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train.py
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train.py
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import argparse
import os
import time
from math import ceil
import tensorflow as tf
import numpy as np
import dataset
from six.moves import cPickle
import re
# https://cv-tricks.com/tensorflow-tutorial/training-convolutional-neural-network-for-image-classification/
def create_weights(shape):
return tf.Variable(tf.truncated_normal(shape, stddev=0.05))
def _activation_summary(x):
"""Helper to create summaries for activations.
Creates a summary that provides a histogram of activations.
Creates a summary that measures the sparsity of activations.
Args:
x: Tensor
Returns:
nothing
"""
# Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training
# session. This helps the clarity of presentation on tensorboard.
tensor_name = x.op.name
tf.summary.histogram(tensor_name + '/activations', x)
tf.summary.scalar(tensor_name + '/sparsity',
tf.nn.zero_fraction(x))
def create_biases(size):
return tf.Variable(tf.constant(0.05, shape=[size]))
def create_convolutional_layer(input,
num_input_channels,
conv_filter_size,
num_filters):
# We shall define the weights that will be trained using create_weights function.
weights = create_weights(shape=[conv_filter_size, conv_filter_size, num_input_channels, num_filters])
# We create biases using the create_biases function. These are also trained.
biases = create_biases(num_filters)
# Creating the convolutional layer
layer = tf.nn.conv2d(input=input,
filter=weights,
strides=[1, 1, 1, 1],
padding='SAME')
layer += biases
# We shall be using max-pooling.
layer = tf.nn.max_pool(value=layer,
ksize=[1, 2, 2, 1],
strides=[1, 2, 2, 1],
padding='SAME')
# Output of pooling is fed to Relu which is the activation function for us.
layer = tf.nn.relu(layer)
return layer
def create_flatten_layer(layer):
# We know that the shape of the layer will be [batch_size img_size img_size num_channels]
# But let's get it from the previous layer.
layer_shape = layer.get_shape()
# Number of features will be img_height * img_width* num_channels. But we shall calculate it in place of hard-coding it.
num_features = layer_shape[1:4].num_elements()
# Now, we Flatten the layer so we shall have to reshape to num_features
layer = tf.reshape(layer, [-1, num_features])
return layer
def create_fc_layer(input,
num_inputs,
num_outputs,
use_relu=True):
# Let's define trainable weights and biases.
weights = create_weights(shape=[num_inputs, num_outputs])
biases = create_biases(num_outputs)
# Fully connected layer takes input x and produces wx+b.Since, these are matrices, we use matmul function in Tensorflow
layer = tf.matmul(input, weights) + biases
if use_relu:
layer = tf.nn.relu(layer)
return layer, weights, biases
def train(args):
batch_size = args.batch_size
# Prepare input data
classes = os.listdir(args.data_dir)
# 20% of the data will automatically be used for validation
validation_size = 0.2
# Multiples of 2 please
img_size = args.image_size
num_channels = 3
if not os.path.isdir(args.save_dir):
os.makedirs(args.save_dir)
if args.init_from:
with open(os.path.join(args.init_from, 'labels.cpkl'), 'rb') as f:
labels = cPickle.load(f)
for label in classes:
if label not in labels:
labels.append(label)
classes = labels
with open(os.path.join(args.init_from, 'config.cpkl'), 'rb') as f:
old_args = cPickle.load(f)
need_to_be_same = ['img_size']
assert all([getattr(args, x) == getattr(old_args, x) for x in need_to_be_same])
num_classes = len(classes)
# We shall load all the training and validation images and labels into memory using openCV and use that during training
train_data, valid_data, filecount = dataset.read_train_sets(args.data_dir, img_size, classes, validation_size=validation_size, max_size=10000)
with open(os.path.join(args.save_dir, 'labels.cpkl'), 'wb') as f:
cPickle.dump(classes, f)
with open(os.path.join(args.save_dir, 'config.cpkl'), 'wb') as f:
cPickle.dump(args, f)
with tf.Session() as sess:
x = tf.placeholder(tf.float32, shape=[None, img_size, img_size, num_channels],
name='x')
# labels
y_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')
y_true_cls = tf.argmax(y_true, axis=1)
# Network graph params
filter_size_conv1 = 3
num_filters_conv1 = 32
filter_size_conv2 = 3
num_filters_conv2 = 64
filter_size_conv3 = 3
num_filters_conv3 = 128
fc_layer_size = img_size
layer_conv1 = create_convolutional_layer(input=x,
num_input_channels=num_channels,
conv_filter_size=filter_size_conv1,
num_filters=num_filters_conv1)
_activation_summary(layer_conv1)
layer_conv2 = create_convolutional_layer(input=layer_conv1,
