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experiments.py
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experiments.py
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import tensorflow as tf
import numpy as np
import utils as ut
import input
import DoomModel as dm
import pickle
from datetime import datetime as dt
import sys
FLAGS = tf.app.flags.FLAGS
def search_learning_rate(lrs=[0.001, 0.0004, 0.0001, 0.00003,],
epochs=500):
FLAGS.suffix = 'grid_lr'
ut.print_info('START: search_learning_rate', color=31)
best_result, best_args = None, None
result_summary, result_list = [], []
for lr in lrs:
ut.print_info('STEP: search_learning_rate', color=31)
FLAGS.learning_rate = lr
model = model_class()
meta, accuracy_by_epoch = model.train(epochs)
result_list.append((ut.to_file_name(meta), accuracy_by_epoch))
best_accuracy = np.min(accuracy_by_epoch)
result_summary.append('\n\r lr:%2.5f \tq:%.2f' % (lr, best_accuracy))
if best_result is None or best_result > best_accuracy:
best_result = best_accuracy
best_args = lr
meta = {'suf': 'grid_lr_bs', 'e': epochs, 'lrs': lrs, 'acu': best_result,
'bs': FLAGS.batch_size, 'h': model.get_layer_info()}
pickle.dump(result_list, open('search_learning_rate%d.txt' % epochs, "wb"))
ut.plot_epoch_progress(meta, result_list)
print(''.join(result_summary))
ut.print_info('BEST Q: %d IS ACHIEVED FOR LR: %f' % (best_result, best_args), 36)
def search_batch_size(bss=[50], strides=[1, 2, 5, 20], epochs=500):
FLAGS.suffix = 'grid_bs'
ut.print_info('START: search_batch_size', color=31)
best_result, best_args = None, None
result_summary, result_list = [], []
print(bss)
for bs in bss:
for stride in strides:
ut.print_info('STEP: search_batch_size %d %d' % (bs, stride), color=31)
FLAGS.batch_size = bs
FLAGS.stride = stride
model = model_class()
start = dt.now()
# meta, accuracy_by_epoch = model.train(epochs * int(bs / bss[0]))
meta, accuracy_by_epoch = model.train(epochs)
meta['str'] = stride
meta['t'] = int((dt.now() - start).seconds)
result_list.append((ut.to_file_name(meta)[22:], accuracy_by_epoch))
best_accuracy = np.min(accuracy_by_epoch)
result_summary.append('\n\r bs:%d \tst:%d \tq:%.2f' % (bs, stride, best_accuracy))
if best_result is None or best_result > best_accuracy:
best_result = best_accuracy
best_args = (bs, stride)
meta = {'suf': 'grid_batch_bs', 'e': epochs, 'acu': best_result,
'h': model.get_layer_info()}
pickle.dump(result_list, open('search_batch_size%d.txt' % epochs, "wb"))
ut.plot_epoch_progress(meta, result_list)
print(''.join(result_summary))
ut.print_info('BEST Q: %d IS ACHIEVED FOR bs, st: %d %d' % (best_result, best_args[0], best_args[1]), 36)
def search_layer_sizes(epochs=500):
FLAGS.suffix = 'grid_h'
ut.print_info('START: search_layer_sizes', color=31)
best_result, best_args = None, None
result_summary, result_list = [], []
for _, h_encoder in enumerate([300, 700, 2500]):
for _, h_decoder in enumerate([300, 700, 2500]):
for _, h_narrow in enumerate([3]):
model = model_class()
model.layer_encoder = h_encoder
model.layer_narrow = h_narrow
model.layer_decoder = h_decoder
layer_info = str(model.get_layer_info())
ut.print_info('STEP: search_layer_sizes: ' + str(layer_info), color=31)
meta, accuracy_by_epoch = model.train(epochs)
result_list.append((layer_info, accuracy_by_epoch))
best_accuracy = np.min(accuracy_by_epoch)
result_summary.append('\n\r h:%s \tq:%.2f' % (layer_info, best_accuracy))
if best_result is None or best_result > best_accuracy:
best_result = best_accuracy
best_args = layer_info
meta = {'suf': 'grid_H_bs', 'e': epochs, 'acu': best_result,
'bs': FLAGS.batch_size, 'h': model.get_layer_info()}
print(''.join(result_summary))
pickle.dump(result_list, open('search_layer_sizes%d.txt' % epochs, "wb"))
ut.print_info('BEST Q: %d IS ACHIEVED FOR H: %s' % (best_result, best_args), 36)
ut.plot_epoch_progress(meta, result_list)
def search_layer_sizes_follow_up():
"""train further 2 best models"""
FLAGS.save_every = 200
for i in range(4):
model = model_class()
model.layer_encoder = 500
model.layer_narrow = 3
model.layer_decoder = 100
model.train(600)
model = model_class()
model.layer_encoder = 500
model.layer_narrow = 12
model.layer_decoder = 500
model.train(600)
def print_reconstructions_along_with_originals():
FLAGS.load_from_checkpoint = './tmp/doom_bs__act|sigmoid__bs|20__h|500|5|500__init|na__inp|cbd4__lr|0.0004__opt|AO'
model = model_class()
files = ut.list_encodings(FLAGS.save_path)
last_encoding = files[-1]
print(last_encoding)
take_only = 20
data = np.loadtxt(last_encoding)[0:take_only]
reconstructions = model.decode(data)
original, _ = input.get_images(FLAGS.input_path, at_most=take_only)
ut.print_side_by_side(original, reconstructions)
def train_couple_8_models():
FLAGS.input_path = '../data/tmp/8_pos_delay_3/img/'
model = model_class()
model.set_layer_sizes([500, 5, 500])
for i in range(10):
model.train(1000)
model = model_class()
model.set_layer_sizes([1000, 10, 1000])
for i in range(20):
model.train(1000)
if __name__ == "__main__":
# run function if provided as console params
epochs = 100
model_class = dm.DoomModel
experiment = search_learning_rate
if len(sys.argv) > 1:
print(sys.argv)
experiment = sys.argv[1]
if experiment not in locals():
ut.print_info('Function "%s" not found. List of available functions:' % experiment)
ut.print_info('\n'.join([x for x in locals() if 'search' in x]))
exit(0)
experiment = locals()[experiment]
if len(sys.argv) > 2:
epochs = int(sys.argv[2])
if len(sys.argv) > 3:
m = __import__(sys.argv[3])
model_class = getattr(m, sys.argv[3])
FLAGS.suffix = 'grid'
# FLAGS.input_path = '../data/tmp/8_pos_delay/img/'
experiment(epochs=epochs)
# search_layer_sizes(epochs=epochs)
# search_batch_size(epochs=epochs)
# FLAGS.batch_size = 40