def _build_net(self):
with tf.name_scope('inputs'):
self.obs = tf.placeholder(tf.float32, [None, self.n_features],
name="observations")
self.actions = tf.placeholder(tf.int32, [
None,
],
name="actions_num")
self.dis_return = tf.placeholder(tf.float32, [None], name="return")
# self.prediction = tf.placeholder(tf.float32, [None, ], name="actions_value")
with tf.name_scope('Actor'):
self.w_u = tf.Variable(tf.random_uniform(
[self.n_features, self.n_actions]),
dtype=tf.float32,
name="w_u")
self.action = tf.matmul(self.obs, self.w_u)
with tf.name_scope('Critic'):
self.w_v = tf.Variable(tf.random_uniform([self.n_features, 1]),
dtype=tf.float32,
name="w_v")
self.prediction = tf.matmul(self.obs, self.w_v)
# # fc1
# layer = tf.layers.dense(
# inputs=self.tf_obs,
# units=self.n_features,
# activation=None, # tanh activation
# kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.),
# bias_initializer=tf.constant_initializer(0.),
# name='fc1'
# )
# # fc2
# all_act = tf.layers.dense(
# inputs=layer,
# units=self.n_actions,
# activation=None,
# kernel_initializer=tf.random_normal_initializer(mean=0, stddev=0.),
# bias_initializer=tf.constant_initializer(0.),
# name='fc2'
# )
self.all_act_prob = tf.nn.softmax(self.action, name='act_prob')
with tf.name_scope('loss'):
# to maximize total reward (log_p * R) is to minimize -(log_p * R), and the tf only have minimize(loss)
self.neg_log_prob = tf.reduce_sum(
-tf.log(self.all_act_prob) *
tf.one_hot(self.actions, self.n_actions),
axis=1) # this is negative log of chosen action
delta = self.dis_return - self.prediction
loss_v = tf.reduce_mean(tf.square(delta))
# loss_u = tf.reduce_mean(self.neg_log_prob * delta) # reward guided loss
with tf.name_scope('update'):
self.w_u = tf.assign_add(
self.w_u,
self.lr_actor * delta *
tf.gradient(self.neg_log_prob, self.w_u))
self.w_v = tf.assign_add(
self.w_v, self.lr_critic * tf.gradients(loss_v, self.w_v))
def test_gradient():
x = 2
z = 1
y = func_y
with tf.GradientTape as t:
k = x - 1
t.watch(k)
y = func_y(k, z)
grad = tf.gradient(y, t)
def CG(b, x0, TOLERANCE=1.0e-10, MAX_ITERATIONS=100):
"""
A function to solve [A]{x} = {b} linear equation system with the
conjugate gradient method.
More at: http://en.wikipedia.org/wiki/Conjugate_gradient_method
========== Parameters ==========
A : array
A real symmetric positive definite matrix.
In our case this will be the Hessian. We want to avoid using this, so it will not be used.
We will use a finite differences to calculate H*d by calling finite_differences(x,d) where x is the current
point and d is the direction of movement
b : vector
The right hand side (RHS) vector of the system.
In our case it will be the gradient of a specific point
We need to be able to calculate gradients at more than
Just that one point
Therefore we will pass this as a function so we can
evaluate the gradient of the function at that point
x0 : vector
The starting guess for the solution. Anything will do.
MAX_ITERATIONS : integer
Maximum number of iterations. Iteration will stop after maxiter
steps even if the specified tolerance has not been achieved.
TOLERANCE : float
Tolerance to achieve. The algorithm will terminate when either
the relative or the absolute residual is below TOLERANCE.
