def experiment_run_at_least_to_at_least_or_most(write_dir='data/exp3b',
atl=0,
atl2=1,
atm=2):
eparams = {
'num_epochs': 4,
'batch_size': 8,
'generator_mode': 'g',
'num_data': 100000,
'eval_steps': 50,
'stop_loss': 0.02
}
hparams = {
'hidden_size':
12,
'num_layers':
2,
'max_len':
20,
'num_classes':
2,
'dropout':
1.0,
'quantifiers': [
quantifiers.at_least_n(atl),
quantifiers.at_least_n_or_at_most_m(atl2, atm)
]
}
num_trials = 30
for idx in range(num_trials):
run_trial(eparams, hparams, idx, write_dir)
def experiment_one_c(write_dir='data/exp1c'):
eparams = {
'num_epochs': 4,
'batch_size': 8,
'generator_mode': 'g',
'num_data': 100000,
'eval_steps': 50,
'stop_loss': 0.02
}
hparams = {
'hidden_size':
12,
'num_layers':
2,
'max_len':
20,
'num_classes':
2,
'dropout':
1.0,
'quantifiers':
[quantifiers.at_least_n(4),
quantifiers.between_m_and_n(6, 10)]
}
num_trials = 30
for idx in range(num_trials):
run_trial(eparams, hparams, idx, write_dir)
def transfer_test():
params = {
'checkpoint_path':
'data/exp1a/trial_0/',
'old_scope':
'/',
'hidden_size':
12,
'num_layers':
2,
'max_len':
20,
'num_classes':
2,
'dropout':
1.0,
'quantifiers':
[quantifiers.at_least_n(4),
quantifiers.at_least_n_or_at_most_m(6, 2)]
}
# print(params['quantifiers'][0]._name)
model = tf.estimator.Estimator(model_fn=transfer_lstm_model_fn,
params=params)
generator = data_gen.DataGenerator(params['max_len'],
params['quantifiers'],
num_data_points=1000)
some_data = generator.get_training_data()
# must train before predict, so we'll give it one example
model.train(input_fn=tf.estimator.inputs.numpy_input_fn(
x={quant_verify.INPUT_FEATURE: np.array([some_data[0][0]])},
y=np.array([some_data[0][1]]),
shuffle=False))
some_inputs = np.array([datum[0] for datum in some_data])
predict_input_fn = tf.estimator.inputs.numpy_input_fn(
x={quant_verify.INPUT_FEATURE: some_inputs}, shuffle=False)
predictions = list(model.predict(input_fn=predict_input_fn))
for idx in range(5):
print('input: {}\nprobs: {}\n'.format(some_inputs[idx],
predictions[idx]['probs']))
def test():
eparams = {
'num_epochs': 4,
'batch_size': 8,
'generator_mode': 'g',
'num_data': 10000,
'eval_steps': 50,
'stop_loss': 0.02
}
hparams = {
'hidden_size': 12,
'num_layers': 2,
'max_len': 20,
'num_classes': 2,
'dropout': 1.0,
'quantifiers': [quantifiers.at_least_n(4), quantifiers.most]
}
for idx in range(2):
run_trial(eparams, hparams, idx)
