def test_func_api_network_with_automatically_handling_container_input_space(self):
# Simple vectors plus image as inputs (see e.g. SAC).
input_space = Dict(A=Float(-1.0, 1.0, shape=(2,)), B=Int(5), C=Float(-1.0, 1.0, shape=(2, 2, 3)), main_axes="B")
output_space = Float(shape=(3,), main_axes="B") # simple output
# Only define a base-core network and let the automation handle the complex input structure via
# `pre-concat` nets.
core_nn = tf.keras.models.Sequential()
core_nn.add(tf.keras.layers.Dense(3, activation="relu"))
core_nn.add(tf.keras.layers.Dense(3))
# Use no distributions.
nn = Network(
network=core_nn,
input_space=input_space,
pre_concat_networks=dict(
# leave "A" out -> "A" input will go unaltered into concat step.
B=lambda i: tf.one_hot(i, depth=input_space["B"].num_categories, axis=-1),
C=tf.keras.layers.Flatten()
),
output_space=output_space,
distributions=False
)
# Simple function call.
input_ = input_space.sample(6)
result = nn(input_)
weights = nn.get_weights()
expected = dense(dense(relu(dense(np.concatenate([
input_["A"],
one_hot(input_["B"], depth=input_space["B"].num_categories),
np.reshape(input_["C"], newshape=(6, -1))
], axis=-1), weights[0], weights[1])), weights[2], weights[3]), weights[4], weights[5])
check(result, expected)
def test_mixture_adapter(self):
input_space = Float(shape=(16,), main_axes="B")
output_space = Float(shape=(3,), main_axes="B")
adapter = MixtureDistributionAdapter(
"normal-distribution-adapter", "beta-distribution-adapter",
output_space=output_space,
activation="relu" # Don't do this in real life! This is just to test.
)
batch_size = 2
inputs = input_space.sample(batch_size)
out = adapter(inputs)
weights = adapter.get_weights()
params0 = np.split(dense(inputs, weights[2], weights[3]), 2, axis=-1)
params0[1] = np.exp(np.clip(params0[1], MIN_LOG_NN_OUTPUT, MAX_LOG_NN_OUTPUT))
params1 = dense(inputs, weights[4], weights[5])
params1 = np.clip(params1, np.log(SMALL_NUMBER), -np.log(SMALL_NUMBER))
params1 = np.log(np.exp(params1) + 1.0) + 1.0
params1 = np.split(params1, 2, axis=-1)
expected = {
"categorical": relu(dense(inputs, weights[0], weights[1])), "parameters0": params0, "parameters1": params1
}
check(out, expected, decimals=5)
def test_plain_output_adapter_with_pre_network(self):
input_space = Float(-1.0, 1.0, shape=(5,), main_axes="B")
output_space = Float(shape=(3,), main_axes="B")
adapter = PlainOutputAdapter(output_space, pre_network=tf.keras.models.Sequential(
tf.keras.layers.Dense(units=10, activation="relu")
))
# Simple function call -> Expect output for all int-inputs.
input_ = input_space.sample(6)
result = adapter(input_)
weights = adapter.get_weights()
expected = dense(relu(dense(input_, weights[0], weights[1])), weights[2], weights[3])
check(result, expected)
def test_layer_network_with_container_output_space(self):
# Using keras layer as network spec.
layer = tf.keras.layers.Dense(10)
nn = Network(
network=layer,
output_space=Dict({"a": Float(shape=(2, 3)), "b": Int(3)})
)
# Simple call -> Should return dict with "a"->float(2,3) and "b"->float(3,)
input_ = Float(-1.0, 1.0, shape=(5,), main_axes="B").sample(5)
result = nn(input_)
weights = nn.get_weights()
expected_a = np.reshape(dense(dense(input_, weights[0], weights[1]), weights[2], weights[3]), newshape=(-1, 2, 3))
expected_b = dense(dense(input_, weights[0], weights[1]), weights[4], weights[5])
check(result, dict(a=expected_a, b=expected_b))
def test_copying_a_network(self):
# Using keras layer as network spec.
layer = tf.keras.layers.Dense(4)
nn = Network(network=layer, output_space=Dict({"a": Float(shape=(2,)), "b": Int(2)}))
# Simple call -> Should return dict with "a"->float(2,) and "b"->float(2,)
input_ = Float(-1.0, 1.0, shape=(5,), main_axes="B").sample(5)
_ = nn(input_)
weights = nn.get_weights()
expected_a = dense(dense(input_, weights[0], weights[1]), weights[2], weights[3])
expected_b = dense(dense(input_, weights[0], weights[1]), weights[4], weights[5])
