def get_mat_id(self, mat_id_name='mat_id'):
"""
Get material ID numbers of the underlying mesh elements.
"""
if self.source is not None:
dm = DatasetManager(dataset=self.source.outputs[0])
mat_id = dm.cell_scalars[mat_id_name]
return mat_id
def test_should_print_ascii(self):
self.maxDiff = None
result = """+---------------------+------------+-----------------------------------------------------------------------------+
| description | identifier | source |
+---------------------+------------+-----------------------------------------------------------------------------+
| my little dataset | one_test | https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv |
| my little dataset 2 | two_test | https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv |
+---------------------+------------+-----------------------------------------------------------------------------+"""
data = DatasetManager("./tests/resources/multiple_data")
printer = Printer(data.get_datasets())
self.assertEqual(result, printer.__repr__())
def test_should_read_yaml_from_dir(self):
expected = {
"one_test": {
"source": "http://source/teste",
"description": "my little dataset"
}
}
data = DatasetManager("./tests/resources/one_data")
self.assertDictEqual(data.get_datasets(), expected)
def test_should_get_dataset(self):
data = DatasetManager("./tests/resources/local_data")
dataset = {
"local_test": {
"source": "./tests/resources/local_data/train.csv",
"description": "my little dataset local"
}
}
self.assertDictEqual(data.get_dataset("local_test"),
dataset.get("local_test"))
def main():
dataset_manager = DatasetManager()
epochs = 10
output_dir = os.path.abspath("workspace")
model = MaskRCNN(output_dir)
ratio = 0.2
datasetA, datasetB = dataset_manager.split_dataset('ade20k_train',
ratio=ratio)
weights = model.train(datasetA, weights=None, epochs=10)
small_datasetB = dataset_manager.random_subset(datasetB, 1000)
result = model.predict(small_datasetB, weights)
def test_should_remove_dataset(self):
data = DatasetManager(self.trash_dir, fs=self.os)
identifier = "data_name"
dataset = {
"identifier": identifier,
"description": "description",
"source": "/tmp/test.csv"
}
data.create_dataset(**dataset)
self.assertTrue(
os.path.isfile("{}/{}.yaml".format(self.trash_dir, identifier)))
self.assertEqual(len(os.listdir(self.trash_dir)), 2)
data.remove_dataset(identifier)
self.assertFalse(
os.path.isfile("{}/{}.yaml".format(self.trash_dir, identifier)))
self.assertEqual(len(os.listdir(self.trash_dir)), 1)
def setUp(self):
with open('training_set_list.pickle', 'rb') as handle:
self.training_dict = pickle.load(handle)
with open('validation_set_list.pickle', 'rb') as handle:
self.validation_dict = pickle.load(handle)
with open('test_set_list.pickle', 'rb') as handle:
self.test_dict = pickle.load(handle)
with open('genres.json') as json_data:
self.genres = json.load(json_data)
with open('labels.json') as json_data:
self.dataset = json.load(json_data)
self.dataset_manager = DatasetManager(self.training_dict,
self.validation_dict,
self.test_dict, self.genres,
self.dataset)
self.batch_size = 50
def evaluate(experiment_name, step=''):
logging.info('*' * 50)
logging.info('RUNNING EVALUATION FOR MODEL: %s', experiment_name)
if step == '':
interesting_checkpoint = tf.train.latest_checkpoint(
os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name))
else:
interesting_checkpoint = os.path.join(CURRENT_DIR, '..', 'checkpoint',
experiment_name,
'step-{}'.format(step))
dataset_manager = DatasetManager()
dataset_manager.boot()
with tf.Graph().as_default() as gr:
logging.info('-- Restoring graph for model: %s',
interesting_checkpoint)
saver = tf.train.import_meta_graph(
'{}.meta'.format(interesting_checkpoint))
logging.info('-- Restored graph for model named: %s',
interesting_checkpoint)
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)).as_default() as sess:
saver.restore(sess=sess, save_path=interesting_checkpoint)
logging.info('-- Restored variables for model named: %s',
interesting_checkpoint)
list_predictions = []
list_labels = []
for docs, labels in dataset_manager.get_test_by_batch(
batch_size=FLAGS.BATCH_SIZE):
tf_input = gr.get_tensor_by_name('input/tf_input:0')
tf_predictions = gr.get_tensor_by_name('prediction:0')
prediction = sess.run(tf_predictions,
feed_dict={tf_input: docs})
