def _init(self,
obs_space,
ac_space,
embedding_shape,
hid_size,
num_hid_layers,
gaussian_fixed_var=True):
self.pdtype = pdtype = make_pdtype(ac_space.shape[0])
batch_size = None
ob = U.get_placeholder(name="ac_de_ob",
dtype=tf.float32,
shape=[batch_size, obs_space.shape[0]])
embedding = U.get_placeholder(
name="ac_de_embedding",
dtype=tf.float32,
shape=[batch_size, embedding_shape
]) ##这里我觉得是一个embedding 的值扩展成sequence_len大小，暂时先不管，等具体做到
# 正则化一下
last_out = U.concatenate([ob, embedding], axis=1)
with tf.variable_scope("ac_de_filter"):
self.ac_rms = RunningMeanStd(shape=obs_space.shape[0] +
embedding_shape)
last_out = tf.clip_by_value(
(last_out - self.ac_rms.mean) / self.ac_rms.std, -5.0, 5.0)
for i in range(num_hid_layers):
last_out = tf.nn.relu(
U.dense(last_out,
hid_size[i],
"ac_de%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space.shape[0], int):
self.mean = U.dense(last_out,
pdtype.param_shape()[0] // 2, "ac_de_final",
U.normc_initializer(1.0))
logstd = tf.get_variable(name="logstd",
shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = U.concatenate([self.mean, self.mean * 0.0 + logstd],
axis=1)
else:
pdparam = U.dense(last_out,
pdtype.param_shape()[0], "ac_de_final",
U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
stochastic = U.get_placeholder(name="stochastic",
dtype=tf.bool,
shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.ac = ac
self._act = U.function([stochastic, ob, embedding], ac)
self._get_pol_mean = U.function([ob, embedding], self.mean)
def _init(self,
obs_space,
embedding_shape,
hid_size,
num_hid_layers,
gaussian_fixed_var=True):
self.pdtype = pdtype = make_pdtype(obs_space.shape[0])
batch_size = None
ob_input = U.get_placeholder(name="ob",
dtype=tf.float32,
shape=[batch_size, obs_space.shape[0]])
embedding = U.get_placeholder(
name="embedding",
dtype=tf.float32,
shape=[
batch_size, embedding_shape
]) ##这里我觉得是一个embedding 的值扩展成sequence_len大小，暂时先不管，等具体做到这里的时候再处理
last_out = U.concatenate(
[ob_input, embedding],
axis=1) ##这里只有policy, 没有 value function, 还有这个要看看concatenate的对不对
# 正则化
with tf.variable_scope("state_de_filter"):
self.state_rms = RunningMeanStd(shape=obs_space.shape[0] +
embedding_shape)
input_z = tf.clip_by_value(
(last_out - self.state_rms.mean) / self.state_rms.std, -5.0, 5.0)
for i in range(num_hid_layers):
input_z = tf.nn.tanh(
U.dense(input_z,
hid_size[i],
"state_de%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(obs_space.shape[0], int):
self.mean = U.dense(input_z,
pdtype.param_shape()[0] // 2, "state_de_final",
U.normc_initializer(0.01))
self.logstd = tf.get_variable(
name="logstd",
shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.zeros_initializer())
pdparam = U.concatenate([self.mean, self.mean * 0.0 + self.logstd],
axis=1)
else:
pdparam = U.dense(last_out,
pdtype.param_shape()[0], "state_de_final",
U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
self._act = U.function([ob_input, embedding], self.pd.sample())
self.get_mean = U.function([ob_input, embedding], self.mean)
def _init(self, obs_space, batch_size, time_steps, LSTM_size, laten_size, gaussian_fixed_var=True): ##等会儿要重点看一下var有没有更新
self.pdtype = pdtype = make_pdtype(laten_size)
obs = U.get_placeholder("en_ob", dtype=tf.float32, shape = [batch_size, time_steps, obs_space.shape[0]])
# 正则化
with tf.variable_scope("obfilter"): ## 看看有没有起效果，我觉得是其效果考虑的
self.obs_rms = RunningMeanStd(shape=obs_space.shape)
obz = tf.clip_by_value((obs - self.obs_rms.mean) / self.obs_rms.std, -5.0, 5.0)
lstm_fw_cell = rnn.BasicLSTMCell(LSTM_size, forget_bias=1.0)
lstm_bw_cell = rnn.BasicLSTMCell(LSTM_size, forget_bias=1.0)
