__author__ = 'Chong Guo <*****@*****.**>'
__copyright__ = 'Copyright 2018, Chong Guo'
__license__ = 'GPL'
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from config import batch_size, display_step, saving_step, summary_path, testing_summary
from common import init_model
from image_helper import concat_images
if __name__ == '__main__':
# Init model
is_training, _, _, loss, predict_rgb, color_image_rgb, gray_image, file_paths = init_model(train=False)
# Init scaffold, hooks and config
scaffold = tf.train.Scaffold()
checkpoint_hook = tf.train.CheckpointSaverHook(checkpoint_dir=summary_path, save_steps=saving_step, scaffold=scaffold)
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True, gpu_options=(tf.GPUOptions(allow_growth=True)))
session_creator = tf.train.ChiefSessionCreator(scaffold=scaffold, config=config, checkpoint_dir=summary_path)
# Create a session for running operations in the Graph
with tf.train.MonitoredSession(session_creator=session_creator, hooks=[checkpoint_hook]) as sess:
print("🤖 Start testing...")
step = 0
avg_loss = 0
while not sess.should_stop():
step += 1
"""Train model."""
import numpy as np
import tensorflow as tf
from matplotlib import pyplot as plt
from config import display_step, summary_path, saving_step, testing_step, training_iters, training_summary
from common import init_model
from image_helper import concat_images
if __name__ == '__main__':
# Init model
is_training, global_step, optimizer, loss, predict_rgb, color_image_rgb, gray_image, _ = init_model(train=True)
# Init scaffold, hooks and config
scaffold = tf.train.Scaffold()
summary_hook = tf.train.SummarySaverHook(output_dir=training_summary, save_steps=display_step, scaffold=scaffold)
checkpoint_hook = tf.train.CheckpointSaverHook(checkpoint_dir=summary_path, save_steps=saving_step, scaffold=scaffold)
num_step_hook = tf.train.StopAtStepHook(num_steps=training_iters)
config = tf.ConfigProto(allow_soft_placement=True, log_device_placement=True, gpu_options=(tf.GPUOptions(allow_growth=True)))
# Create a session for running operations in the Graph
with tf.train.MonitoredTrainingSession(checkpoint_dir=summary_path,hooks=[summary_hook, checkpoint_hook, num_step_hook],scaffold=scaffold,config=config) as sess:
print("🤖 Start training...")
while not sess.should_stop():
# Run optimizer
_, step, l, pred, color, gray = sess.run([optimizer, global_step, loss, predict_rgb, color_image_rgb, gray_image] , feed_dict={is_training: True})
"""
Training model
"""
import sys
import numpy as np
from matplotlib import pyplot as plt
from config import *
from common import init_model
from image_helper import (concat_images)
if __name__ == '__main__':
# Init model
is_training, global_step, uv, optimizer, cost, predict, predict_rgb, color_image_rgb, gray_image_rgb, file_paths = init_model(
train=True)
# Saver
print "Init model saver"
saver = tf.train.Saver()
# Init the graph
print "Init graph"
init = tf.initialize_all_variables()
# Create a session for running operations in the Graph
with tf.Session() as sess:
# Initialize the variables.
sess.run(init)
# Merge all summaries
import common
parser = argparse.ArgumentParser()
parser.add_argument("--input-path")
parser.add_argument("--model-dir-path")
args = parser.parse_args()
training_data = common.read_data(args.input_path)
dictionary, reverse_dictionary = common.build_dataset(training_data, dict())
vocab_size = len(dictionary)
# Parameters
learning_rate = 0.001
n_input = 3
pred, x, y = common.init_model(dictionary, n_input)
saver = tf.train.Saver()
# Loss and optimizer
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=pred, labels=y))
optimizer = tf.train.RMSPropOptimizer(
learning_rate=learning_rate).minimize(cost)
# Model evaluation
correct_pred = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
# training_iters = 50000
training_iters = 1