list_predictions.extend(prediction)
list_labels.extend(labels)
logging.debug('-- Prediction length: %s/%s',
len(list_predictions),
dataset_manager.test_y.shape[0])
logging.info('-- Report for model: %s', experiment_name)
logging.info(
classification_report(y_true=list_labels,
y_pred=list_predictions,
digits=4))
logging.info(
confusion_matrix(y_true=list_labels, y_pred=list_predictions))
def add_subdomains_surface(obj,
position,
mat_id_name='mat_id',
threshold_limits=(None, None),
**kwargs):
dm = DatasetManager(dataset=obj.outputs[0])
mat_id = dm.cell_scalars[mat_id_name]
rm = mat_id.min(), mat_id.max()
active = mlab.pipeline.set_active_attribute(obj)
active.cell_scalars_name = mat_id_name
aa = mlab.pipeline.set_active_attribute(obj)
aa.cell_scalars_name = mat_id_name
threshold = mlab.pipeline.threshold(aa)
threshold.threshold_filter.progress = 1.0
if threshold_limits[0] is not None:
threshold.lower_threshold = threshold_limits[0] + 0.1
if threshold_limits[1] is not None:
threshold.upper_threshold = threshold_limits[1] - 0.1
threshold.auto_reset_lower = False
threshold.auto_reset_upper = False
surface = mlab.pipeline.surface(threshold, opacity=0.3)
surface.actor.actor.position = position
module_manager = surface.parent
lm = module_manager.scalar_lut_manager
lm.lut_mode = 'Blues'
if (rm[1] - rm[0]) == 1:
lm.reverse_lut = True
surface2 = mlab.pipeline.surface(active, opacity=0.2)
surface2.actor.actor.position = position
module_manager = surface2.parent
module_manager.scalar_lut_manager.lut_mode = 'Blues'
return surface, surface2
def main():
output_dir = os.path.abspath("workspace")
dataset_manager = DatasetManager()
model = MaskRCNN(output_dir)
ratio = 0.2
datasetA, datasetB = dataset_manager.split_dataset('ade20k_train',
ratio=ratio)
weights = model.train(datasetA, weights=None, epochs=10)
while True:
small_datasetB = dataset_manager.random_subset(datasetB, 100)
predictions = model.predict(small_datasetB, weights)
annotations = annotator.filter(predictions)
new_dataset = dataset_manager.create_dataset_with_new_annotations(
datasetB, annotations)
weights = model.train(new_dataset, weights, epochs=1)
def test_should_create_dataset_with_custom_data(self):
data = DatasetManager(self.trash_dir, fs=self.os)
identifier = "data_name_custom"
dataset = {
"identifier": identifier,
"description": "description",
"source": "/tmp/test.csv"
}
data.create_dataset(**dataset)
self.assertTrue(
self.os.isfile("{}/{}.yaml".format(self.trash_dir, identifier)))
self.assertEqual(len(os.listdir(self.trash_dir)), 2)
loaded_dataset = data.get_datasets()
self.assertEqual(list(loaded_dataset.keys()), [identifier])
datasource_configs = loaded_dataset.get(identifier)
self.assertEqual(datasource_configs["description"],
dataset["description"])
self.assertEqual(datasource_configs["source"], dataset["source"])
def test_should_read_multiple_yaml_from_dir(self):
expected = {
"one_test": {
"source":
"https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv",
"description": "my little dataset"
},
"two_test": {
"source":
"https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv",
"description": "my little dataset 2"
}
}
data = DatasetManager("./tests/resources/multiple_data", fs=self.os)
result = list(data.get_datasets().keys())
result.sort()
expected = ["one_test", "two_test"]
self.assertListEqual(expected, result)
def predict(list_docs, experiment_name, step='', batch_size=64):
logging.info('*' * 50)
logging.info('RUNNING PREDICT FOR MODEL: %s', experiment_name)
if step == '':
interesting_checkpoint = tf.train.latest_checkpoint(os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name))
else:
interesting_checkpoint = os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name, 'step-{}'.format(step))
dataset_manager = DatasetManager()
dataset_manager.boot()
list_preprocessed_sentences = preprocessor.preprocess(list_docs)
list_vecs = dataset_manager.text2vec.doc_to_vec(list_preprocessed_sentences)
print(dataset_manager.text2vec.vec_to_doc(list_vecs))
list_vecs = dataset_manager.equalize_vector_length_to_np(list_vectors=list_vecs,
max_length=model_v6.SENTENCE_LENGTH_MAX)
with tf.Graph().as_default() as gr:
logging.info('-- Restoring graph for model: %s', interesting_checkpoint)
saver = tf.train.import_meta_graph('{}.meta'.format(interesting_checkpoint))
