def test_save_load():
cfg = {"rng": np.random}
input_shape = 8
output_shape = 8
instructions = \
{
"init_std": 0.05,
"layers": [64, 64],
"layer_functions": ['relu', 'relu'],
"layer_extras": ['bn', 'bn'],
"output_function": 'linear',
"output_extras": 'bn',
}
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
inp = np.ones(input_shape)
out = policy.activate(inp)
print("\nOUTPUT ON ONES BEFORE SAVING:", out)
policy.save("data/experiments/exp_name/epochs/epoch_0/policy")
policy.load("data/experiments/exp_name/epochs/epoch_0/policy")
out = policy.activate(inp)
print("OUTPUT ON ONES AFTER SAVING:", out)
def test_bn():
cfg = ConfigLoader.load_config(file_name="test_config.json")
input_shape = 8
output_shape = 8
instructions = cfg["policy"]
#parser = MiscParsingFunctions.parse_policy_action_function(cfg["policy"]["action_parser"])
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
inp = np.ones(input_shape)
output = policy.activate(inp)
print("POLICY OUTPUT ON ONES OF SHAPE", inp.shape, "=", output.shape,
"EXPECTED", output_shape)
action_parsers = {
"linear": parsers.linear_parse,
"random sample": parsers.random_sample,
"arg max": parsers.argmax_sample
}
for name, parser in action_parsers.items():
policy.action_parser = parser
print("ATTEMPTING PARSER", name)
print("RAW POLICY OUTPUT:", policy.activate(inp)[0])
print("SUM:", sum(policy.activate(inp)[0]))
action = policy.get_action(inp)
print("POLICY ACTION FROM PARSER", name, "=", action)
print()
def run_test():
cfg = {"rng": np.random}
input_shape = 2
output_shape = 2
instructions = \
{
"init_std": 0.05,
"layers" : [1],
"layer_functions" : ['relu'],
"layer_extras" : ['bn'],
"output_function" : 'linear',
"output_extras" : 'bn',
}
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
flat = np.random.randn(policy.num_params)
print("POLICY FLAT BEFORE SETTING:", policy.get_trainable_flat())
policy.set_trainable_flat(flat)
print("POLICY FLAT AFTER SETTING:", policy.get_trainable_flat())
print("FLAT SHOULD NOW BE:", flat)
def test_batch_bn():
cfg = ConfigLoader.load_config(file_name="test_config.json")
input_shape = 8
output_shape = 8
instructions = cfg["policy"]
parser = MiscParsingFunctions.parse_policy_action_function(
cfg["policy"]["action_parser"])
policy = FeedForward(input_shape, output_shape, parser, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
batch = [np.ones(input_shape) for _ in range(500)]
output = policy.activate_batch(batch)
print("OUTPUT ON ONES BATCH OF 500:", output.shape, "EXPECTED 500 OF",
output_shape)
action_parsers = {
"linear": parsers.linear_parse,
"random sample": parsers.random_sample,
"arg max": parsers.argmax_sample
}
for name, parser in action_parsers.items():
policy.action_parser = parser
action = policy.get_actions_on_batch(batch)
print("POLICY ACTIONS FROM PARSER", name, "=", action)
print()
def run_test():
cfg = {"rng": np.random}
input_shape = 8
output_shape = 8
instructions = \
{
"init_std": 0.05,
"layers" : [64,64],
"layer_functions" : ['relu', 'relu'],
"layer_extras" : ['bn', 'bn'],
"output_function" : 'linear',
"output_extras" : 'bn',
}
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
def run_test():
# cfg = {"rng": np.random}
# input_shape = 8
# output_shape = 8
# instructions = \
# {
# "init_std": 0.05,
# "layers": [64, 64],
# "layer_functions": ['relu', 'relu'],
# "layer_extras": ['bn', 'bn'],
# "output_function": 'linear',
# "output_extras": 'bn',
# }
cfg = ConfigLoader.load_config(file_name="test_config.json")
env = EnvironmentFactory.get_from_config(cfg)
input_shape = env.get_policy_input_shape()
output_shape = env.get_policy_output_shape()
instructions = cfg["policy"]
cfg["rng"] = np.random.RandomState(cfg["seed"])
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
num = np.prod(input_shape)
vbn = [np.random.randn(num) for _ in range(1000)]
inp = np.ones(num)
out = policy.activate(inp)
print("\nOUTPUT ON ONES BEFORE VBN:", out)
policy.compute_virtual_normalization(vbn)
out = policy.activate(inp)
print("OUTPUT ON ONES AFTER VBN:", out)
policy.save("data/test")
out = policy.activate(inp)
print("OUTPUT ON ONES AFTER SAVE:", out)
del policy
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
policy.load("data/test")
out = policy.activate(inp)
print("OUTPUT ON ONES AFTER LOAD:", out)
policy.compute_virtual_normalization(vbn)
out = policy.activate(inp)
print("OUTPUT ON ONES AFTER LOAD AND VBN:", out)
def test_save_load_vbn():
cfg = {"rng": np.random}
input_shape = 8
output_shape = 8
instructions = \
{
"init_std": 0.05,
"layers": [64, 64],
"layer_functions": ['relu', 'relu'],
"layer_extras": ['bn', 'bn'],
"output_function": 'linear',
"output_extras": 'bn',
}
policy = FeedForward(input_shape, output_shape, None, cfg)
policy.build_model(instructions)
print("BUILT POLICY LAYERS:")
for layer in policy.model:
print(layer)
vbn = [np.random.randn(input_shape) for _ in range(1000)]
policy.compute_virtual_normalization(vbn)
inp = np.ones(input_shape)
out = policy.activate(inp)
print("\nOUTPUT ON ONES WITH VBN BEFORE SAVING:", out)
policy.save("data/experiments/exp_name/epochs/epoch_0/policy")
out = policy.activate(inp)
print("\nOUTPUT ON ONES WITH VBN AFTER SAVING:", out)
policy.set_trainable_flat(policy.get_trainable_flat() +
np.random.randn(policy.num_params))
out = policy.activate(inp)
print("\nJIGGLED OUTPUT ON ONES WITH VBN BEFORE LOADING", out)
policy.load("data/experiments/exp_name/epochs/epoch_0/policy")
out = policy.activate(inp)
print("OUTPUT ON ONES WITH VBN AFTER LOADING:", out)