from data_providers import QuickDrawImageDataProvider
from network_builder import ClassifierNetworkGraph
from utils.parser_utils import ParserClass
from utils.storage import build_experiment_folder, save_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
parser_extractor = ParserClass(
parser=parser) # creates a parser class to process the parsed input
batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
strided_dim_reduction, experiment_prefix, dropout_rate_value, rnn_dropout_rate_value, layer_stage_sizes, \
rnn_cell_type, bidirectional, rnn_stage_sizes, conv_rnn_sizes, num_classes_use, inner_layer_depth, \
filter_size, num_dense_layers, num_dense_units, network_name, rotate = parser_extractor.get_argument_variables()
# returns a list of objects that contain our parsed input
convnet_desc = ""
if batch_norm:
convnet_desc = convnet_desc + "BN"
for ls in layer_stage_sizes:
convnet_desc = "{}_{}".format(convnet_desc, ls)
if network_name == 'fcn':
experiment_name = "exp{}_{}_{}_layers".format(experiment_prefix,
network_name,
len(layer_stage_sizes))
elif network_name == 'convn':
if rotate:
import tensorflow as tf
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
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(
description='Welcome to CNN experiments script'
) # generates an argument parser
parser_extractor = ParserClass(
parser=parser) # creates a parser class to process the parsed input
batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, batch_norm, \
strided_dim_reduction, experiment_prefix, dropout_rate_value = parser_extractor.get_argument_variables()
# returns a list of objects that contain
# our parsed input
experiment_name = "experiment_{}_batch_size_{}_bn_{}_mp_{}".format(
experiment_prefix, batch_size, batch_norm, strided_dim_reduction)
# generate experiment name
rng = np.random.RandomState(seed=seed) # set seed
train_data = CIFAR10DataProvider(which_set="train",
batch_size=batch_size,
rng=rng)
val_data = CIFAR10DataProvider(which_set="valid",
batch_size=batch_size,
rng=rng)
for word, index in vocab_processor.vocabulary_._mapping.items():
vocab[word] = index + 1
words.append(word)
return vocab, words
tf.reset_default_graph() # resets any previous graphs to clear memory
parser = argparse.ArgumentParser(
description='Welcome to CNN experiments script'
) # generates an argument parser
parser_extractor = ParserClass(
parser=parser) # creates a parser class to process the parsed input
batch_size, seed, epochs, logs_path, continue_from_epoch, tensorboard_enable, embedding_dim, \
filter_sizes, num_filters, experiment_prefix, dropout_rate_value, pt_embeddings, static_embeddings, \
l2_norm, activation, typec, cell, hidden_unit, num_units = parser_extractor.get_argument_variables()
# returns a list of objects that contain
# our parsed input
experiment_name = "experiment_{}_batch_size_{}_ptembed_{}".format(
experiment_prefix, batch_size, pt_embeddings)
# generate experiment name
rng = np.random.RandomState(seed=seed) # set seed
train_data = TwitterDataProvider(which_set="train",
batch_size=batch_size,
rng=rng)
val_data = TwitterDataProvider(which_set="valid",
batch_size=batch_size,
