def __init__(self):
self.env_name = "carracing"
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
self.vae.load_json('vae/vae.json')
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, 3)
self.bias_output = np.random.randn(3)
self.param_count = ((self.input_size + 1) *
self.hidden_size) + (self.hidden_size * 3 + 3)
else:
self.weight = np.random.randn(self.input_size, 3)
self.bias = np.random.randn(3)
self.param_count = (self.input_size) * 3 + 3
self.render_mode = False
class Model:
''' simple one layer model for car racing '''
def __init__(self):
self.env_name = "carracing"
self.vae = ConvVAE(batch_size=1,
gpu_mode=False,
is_training=False,
reuse=True)
self.vae.load_json('vae/vae.json')
self.rnn = MDNRNN(hps_sample, gpu_mode=False, reuse=True)
self.rnn.load_json('rnn/rnn.json')
self.state = rnn_init_state(self.rnn)
self.rnn_mode = True
self.input_size = rnn_output_size(EXP_MODE)
self.z_size = 32
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
self.hidden_size = 40
self.weight_hidden = np.random.randn(self.input_size,
self.hidden_size)
self.bias_hidden = np.random.randn(self.hidden_size)
self.weight_output = np.random.randn(self.hidden_size, 3)
self.bias_output = np.random.randn(3)
self.param_count = ((self.input_size + 1) *
self.hidden_size) + (self.hidden_size * 3 + 3)
else:
self.weight = np.random.randn(self.input_size, 3)
self.bias = np.random.randn(3)
self.param_count = (self.input_size) * 3 + 3
self.render_mode = False
def make_env(self, seed=-1, render_mode=False):
self.render_mode = render_mode
self.env = make_env(self.env_name, seed=seed, render_mode=render_mode)
def reset(self):
self.state = rnn_init_state(self.rnn)
def encode_obs(self, obs):
# convert raw obs to z, mu, logvar
result = np.copy(obs).astype(np.float) / 255.0
result = result.reshape(1, 64, 64, 3)
mu, logvar = self.vae.encode_mu_logvar(result)
mu = mu[0]
logvar = logvar[0]
s = logvar.shape
z = mu + np.exp(logvar / 2.0) * np.random.randn(*s)
return z, mu, logvar
def decode_obs(self, z):
# decode the latent vector
img = self.vae.decode(z.reshape(1, self.z_size)) * 255.
img = np.round(img).astype(np.uint8)
img = img.reshape(64, 64, 3)
return img
def get_action(self, z):
h = rnn_output(self.state, z, EXP_MODE)
'''
action = np.dot(h, self.weight) + self.bias
action[0] = np.tanh(action[0])
action[1] = sigmoid(action[1])
action[2] = clip(np.tanh(action[2]))
'''
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
h = np.tanh(np.dot(h, self.weight_hidden) + self.bias_hidden)
action = np.tanh(np.dot(h, self.weight_output) + self.bias_output)
else:
action = np.tanh(np.dot(h, self.weight) + self.bias)
action[1] = (action[1] + 1.0) / 2.0
action[2] = clip(action[2])
self.state = rnn_next_state(self.rnn, z, action, self.state)
return action
def set_model_params(self, model_params):
if EXP_MODE == MODE_Z_HIDDEN: # one hidden layer
params = np.array(model_params)
cut_off = (self.input_size + 1) * self.hidden_size
params_1 = params[:cut_off]
params_2 = params[cut_off:]
self.bias_hidden = params_1[:self.hidden_size]
self.weight_hidden = params_1[self.hidden_size:].reshape(
self.input_size, self.hidden_size)
self.bias_output = params_2[:3]
self.weight_output = params_2[3:].reshape(self.hidden_size, 3)
else:
self.bias = np.array(model_params[:3])
self.weight = np.array(model_params[3:]).reshape(
self.input_size, 3)
def load_model(self, filename):
with open(filename) as f:
data = json.load(f)
print('loading file %s' % (filename))
self.data = data
model_params = np.array(data[0]) # assuming other stuff is in data
self.set_model_params(model_params)
def get_random_model_params(self, stdev=0.1):
return np.random.randn(self.param_count) * stdev
N_data = len(data_mu) # should be 10k
batch_size = hps_model.batch_size
# save 1000 initial mu and logvars:
initial_mu = np.copy(data_mu[:1000, 0, :] * 10000).astype(np.int).tolist()
initial_logvar = np.copy(data_logvar[:1000, 0, :] * 10000).astype(
np.int).tolist()
with open(os.path.join("tf_initial_z", "initial_z.json"), 'wt') as outfile:
json.dump([initial_mu, initial_logvar],
outfile,
sort_keys=True,
indent=0,
separators=(',', ': '))
reset_graph()
rnn = MDNRNN(hps_model)
# train loop:
hps = hps_model
start = time.time()
for local_step in range(hps.num_steps):
step = rnn.sess.run(rnn.global_step)
curr_learning_rate = (hps.learning_rate - hps.min_learning_rate) * (
hps.decay_rate)**step + hps.min_learning_rate
raw_z, raw_a = random_batch()
inputs = np.concatenate((raw_z[:, :-1, :], raw_a[:, :-1, :]), axis=2)
outputs = raw_z[:, 1:, :] # teacher forcing (shift by one predictions)
feed = {