logging.info('-- Restored graph for model named: %s', interesting_checkpoint)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)).as_default() as sess:
saver.restore(sess=sess, save_path=interesting_checkpoint)
logging.info('-- Restored variables for model named: %s', interesting_checkpoint)
list_predictions = []
num_steps = len(list_vecs) // batch_size
logging.info('There will be %s steps', num_steps + 1)
for i in range(num_steps + 1):
tf_input = gr.get_tensor_by_name('input/tf_input:0')
tf_predictions = gr.get_tensor_by_name('prediction:0')
prediction = sess.run(tf_predictions, feed_dict={
tf_input: list_vecs[i*batch_size: (i+1)*batch_size]
})
list_predictions.extend([dataset_manager.LABEL_UNMAPPING[p] for p in prediction])
return list_predictions
def test_should_print_html(self):
self.maxDiff = None
result = """<table>
<tr>
<th>description</th>
<th>identifier</th>
<th>source</th>
</tr>
<tr>
<td>my little dataset</td>
<td>one_test</td>
<td>https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv</td>
</tr>
<tr>
<td>my little dataset 2</td>
<td>two_test</td>
<td>https://raw.githubusercontent.com/pcsanwald/kaggle-titanic/master/train.csv</td>
</tr>
</table>"""
data = DatasetManager("./tests/resources/multiple_data")
printer = Printer(data.get_datasets())
self.assertEqual(result, printer._repr_html_())
def test_should_create_dataset(self):
data = DatasetManager(self.trash_dir, fs=self.os)
identifier = "data_name"
dataset = {
"identifier": identifier,
"description": "description",
"source": "/tmp/test.csv",
}
data.create_dataset(**dataset)
loaded_datasets = data.get_datasets()
dataset_config = loaded_datasets.get(identifier)
self.assertTrue(
self.os.isfile("{}/{}.yaml".format(self.trash_dir, identifier)))
self.assertEqual(len(self.os.listdir(self.trash_dir)), 2)
self.assertEqual(list(loaded_datasets.keys())[0], identifier)
self.assertEqual(dataset_config.get("description"),
dataset["description"])
self.assertEqual(dataset_config.get("source"), dataset["source"])
def create_source(self):
"""
Create a VTK source from data in a SfePy-supported file.
Notes
-----
All data need to be set here, otherwise time stepping will not
work properly - data added by user later will be thrown away on
time step change.
"""
if self.io is None:
self.read_common(self.filename)
dataset = self.create_dataset()
try:
out = self.io.read_data(self.step)
except ValueError:
out = None
if out is not None:
self.add_data_to_dataset(dataset, out)
if self.mat_id_name is not None:
mat_id = self.mesh.cmesh.cell_groups
if self.single_color:
rm = mat_id.min(), mat_id.max()
mat_id[mat_id > rm[0]] = rm[1]
dm = DatasetManager(dataset=dataset)
dm.add_array(mat_id, self.mat_id_name, 'cell')
src = VTKDataSource(data=dataset)
# src.print_traits()
# debug()
return src
def test_should_get_dataset_unknown(self):
data = DatasetManager("./tests/resources/local_data")
with self.assertRaises(IOError):
data.get_dataset("unknown_test")
def main():
# Load dataset manager
with open('training_set_list.pickle', 'rb') as handle:
training_set = pickle.load(handle)
with open('validation_set_list.pickle', 'rb') as handle:
validation_set = pickle.load(handle)
with open('test_set_list.pickle', 'rb') as handle:
test_set = pickle.load(handle)
with open('genres.json') as json_data:
genres = json.load(json_data)
with open('labels.json') as json_data:
labels = json.load(json_data)
dataset_manager = DatasetManager(training_set, validation_set, test_set,
genres, labels)
batch_size = 1
n_classes = 26
# Graph input
x = tf.placeholder(tf.float32, [batch_size, 227, 227, 3])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_var = tf.placeholder(tf.float32)
# Model
pred = Model.alexnet(x, keep_var) # definition of the network architecture
# Loss and optimize
# Init
init = tf.global_variables_initializer()
# Initialize an saver for store model checkpoints
saver = tf.train.Saver()
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Load pretrained model
# Skip weights from fc8 (fine-tuning)
# load_with_skip('pretrained_alexnet.npy', sess, ['fc8'])
# saver.restore(sess, "saved_models/MSE_without_data_augmentation_0.75_0.001/film_genre_model.ckpt")
saver.restore(
sess, "saved_models/models/model_dropout05_mean_square_error.ckpt")
print('Model Restored')
test_map_global = 0.
