In the following code, we have quoted the source code of the implemented emotion classifier:
import tensorflow as tf
import numpy as np
import os, sys, inspect
from datetime import datetime
import EmotionDetectorUtils
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string("data_dir", "EmotionDetector/", "Path to data files")
tf.flags.DEFINE_string("logs_dir", "logs/EmotionDetector_logs/", "Path to where log files are to be saved")
tf.flags.DEFINE_string("mode", "train", "mode: train (Default)/ test")
BATCH_SIZE = 128
LEARNING_RATE = 1e-3
MAX_ITERATIONS = 1001
REGULARIZATION = 1e-2
IMAGE_SIZE = 48
NUM_LABELS = 7
VALIDATION_PERCENT = 0.1
def add_to_regularization_loss(W, b):
tf.add_to_collection("losses", tf.nn.l2_loss(W))
tf.add_to_collection("losses", tf.nn.l2_loss(b))
def weight_variable(shape, stddev=0.02, name=None):
initial = tf.truncated_normal(shape, stddev=stddev)
if name is None:
return tf.Variable(initial)
else:
return tf.get_variable(name, initializer=initial)
def bias_variable(shape, name=None):
initial = tf.constant(0.0, shape=shape)
if name is None:
return tf.Variable(initial)
else:
return tf.get_variable(name, initializer=initial)
def conv2d_basic(x, W, bias):
conv = tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
return tf.nn.bias_add(conv, bias)
def max_pool_2x2(x):
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1], padding="SAME")
def emotion_cnn(dataset):
with tf.name_scope("conv1") as scope:
tf.summary.histogram("W_conv1", weights['wc1'])
tf.summary.histogram("b_conv1", biases['bc1'])
conv_1 = tf.nn.conv2d(dataset, weights['wc1'],
strides=[1, 1, 1, 1], padding="SAME")
h_conv1 = tf.nn.bias_add(conv_1, biases['bc1'])
h_1 = tf.nn.relu(h_conv1)
h_pool1 = max_pool_2x2(h_1)
add_to_regularization_loss(weights['wc1'], biases['bc1'])
with tf.name_scope("conv2") as scope:
tf.summary.histogram("W_conv2", weights['wc2'])
tf.summary.histogram("b_conv2", biases['bc2'])
conv_2 = tf.nn.conv2d(h_pool1, weights['wc2'],
strides=[1, 1, 1, 1], padding="SAME")
h_conv2 = tf.nn.bias_add(conv_2, biases['bc2'])
#h_conv2 = conv2d_basic(h_pool1, weights['wc2'], biases['bc2'])
h_2 = tf.nn.relu(h_conv2)
h_pool2 = max_pool_2x2(h_2)
add_to_regularization_loss(weights['wc2'], biases['bc2'])
with tf.name_scope("fc_1") as scope:
prob=0.5
image_size = IMAGE_SIZE / 4
h_flat = tf.reshape(h_pool2, [-1, image_size * image_size *
64])
tf.summary.histogram("W_fc1", weights['wf1'])
tf.summary.histogram("b_fc1", biases['bf1'])
h_fc1 = tf.nn.relu(tf.matmul(h_flat, weights['wf1']) +
biases['bf1'])
h_fc1_dropout = tf.nn.dropout(h_fc1, prob)
with tf.name_scope("fc_2") as scope:
tf.summary.histogram("W_fc2", weights['wf2'])
tf.summary.histogram("b_fc2", biases['bf2'])
#pred = tf.matmul(h_fc1, weights['wf2']) + biases['bf2']
pred = tf.matmul(h_fc1_dropout, weights['wf2']) + biases['bf2']
return pred
weights = {
'wc1': weight_variable([5, 5, 1, 32], name="W_conv1"),
'wc2': weight_variable([3, 3, 32, 64],name="W_conv2"),
'wf1': weight_variable([(IMAGE_SIZE / 4) *
(IMAGE_SIZE / 4) * 64, 256],name="W_fc1"),
'wf2': weight_variable([256, NUM_LABELS], name="W_fc2")
}
biases = {
'bc1': bias_variable([32], name="b_conv1"),
'bc2': bias_variable([64], name="b_conv2"),
'bf1': bias_variable([256], name="b_fc1"),
'bf2': bias_variable([NUM_LABELS], name="b_fc2")
}
def loss(pred, label):
cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=label))
tf.summary.scalar('Entropy', cross_entropy_loss) reg_losses = tf.add_n(tf.get_collection("losses"))
tf.scalar_summary('Reg_loss', reg_losses)
return cross_entropy_loss + REGULARIZATION * reg_losses
def train(loss, step):
return tf.train.AdamOptimizer(LEARNING_RATE).
minimize(loss, global_step=step)
def get_next_batch(images, labels, step):
offset = (step * BATCH_SIZE) % (images.shape[0] - BATCH_SIZE)
batch_images = images[offset: offset + BATCH_SIZE]
batch_labels = labels[offset:offset + BATCH_SIZE]
return batch_images, batch_labels
def main(argv=None):
train_images,
train_labels,
valid_images,
valid_labels,
test_images =
EmotionDetectorUtils.read_data(FLAGS.data_dir)
print "Train size: %s" % train_images.shape[0]
print 'Validation size: %s' % valid_images.shape[0]
print "Test size: %s" % test_images.shape[0]
global_step = tf.Variable(0, trainable=False)
dropout_prob = tf.placeholder(tf.float32)
input_dataset = tf.placeholder(tf.float32,
[None,
IMAGE_SIZE,
IMAGE_SIZE, 1],name="input")
input_labels = tf.placeholder(tf.float32,
[None, NUM_LABELS])
pred = emotion_cnn(input_dataset)
output_pred = tf.nn.softmax(pred,name="output")
loss_val = loss(pred, input_labels)
train_op = train(loss_val, global_step)
summary_op = tf.summary.merge_all()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
summary_writer = tf.summary.FileWriter(FLAGS.logs_dir,
sess.graph_def)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print "Model Restored!"
for step in xrange(MAX_ITERATIONS):
batch_image, batch_label = get_next_batch(train_images,
train_labels,
step)
feed_dict = {input_dataset: batch_image,
input_labels: batch_label}
sess.run(train_op, feed_dict=feed_dict)
if step % 10 == 0:
train_loss,
summary_str = sess.run([loss_val,
summary_op],
feed_dict=feed_dict)
summary_writer.add_summary(summary_str,
global_step=step)
print "Training Loss: %f" % train_loss
if step % 100 == 0:
valid_loss =
sess.run(loss_val,
feed_dict={input_dataset:
valid_images,
input_labels:
valid_labels})
print "%s Validation Loss: %f"
% (datetime.now(), valid_loss)
saver.save(sess, FLAGS.logs_dir
+ 'model.ckpt',
global_step=step)
if __name__ == "__main__":
tf.app.run()