def test_log_likelihood_sym(self, output_dim, input_shape):
gmr = GaussianMLPRegressorWithModel(
input_shape=input_shape,
output_dim=output_dim,
optimizer=PenaltyLbfgsOptimizer,
optimizer_args=dict())
new_input_var = tf.placeholder(
tf.float32, shape=(None, ) + input_shape)
new_ys_var = tf.placeholder(
dtype=tf.float32, name='ys', shape=(None, output_dim))
data = np.random.random(size=input_shape)
label = np.ones(output_dim)
outputs = gmr.log_likelihood_sym(
new_input_var, new_ys_var, name='ll_sym')
ll_from_sym = self.sess.run(
outputs, feed_dict={
new_input_var: [data],
new_ys_var: [label]
})
mean, log_std = gmr._f_pdists([data])
ll = gmr.model.networks['default'].dist.log_likelihood(
[label], dict(mean=mean, log_std=log_std))
assert np.allclose(ll, ll_from_sym, rtol=0, atol=1e-5)
def __init__(
self,
env_spec,
subsample_factor=1.,
num_seq_inputs=1,
regressor_args=None,
name='GaussianMLPBaselineWithModel',
):
"""
Gaussian MLP Baseline with Model.
It fits the input data to a gaussian distribution estimated by
a MLP.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
subsample_factor (float): The factor to subsample the data. By
default it is 1.0, which means using all the data.
num_seq_inputs (float): Number of sequence per input. By default
it is 1.0, which means only one single sequence.
regressor_args (dict): Arguments for regressor.
"""
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianMLPRegressorWithModel(
input_shape=(env_spec.observation_space.flat_dim *
num_seq_inputs, ),
output_dim=1,
name=name,
**regressor_args)
self.name = name
def __init__(
self,
env_spec,
subsample_factor=1.,
num_seq_inputs=1,
regressor_args=None,
name='GaussianMLPBaselineWithModel',
):
"""
Constructor.
:param env_spec:
:param subsample_factor:
:param num_seq_inputs:
:param regressor_args:
"""
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianMLPRegressorWithModel(
input_shape=(env_spec.observation_space.flat_dim *
num_seq_inputs, ),
output_dim=1,
name=name,
**regressor_args)
self.name = name
def test_is_pickleable(self):
gmr = GaussianMLPRegressorWithModel(input_shape=(1, ), output_dim=1)
with tf.variable_scope(
'GaussianMLPRegressorWithModel/GaussianMLPRegressorModel',
reuse=True):
bias = tf.get_variable('dist_params/mean_network/hidden_0/bias')
bias.load(tf.ones_like(bias).eval())
result1 = gmr.predict(np.ones((1, 1)))
h = pickle.dumps(gmr)
with tf.Session(graph=tf.Graph()):
gmr_pickled = pickle.loads(h)
result2 = gmr_pickled.predict(np.ones((1, 1)))
assert np.array_equal(result1, result2)
def test_fit_smaller_subsample_factor(self):
gmr = GaussianMLPRegressorWithModel(
input_shape=(1, ), output_dim=1, subsample_factor=0.9)
data = np.linspace(-np.pi, np.pi, 1000)
obs = [{'observations': [[x]], 'returns': [np.sin(x)]} for x in data]
observations = np.concatenate([p['observations'] for p in obs])
returns = np.concatenate([p['returns'] for p in obs])
for _ in range(150):
gmr.fit(observations, returns.reshape((-1, 1)))
paths = {
'observations': [[-np.pi], [-np.pi / 2], [-np.pi / 4], [0],
[np.pi / 4], [np.pi / 2], [np.pi]]
}
prediction = gmr.predict(paths['observations'])
expected = [[0], [-1], [-0.707], [0], [0.707], [1], [0]]
assert np.allclose(prediction, expected, rtol=0, atol=0.1)
class GaussianMLPBaselineWithModel(Baseline):
"""A value function using Gaussian MLP network."""
def __init__(
self,
env_spec,
subsample_factor=1.,
num_seq_inputs=1,
regressor_args=None,
name='GaussianMLPBaselineWithModel',
):
"""
Constructor.
:param env_spec:
:param subsample_factor:
:param num_seq_inputs:
:param regressor_args:
"""
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianMLPRegressorWithModel(
input_shape=(env_spec.observation_space.flat_dim *
num_seq_inputs, ),
output_dim=1,
name=name,
**regressor_args)
self.name = name
@overrides
def fit(self, paths):
"""Fit regressor based on paths."""
observations = np.concatenate([p['observations'] for p in paths])
returns = np.concatenate([p['returns'] for p in paths])
self._regressor.fit(observations, returns.reshape((-1, 1)))
@overrides
def predict(self, path):
"""Predict value based on paths."""
return self._regressor.predict(path['observations']).flatten()
@overrides
def get_param_values(self, **tags):
"""Get parameter values."""
return self._regressor.get_param_values(**tags)
@overrides
def set_param_values(self, flattened_params, **tags):
"""Set parameter values to val."""
self._regressor.set_param_values(flattened_params, **tags)
@overrides
def get_params_internal(self, **tags):
"""Get internal parameters."""