test_count = 0
# test accuracy by group of batch_size images
for _ in range(int(len(dataset_manager.test_list) / batch_size) + 1):
batch_tx, batch_ty = dataset_manager.next_batch(batch_size, 'test')
# print(batch_tx[0], batch_ty[0])
test_output = sess.run(pred, feed_dict={x: batch_tx, keep_var: 1})
# print(test_output[0])
MAP = mean_average_precision(test_output, batch_ty)
test_map_global += MAP
test_count += 1
test_map_global /= test_count
print("Global Test Accuracy = {:.4f}".format(test_map_global))
# Load one image
img = cv2.imread('saved_models/images_tests/yellow.jpg')
img = cv2.resize(img, (227, 227))
img = img.astype(np.float32)
img -= np.array([104., 117., 124.])
print(img)
test_output = sess.run(pred,
feed_dict={
x: np.reshape(img, (1, 227, 227, 3)),
keep_var: 1
})
score_dict = {}
for score, genre in zip(test_output[0], genres):
score_dict[genre] = score
print(list(reversed(sorted(score_dict.items(), key=lambda x: x[1]))))
def add_data_to_dataset(self, dataset, data):
"""Add point and cell data to the dataset."""
dim = self.dim
sym = (dim + 1) * dim / 2
dm = DatasetManager(dataset=dataset)
for key, val in data.iteritems():
vd = val.data
## print vd.shape
if val.mode == 'vertex':
if vd.shape[1] == 1:
aux = vd.reshape((vd.shape[0],))
elif vd.shape[1] == 2:
zz = nm.zeros((vd.shape[0], 1), dtype=vd.dtype)
aux = nm.c_[vd, zz]
elif vd.shape[1] == 3:
aux = vd
else:
raise ValueError('unknown vertex data format! (%s)'\
% vd.shape)
dm.add_array(aux, key, 'point')
elif val.mode == 'cell':
ne, aux, nr, nc = vd.shape
if (nr == 1) and (nc == 1):
aux = vd.reshape((ne,))
elif (nr == dim) and (nc == 1):
if dim == 3:
aux = vd.reshape((ne, dim))
else:
zz = nm.zeros((vd.shape[0], 1), dtype=vd.dtype);
aux = nm.c_[vd.squeeze(), zz]
elif (((nr == sym) or (nr == (dim * dim))) and (nc == 1)) \
or ((nr == dim) and (nc == dim)):
vd = vd.squeeze()
if dim == 3:
if nr == sym:
aux = vd[:,[0,3,4,3,1,5,4,5,2]]
elif nr == (dim * dim):
aux = vd[:,[0,3,4,6,1,5,7,8,2]]
else:
aux = vd.reshape((vd.shape[0], dim*dim))
else:
zz = nm.zeros((vd.shape[0], 1), dtype=vd.dtype);
if nr == sym:
aux = nm.c_[vd[:,[0,2]], zz, vd[:,[2,1]],
zz, zz, zz, zz]
elif nr == (dim * dim):
aux = nm.c_[vd[:,[0,2]], zz, vd[:,[3,1]],
zz, zz, zz, zz]
else:
aux = nm.c_[vd[:,0,[0,1]], zz, vd[:,1,[0,1]],
zz, zz, zz, zz]
dm.add_array(aux, key, 'cell')
from flask import Flask, send_file
from flask_restful import Resource, Api, reqparse
from flask_cors import CORS
import numpy as np
from data_backend import Dataset as HDF_Dataset
from dataset_manager import DatasetManager
from thumbnailer import Thumbnailer
from utils import merge_overlapping_filters
DATASET_PATH = "./datasets"
dataset_manager = DatasetManager(DATASET_PATH)
API_BASE_STR = "/api/v1"
# Init thumbnails (clean directory)
thumbnailer = Thumbnailer("./thumbnails")
thumbnailer.clean();
dataset_list = []
for dset_index, name in enumerate(dataset_manager.get_dataset_names()):
dset = HDF_Dataset(DATASET_PATH, name)
dataset_list.append({
"id": dset_index,
"name": name,
"device": {
"name": dset.device.name,
"version": dset.device.version
},
"subsets": [
{
"id": subset_index,
"name": subset,
def run(experiment_name):
with tf.Graph().as_default() as gr:
with tf.variable_scope('input'):
tf_input = tf.placeholder(dtype=tf.int32,
shape=[None, model.SENTENCE_LENGTH_MAX],
name='tf_input')
tf_labels = tf.placeholder(dtype=tf.int32,
shape=[None],
name='tf_labels')
tf_logits = model.inference(tf_input)
tf_loss = model.loss(tf_logits, tf_labels)
tf_optimizer, tf_global_step = model.optimize(tf_loss)
model.measure_acc(tf_logits, tf_labels)
tf_all_summary = tf.summary.merge_all()
tf_train_writer = tf.summary.FileWriter(logdir=os.path.join(
CURRENT_DIR, '..', 'summary', 'train_' + experiment_name),
graph=gr)
tf_test_writer = tf.summary.FileWriter(logdir=os.path.join(
CURRENT_DIR, '..', 'summary', 'test_' + experiment_name),
graph=gr)
tf_embedding_writer = tf.summary.FileWriter(logdir=os.path.join(
CURRENT_DIR, '..', 'checkpoint', experiment_name))
# Visual word embedding
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = 'embedding/word_embeddings' # Reference model_v6.py
embedding.metadata_path = os.path.join(CURRENT_DIR, 'data',
DatasetManager.VOCAB_FILE)
projector.visualize_embeddings(tf_embedding_writer, config)
saver = tf.train.Saver(max_to_keep=5,
keep_checkpoint_every_n_hours=0.03)
logging.info('Graph size: %s', utils.count_trainable_variables())
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.GPU)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options,
allow_soft_placement=True,
log_device_placement=FLAGS.LOG_DEVICE_PLACEMENT)).as_default(
) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
dataset_manager = DatasetManager()
dataset_manager.boot()
for docs, labels in dataset_manager.get_batch(
batch_size=FLAGS.BATCH_SIZE,
number_epochs=FLAGS.NUMBER_EPOCHS):
_, global_step = sess.run([tf_optimizer, tf_global_step],
feed_dict={
tf_input: docs,
tf_labels: labels
})
summary_interval_step = 10
if global_step % summary_interval_step == 0:
logging.debug('Global step: %s', global_step)
train_summary_data = sess.run(tf_all_summary,
feed_dict={
tf_input: docs,
tf_labels: labels
})
tf_train_writer.add_summary(train_summary_data,
global_step=global_step)
if global_step % summary_interval_step == 0:
docs_test, labels_test = dataset_manager.get_test_set(
FLAGS.TEST_SIZE, is_shuffled=True)
test_summary_data = sess.run(tf_all_summary,
feed_dict={
tf_input: docs_test,
tf_labels: labels_test
})
tf_test_writer.add_summary(test_summary_data,
global_step=global_step)
if global_step % 200 == 0:
path_to_save = os.path.join(CURRENT_DIR, '..',
'checkpoint', experiment_name)
if not os.path.exists(path_to_save):
os.makedirs(path_to_save)
saved_file = saver.save(sess,
save_path=os.path.join(
path_to_save, 'step'),
global_step=global_step,
write_meta_graph=True)
logging.debug('Saving model at %s', saved_file)
def run(experiment_name):
BEST_THRES = 3
WORST_THRES = 3
POPULATION_STEPS = 500
ITERATIONS = 100
POPULATION_SIZE = 10
accuracy_hist = np.zeros((POPULATION_SIZE, POPULATION_STEPS))
l1_scale_hist = np.zeros((POPULATION_SIZE, POPULATION_STEPS))
best_accuracy_hist = np.zeros((POPULATION_STEPS, ))
best_l1_scale_hist = np.zeros((POPULATION_STEPS, ))
with tf.Graph().as_default() as gr:
with tf.variable_scope('input'):
tf_input = tf.placeholder(
dtype=tf.int32,
shape=[