outputs, output_state = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell, lstm_bw_cell, obz, dtype=tf.float32)
outputs_average = tf.reduce_mean(outputs[0], axis=1)
if gaussian_fixed_var and isinstance(laten_size, int):
self.mean = U.dense(outputs_average, pdtype.param_shape()[0] // 2, "dblstmfin", U.normc_initializer(1.0))
self.logstd = U.dense(outputs_average, pdtype.param_shape()[0] // 2, "dblstm_logstd", U.normc_initializer(1.0))
# self.logstd = tf.get_variable(name="logstd", shape=[1, pdtype.param_shape()[0] // 2],
# initializer=tf.constant_initializer(0.1)) ##这个地方是不是也是有问题的
pdparam = U.concatenate([self.mean, self.mean * 0.0 + self.logstd], axis=1)
else:
pdparam = U.dense(outputs_average, pdtype.param_shape()[0], "dblstmfin", U.normc_initializer(0.1))
self.pd = pdtype.pdfromflat(pdparam)
self._encode = U.function([obs], self.pd.sample())
self._get_mean = U.function([obs], self.mean)
def make_model(name, obs_space, action_space, reg=1e-7, noisy_head=False):
cube = tf.keras.Input(shape=obs_space['cube'].shape)
arm = tf.keras.Input(shape=obs_space['arm'].shape)
features = concatenate([arm, cube])
base = make_mlp([400, 300], 'tanh', reg)(features)
head = DuelingModel([512], action_space.n, reg, noisy=noisy_head)(base)
model = tf.keras.Model(inputs={
'cube': cube,
'arm': arm
},
outputs=head,
name=name)
return model
def make_uni_base(img, feat, reg):
flat_base = None
cnn_bases = list()
if len(feat) > 0:
feat_base = concatenate(list(feat.values()))
flat_base = make_mlp([64, 64], 'relu', reg)(feat_base)
if len(img) > 0:
for i in img.values():
normalized = i / 255
cnn = make_impala_cnn((16, 32), reg, flat=False,
use_bn=False)(normalized)
cnn_bases.append(cnn)
if flat_base is not None:
cnn_shape = cnn_bases[0].shape[1:-1]
assert all([c.shape[1:-1] == cnn_shape for c in cnn_bases
]), [c.shape[1:-1] for c in cnn_bases]
flat_base = tf.keras.layers.Reshape((1, 1, 64))(flat_base)
flat_base = tf.keras.layers.UpSampling2D(cnn_shape)(flat_base)
cnn_bases.append(flat_base)
merge_bases = concatenate(cnn_bases)
flat_base = make_impala_cnn((64, ), reg, flat=True,
use_bn=False)(merge_bases)
return flat_base
def make_model(name, obs_space, action_space, reg=1e-6):
pov = tf.keras.Input(shape=obs_space['pov'].shape)
arm = tf.keras.Input(shape=obs_space['arm'].shape)
normalized_pov = pov / 255
pov_base = make_cnn([32, 32, 32, 32], [3, 3, 3, 3], [2, 2, 2, 2], 'tanh',
reg)(normalized_pov)
angles_base = make_mlp([512, 256], 'tanh', reg)(arm)
base = concatenate([pov_base, angles_base])
head = DuelingModel([1024], action_space.n, reg)(base)
model = tf.keras.Model(inputs={
'pov': pov,
'arm': arm
},
outputs=head,
name=name)
return model
def _init(self,
ob_space,
ac_space,
hid_size,
num_hid_layers,
vae_pol_mean,
gaussian_fixed_var=True):
assert isinstance(ob_space, gym.spaces.Box)
self.pdtype = pdtype = make_pdtype(ac_space)
sequence_length = None
ob = U.get_placeholder(name="ob",
dtype=tf.float32,
shape=[sequence_length] + list(ob_space.shape))
with tf.variable_scope("obfilter"):
self.ob_rms = RunningMeanStd(shape=ob_space.shape)
obz = tf.clip_by_value((ob - self.ob_rms.mean) / self.ob_rms.std, -5.0,
5.0)
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
U.dense(last_out,
hid_size[i],
"vffc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
self.vpred = U.dense(last_out,
1,
"vffinal",
weight_init=U.normc_initializer(1.0))[:, 0]
last_out = obz
for i in range(num_hid_layers):
last_out = tf.nn.tanh(
U.dense(last_out,
hid_size[i],
"polfc%i" % (i + 1),
weight_init=U.normc_initializer(1.0)))
if gaussian_fixed_var and isinstance(ac_space, gym.spaces.Box):
mean = U.dense(last_out,
