import tensorflow as tf
import net
import numpy as np
net.build()
vars = tf.all_variables()
saver = tf.train.Saver()
sess = tf.Session()
print([var.name for var in vars])
mat = list(filter(lambda x: x.name == 'linear_1/weights:0', vars))[0]
saver.restore(sess, './log/checkpoint-40000')
W = sess.run(mat)
print(W.shape)
np.savez('mat.npz', mat=W)
def run_training(extra_opts={}):
start = datetime.datetime.now()
start_str = start.strftime('%d-%m-%Y_%H_%M')
train, validation = input_data.read_data_sets(FLAGS.data_dir)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
net.build(extra_opts)
#Precision summaries
precision_train = tf.Variable(0.0,
trainable=False,
name='precision_train')
precision_validation = tf.Variable(0.0,
trainable=False,
name='precision_validation')
precision_train_summary = tf.summary.scalar('precision/train',
precision_train)
precision_validation_summary = tf.summary.scalar(
'precision/validation', precision_validation)
graph = tf.get_default_graph()
loss = graph.get_tensor_by_name('loss:0')
train_op = graph.get_tensor_by_name('train_op:0')
correct_count = graph.get_tensor_by_name('correct_count:0')
#Create summary stuff
regular_summaries_names = ['loss', 'learning_rate']
regular_summaries_list = []
for name in regular_summaries_names:
summary = graph.get_tensor_by_name('summary/' + name + ':0')
regular_summaries_list.append(summary)
regular_summaries = tf.summary.merge(regular_summaries_list,
name='summary/regular_summaries')
# Create a saver for writing training checkpoints.
saver = tf.train.Saver(tf.global_variables())
# Run the Op to initialize the variables.
init = tf.global_variables_initializer()
# Create a session for running Ops on the Graph.
sess = tf.Session(graph=graph,
config=tf.ConfigProto(
intra_op_parallelism_threads=3,
inter_op_parallelism_threads=3))
sess.run(init)
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.log_dir,
graph=tf.get_default_graph())
# And then after everything is built, start the training loop.
for step in xrange(1, FLAGS.max_steps + 1):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(train.next_batch(FLAGS.batch_size))
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % FLAGS.overview_steps == 0:
# Print status to stdout.
data_per_sec = FLAGS.batch_size / duration
print('Step %d: loss = %.2f (%.3f sec) [%.2f data/s]' %
(step, loss_value, duration, data_per_sec))
# Update the events file.
summary_str = sess.run(regular_summaries, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# Save a checkpoint and evaluate the model periodically.
if (step) % FLAGS.evaluation_steps == 0 or step == FLAGS.max_steps:
saver.save(sess,
FLAGS.log_dir + '/checkpoint',
global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
precision_t, obj_t = do_eval(sess, correct_count, loss, train)
sess.run(precision_train.assign(precision_t))
# Evaluate against the validation set.
print('Validation Data Eval:')
precision_v, obj_v = do_eval(sess, correct_count, loss,
validation)
sess.run(precision_validation.assign(precision_v))
summary_str_0, summary_str_1 = sess.run(
[precision_train_summary, precision_validation_summary])
summary_writer.add_summary(summary_str_0, step)
summary_writer.add_summary(summary_str_1, step)
os.makedirs('./results', exist_ok=True)
res_file = open('./results/res_' + str(start_str), 'w')
res_file.write('Iterations: ' + str(step) + '\n')
now = datetime.datetime.now()
delta = now - start
res_file.write('Learning time: {0:.2f} minutes\n'.format(
delta.total_seconds() / 60.0))
res_file.write(
'Train precision: {0:.5f}\n'.format(precision_t))
res_file.write('Train loss: {0:.5f}\n'.format(obj_t))
res_file.write(
'Validation precision: {0:.5f}\n'.format(precision_v))
res_file.write('Validation loss: {0:.5f}\n'.format(obj_v))
res_file.write('Extra opts: ' + str(extra_opts) + '\n')
res_file.write('Code:\n')
net_file = open('./net.py', 'r')
shutil.copyfileobj(net_file, res_file)
net_file.close()
res_file.close()
def run_training(extra_opts={}):
start = datetime.datetime.now()
start_str = start.strftime('%d-%m-%Y_%H_%M')
train, validation = input_data.read_data_sets(FLAGS.data_dir)