# Do the copy.
nn_copy = nn.copy()
result = nn_copy(input_)
check(result, dict(a=expected_a, b=expected_b))
def test_func_api_network_with_primitive_int_output_space_and_distribution(self):
input_space = Float(-1.0, 1.0, shape=(3,), main_axes="B")
output_space = Int(5, main_axes="B")
# Using keras functional API to create network.
i = tf.keras.layers.Input(shape=(3,))
d = tf.keras.layers.Dense(10)(i)
e = tf.keras.layers.Dense(5)(i)
o = tf.concat([d, e], axis=-1)
network = tf.keras.Model(inputs=i, outputs=o)
# Use default distributions (i.e. categorical for Int).
nn = Network(
network=network,
output_space=output_space,
distributions="default"
)
input_ = input_space.sample(1000)
result = nn(input_)
# Check the sample for a proper mean value.
check(np.mean(result), 2, decimals=0)
# Function call with value -> Expect probabilities for given int-values.
input_ = input_space.sample(6)
values = output_space.sample(6)
result = nn(input_, values)
weights = nn.get_weights()
expected = dense(np.concatenate(
[dense(input_, weights[0], weights[1]), dense(input_, weights[2], weights[3])],
axis=-1
), weights[4], weights[5])
expected = softmax(expected)
expected = np.sum(expected * one_hot(values, depth=output_space.num_categories), axis=-1)
check(result, expected)
# Function call with "likelihood" option set -> Expect sample plus probabilities for sampled int-values.
input_ = input_space.sample(1000)
sample, probs = nn(input_, likelihood=True)
check(np.mean(sample), 2, decimals=0)
check(np.mean(probs), 1.0 / output_space.num_categories, decimals=1)
def test_subclassing_network_with_primitive_int_output_space(self):
input_space = Float(-1.0, 1.0, shape=(5,), main_axes="B")
output_space = Int(3, main_axes="B")
# Using keras subclassing.
network = self.MyModel()
nn = Network(network=network, output_space=output_space)
# Simple function call -> Expect output for all int-inputs.
input_ = input_space.sample(6)
result = nn(input_)
weights = nn.get_weights()
expected = dense(np.concatenate(
[dense(input_, weights[0], weights[1]), dense(input_, weights[2], weights[3])],
axis=-1
), weights[4], weights[5])
check(result, expected)
# Function call with value -> Expect output for only that int-value
input_ = input_space.sample(6)
values = output_space.sample(6)
result = nn(input_, values)
weights = nn.get_weights()
expected = dense(np.concatenate(
[dense(input_, weights[0], weights[1]), dense(input_, weights[2], weights[3])],
axis=-1
), weights[4], weights[5])
expected = np.sum(expected * one_hot(values, depth=output_space.num_categories), axis=-1)
check(result, expected)
def test_normal_adapter(self):
input_space = Float(shape=(8,), main_axes="B")
output_space = Float(shape=(3, 2), main_axes="B")
adapter = NormalDistributionAdapter(output_space=output_space, activation="linear")
batch_size = 3
inputs = input_space.sample(batch_size)
out = adapter(inputs)
weights = adapter.get_weights()
expected = np.split(np.reshape(dense(inputs, weights[0], weights[1]), newshape=(batch_size, 3, 4)), 2, axis=-1)
expected[1] = np.clip(expected[1], MIN_LOG_NN_OUTPUT, MAX_LOG_NN_OUTPUT)
expected[1] = np.exp(expected[1])
check(out, expected, decimals=5)
def test_categorical_adapter(self):
input_space = Float(shape=(16,), main_axes="B")
output_space = Int(2, shape=(3, 2), main_axes="B")
adapter = CategoricalDistributionAdapter(
output_space=output_space, kernel_initializer="ones", activation="relu"
)
batch_size = 2
inputs = input_space.sample(batch_size)
out = adapter(inputs)
weights = adapter.get_weights()
expected = np.reshape(relu(dense(inputs, weights[0], weights[1])), newshape=(batch_size, 3, 2, 2))
check(out, expected, decimals=5)
def test_bernoulli_adapter(self):
input_space = Float(shape=(16,), main_axes="B")
output_space = Bool(shape=(2,), main_axes="B")
adapter = BernoulliDistributionAdapter(output_space=output_space, activation="relu")
batch_size = 32
inputs = input_space.sample(batch_size)
out = adapter(inputs)
weights = adapter.get_weights()