pdtype.param_shape()[0] // 2, "polfinal",
U.normc_initializer(0.01)) + vae_pol_mean
logstd = tf.get_variable(name="logstd",
shape=[1, pdtype.param_shape()[0] // 2],
initializer=tf.constant_initializer(0.1))
pdparam = U.concatenate([mean, mean * 0.0 + logstd], axis=1)
else:
pdparam = U.dense(last_out,
pdtype.param_shape()[0], "polfinal",
U.normc_initializer(0.01))
self.pd = pdtype.pdfromflat(pdparam)
self.state_in = []
self.state_out = []
# change for BC
#stochastic = tf.placeholder(dtype=tf.bool, shape=())
stochastic = U.get_placeholder(name="stochastic",
dtype=tf.bool,
shape=())
ac = U.switch(stochastic, self.pd.sample(), self.pd.mode())
self.ac = ac
self._act = U.function([stochastic, ob], [ac, self.vpred])
def learn(encoder,
action_decorder,
state_decorder,
embedding_shape,
*,
dataset,
logdir,
batch_size,
time_steps,
epsilon=0.001,
lr_rate=1e-3):
lstm_encoder = encoder("lstm_encoder")
ac_decoder = action_decorder("ac_decoder")
state_decoder = state_decorder("state_decoder") #换成了mlp
obs = U.get_placeholder_cached(name="obs") ##for encoder
ob = U.get_placeholder_cached(name="ob")
embedding = U.get_placeholder_cached(name="embedding")
# obss = U.get_placeholder_cached(name="obss") ## for action decoder， 这个state decoder是不是也可以用, 是不是应该改成obs
# ## for action decoder, 这个state decoder应该也是可以用的
# embeddingss = U.get_placeholder_cached(name="embeddingss")
ac = ac_decoder.pdtype.sample_placeholder([None])
obs_out = state_decoder.pdtype.sample_placeholder([None])
# p(z) 标准正太分布, state先验分布？？？是不是应该换成demonstration的标准正态分布？？？？ 可以考虑一下这个问题
from common.distributions import make_pdtype
p_z_pdtype = make_pdtype(embedding_shape)
p_z_params = U.concatenate([
tf.zeros(shape=[embedding_shape], name="mean"),
tf.zeros(shape=[embedding_shape], name="logstd")
],
axis=-1)
p_z = p_z_pdtype.pdfromflat(p_z_params)
recon_loss = -tf.reduce_mean(
tf.reduce_sum(ac_decoder.pd.logp(ac) + state_decoder.pd.logp(obs_out),
axis=0)) ##这个地方还要再改
kl_loss = lstm_encoder.pd.kl(p_z) ##p(z)：标准正太分布, 这个看起来是不是也不太对！！！！
vae_loss = recon_loss + kl_loss ###vae_loss 应该是一个batch的
ep_stats = stats(["recon_loss", "kl_loss", "vae_loss"])
losses = [recon_loss, kl_loss, vae_loss]
## var_list
var_list = []
en_var_list = lstm_encoder.get_trainable_variables()
var_list.extend(en_var_list)
# ac_de_var_list = ac_decoder.get_trainable_variables()
# var_list.extend(ac_de_var_list)
state_de_var_list = state_decoder.get_trainable_variables()
var_list.extend(state_de_var_list)
# compute_recon_loss = U.function([ob, obs, embedding, obss, embeddingss, ac, obs_out], recon_loss)
compute_losses = U.function([obs, ob, embedding, ac, obs_out], losses)
compute_grad = U.function([obs, ob, embedding, ac, obs_out],
U.flatgrad(vae_loss,
var_list)) ###这里没有想好！！！，可能是不对的！！
adam = MpiAdam(var_list, epsilon=epsilon)
U.initialize()
adam.sync()
writer = U.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
# =========================== TRAINING ===================== #
iters_so_far = 0
saver = tf.train.Saver(var_list=tf.trainable_variables(), max_to_keep=100)
saver_encoder = tf.train.Saver(var_list=en_var_list, max_to_keep=100)
# saver_pol = tf.train.Saver(var_list=ac_de_var_list, max_to_keep=100) ##保留一下policy的参数，但是这个好像用不到哎
while True:
logger.log("********** Iteration %i ************" % iters_so_far)
recon_loss_buffer = deque(maxlen=100)
kl_loss_buffer = deque(maxlen=100)
vae_loss_buffer = deque(maxlen=100)
for observations in dataset.get_next_batch(batch_size=time_steps):
observations = observations.transpose((1, 0))
embedding_now = lstm_encoder.get_laten_vector(observations)
embeddings = np.array([embedding_now for _ in range(time_steps)])