None, model_population_based_tunning.SENTENCE_LENGTH_MAX
],
name='tf_input')
tf_labels = tf.placeholder(dtype=tf.int32,
shape=[None],
name='tf_labels')
models = [
create_model(
i, is_included_regularization=FLAGS.IS_INCLUDED_REGULARIZATION)
for i in range(10)
]
# It will help us with creation of different scope_name for each model
for index, model in enumerate(models):
with tf.variable_scope(str(index)):
model.boot(tf_input, tf_labels)
logging.info('Graph size: %s', utils.count_trainable_variables())
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=FLAGS.GPU)
with tf.Session(config=tf.ConfigProto(
gpu_options=gpu_options,
allow_soft_placement=True,
log_device_placement=FLAGS.LOG_DEVICE_PLACEMENT)).as_default(
) as sess:
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
dataset_manager = DatasetManager()
dataset_manager.boot()
dataset_generator = dataset_manager.get_batch(
batch_size=FLAGS.BATCH_SIZE,
number_epochs=10 * FLAGS.NUMBER_EPOCHS)
for i in range(POPULATION_STEPS):
# Copy best
sess.run([
m.get_copy_from_op(models[0])
for m in models[-WORST_THRES:]
])
# Perturb others
sess.run([m.l1_scale_perturb_op for m in models[BEST_THRES:]])
# Training
for _ in range(ITERATIONS):
docs, labels = next(dataset_generator)
sess.run([m.tf_optimizer for m in models],
feed_dict={
tf_input: docs,
tf_labels: labels
})
docs, labels = next(dataset_generator)
# Evaluate
l1_scales = sess.run({m: m.l1_scale for m in models})
accuracies = sess.run({m: m.tf_acc
for m in models},
feed_dict={
tf_input: docs,
tf_labels: labels
})
models.sort(key=lambda m: accuracies[m], reverse=True)
# Logging
best_accuracy_hist[i] = accuracies[models[0]]
best_l1_scale_hist[i] = l1_scales[models[0]]
for m in models:
l1_scale_hist[m.model_id, i] = l1_scales[m]
accuracy_hist[m.model_id, i] = accuracies[m]
with open('temp', 'w') as output_f:
json.dump(
{
'accuracy_hist': accuracy_hist,
'l1_scale_hist': l1_scale_hist,
'best_accuracy_hist': best_accuracy_hist,
'best_l1_scale_hist': best_l1_scale_hist
}, output_f)
import sys
from dataset_manager import DatasetManager
from gloss_lookup import GlossLookup
# Instantiate singleton classes
gl = GlossLookup()
dm = DatasetManager()
def menu():
print()
print(15 * '-', 'MAIN MENU', 15 * '-')
print('[1] Gloss Lookup' + '\n[2] Dataset manager' + '\n[3] Exit program')
print(41 * '-')
def glossLookup():
loop = True
while loop:
gl.menu()
answer = int(input('Enter the class (an integer between 0 to 999): '))
if gl.wordExists(answer):
print('Word: ' + gl.searchGlossary(answer))
loop = False
else:
print('Invalid class. Please enter a valid class.')
def datasetManager():
loop = True
params = "pd_fixed_trainratio80_outcome_all_data_singletask"
#params = "lstmsize%s_dropout%s_shared%s_specialized%s"%(lstmsize, dropout, n_shared_layers, n_specialized_layers)
checkpoint_prefix = os.path.join(
output_dir, "checkpoints/model_%s_%s" % (dataset_name, params))
model_filename = glob.glob("%s*.hdf5" % checkpoint_prefix)[-1]
#model_filename = "code/output_files/models/model_28-1.51.h5"
results_file = os.path.join(
output_dir, "evaluation_results/results_%s_%s_%s.csv" %
(cls_method, dataset_name, params))
##### MAIN PART ######
print('Preparing data...')