# Tell TensorFlow that the model will be built into the default Graph.
with tf.Graph().as_default():
net.build(extra_opts)
#Precision summaries
precision_train = tf.Variable(0.0, trainable=False, name='precision_train')
precision_validation = tf.Variable(0.0, trainable=False, name='precision_validation')
precision_train_summary = tf.summary.scalar('precision/train',
precision_train)
precision_validation_summary = tf.summary.scalar('precision/validation',
precision_validation)
graph = tf.get_default_graph()
loss = graph.get_tensor_by_name('loss:0')
train_op = graph.get_tensor_by_name('train_op:0')
correct_count = graph.get_tensor_by_name('correct_count:0')
#Create summary stuff
regular_summaries_names = ['loss', 'learning_rate']
regular_summaries_list = []
for name in regular_summaries_names:
summary = graph.get_tensor_by_name('summary/' + name + ':0')
regular_summaries_list.append(summary)
regular_summaries = tf.summary.merge(regular_summaries_list, name='summary/regular_summaries')
# Create a saver for writing training checkpoints.
saver = tf.train.Saver(tf.global_variables())
# Run the Op to initialize the variables.
init = tf.global_variables_initializer()
# Create a session for running Ops on the Graph.
sess = tf.Session(graph=graph,
config=tf.ConfigProto(intra_op_parallelism_threads=3, inter_op_parallelism_threads = 3))
sess.run(init)
# Instantiate a SummaryWriter to output summaries and the Graph.
summary_writer = tf.summary.FileWriter(FLAGS.log_dir,
graph=tf.get_default_graph())
# And then after everything is built, start the training loop.
for step in xrange(1, FLAGS.max_steps + 1):
start_time = time.time()
# Fill a feed dictionary with the actual set of images and labels
# for this particular training step.
feed_dict = fill_feed_dict(train.next_batch(FLAGS.batch_size))
# Run one step of the model. The return values are the activations
# from the `train_op` (which is discarded) and the `loss` Op. To
# inspect the values of your Ops or variables, you may include them
# in the list passed to sess.run() and the value tensors will be
# returned in the tuple from the call.
_, loss_value = sess.run([train_op, loss],
feed_dict=feed_dict)
duration = time.time() - start_time
# Write the summaries and print an overview fairly often.
if step % FLAGS.overview_steps == 0:
# Print status to stdout.
data_per_sec = FLAGS.batch_size / duration
print('Step %d: loss = %.2f (%.3f sec) [%.2f data/s]' %
(step, loss_value, duration, data_per_sec))
# Update the events file.
summary_str = sess.run(regular_summaries, feed_dict=feed_dict)
summary_writer.add_summary(summary_str, step)
# Save a checkpoint and evaluate the model periodically.
if (step) % FLAGS.evaluation_steps == 0 or step == FLAGS.max_steps:
saver.save(sess, FLAGS.log_dir +'/checkpoint', global_step=step)
# Evaluate against the training set.
print('Training Data Eval:')
precision_t, obj_t = do_eval(sess,
correct_count,
loss,
train)
sess.run(precision_train.assign(precision_t))
# Evaluate against the validation set.
print('Validation Data Eval:')
precision_v, obj_v = do_eval(sess,
correct_count,
loss,
validation)
sess.run(precision_validation.assign(precision_v))
summary_str_0, summary_str_1 = sess.run([precision_train_summary, precision_validation_summary])
summary_writer.add_summary(summary_str_0, step)
summary_writer.add_summary(summary_str_1, step)
os.makedirs('./results', exist_ok=True)
res_file = open('./results/res_' + str(start_str), 'w')
res_file.write('Iterations: ' + str(step) + '\n')
now = datetime.datetime.now()
delta = now - start
res_file.write('Learning time: {0:.2f} minutes\n'.format(delta.total_seconds() / 60.0))
res_file.write('Train precision: {0:.5f}\n'.format(precision_t))
res_file.write('Train loss: {0:.5f}\n'.format(obj_t))
res_file.write('Validation precision: {0:.5f}\n'.format(precision_v))
res_file.write('Validation loss: {0:.5f}\n'.format(obj_v))
res_file.write('Extra opts: ' + str(extra_opts) + '\n')
res_file.write('Code:\n')
net_file = open('./net.py', 'r')
shutil.copyfileobj(net_file, res_file)
net_file.close()
res_file.close()