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Commit 9f006a6b authored by Tiago de Freitas Pereira's avatar Tiago de Freitas Pereira
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#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
# @date: Wed 11 May 2016 09:39:36 CEST
"""
Simple script that trains MNIST with LENET using Tensor flow
Usage:
train_mnist.py [--batch-size=<arg> --iterations=<arg> --validation-interval=<arg> --use-gpu]
train_mnist.py -h | --help
Options:
-h --help Show this screen.
--batch-size=<arg> [default: 1]
--iterations=<arg> [default: 30000]
--validation-interval=<arg> [default: 100]
"""
from docopt import docopt
import tensorflow as tf
from .. import util
from ..DataShuffler import *
from ..lenet import Lenet
from matplotlib.backends.backend_pdf import PdfPages
import sys
SEED = 10
from ..DataShuffler import *
def compute_euclidean_distance(x, y):
"""
Computes the euclidean distance between two tensorflow variables
"""
#d = tf.square(tf.sub(x, y))
#d = tf.sqrt(tf.reduce_sum(d)) # What about the axis ???
d = tf.sqrt(tf.reduce_sum(tf.square(tf.sub(x, y)), 1))
return d
def compute_contrastive_loss(left_feature, right_feature, label, margin):
"""
Compute the contrastive loss as in
http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf
L = 0.5 * (Y) * D^2 + 0.5 * (1-Y) * {max(0, margin - D)}^2
**Parameters**
left_feature: First element of the pair
right_feature: Second element of the pair
label: Label of the pair (0 or 1)
margin: Contrastive margin
**Returns**
Return the loss operation
"""
#label = tf.to_float(label)
#one = tf.constant(1.0)
#zero = tf.constant(0.0)
#half = tf.constant(0.5)
#m = tf.constant(margin)
#d = compute_euclidean_distance(left_feature, right_feature)
#first_part = tf.mul(label, tf.square(d))# (Y)*(d^2)
#max_part = tf.square(tf.maximum(m-d, zero))
#second_part = tf.mul(one-label, max_part) # (1-Y) * max(margin - d, 0)
#loss = half * tf.reduce_sum(first_part + second_part)
#return loss
# Stack overflow "fix"
label = tf.to_float(label)
one = tf.constant(1.0)
d = compute_euclidean_distance(left_feature, right_feature)
first_part = tf.mul(one - label, tf.square(d)) # (Y-1)*(d^2)
max_part = tf.square(tf.maximum(margin - d, 0))
second_part = tf.mul(label, max_part) # (Y) * max((margin - d)^2, 0)
loss = 0.5 * tf.reduce_mean(first_part + second_part)
return loss
def main():
args = docopt(__doc__, version='Mnist training with TensorFlow')
BATCH_SIZE = int(args['--batch-size'])
ITERATIONS = int(args['--iterations'])
VALIDATION_TEST = int(args['--validation-interval'])
perc_train = 0.9
CONTRASTIVE_MARGIN = 0.1
USE_GPU = args['--use-gpu']
#print("Load data")
#sys.stdout.flush()
data, labels = util.load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
data_shuffler = DataShuffler(data, labels, scale=True)
#print("A")
#sys.stdout.flush()
# Siamease place holders
train_left_data = tf.placeholder(tf.float32, shape=(BATCH_SIZE*2, 28, 28, 1), name="left")
train_right_data = tf.placeholder(tf.float32, shape=(BATCH_SIZE * 2, 28, 28, 1), name="right")
labels_data = tf.placeholder(tf.int32, shape=BATCH_SIZE*2)
validation_data = tf.placeholder(tf.float32, shape=(data_shuffler.validation_data.shape[0], 28, 28, 1))
#print("B")
#sys.stdout.flush()
# Creating the architecture
lenet_architecture = Lenet(seed=SEED, use_gpu=USE_GPU)
lenet_train_left = lenet_architecture.create_lenet(train_left_data)
lenet_train_right = lenet_architecture.create_lenet(train_right_data)
lenet_validation = lenet_architecture.create_lenet(validation_data, train=False)
#print("C")
#sys.stdout.flush()
