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Add support for retrieving best epoch and val score when reloading a model

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This commit is contained in:
AntreasAntoniou 2018-02-16 23:44:04 +00:00
parent f707b10ef0
commit 8ac193d906
6 changed files with 45 additions and 25 deletions
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9
cifar100_network_trainer.py
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@ -5,7 +5,7 @@ import tqdm
from data_providers import CIFAR100DataProvider
from network_builder import ClassifierNetworkGraph
from utils.parser_utils import ParserClass
from utils.storage import build_experiment_folder, save_statistics
from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
@ -64,8 +64,6 @@ total_train_batches = train_data.num_batches
total_val_batches = val_data.num_batches
total_test_batches = test_data.num_batches
best_epoch = 0
if tensorboard_enable:
print("saved tensorboard file at", logs_filepath)
writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
@ -76,13 +74,16 @@ with tf.Session() as sess:
sess.run(init) # actually running the initialization op
train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
val_saver = tf.train.Saver()
best_val_accuracy = 0.
best_epoch = 0
# training or inference
if continue_from_epoch != -1:
train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
continue_from_epoch)) # restore previous graph to continue operations
best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
print(best_val_accuracy, best_epoch)
best_val_accuracy = 0.
with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
for e in range(start_epoch, epochs):
total_c_loss = 0.

9
cifar10_network_trainer.py
View File

@ -5,7 +5,7 @@ import tqdm
from data_providers import CIFAR10DataProvider
from network_builder import ClassifierNetworkGraph
from utils.parser_utils import ParserClass
from utils.storage import build_experiment_folder, save_statistics
from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
@ -64,7 +64,7 @@ total_train_batches = train_data.num_batches
total_val_batches = val_data.num_batches
total_test_batches = test_data.num_batches
best_epoch = 0
if tensorboard_enable:
print("saved tensorboard file at", logs_filepath)
@ -76,13 +76,16 @@ with tf.Session() as sess:
sess.run(init) # actually running the initialization op
train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
val_saver = tf.train.Saver()
best_val_accuracy = 0.
best_epoch = 0
# training or inference
if continue_from_epoch != -1:
train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
continue_from_epoch)) # restore previous graph to continue operations
best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
print(best_val_accuracy, best_epoch)
best_val_accuracy = 0.
with tqdm.tqdm(total=epochs-start_epoch) as epoch_pbar:
for e in range(start_epoch, epochs):
total_c_loss = 0.

9
emnist_network_trainer.py
View File

@ -5,7 +5,7 @@ import tqdm
from data_providers import EMNISTDataProvider
from network_builder import ClassifierNetworkGraph
from utils.parser_utils import ParserClass
from utils.storage import build_experiment_folder, save_statistics
from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
@ -64,8 +64,6 @@ total_train_batches = train_data.num_batches
total_val_batches = val_data.num_batches
total_test_batches = test_data.num_batches
best_epoch = 0
if tensorboard_enable:
print("saved tensorboard file at", logs_filepath)
writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
@ -76,13 +74,16 @@ with tf.Session() as sess:
sess.run(init) # actually running the initialization op
train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
val_saver = tf.train.Saver()
best_val_accuracy = 0.
best_epoch = 0
# training or inference
if continue_from_epoch != -1:
train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
continue_from_epoch)) # restore previous graph to continue operations
best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
print(best_val_accuracy, best_epoch)
best_val_accuracy = 0.
with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
for e in range(start_epoch, epochs):
total_c_loss = 0.

10
msd10_network_trainer.py
View File

@ -5,7 +5,7 @@ import tqdm
from data_providers import MSD10GenreDataProvider
from network_builder import ClassifierNetworkGraph
from utils.parser_utils import ParserClass
from utils.storage import build_experiment_folder, save_statistics
from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
@ -64,8 +64,6 @@ total_train_batches = train_data.num_batches
total_val_batches = val_data.num_batches
total_test_batches = test_data.num_batches
best_epoch = 0
if tensorboard_enable:
print("saved tensorboard file at", logs_filepath)
writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
@ -76,14 +74,16 @@ with tf.Session() as sess:
sess.run(init) # actually running the initialization op
train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
val_saver = tf.train.Saver()
best_val_accuracy = 0.
best_epoch = 0
# training or inference
if continue_from_epoch != -1:
train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
continue_from_epoch)) # restore previous graph to continue operations
best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
print(best_val_accuracy, best_epoch)
best_val_accuracy = 0.
with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
for e in range(start_epoch, epochs):
total_c_loss = 0.

9
msd25_network_trainer.py
View File

@ -5,7 +5,7 @@ import tqdm
from data_providers import MSD10GenreDataProvider
from network_builder import ClassifierNetworkGraph
from utils.parser_utils import ParserClass
from utils.storage import build_experiment_folder, save_statistics
from utils.storage import build_experiment_folder, save_statistics, get_best_validation_model_statistics
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(description='Welcome to CNN experiments script') # generates an argument parser
@ -64,8 +64,6 @@ total_train_batches = train_data.num_batches
total_val_batches = val_data.num_batches
total_test_batches = test_data.num_batches
best_epoch = 0
if tensorboard_enable:
print("saved tensorboard file at", logs_filepath)
writer = tf.summary.FileWriter(logs_filepath, graph=tf.get_default_graph())
@ -76,13 +74,16 @@ with tf.Session() as sess:
sess.run(init) # actually running the initialization op
train_saver = tf.train.Saver() # saver object that will save our graph so we can reload it later for continuation of
val_saver = tf.train.Saver()
best_val_accuracy = 0.
best_epoch = 0
# training or inference
if continue_from_epoch != -1:
train_saver.restore(sess, "{}/{}_{}.ckpt".format(saved_models_filepath, experiment_name,
continue_from_epoch)) # restore previous graph to continue operations
best_val_accuracy, best_epoch = get_best_validation_model_statistics(logs_filepath, "result_summary_statistics")
print(best_val_accuracy, best_epoch)
best_val_accuracy = 0.
with tqdm.tqdm(total=epochs - start_epoch) as epoch_pbar:
for e in range(start_epoch, epochs):
total_c_loss = 0.

16
utils/storage.py
View File

@ -1,5 +1,5 @@
import csv
import numpy as np
def save_statistics(log_dir, statistics_file_name, list_of_statistics, create=False):
"""
@ -42,6 +42,20 @@ def load_statistics(log_dir, statistics_file_name):
data_dict[key].append(item)
return data_dict
def get_best_validation_model_statistics(log_dir, statistics_file_name):
"""
Returns the best val epoch and val accuracy from a log csv file
:param log_dir: The log directory the file is saved in
:param statistics_file_name: The log file name
:return: The best validation accuracy and the epoch at which it is produced
"""
log_file_dict = load_statistics(statistics_file_name=statistics_file_name, log_dir=log_dir)
val_acc = np.array(log_file_dict['val_c_accuracy'], dtype=np.float32)
best_val_acc = np.max(val_acc)
best_val_epoch = np.argmax(val_acc)
return best_val_acc, best_val_epoch
def build_experiment_folder(experiment_name, log_path):
saved_models_filepath = "{}/{}/{}".format(log_path, experiment_name.replace("%.%", "/"), "saved_models")