def validate_bool_var(bool_var, scores, options, baseline):
temp = config[bool_var]
config[bool_var] = not temp
score = run(config)
config[bool_var] = temp
diff = (baseline - score) if temp else (score - baseline)
scores.append(diff)
options.append(bool_var)
visualize(scores, options)
def validate_bool_var(bool_var, scores, options, baseline):
temp = config[bool_var]
config[bool_var] = not temp
score = run(config)
config[bool_var] = temp
diff = (baseline - score) if temp else (score - baseline)
scores.append(diff)
options.append(bool_var)
visualize(scores, options)
plt.title('validation on the size of the word2vec dimensionality')
plt.ylabel('accuracy')
plt.xlabel('vector size')
plt.grid(True)
plt.savefig(base_folder + 'word2vec_dimensions_validation.pdf')
if __name__ == '__main__':
config = load_config()
base_folder = config['base_folder']
if not os.path.exists(base_folder):
os.makedirs(base_folder)
json.dump(config, open(base_folder + 'configuration.json','w'), indent=4, sort_keys=True)
scores = []
params = []
for i in range(1, 250, 10):
print("Validate parameter " + str(i))
config['lstm-layers'][0]['output-size'] = i
score = run(config)
scores.append(score)
params.append(i)
plot_scores(params, scores)
print(params)
print(scores)
plot_scores(params, scores)
plt.close()
if __name__ == "__main__":
config = load_config()
base_folder = config["base_folder"]
if not os.path.exists(base_folder):
os.makedirs(base_folder)
json.dump(config, open(base_folder + "configuration.json", "w"), indent=4, sort_keys=True)
scores = []
options = []
baseline = run(config)
# inits = ['zero', 'glorot_uniform', 'glorot_normal', 'he_normal', 'he_uniform', 'uniform', 'lecun_uniform', 'normal', ]
# init_inner = ['identity', 'orthogonal']
# activations = ['linear', 'tanh', 'sigmoid', 'hard_sigmoid', 'relu', 'softplus']
#
# for activation in activations:
# config['lstm-activation'] = activation
# score = run(config)
# scores.append(score - baseline)
# options.append('lstm-activation-' + activation)
# visualize(scores, options)
for bool_var in [
"tokenizer-german-split-compound-words",
"use-textblob-de",
plt.grid(True)
plt.savefig(base_folder + 'word2vec_dimensions_validation.pdf')
if __name__ == '__main__':
config = load_config()
base_folder = config['base_folder']
if not os.path.exists(base_folder):
os.makedirs(base_folder)
json.dump(config,
open(base_folder + 'configuration.json', 'w'),
indent=4,
sort_keys=True)
scores = []
params = []
for i in range(1, 250, 10):
print("Validate parameter " + str(i))
config['lstm-layers'][0]['output-size'] = i
score = run(config)
scores.append(score)
params.append(i)
plot_scores(params, scores)
print(params)
print(scores)
plot_scores(params, scores)
if __name__ == '__main__':
config = load_config()
base_folder = config['base_folder']
if not os.path.exists(base_folder):
os.makedirs(base_folder)
json.dump(config,
open(base_folder + 'configuration.json', 'w'),
indent=4,
sort_keys=True)
scores = []
options = []
baseline = run(config)
# inits = ['zero', 'glorot_uniform', 'glorot_normal', 'he_normal', 'he_uniform', 'uniform', 'lecun_uniform', 'normal', ]
# init_inner = ['identity', 'orthogonal']
# activations = ['linear', 'tanh', 'sigmoid', 'hard_sigmoid', 'relu', 'softplus']
#
# for activation in activations:
# config['lstm-activation'] = activation
# score = run(config)
# scores.append(score - baseline)
# options.append('lstm-activation-' + activation)
# visualize(scores, options)
for bool_var in [
'tokenizer-german-split-compound-words', 'use-textblob-de',
"only-fr-descriptions", "only-it-descriptions",