# Parameters are the plain logits (no sigmoid).
expected = relu(dense(inputs, weights[0], weights[1]))
check(out, expected, decimals=5)
def test_plain_output_adapter(self):
input_space = Float(-1.0, 1.0, shape=(5,), main_axes="B")
output_space = Float(shape=(3,), main_axes="B")
adapter = PlainOutputAdapter(output_space)
# Simple function call -> Expect output for all int-inputs.
input_ = input_space.sample(6)
result = adapter(input_)
weights = adapter.get_weights()
expected = dense(input_, weights[0], weights[1])
check(result, expected)
def test_beta_adapter(self):
input_space = Float(shape=(8,), main_axes="B")
output_space = Float(shape=(3, 2), main_axes="B")
adapter = BetaDistributionAdapter(output_space=output_space)
batch_size = 5
inputs = input_space.sample(batch_size)
out = adapter(inputs)
weights = adapter.get_weights()
expected = np.reshape(dense(inputs, weights[0], weights[1]), newshape=(batch_size, 3, 4))
expected = np.clip(expected, np.log(SMALL_NUMBER), -np.log(SMALL_NUMBER))
expected = np.log(np.exp(expected) + 1.0) + 1.0
expected = np.split(expected, 2, axis=-1)
check(out, expected, decimals=5)
def test_copying_an_adapter(self):
input_space = Float(-1.0, 1.0, shape=(5,), main_axes="B")
output_space = Float(shape=(3,), main_axes="B")
adapter = PlainOutputAdapter(output_space, pre_network=None)
# Simple function call -> Expect output for all int-inputs.
input_ = input_space.sample(3)
result = adapter(input_)
weights = adapter.get_weights()
expected = dense(input_, weights[0], weights[1])
check(result, expected)
new_adapter = adapter.copy()
new_weights = new_adapter.get_weights()
# Check all weights.
check(weights, new_weights)
# Do a pass and double-check.
result = new_adapter(input_)
check(result, expected)
def test_func_api_network_with_manually_handling_container_input_space(self):
# Simple vector plus image as inputs (see e.g. SAC).
input_space = Dict(A=Float(-1.0, 1.0, shape=(2,)), B=Float(-1.0, 1.0, shape=(2, 2, 3)), main_axes="B")
output_space = Float(shape=(3,), main_axes="B") # simple output
# Using keras functional API to create network.
keras_input = input_space.create_keras_input()
# Simply flatten an concat everything, then output.
o = tf.keras.layers.Flatten()(keras_input["B"])
o = tf.concat([keras_input["A"], o], axis=-1)
network = tf.keras.Model(inputs=keras_input, outputs=o)
# Use no distributions.
nn = Network(
network=network,
output_space=output_space,
distributions=False
)
# Simple function call.
input_ = input_space.sample(6)
result = nn(input_)
weights = nn.get_weights()
expected = dense(np.concatenate([input_["A"], np.reshape(input_["B"], newshape=(6, -1))], axis=-1), weights[0], weights[1])
check(result, expected)
# Function call with value -> Expect error as we only have float outputs (w/o distributions).
input_ = input_space.sample(6)
values = output_space.sample(6)
error = True
try:
nn(input_, values)
error = False
except SurrealError:
pass
self.assertTrue(error)
def test_dueling_network(self):
input_space = Float(-1.0, 1.0, shape=(2,), main_axes="B")
output_space = Dict({"A": Float(shape=(4,)), "V": Float()}, main_axes="B") # V=single node
# Using keras layer as main network spec.
layer = tf.keras.layers.Dense(5)
nn = Network(
network=layer,
output_space=output_space,
# Only two output components are distributions (a and b), the others not (c=Float, d=Int).
adapters=dict(A=dict(pre_network=tf.keras.layers.Dense(2)), V=dict(pre_network=tf.keras.layers.Dense(3)))
)
# Simple call -> Should return sample dict.
input_ = input_space.sample(10)
result = nn(input_)
weights = nn.get_weights()
expected_a = dense(dense(dense(input_, weights[0], weights[1]), weights[2], weights[3]), weights[4], weights[5])
expected_v = np.reshape(
dense(dense(dense(input_, weights[0], weights[1]), weights[6], weights[7]), weights[8], weights[9]),
newshape=(10,)
)
check(result["A"], expected_a, decimals=5)
check(result["V"], expected_v, decimals=5)
def qt(s):
return dense(dense(s, weights_qt[0], weights_qt[1]), weights_qt[2], weights_qt[3])
def q(s, a):
return np.sum(dense(dense(s, weights_q[0], weights_q[1]), weights_q[2], weights_q[3]) * one_hot(a, depth=4), axis=-1)
