eval(feed_dict={data_node: validation_data,
labels_node: validation_labels,
dropout_keeprate_node: 1.0})) * 100.0
test_error_rate[rate_record_index] = (1.0 - tf_pred_accuracy.eval(feed_dict={data_node: test_data,
labels_node: test_labels,
dropout_keeprate_node: 1.0})) * 100.0
# tb_summary_cost_result, tb_summary_accuracy_result = sess.run([tb_summary_cost,tb_summary_accuracy],
# feed_dict={data_node: train_data,
# labels_node: train_labels,
# dropout_keeprate_node:1.0})
# file_writer.add_summary(summary_str,step)
print('At epoch = %d, elapsed_time = %.1f ms' % (epoch, elapsed_time))
print("Training set avg cost (avg over minibatches)=%.2f" % avg_cost)
print("Training set Err rate (avg over minibatches)= %.2f %% " % (train_error_rate[rate_record_index]))
print("Validation set Err rate (total batch)= %.2f %%" % (validation_error_rate[rate_record_index]))
print("Test Set Err. rate (total batch) = %.2f %%" % (test_error_rate[rate_record_index]) )
print("--------------------------------------------")
rate_record_index += 1
print("Training finished!")
#file_writer.close()
# Training result visualization ===============================================
hfig1 = plt.figure(1, figsize=(10, 10))
err_rate_index = np.array([elem for elem in range(train_error_rate.shape[0])])
plt.plot(err_rate_index, train_error_rate, label='Training err', color='r', marker='o')
plt.plot(err_rate_index, validation_error_rate, label='Validation err', color='b', marker='x')
plt.plot(err_rate_index, test_error_rate, label='Test err', color='g', marker='d')
plt.legend()
plt.title('Train/Valid/Test Error rate')
plt.xlabel('Iteration epoch')
plt.ylabel('error Rate')
plt.show()