num_input_channels=num_filters_conv1,
conv_filter_size=filter_size_conv2,
num_filters=num_filters_conv2)
layer_conv3 = create_convolutional_layer(input=layer_conv2,
num_input_channels=num_filters_conv2,
conv_filter_size=filter_size_conv3,
num_filters=num_filters_conv3)
layer_flat = create_flatten_layer(layer_conv3)
layer_fc1, a, b = create_fc_layer(input=layer_flat,
num_inputs=layer_flat.get_shape()[1:4].num_elements(),
num_outputs=fc_layer_size,
use_relu=True)
_activation_summary(layer_fc1)
layer_fc2, wei, bia = create_fc_layer(input=layer_fc1,
num_inputs=fc_layer_size,
num_outputs=num_classes,
use_relu=False)
_activation_summary(layer_fc2)
y_pred = tf.nn.softmax(layer_fc2, name='y_pred')
y_pred_cls = tf.argmax(y_pred, axis=1)
sess.run(tf.global_variables_initializer())
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=layer_fc2,
labels=y_true)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer(learning_rate=args.learning_rate).minimize(cost)
correct_prediction = tf.equal(y_pred_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
sess.run(tf.global_variables_initializer())
tf.summary.scalar('train_loss', cost)
for var in tf.trainable_variables():
tf.summary.histogram(var.op.name, var)
summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter(os.path.join(args.log_dir, time.strftime("%Y-%m-%d-%H-%M-%S")))
writer.add_graph(sess.graph)
saver = tf.train.Saver(tf.global_variables())
if args.init_from:
print(f'Restoring save from folder {args.init_from}')
saver.restore(sess, tf.train.latest_checkpoint(args.init_from))
num_batches = ceil(filecount/batch_size)
def show_progress(epoch, loss, batch, duration):
print(f"Training Epoch {epoch}/{args.num_epochs} Batch: {batch}/{num_batches} Loss: {loss:.3f}, Time: {duration:.02f}s")
train_data = train_data.batch(batch_size)
iterator = train_data.make_initializable_iterator()
next_element = iterator.get_next()
valid_data = valid_data.batch(batch_size)
valid_data = valid_data.repeat()
valid_iterator = valid_data.make_initializable_iterator()
next_valid = valid_iterator.get_next()
sess.run(valid_iterator.initializer)
for epoch in range(args.num_epochs):
sess.run(iterator.initializer)
batch = 1
while True:
start = time.time()
try:
x_batch, y_true_batch = sess.run(next_element)
except tf.errors.OutOfRangeError:
acc = sess.run(accuracy, feed_dict=feed_dict_tr)
val_acc = sess.run(accuracy, feed_dict=feed_dict_val)
msg = "Training Epoch {0} --- Training Accuracy: {1:>6.1%}, Validation Accuracy: {2:>6.1%}"
print(msg.format(epoch + 1, acc, val_acc))
break
x_valid_batch, y_valid_batch = sess.run(next_valid)
feed_dict_tr = {x: x_batch,
y_true: y_true_batch}
feed_dict_val = {x: x_valid_batch,
y_true: y_valid_batch}
sess.run(optimizer, feed_dict=feed_dict_tr)
val_loss = sess.run(cost, feed_dict=feed_dict_val)
# This speeds up training by a lot
#writer.add_summary(summ, epoch*num_batches + batch)
show_progress(epoch, val_loss, batch, time.time() - start)
if (epoch * num_batches + batch) % args.save_every == 0\
or (epoch == args.num_epochs-1 and batch == num_batches):
checkpoint_path = os.path.join(args.save_dir, 'model.ckpt')
checkpoint_path = saver.save(sess, checkpoint_path, global_step=epoch*num_batches+batch)
print(f'Saved checkpoint {checkpoint_path}')
batch += 1
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--batch-size', type=int, default=70,
help='Size of each batch')
parser.add_argument('--image-size', type=int, default=128,
help='Size that all images will be resized to')
parser.add_argument('--save-dir', type=str, default='model',
help='The directory where the models are saved')
parser.add_argument('--log-dir', type=str, default='logs',
help='Directory for tensorboard logs')
parser.add_argument('--save-every', type=int, default=1000,
help="How many passes have to be between each checkpoint")
parser.add_argument('--init-from', type=str, default=None,
help="How many passes have to be between each checkpoint")
parser.add_argument('--continue', action='store_true', dest='continue_',
help='If this is set will continue from last save and restore all parameters.\n'
'This means every setting you use with this will be ignored except init-from.')
parser.add_argument('--data-dir', type=str, default='data2',
help='Directory where the data is located in.\n'
'Directory must have subdirectories that include the training pics')
parser.add_argument('--num-epochs', type=int, default=50,
help='number of epochs. Number of full passes through the training examples.')
parser.add_argument('--learning-rate', type=float, default=1e-5,
help='The learning rate for the optimizer')
args = parser.parse_args()
train(args)