def add_summaries(self, sess):
if self.config.load:
path_ = "../results/tensorboard"
else :
path_ = "../bin/results/tensorboard"
summary_writer_train = tf.summary.FileWriter(path_ + "/train", sess.graph)
summary_writer_val = tf.summary.FileWriter(path_ + "/val", sess.graph)
summary_writer_test = tf.summary.FileWriter(path_+ "/test", sess.graph)
summary_writers = {'train': summary_writer_train, 'val': summary_writer_val, 'test': summary_writer_test}
return summary_writers
python类Summary()的实例源码
__main__.py 文件源码
项目：Neural-Architecture-Search-with-RL
作者: dhruvramani
项目源码
文件源码
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def set_model(self, model):
self.model = model
self.sess = K.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
if hasattr(tf, 'histogram_summary'):
tf.histogram_summary(weight.name, weight)
else:
tf.summary.histogram(weight.name, weight)
if self.write_images:
w_img = tf.squeeze(weight)
shape = w_img.get_shape()
if len(shape) > 1 and shape[0] > shape[1]:
w_img = tf.transpose(w_img)
if len(shape) == 1:
w_img = tf.expand_dims(w_img, 0)
w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
if hasattr(tf, 'image_summary'):
tf.image_summary(weight.name, w_img)
else:
tf.summary.image(weight.name, w_img)
if hasattr(layer, 'output'):
if hasattr(tf, 'histogram_summary'):
tf.histogram_summary('{}_out'.format(layer.name),
layer.output)
else:
tf.summary.histogram('{}_out'.format(layer.name),
layer.output)
if hasattr(tf, 'merge_all_summaries'):
self.merged = tf.merge_all_summaries()
else:
self.merged = tf.summary.merge_all()
if self.write_graph:
if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
self.writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
elif parse_version(tf.__version__) >= parse_version('0.8.0'):
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
else:
if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
self.writer = tf.summary.FileWriter(self.log_dir)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def set_model(self, model):
self.model = model
self.sess = K.get_session()
if self.histogram_freq and self.merged is None:
for layer in self.model.layers:
for weight in layer.weights:
if hasattr(tf, 'histogram_summary'):
tf.histogram_summary(weight.name, weight)
else:
tf.summary.histogram(weight.name, weight)
if self.write_images:
w_img = tf.squeeze(weight)
shape = w_img.get_shape()
if len(shape) > 1 and shape[0] > shape[1]:
w_img = tf.transpose(w_img)
if len(shape) == 1:
w_img = tf.expand_dims(w_img, 0)
w_img = tf.expand_dims(tf.expand_dims(w_img, 0), -1)
if hasattr(tf, 'image_summary'):
tf.image_summary(weight.name, w_img)
else:
tf.summary.image(weight.name, w_img)
if hasattr(layer, 'output'):
if hasattr(tf, 'histogram_summary'):
tf.histogram_summary('{}_out'.format(layer.name),
layer.output)
else:
tf.summary.histogram('{}_out'.format(layer.name),
layer.output)
if hasattr(tf, 'merge_all_summaries'):
self.merged = tf.merge_all_summaries()
else:
self.merged = tf.summary.merge_all()
if self.write_graph:
if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
self.writer = tf.summary.FileWriter(self.log_dir,
self.sess.graph)
elif parse_version(tf.__version__) >= parse_version('0.8.0'):
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph)
else:
self.writer = tf.train.SummaryWriter(self.log_dir,
self.sess.graph_def)
else:
if hasattr(tf, 'summary') and hasattr(tf.summary, 'FileWriter'):
self.writer = tf.summary.FileWriter(self.log_dir)
else:
self.writer = tf.train.SummaryWriter(self.log_dir)
def __init__(self, sess, state_dim, action_dim, learning_rate, gamma, t_replace_iter,
a_ma, a2_ma, agent_id, agent_num):
self.agent_id = agent_id
self.agent_num = agent_num
self.sess = sess
self.s_dim = state_dim
self.a_dim = action_dim
self.lr = learning_rate
self.gamma = gamma
self.t_replace_iter = t_replace_iter
self.t_replace_counter = 0
with tf.variable_scope('Critic{}'.format(self.agent_id)):
# Input (s, a), output q
local_a = a_ma[agent_id]
self.a_ma = tf.concat(a_ma, axis=1)
self.q = self._build_critic_net(X_MA, self.a_ma, 'eval_net', trainable=True)
# Input (s_, a_), output q_ for q_target
a2_ma = tf.concat(a2_ma, axis=1)
self.q_ = self._build_critic_net(X2_MA, a2_ma, 'target_net', trainable=False) # target_q is based on a_ from Actor's target_net
self.e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic{}/eval_net'.format(agent_id))
self.t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic{}/target_net'.format(agent_id))
with tf.variable_scope('target_q{}'.format(self.agent_id)):
self.target_q = R + self.gamma * self.q_
with tf.variable_scope('TD_error{}'.format(self.agent_id)):
self.loss = tf.reduce_mean(tf.squared_difference(self.target_q, self.q)) # MSE
with tf.variable_scope('C_train{}'.format(self.agent_id)):
self.train_ops = []
self.train_ops.append(tf.train.AdamOptimizer(self.lr).minimize(
self.loss, var_list=self.e_params)) # C train only update c network, don't update a
self.train_ops.append(self.loss) # for tf.summary
with tf.variable_scope('a_grad{}'.format(self.agent_id)):
# tensor of gradients of each sample (None, a_dim)
self.a_grads = tf.gradients(self.q, local_a)[0] # only get dq/da, throw dq/dw
self.train_ops.append(self.a_grads)
def __init__(self, sess, state_dim, action_dim, learning_rate, gamma, t_replace_iter,
a_ma, a2_ma, agent_id, agent_num):
self.agent_id = agent_id
self.agent_num = agent_num
self.sess = sess
self.s_dim = state_dim
self.a_dim = action_dim
self.lr = learning_rate
self.gamma = gamma
self.t_replace_iter = t_replace_iter
self.t_replace_counter = 0
with tf.variable_scope('Critic{}'.format(self.agent_id)):
# Input (s, a), output q
local_a = a_ma[agent_id]
self.a_ma = tf.concat(a_ma, axis=1)
self.q = self._build_critic_net(X_MA, self.a_ma, 'eval_net', trainable=True)
# Input (s_, a_), output q_ for q_target
a2_ma = tf.concat(a2_ma, axis=1)
self.q_ = self._build_critic_net(X2_MA, a2_ma, 'target_net', trainable=False) # target_q is based on a_ from Actor's target_net
self.e_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic{}/eval_net'.format(agent_id))
self.t_params = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='Critic{}/target_net'.format(agent_id))
with tf.variable_scope('target_q{}'.format(self.agent_id)):
self.target_q = R + self.gamma * self.q_
with tf.variable_scope('TD_error{}'.format(self.agent_id)):
self.loss = tf.reduce_mean(tf.squared_difference(self.target_q, self.q)) # MSE
with tf.variable_scope('C_train{}'.format(self.agent_id)):
self.train_ops = []
self.train_ops.append(tf.train.AdamOptimizer(self.lr).minimize(
self.loss, var_list=self.e_params)) # C train only update c network, don't update a
self.train_ops.append(self.loss) # for tf.summary
with tf.variable_scope('a_grad{}'.format(self.agent_id)):
# tensor of gradients of each sample (None, a_dim)
self.a_grads = tf.gradients(self.q, local_a)[0] # only get dq/da, throw dq/dw
self.train_ops.append(self.a_grads)
