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main_train_networks.py
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main_train_networks.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Jan 31 18:53:31 2018
@author: guy.hacohen
"""
import numpy as np
import os.path
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import datasets.cifar100_subset
import datasets.cifar10
import datasets.cifar100
import models.cifar100_model
import train_keras_model
import transfer_learning
import pickle
import argparse
import time
import scipy
import matplotlib.pyplot as plt
def exponent_decay_lr_generator(decay_rate, minimum_lr, batch_to_decay):
cur_lr = None
def exponent_decay_lr(initial_lr, batch, history):
nonlocal cur_lr
if batch == 0:
cur_lr = initial_lr
if (batch % batch_to_decay) == 0 and batch !=0:
new_lr = cur_lr / decay_rate
cur_lr = max(new_lr, minimum_lr)
return cur_lr
return exponent_decay_lr
def exponent_data_function_generator(dataset, order, batches_to_increase,
increase_amount, starting_percent,
batch_size=100):
size_data = dataset.x_train.shape[0]
cur_percent = 1
cur_data_x = dataset.x_train
cur_data_y = dataset.y_test_labels
def data_function(x, y, batch, history, model):
nonlocal cur_percent, cur_data_x, cur_data_y
if batch % batches_to_increase == 0:
if batch == 0:
percent = starting_percent
else:
percent = min(cur_percent*increase_amount, 1)
if percent != cur_percent:
cur_percent = percent
data_limit = np.int(np.ceil(size_data * percent))
new_data = order[:data_limit]
cur_data_x = dataset.x_train[new_data, :, :, :]
cur_data_y = dataset.y_train_labels[new_data, :]
return cur_data_x, cur_data_y
return data_function
def order_by_loss(dataset, model):
size_train = len(dataset.y_train)
scores = model.predict(dataset.x_train)
hardness_score = scores[list(range(size_train)), dataset.y_train]
res = np.asarray(sorted(range(len(hardness_score)), key=lambda k: hardness_score[k], reverse=True))
return res
def balance_order(order, dataset):
num_classes = dataset.n_classes
size_each_class = dataset.x_train.shape[0] // num_classes
class_orders = []
for cls in range(num_classes):
class_orders.append([i for i in range(len(order)) if dataset.y_train[order[i]] == cls])
new_order = []
## take each group containing the next easiest image for each class,
## and putting them according to diffuclt-level in the new order
for group_idx in range(size_each_class):
group = sorted([class_orders[cls][group_idx] for cls in range(num_classes)])
for idx in group:
new_order.append(order[idx])
return new_order
def data_function_from_input(curriculum, batch_size,
dataset, order, batch_increase,
increase_amount, starting_percent):
if curriculum == "random":
np.random.shuffle(order)
if curriculum == "None" or curriculum == "vanilla":
data_function = train_keras_model.basic_data_function
elif curriculum in ["curriculum", "vanilla", "anti", "random"]:
data_function = exponent_data_function_generator(dataset, order, batch_increase, increase_amount,
starting_percent, batch_size=batch_size)
else:
print("unsupprted condition (not vanilla/curriculum/random/anti)")
print("got the value:", curriculum)
raise ValueError
return data_function
def load_dataset(dataset_name):
if dataset_name.startswith('cifar100_subset'):
superclass_idx = int(dataset_name[len("cifar100_subset_"):])
dataset = datasets.cifar100_subset.Cifar100_Subset(supeclass_idx=superclass_idx,
normalize=False)
elif dataset_name == "cifar10":
dataset = datasets.cifar10.Cifar10(normalize=False)
elif dataset_name == "cifar100":
dataset = datasets.cifar100.Cifar100(normalize=False)
else:
print("do not support datset: %s" % dataset_name)
raise ValueError
return dataset
def load_model():
return models.cifar100_model.Cifar100_Model()
def load_order(order_name, dataset):
classic_networks = ["vgg16", "vgg19", "inception", "xception", "resnet"]
if order_name in classic_networks:
network_name = order_name
if not transfer_learning.svm_scores_exists(dataset,
network_name=network_name):
if order_name == "inception":
(transfer_values_train, transfer_values_test) = transfer_learning.get_transfer_values_inception(dataset)
else:
(transfer_values_train, transfer_values_test) = transfer_learning.get_transfer_values_classic_networks(dataset,
network_name)
else:
(transfer_values_train, transfer_values_test) = (None, None)
train_scores, test_scores = transfer_learning.get_svm_scores(transfer_values_train, dataset.y_train,
transfer_values_test, dataset.y_test, dataset,
network_name=network_name)
order = transfer_learning.rank_data_according_to_score(train_scores, dataset.y_train)
else:
print("do not support order: %s" % args.order)
raise ValueError
return order
def combine_histories(history_list):
num_repeats = len(history_list)
combined_history = history_list[0].copy()
for key in ["loss", "acc", "val_loss", "val_acc"]:
size_key = len(history_list[0][key])
results = np.zeros((num_repeats, size_key))
for i in range(num_repeats):
results[i, :] = history_list[i][key]