embeddings_reshape = embeddings.reshape((time_steps, -1))
actions = ac_decoder.act(stochastic=True,
ob=observations,
embedding=embeddings_reshape)
state_outputs = state_decoder.get_outputs(
observations.reshape(time_steps, -1, 1),
embeddings) ##还没有加混合高斯......乱加了一通，已经加完了
recon_loss, kl_loss, vae_loss = compute_losses(
observations, observations.reshape(batch_size, time_steps,
-1), embeddings_reshape,
observations.reshape(time_steps, -1, 1), embeddings, actions,
state_outputs)
g = compute_grad(observations,
observations.reshape(batch_size, time_steps,
-1), embeddings_reshape,
observations.reshape(time_steps, -1, 1),
embeddings, actions, state_outputs)
adam.update(g, lr_rate)
recon_loss_buffer.append(recon_loss)
kl_loss_buffer.append(kl_loss)
vae_loss_buffer.append(vae_loss)
ep_stats.add_all_summary(writer, [
np.mean(recon_loss_buffer),
np.mean(kl_loss_buffer),
np.mean(vae_loss_buffer)
], iters_so_far)
logger.record_tabular("recon_loss", recon_loss)
logger.record_tabular("kl_loss", kl_loss)
logger.record_tabular("vae_loss", vae_loss)
logger.dump_tabular()
if (iters_so_far % 10 == 0 and iters_so_far != 0):
save(saver=saver,
sess=tf.get_default_session(),
logdir=logdir,
step=iters_so_far)
save(saver=saver_encoder,
sess=tf.get_default_session(),
logdir="./vae_saver",
step=iters_so_far)
# save(saver=saver_pol, sess=tf.get_default_session(), logdir="pol_saver", step=iters_so_far)
iters_so_far += 1
def learn(env, encoder, action_decorder, state_decorder, embedding_shape,*, dataset, optimizer, logdir, batch_size, time_steps, adam_epsilon = 0.001, lr_rate = 1e-4, vae_beta = 8):
lstm_encoder = encoder("lstm_encoder")
ac_decoder = action_decorder("ac_decoder")
state_decoder = state_decorder("state_decoder") #这个地方有问题
ac_de_ob = U.get_placeholder_cached(name="ac_de_ob")
en_ob = U.get_placeholder_cached(name="en_ob") ##for encoder
state_de_ob = U.get_placeholder_cached(name="state_de_ob") ## for action decoder， 这个state decoder是不是也可以用, 是不是应该改成obs
ac_de_embedding = U.get_placeholder_cached(name="ac_de_embedding") ## for action decoder, 这个state decoder应该也是可以用的
state_de_embedding = U.get_placeholder_cached(name="state_de_embedding")
# ac = ac_decoder.pdtype.sample_placeholder([None])
ob_next = tf.placeholder(name="ob_next", shape=[None, ob_shape], dtype=tf.float32)
# ob_next_ac = tf.placeholder(name="ob_next_ac", shape=[ob_shape], dtype=tf.float32)
# obs_out = state_decoder.pdtype.sample_placeholder([None])
# p(z) 标准正太分布
from common.distributions import make_pdtype
p_z_pdtype = make_pdtype(embedding_shape)
p_z_params = U.concatenate([tf.zeros(shape=[embedding_shape], name="mean"), tf.zeros(shape=[embedding_shape], name="logstd")], axis=-1)
p_z = p_z_pdtype.pdfromflat(p_z_params)
# recon_loss 里再加一个,对于action的
recon_loss = -tf.reduce_sum(state_decoder.pd.logp(ob_next))
# kl_loss = lstm_encoder.pd.kl(p_z)[0] ##p(z)：标准正太分布, 这个看起来是不是也不太对！！！！
# kl_loss = tf.maximum(lstm_encoder.pd.kl(p_z)[0], tf.constant(5.00)) ##p(z)：标准正太分布, 这个看起来是不是也不太对！！！！
kl_loss = lstm_encoder.pd.kl(p_z)[0]
vae_loss = tf.reduce_mean(recon_loss + vae_beta * kl_loss) ###vae_loss 应该是一个batch的
ep_stats = stats(["recon_loss", "kl_loss", "vae_loss"])
losses = [recon_loss, kl_loss, vae_loss]
# 均方误差去训练 action,把得到的action step 一下,得到x(t+1),然后用均方误差loss,或者可以试试交叉熵
## var_list
var_list = []
en_var_list = lstm_encoder.get_trainable_variables()
var_list.extend(en_var_list)
# ac_de_var_list = ac_decoder.get_trainable_variables()
# var_list.extend(ac_de_var_list)
state_de_var_list = state_decoder.get_trainable_variables()
var_list.extend(state_de_var_list)
