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Created Oct 23, 2017
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training on faces with tf
""" Build an Image Dataset in TensorFlow.
For this example, you need to make your own set of images (JPEG).
We will show 2 different ways to build that dataset:
- From a root folder, that will have a sub-folder containing images for each class
```
ROOT_FOLDER
|-------- SUBFOLDER (CLASS 0)
| |
| | ----- image1.jpg
| | ----- image2.jpg
| | ----- etc...
|
|-------- SUBFOLDER (CLASS 1)
| |
| | ----- image1.jpg
| | ----- image2.jpg
| | ----- etc...
```
- From a plain text file, that will list all images with their class ID:
```
/path/to/image/1.jpg CLASS_ID
/path/to/image/2.jpg CLASS_ID
/path/to/image/3.jpg CLASS_ID
/path/to/image/4.jpg CLASS_ID
etc...
```
Below, there are some parameters that you need to change (Marked 'CHANGE HERE'),
such as the dataset path.
Author: Aymeric Damien
Project: https://github.com/aymericdamien/TensorFlow-Examples/
"""
from __future__ import print_function
import tensorflow as tf
import os
# Dataset Parameters - CHANGE HERE
MODE = 'folder' # or 'file', if you choose a plain text file (see above).
DATASET_PATH = '/media/sf_data/faces/tv10/' # the dataset file or root folder path.
# Image Parameters
N_CLASSES = 30 # CHANGE HERE, total number of classes
IMG_HEIGHT = 64 # CHANGE HERE, the image height to be resized to
IMG_WIDTH = 64 # CHANGE HERE, the image width to be resized to
CHANNELS = 1 # The 3 color channels, change to 1 if grayscale
# Reading the dataset
# 2 modes: 'file' or 'folder'
def read_images(dataset_path, mode, batch_size):
imagepaths, labels = list(), list()
if mode == 'file':
# Read dataset file
data = open(dataset_path, 'r').read().splitlines()
for d in data:
imagepaths.append(d.split(' ')[0])
labels.append(int(d.split(' ')[1]))
elif mode == 'folder':
# An ID will be affected to each sub-folders by alphabetical order
label = 0
# List the directory
try: # Python 2
classes = sorted(os.walk(dataset_path).next()[1])
except Exception: # Python 3
classes = sorted(os.walk(dataset_path).__next__()[1])
# List each sub-directory (the classes)
for c in classes:
c_dir = os.path.join(dataset_path, c)
try: # Python 2
walk = os.walk(c_dir).next()
except Exception: # Python 3
walk = os.walk(c_dir).__next__()
# Add each image to the training set
for sample in walk[2]:
# Only keeps jpeg images
if sample.endswith('.png'):
imagepaths.append(os.path.join(c_dir, sample))
labels.append(label)
label += 1
else:
raise Exception("Unknown mode.")
# Convert to Tensor
imagepaths = tf.convert_to_tensor(imagepaths, dtype=tf.string)
labels = tf.convert_to_tensor(labels, dtype=tf.int32)
# Build a TF Queue, shuffle data
image, label = tf.train.slice_input_producer([imagepaths, labels],
shuffle=True)
# Read images from disk
image = tf.read_file(image)
image = tf.image.decode_png(image, channels=CHANNELS)
# Resize images to a common size
image = tf.image.resize_images(image, [IMG_HEIGHT, IMG_WIDTH])
# Normalize
image = image * 1.0/127.5 - 1.0
# Create batches
X, Y = tf.train.batch([image, label], batch_size=batch_size,
capacity=batch_size * 8,
num_threads=4)
return X, Y
# -----------------------------------------------
# THIS IS A CLASSIC CNN (see examples, section 3)
# -----------------------------------------------
# Note that a few elements have changed (usage of queues).
# Parameters
learning_rate = 0.001
num_steps = 10
batch_size = 128
display_step = 1
# Network Parameters
dropout = 0.75 # Dropout, probability to keep units
# Build the data input
X, Y = read_images(DATASET_PATH, MODE, batch_size)
# Create model
def conv_net(x, n_classes, dropout, reuse, is_training):
# Define a scope for reusing the variables
with tf.variable_scope('ConvNet', reuse=reuse):
# Convolution Layer with 32 filters and a kernel size of 5
conv1 = tf.layers.conv2d(x, 32, 5, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
conv1 = tf.layers.max_pooling2d(conv1, 2, 2)
# Convolution Layer with 32 filters and a kernel size of 5
conv2 = tf.layers.conv2d(conv1, 64, 3, activation=tf.nn.relu)
# Max Pooling (down-sampling) with strides of 2 and kernel size of 2
conv2 = tf.layers.max_pooling2d(conv2, 2, 2)
# Flatten the data to a 1-D vector for the fully connected layer
fc1 = tf.contrib.layers.flatten(conv2)
# Fully connected layer (in contrib folder for now)
fc1 = tf.layers.dense(fc1, 1024)
# Apply Dropout (if is_training is False, dropout is not applied)
fc1 = tf.layers.dropout(fc1, rate=dropout, training=is_training)
# Output layer, class prediction
out = tf.layers.dense(fc1, n_classes)
# Because 'softmax_cross_entropy_with_logits' already apply softmax,
# we only apply softmax to testing network
out = tf.nn.softmax(out) if not is_training else out
return out
# Because Dropout have different behavior at training and prediction time, we
# need to create 2 distinct computation graphs that share the same weights.
# Create a graph for training
logits_train = conv_net(X, N_CLASSES, dropout, reuse=False, is_training=True)
# Create another graph for testing that reuse the same weights
logits_test = conv_net(X, N_CLASSES, dropout, reuse=True, is_training=False)
# Define loss and optimizer (with train logits, for dropout to take effect)
loss_op = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=logits_train, labels=Y))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
# Evaluate model (with test logits, for dropout to be disabled)
correct_pred = tf.equal(tf.argmax(logits_test, 1), tf.cast(Y, tf.int64))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# Initialize the variables (i.e. assign their default value)
init = tf.global_variables_initializer()
# Saver object
saver = tf.train.Saver()
# Start training
with tf.Session() as sess:
# Run the initializer
sess.run(init)
# Start the data queue
tf.train.start_queue_runners()
# Training cycle
for step in range(1, num_steps+1):
if step % display_step == 0:
# Run optimization and calculate batch loss and accuracy
_, loss, acc = sess.run([train_op, loss_op, accuracy])
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss) + ", Training Accuracy= " + \
"{:.3f}".format(acc))
else:
# Only run the optimization op (backprop)
sess.run(train_op)
print("Optimization Finished!")
# Save your model
saver.save(sess, 'my_tf_model')
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