return self._regressor.get_params_internal(**tags)
def test_fit_unnormalized(self):
gmr = GaussianMLPRegressorWithModel(
input_shape=(1, ),
output_dim=1,
subsample_factor=0.9,
normalize_inputs=False,
normalize_outputs=False)
data = np.linspace(-np.pi, np.pi, 1000)
obs = [{'observations': [[x]], 'returns': [np.sin(x)]} for x in data]
observations = np.concatenate([p['observations'] for p in obs])
returns = np.concatenate([p['returns'] for p in obs])
for _ in range(150):
gmr.fit(observations, returns.reshape((-1, 1)))
paths = {
'observations': [[-np.pi], [-np.pi / 2], [-np.pi / 4], [0],
[np.pi / 4], [np.pi / 2], [np.pi]]
}
prediction = gmr.predict(paths['observations'])
expected = [[0], [-1], [-0.707], [0], [0.707], [1], [0]]
assert np.allclose(prediction, expected, rtol=0, atol=0.1)
x_mean = self.sess.run(gmr.model.networks['default'].x_mean)
x_mean_expected = np.zeros_like(x_mean)
x_std = self.sess.run(gmr.model.networks['default'].x_std)
x_std_expected = np.ones_like(x_std)
assert np.array_equal(x_mean, x_mean_expected)
assert np.array_equal(x_std, x_std_expected)
y_mean = self.sess.run(gmr.model.networks['default'].y_mean)
y_mean_expected = np.zeros_like(y_mean)
y_std = self.sess.run(gmr.model.networks['default'].y_std)
y_std_expected = np.ones_like(y_std)
assert np.allclose(y_mean, y_mean_expected)
assert np.allclose(y_std, y_std_expected)
def test_is_pickleable2(self):
gmr = GaussianMLPRegressorWithModel(input_shape=(1, ), output_dim=1)
with tf.variable_scope(
'GaussianMLPRegressorWithModel/GaussianMLPRegressorModel',
reuse=True):
x_mean = tf.get_variable('normalized_vars/x_mean')
x_mean.load(tf.ones_like(x_mean).eval())
x1 = gmr.model.networks['default'].x_mean.eval()
h = pickle.dumps(gmr)
with tf.Session(graph=tf.Graph()):
gmr_pickled = pickle.loads(h)
x2 = gmr_pickled.model.networks['default'].x_mean.eval()
assert np.array_equal(x1, x2)
def test_fit_normalized(self):
gmr = GaussianMLPRegressorWithModel(input_shape=(1, ), output_dim=1)
data = np.linspace(-np.pi, np.pi, 1000)
obs = [{'observations': [[x]], 'returns': [np.sin(x)]} for x in data]
observations = np.concatenate([p['observations'] for p in obs])
returns = np.concatenate([p['returns'] for p in obs])
returns = returns.reshape((-1, 1))
for _ in range(150):
gmr.fit(observations, returns)
paths = {
'observations': [[-np.pi], [-np.pi / 2], [-np.pi / 4], [0],
[np.pi / 4], [np.pi / 2], [np.pi]]
}
prediction = gmr.predict(paths['observations'])
expected = [[0], [-1], [-0.707], [0], [0.707], [1], [0]]
assert np.allclose(prediction, expected, rtol=0, atol=0.1)
x_mean = self.sess.run(gmr.model.networks['default'].x_mean)
x_mean_expected = np.mean(observations, axis=0, keepdims=True)
x_std = self.sess.run(gmr.model.networks['default'].x_std)
x_std_expected = np.std(observations, axis=0, keepdims=True)
assert np.allclose(x_mean, x_mean_expected)
assert np.allclose(x_std, x_std_expected)
y_mean = self.sess.run(gmr.model.networks['default'].y_mean)
y_mean_expected = np.mean(returns, axis=0, keepdims=True)
y_std = self.sess.run(gmr.model.networks['default'].y_std)
y_std_expected = np.std(returns, axis=0, keepdims=True)
assert np.allclose(y_mean, y_mean_expected)
assert np.allclose(y_std, y_std_expected)
class GaussianMLPBaselineWithModel(Baseline):
"""A value function using Gaussian MLP network."""
def __init__(
self,
env_spec,
subsample_factor=1.,
num_seq_inputs=1,
regressor_args=None,
name='GaussianMLPBaselineWithModel',
):
"""
Gaussian MLP Baseline with Model.
It fits the input data to a gaussian distribution estimated by
a MLP.
Args:
env_spec (garage.envs.env_spec.EnvSpec): Environment specification.
subsample_factor (float): The factor to subsample the data. By
default it is 1.0, which means using all the data.
num_seq_inputs (float): Number of sequence per input. By default
it is 1.0, which means only one single sequence.
regressor_args (dict): Arguments for regressor.
"""
super().__init__(env_spec)
if regressor_args is None:
regressor_args = dict()
self._regressor = GaussianMLPRegressorWithModel(
input_shape=(env_spec.observation_space.flat_dim *
num_seq_inputs, ),
output_dim=1,
name=name,
**regressor_args)
self.name = name
@overrides
def fit(self, paths):
"""Fit regressor based on paths."""
observations = np.concatenate([p['observations'] for p in paths])
returns = np.concatenate([p['returns'] for p in paths])
self._regressor.fit(observations, returns.reshape((-1, 1)))
@overrides
def predict(self, path):
"""Predict value based on paths."""
return self._regressor.predict(path['observations']).flatten()
@overrides
def get_param_values(self, **tags):
"""Get parameter values."""
return self._regressor.get_param_values(**tags)
@overrides
def set_param_values(self, flattened_params, **tags):
"""Set parameter values to val."""
self._regressor.set_param_values(flattened_params, **tags)
@overrides
def get_params_internal(self, **tags):
"""Get internal parameters."""
return self._regressor.get_params_internal(**tags)