# compute_recon_loss = U.function([ob, obs, embedding, obss, embeddingss, ac, obs_out], recon_loss)
compute_losses = U.function([en_ob, ac_de_ob, state_de_ob, ac_de_embedding, state_de_embedding, ob_next], losses)
compute_grad = U.function([en_ob, ac_de_ob, state_de_ob, ac_de_embedding, state_de_embedding, ob_next], U.flatgrad(vae_loss, var_list)) ###这里没有想好！！！，可能是不对的！！
adam = MpiAdam(var_list, epsilon=adam_epsilon)
U.initialize()
adam.sync()
writer = U.FileWriter(logdir)
writer.add_graph(tf.get_default_graph())
# =========================== TRAINING ===================== #
iters_so_far = 0
saver = tf.train.Saver(var_list=var_list, max_to_keep=100)
saver_encoder = tf.train.Saver(var_list = en_var_list, max_to_keep=100)
# saver_pol = tf.train.Saver(var_list=ac_de_var_list, max_to_keep=100) ##保留一下policy的参数，但是这个好像用不到哎
while iters_so_far < 50:
## 加多轮
logger.log("********** Iteration %i ************" % iters_so_far)
## 要不要每一轮调整一下batch_size
recon_loss_buffer = deque(maxlen=100)
# recon_loss2_buffer = deque(maxlen=100)
kl_loss_buffer = deque(maxlen=100)
vae_loss_buffer = deque(maxlen=100)
# i = 0
for obs_and_next in dataset.get_next_batch(batch_size=time_steps):
# print(i)
# i += 1
observations = obs_and_next[0].transpose((1, 0))[:-1]
ob_next = obs_and_next[0].transpose(1, 0)[state_decoder.receptive_field:, :]
embedding_now = lstm_encoder.get_laten_vector(obs_and_next[0].transpose((1, 0)))
embeddings = np.array([embedding_now for _ in range(time_steps - 1)])
embeddings_reshape = embeddings.reshape((time_steps-1, -1))
actions = ac_decoder.act(stochastic=True, ob=observations, embedding=embeddings_reshape)
ob_next_ac = get_ob_next_ac(env, observations[-1], actions[0]) ##这个还需要再修改 #########################################3
# state_outputs = state_decoder.get_outputs(observations.reshape(1, time_steps, -1), embedding_now.reshape((1, 1, -1))) ##还没有加混合高斯......乱加了一通，已经加完了
# recon_loss = state_decoder.recon_loss(observations.reshape(1, time_steps, -1), embedding_now.reshape((1, 1, -1)))
recon_loss, kl_loss, vae_loss = compute_losses(obs_and_next[0].transpose((1, 0)).reshape(1, time_steps, -1), observations.reshape(time_steps-1,-1),
observations.reshape(1, time_steps-1, -1), embeddings_reshape, embedding_now.reshape((1,1, -1)), ob_next)
g = compute_grad(obs_and_next[0].transpose((1, 0)).reshape(1, time_steps, -1), observations.reshape(time_steps-1,-1),
observations.reshape(1, time_steps-1, -1), embeddings_reshape, embedding_now.reshape((1,1, -1)), ob_next)
# logger.record_tabular("recon_loss", recon_loss)
# logger.record_tabular("recon_loss2", recon_loss2)
# logger.record_tabular("kl_loss", kl_loss)
# logger.record_tabular("vae_loss", vae_loss)
# logger.dump_tabular()
adam.update(g, lr_rate)
recon_loss_buffer.append(recon_loss)
# recon_loss2_buffer.append(recon_loss2)
kl_loss_buffer.append(kl_loss)
vae_loss_buffer.append(vae_loss)
ep_stats.add_all_summary(writer, [np.mean(recon_loss_buffer), np.mean(kl_loss_buffer),
np.mean(vae_loss_buffer)], iters_so_far)
logger.record_tabular("recon_loss", recon_loss)
# logger.record_tabular("recon_loss2", recon_loss2)
logger.record_tabular("kl_loss", kl_loss)
logger.record_tabular("vae_loss", vae_loss)
logger.dump_tabular()
if(iters_so_far % 10 == 0 and iters_so_far != 0):
save(saver=saver, sess=tf.get_default_session(), logdir=logdir, step=iters_so_far)
save(saver=saver_encoder, sess=tf.get_default_session(),logdir="./vae_saver", step=iters_so_far)
# save(saver=saver_pol, sess=tf.get_default_session(), logdir="pol_saver", step=iters_so_far)
iters_so_far += 1
if iters_so_far < 6:
lr_rate /= 2
def _create_network(
self, obs_shape,
embedding_shape): ## , input_batch, global_condition_batch
'''Construct the WaveNet network.'''