start = time.time()
dataset_manager = DatasetManager(dataset_name)
data = dataset_manager.read_dataset()
train, test = dataset_manager.split_data(
data, train_ratio, split=data_split_type
) # to reproduce results of Tax et al., use 'ordered' instead of 'temporal'
dt_train = dataset_manager.encode_data_with_label_all_data(train)
dt_test = dataset_manager.encode_data_with_label_all_data(test)
if normalize_over == "train":
dataset_manager.calculate_divisors(dt_train)
elif normalize_over == "all":
dt_all = dataset_manager.extract_timestamp_features(data)
dt_all = dataset_manager.extract_duration_features(dt_all)
dataset_manager.calculate_divisors(dt_all)
else:
def test_should_remove_unknown_dataset(self):
data = DatasetManager("./tests/resources/local_data", fs=self.os)
with self.assertRaises(IOError):
data.remove_dataset("unknown_dataset")
def predict(list_sentences,
output_file,
experiment_name,
step='',
list_labels=[]):
dataset_manager = DatasetManager()
dataset_manager.boot()
list_preprocessed_sentences = preprocessor.preprocess(list_sentences)
list_vecs = dataset_manager.text2vec.doc_to_vec(
list_preprocessed_sentences)
list_vecs = dataset_manager.equalize_vector_length_to_np(
list_vectors=list_vecs, max_length=model_v1.SENTENCE_LENGTH_MAX)
list_labels = dataset_manager.convert_labels_to_np(list_labels)
if step == '':
interesting_checkpoint = tf.train.latest_checkpoint(
os.path.join(CURRENT_DIR, '..', 'checkpoint', experiment_name))
else:
interesting_checkpoint = os.path.join(CURRENT_DIR, '..', 'checkpoint',
experiment_name,
'step-{}'.format(step))
with tf.Graph().as_default() as gr:
logging.info('-- Restoring graph for model: %s',
interesting_checkpoint)
saver = tf.train.import_meta_graph(
'{}.meta'.format(interesting_checkpoint))
logging.info('-- Restored graph for model named: %s',
interesting_checkpoint)
with tf.Session(config=tf.ConfigProto(
allow_soft_placement=True)).as_default() as sess:
saver.restore(sess=sess, save_path=interesting_checkpoint)
logging.info('-- Restored variables for model named: %s',
interesting_checkpoint)
tf_input = gr.get_tensor_by_name('input/tf_input:0')
tf_predictions = gr.get_tensor_by_name('prediction:0')
prediction = sess.run(tf_predictions,
feed_dict={tf_input: list_vecs})
if len(list_labels) != 0:
logging.info('-- Report for model: %s', experiment_name)
logging.info(
classification_report(y_true=list_labels,
y_pred=prediction))
result_dict = dict()
result_dict['sentence'] = list_sentences
result_dict['pre-processed'] = list_preprocessed_sentences
result_dict[
'pre-processed_recover'] = dataset_manager.text2vec.vec_to_doc(
list_vecs)
result_dict['predict'] = prediction
if len(list_labels) != 0:
result_dict['label'] = list_labels
pd.DataFrame(result_dict).to_csv(output_file, index=None)
logging.debug('Saved result at %s', output_file)
def main():
# Load dataset manager
with open('training_set_list.pickle', 'rb') as handle:
training_set = pickle.load(handle)
with open('validation_set_list.pickle', 'rb') as handle:
validation_set = pickle.load(handle)
with open('test_set_list.pickle', 'rb') as handle:
test_set = pickle.load(handle)
with open('genres.json') as json_data:
genres = json.load(json_data)
with open('labels.json') as json_data:
labels = json.load(json_data)
log_file_name = str(datetime.now()) + '-logs.txt'
with open("logs/" + log_file_name, 'w') as log_file:
log_file.write('Training logs \n')
iteration_file_name = str(datetime.now()) + '-iteration.txt'
with open("logs/" + iteration_file_name, 'w') as log_file:
log_file.write('Training iterations \n')
best_iteration_file_name = str(datetime.now()) + '-best_iteration.txt'
with open("logs/" + best_iteration_file_name, 'w') as log_file:
log_file.write('Best Training iterations \n')
dataset_manager = DatasetManager(training_set, validation_set, test_set,