combined_history[key] = np.mean(results, axis=0)
if key == "acc":
if num_repeats >1:
combined_history["std_acc"] = scipy.stats.sem(results, axis=0)
else:
combined_history["std_acc"] = None
if key == "val_acc":
if num_repeats >1:
combined_history["std_val_acc"] = scipy.stats.sem(results, axis=0)
else:
combined_history["std_val_acc"] = None
return combined_history
def graph_from_history(history, plot_train=False, plot_test=True):
fig, axs = plt.subplots(figsize=(10,5))
if plot_train:
x = np.array(history['batch_num'])
y = history['acc'][x]
error = history['std_acc'][x]
if error is not None:
axs.errorbar(x, y, error, marker='^', label="train")
else:
plt.plot(x, y, label="train")
if plot_test:
x = np.array(history['batch_num'])
y = history['val_acc']
error = history['std_val_acc']
if error is not None:
axs.errorbar(x, y, error, marker='^', label="test")
else:
plt.plot(x, y, label="test")
axs.set_xlabel("batch number")
axs.set_ylabel("accuracy")
plt.legend()
# axs.legend(loc="best")
def run_expriment(args):
dataset = load_dataset(args.dataset)
model_lib = load_model()
size_train = dataset.x_train.shape[0]
num_batches = (args.num_epochs * size_train) // args.batch_size
lr_scheduler = exponent_decay_lr_generator(args.lr_decay_rate,
args.minimal_lr,
args.lr_batch_size)
order = load_order(args.order, dataset)
order = balance_order(order, dataset)
if args.curriculum == "anti":
order = np.flip(order, 0)
elif args.curriculum == "random":
np.random.shuffle(order)
elif (args.curriculum not in ["None", "curriculum", "vanilla"]):
print("--curriculum value of %s is not supported!" % args.curriculum)
raise ValueError
dataset.normalize_dataset()
if args.output_path:
output_path = args.output_path
else:
output_path = None
## start expriment
start_time_all = time.time()
histories =[]
for repeat in range(args.repeats):
data_function = data_function_from_input(args.curriculum,
args.batch_size,
dataset,
order,
args.batch_increase,
args.increase_amount,
args.starting_percent)
print("starting repeat number: " + str(repeat + 1))
model = model_lib.build_classifier_model(dataset)
train_keras_model.compile_model(model,
initial_lr=args.learning_rate,
loss='categorical_crossentropy',
optimizer="sgd")
history = train_keras_model.train_model_batches(model,
dataset,
num_batches,
verbose=args.verbose,
batch_size=args.batch_size,
initial_lr=args.learning_rate,
lr_scheduler=lr_scheduler,
data_function=data_function)
histories.append(history)
print("time all: --- %s seconds ---" % (time.time() - start_time_all))
combined_history = combine_histories(histories)
if output_path:
with open(output_path + "_history", 'wb') as file_pi:
pickle.dump(combined_history, file_pi)
print("training acc:", combined_history['acc'][-1])
print("test acc:", combined_history['val_acc'][-1])
graph_from_history(combined_history, plot_train=False, plot_test=True)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='')
parser.add_argument("--dataset", default="cifar100_subset_16", help="dataset to use")
parser.add_argument("--output_path", default=r'', help="where to save the model")
parser.add_argument("--verbose", default=True, type=bool, help="print more stuff")
parser.add_argument("--curriculum", "-cl", default="curriculum", help="which test case to use. supports: vanilla, curriculum, anti and random")
parser.add_argument("--batch_size", default=100, type=int, help="determine batch size")
parser.add_argument("--num_epochs", default=140, type=int, help="number of epochs to train on")
# parser.add_argument("--num_epochs", default=10, type=int)
# lr params
parser.add_argument("--learning_rate", "-lr", default=0.035, type=float)
parser.add_argument("--lr_decay_rate", default=1.5, type=float)
parser.add_argument("--minimal_lr", default=1e-4, type=float)
parser.add_argument("--lr_batch_size", default=300, type=int)
# parser.add_argument("--learning_rate", "-lr", default=0.05, type=float, help="initial learning")
# parser.add_argument("--lr_decay_rate", default=1.8, type=float, help="factor by which we drop learning rate exponentially")
# parser.add_argument("--minimal_lr", default=1e-4, type=float, help="min learning rate we allow")
# parser.add_argument("--lr_batch_size", default=500, type=int, help="interval of batches in which we drop the learning rate")
# curriculum params
parser.add_argument("--batch_increase", default=100, type=int, help="interval of batches to increase the amount of data we sample from")
parser.add_argument("--increase_amount", default=1.9, type=float, help="factor by which we increase the amount of data we sample from")
parser.add_argument("--starting_percent", default=100/2500, type=float, help="percent of data to sample from in the inital batch")
parser.add_argument("--order", default="inception", help="determine the order of the examples, supports transfer learning. options: inception, vgg16, vgg19, xception, resnet")
parser.add_argument("--repeats", default=1, type=int, help="number of times to repeat the experiment")
args = parser.parse_args()
run_expriment(args)