# Defining the constrastive loss
#left_output = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(lenet_train_left, labels_data))
#right_output = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(lenet_train_right, labels_data))
#loss = compute_contrastive_loss(tf.nn.softmax(lenet_train_left), tf.nn.softmax(lenet_train_right), labels_data, CONTRASTIVE_MARGIN)
loss = compute_contrastive_loss(lenet_train_left, lenet_train_right, labels_data, CONTRASTIVE_MARGIN)
#print("D")
#sys.stdout.flush()
#regularizer = (tf.nn.l2_loss(lenet_architecture.W_fc1) + tf.nn.l2_loss(lenet_architecture.b_fc1) +
# tf.nn.l2_loss(lenet_architecture.W_fc2) + tf.nn.l2_loss(lenet_architecture.b_fc2))
#loss += 5e-4 * regularizer
# Defining training parameters
batch = tf.Variable(0)
learning_rate = tf.train.exponential_decay(
0.001, # Learning rate
batch * BATCH_SIZE,
data_shuffler.train_data.shape[0],
0.95 # Decay step
)
#print("E")
#sys.stdout.flush()
#optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=batch)
optimizer = tf.train.MomentumOptimizer(learning_rate, momentum=0.99, use_locking=False, name='Momentum').minimize(loss, global_step=batch)
#validation_prediction = tf.nn.softmax(lenet_validation)
#print("Initializing")
#sys.stdout.flush()
# Training
with tf.Session() as session:
#print("INITIALIZE ALL VARIABLES")
#sys.stdout.flush()
tf.initialize_all_variables().run()
#print("INITIALIZE ALL VARIABLES - OK")
#sys.stdout.flush()
#pp = PdfPages("groups.pdf")
for step in range(ITERATIONS):
batch_left, batch_right, labels = data_shuffler.get_pair(BATCH_SIZE)
#print("FEED DICT")
#sys.stdout.flush()
feed_dict = {train_left_data: batch_left,
train_right_data: batch_right,
labels_data: labels}
#print("Run")
#sys.stdout.flush()
_, l, lr = session.run([optimizer, loss, learning_rate], feed_dict=feed_dict)
#print("Ok")
#sys.stdout.flush()
if step % VALIDATION_TEST == 0:
batch_train_data, batch_train_labels = data_shuffler.get_batch(
data_shuffler.validation_data.shape[0],
train_dataset=True)
features_train = session.run(lenet_validation,
feed_dict={validation_data: batch_train_data[:]})
batch_validation_data, batch_validation_labels = data_shuffler.get_batch(data_shuffler.validation_data.shape[0],
train_dataset=False)
features_validation = session.run(lenet_validation,
feed_dict={validation_data: batch_validation_data[:]})
#eer = util.compute_eer(features_train, batch_train_labels, features_validation, batch_validation_labels, 10)
#print("Step {0}. Loss = {1}, Lr={2}, EER = {3}".
# format(step, l, lr, eer))
accuracy = util.compute_accuracy(features_train, batch_train_labels, features_validation, batch_validation_labels, 10)
print("Step {0}. Loss = {1}, Lr={2}, Acc = {3}".
format(step, l, lr, accuracy))
sys.stdout.flush()
#fig = util.plot_embedding_lda(features_validation, batch_validation_labels)
#pp.savefig(fig)
#accuracy_train = util.evaluate_softmax(batch_train_data, batch_train_labels, session,
# tf.nn.softmax(lenet_validation),
# validation_data)
#accuracy_validation = util.evaluate_softmax(batch_validation_data, batch_validation_labels, session,
# tf.nn.softmax(lenet_validation), validation_data)
#
#print("Step {0}. Loss = {1}, Lr={2}, Accuracy train = {3}, Accuracy validation = {4}".
# format(step, l, lr, accuracy_train, accuracy_validation))
#print("EER = {0}".format(eer))
#print("Step {0}. Loss = {1}, Lr={2}, Accuracy train = {3}, Accuracy validation = {4}".
# format(step, l, lr, accuracy_train, accuracy_validation))
print("Step {0}. Loss = {1}, Lr={2}, Acc = {3}".
format(step, l, lr, accuracy))
#pp.close()
print("End !!")
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