import common.tf_util as U
outputs = []
sequence_length = 1
input_batch = U.get_placeholder(
name="state_de_ob",
dtype=tf.float32,
shape=[batch_size, self.time_steps - 1,
obs_shape.shape[0]]) ##input_batch是3D的
global_condition_batch = U.get_placeholder(
name="state_de_embedding",
dtype=tf.float32,
shape=[batch_size, 1, embedding_shape])
current_layer = input_batch
# Pre-process the input with a regular convolution
current_layer = self._create_causal_layer(current_layer) ##这里不行
#output_width = tf.shape(input_batch)[1] - self.receptive_field + 1
output_width = input_batch.shape[1] - self.receptive_field + 1
# Add all defined dilation layers.
with tf.name_scope('dilated_stack'):
for layer_index, dilation in enumerate(self.dilations):
with tf.name_scope('layer{}'.format(layer_index)):
output, current_layer = self._create_dilation_layer(
current_layer, layer_index, dilation,
global_condition_batch, output_width)
outputs.append(output)
with tf.name_scope('postprocessing'):
# Perform (+) -> ReLU -> 1x1 conv -> ReLU -> 1x1 conv to
# postprocess the output.
w1 = self.variables['postprocessing']['postprocess1']
w2 = self.variables['postprocessing']['postprocess2']
if self.use_biases:
b1 = self.variables['postprocessing']['postprocess1_bias']
b2 = self.variables['postprocessing']['postprocess2_bias']
if self.histograms:
tf.histogram_summary('postprocess1_weights', w1)
tf.histogram_summary('postprocess2_weights', w2)
if self.use_biases:
tf.histogram_summary('postprocess1_biases', b1)
tf.histogram_summary('postprocess2_biases', b2)
# We skip connections from the outputs of each layer, adding them
# all up here.
total = sum(outputs)
transformed1 = tf.nn.relu(total)
conv1 = tf.nn.conv1d(transformed1, w1, stride=1, padding="SAME")
if self.use_biases:
conv1 = tf.add(conv1, b1)
transformed2 = tf.nn.relu(conv1)
conv2 = tf.nn.conv1d(transformed2, w2, stride=1, padding="SAME")
if self.use_biases:
conv2 = tf.add(conv2, b2)
# print(conv2)
# ========= add by myself =============== #
# self.mean = tf.reduce_mean(conv2, axis=1) ###去均值作为每一个维度的
# self.logstd = tf.get_variable(name="wave_logstd", shape=[1, self.pdtype.param_shape()[0]//2], initializer=tf.zeros_initializer())
# pdparam = U.concatenate([self.mean, self.mean * 0.0 + self.logstd], axis=1)
# self.pd = self.pdtype.pdfromflat(pdparam)
#
# self._act = U.function([input_batch, global_condition_batch], [self.pd.sample()])
# # for debug
# self.get_mean = U.function([input_batch, global_condition_batch], self.mean)
conv2 = tf.reshape(conv2, [-1, self.quantization_channels])
self.mean = U.dense(conv2, 63, "wave_mean",
U.normc_initializer(1.0)) ## 48 * 63
self.logstd = U.dense(
conv2, 63, "wave_logstd",
weight_init=U.normc_initializer(1.0)) ## 48 * 63
# self.logstd = tf.get_variable(name="wave_logstd", shape=[1, self.pdtype.param_shape()[0] // 2],
# initializer=tf.zeros_initializer()) ## 这个地方的大小有待商榷
pdparm = U.concatenate([self.mean, self.mean * 0.0 + self.logstd],
axis=1)
self.pd = self.pdtype.pdfromflat(pdparm)
# target_output = tf.slice(input_batch, [0, self.receptive_field, 0], [-1, -1, -1])
self._act = U.function([input_batch, global_condition_batch],
[self.pd.sample()])