genres, labels)
# Learning params
learning_rate = 0.001
batch_size = 50
# Nombre d'iterations
training_iters = 1000
# display training information (loss, training accuracy, ...) every 10
# iterations
local_train_step = 20
global_validation_step = 25 # test every global_test_step iterations
global_train_step = 100
# Network params
n_classes = 26
keep_rate = 0.5 # for dropout
# Graph input
x = tf.placeholder(tf.float32, [batch_size, 227, 227, 3])
y = tf.placeholder(tf.float32, [None, n_classes])
keep_var = tf.placeholder(tf.float32)
# Model
pred = Model.alexnet(x, keep_var) # definition of the network architecture
# Loss and optimizer
# loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=y))
loss = tf.sqrt(tf.reduce_mean(tf.square(tf.subtract(y, pred))))
# optimizer = tf.train.GradientDescentOptimizer(
# learning_rate=learning_rate).minimize(loss)
optimizer = tf.train.RMSPropOptimizer(
learning_rate=learning_rate).minimize(loss)
# Init
init = tf.global_variables_initializer()
# Initialize an saver for store model checkpoints
saver = tf.train.Saver()
# To do early stopping
max_validation_map = 0
# Launch the graph
with tf.Session() as sess:
sess.run(init)
# Load pretrained model
# Skip weights from fc8 (fine-tuning)
load_with_skip('pretrained_alexnet.npy', sess, ['fc8'])
print('Start training.')
step = 1
while step < training_iters:
# print("Iter ", step)
with open("logs/" + iteration_file_name, 'a') as log_file:
log_file.write("Iter {} \n".format(step))
batch_xs, batch_ys = dataset_manager.next_batch(
batch_size, 'train')
sess.run(optimizer,
feed_dict={
x: batch_xs,
y: batch_ys,
keep_var: keep_rate
})
# Display on batch training status
if step % local_train_step == 0:
local_train_output = sess.run(pred,
feed_dict={
x: batch_xs,
keep_var: 1
})
MAP = mean_average_precision(local_train_output, batch_ys)
batch_loss = sess.run(loss,
feed_dict={
x: batch_xs,
y: batch_ys,
keep_var: 1.
})
with open("logs/" + log_file_name, 'a') as log_file:
log_file.write("Iter {} Training Loss = {:.4f}, "
"Mean average precision = {:.4f} \n".format(
step, batch_loss, MAP))
# Display global training error
if step % global_train_step == 0:
train_map_global = 0.
test_count = 0
# test accuracy by group of batch_size images
for _ in range(
int(len(dataset_manager.training_list) / batch_size) +
1):
batch_tx, batch_ty = dataset_manager.next_batch(
batch_size, 'train')
test_output = sess.run(pred,
feed_dict={
x: batch_tx,
keep_var: 1
})
MAP = mean_average_precision(test_output, batch_ty)
train_map_global += MAP
test_count += 1
train_map_global /= test_count
with open("logs/" + log_file_name, 'a') as log_file:
log_file.write(
"Global Training Accuracy = {:.4f} \n".format(
train_map_global))
# Display global testing error
if step % global_validation_step == 0:
validation_map_global = 0.
validation_count = 0
# test accuracy by group of batch_size images
for _ in range(
int(len(dataset_manager.test_list) / batch_size) + 1):
batch_tx, batch_ty = dataset_manager.next_batch(
batch_size, 'val')
test_output = sess.run(pred,
feed_dict={
x: batch_tx,
keep_var: 1
})
MAP = mean_average_precision(test_output, batch_ty)
validation_map_global += MAP
validation_count += 1
validation_map_global /= validation_count
with open("logs/" + log_file_name, 'a') as log_file:
log_file.write(
"Iter {} Global Validation Accuracy = {:.4f} \n".
format(step, validation_map_global))
if validation_map_global >= max_validation_map:
max_validation_map = validation_map_global
with open("logs/" + best_iteration_file_name,
'a') as log_file:
log_file.write("Iter {} \n".format(step))
# Save model
saver.save(
sess,
"saved_models/model_dropout05_mean_square_error.ckpt")
step += 1
# print("Finish!")
with open("logs/finish", 'w') as finish_file:
finish_file.write("Finish")