"""
# Initializations
x = x0
d = -tf.gradient(Loss, x)
r0 = b - finite_differences(x,d)
# Start iterations
for i in range(MAX_ITERATIONS):
a = float(np.dot(d.T, r0) / np.dot(d.T, finite_differences(x, d)))
x = x - a * gradient(x)
ri = r0 - np.dot(finite_differences(x, d), d)
# print i, np.linalg.norm(ri)
# Checks stopping condition
if np.linalg.norm(ri) < TOLERANCE:
return x
# Otherwise go on to find new direction
b = float(np.dot(gradient(x).T, finite_differences(x, d)))
d = - gradient(x) + b * d
r0 = ri
return x
def mle_loss(B, W, mu, tau):
"""
Calculate maximize log-likelihood
Parameters:
--------
B: N by 1 vector. dtype: tf.float32
W: N by N matrix (?). dtype: tf.float32
mu: a scalar. dtype: tf.float32
tau: a scalar, same as sigma in gaussian distribution. dtype: tf.float32
"""
J = tf.gradient(B, [W])
_B = tf.math.squre(B[1:] - B[:-1] - mu) - tf.math.log(tau)
# TODO: multiply by Jaccobia matrix, unclear
log_prob = _B * tf.linalg.det(J)
neg_log_likelihood = tf.reduce_sum(log_prob)
return neg_log_likelihood
def wasserstein_loss(real_scores, fake_scores):
batch_size = real_scores.shape[0]
avg_real_scores = tf.math.reduce_mean(real_scores)
avg_fake_scores = tf.math.reduce_mean(avg_fake_scores)
gen_loss = -avg_fake_score
alpha = tf.random.uniform([batch_size, 1, 1, 1])
interpolated = (alpha * generated) + ((1 - alpha) * real)
critic_interpolated = discriminator(interpolated)
critic_gradient = tf.gradient(critic_interpolated, interpolated)
norm_critic_gradient = tf.math.sqrt(
tf.reduce_sum(tf.math.square(critic_gradient), [1, 2, 3]))
norm_critic_center = norm_critic_gradient - 1
gradient_penalty = tf.math.square(norm_critic_center)
discrim_loss = -avg_real_scores + avg_fake_scores + (gp_weight *
gradient_penalty)
return gen_loss, discrim_loss
def wgangp_loss(logits_real, logits_fake, batch_size, x, G_sample):
"""Compute the WGAN-GP loss.
Inputs:
- logits_real: Tensor, shape [batch_size, 1], output of discriminator
Log probability that the image is real for each real image
- logits_fake: Tensor, shape[batch_size, 1], output of discriminator
Log probability that the image is real for each fake image
- batch_size: The number of examples in this batch
- x: the input (real) images for this batch
- G_sample: the generated (fake) images for this batch [batch_size,784]
Returns:
- D_loss: discriminator loss scalar
- G_loss: generator loss scalar
"""
# TODO: compute D_loss and G_loss
D_loss = tf.reduce_mean(logits_fake-logits_real)
G_loss = -tf.reduce_mean(logits_fake)
# lambda from the paper
lam = 10
# random sample of batch_size (tf.random_uniform)
eps = tf.random_uniform([batch_size,1])
x_hat = eps * x + (1-eps)* G_sample
# Gradients of Gradients is kind of tricky!
with tf.variable_scope('',reuse=True) as scope:
D_x_hat = discriminator(x_hat)
grad_D_x_hat = tf.gradient(D_x_hat,x_hat)[0]
grad_norm = tf.norm(grad_D_x_hat,axis = 1)
grad_pen = lam * tf.reduce_mean((grad_norm-1)**2)
D_loss += grad_pen
return D_loss, G_loss
def network_mnist(images, labels,
mode): #features=images,labels,mode=TEST or TRAIN
# Input Layer
input_layer = tf.reshape(images["x"], [-1, 28, 28, 1])
# Convolutional Layer #1
conv1 = tf.layers.conv2d(inputs=input_layer,
filters=32,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
# Pooling Layer #1
pool1 = tf.layers.max_pooling2d(inputs=conv1, pool_size=[2, 2], strides=2)
# Convolutional Layer #2 and Pooling Layer #2
conv2 = tf.layers.conv2d(inputs=pool1,
filters=64,
kernel_size=[5, 5],
padding="same",
activation=tf.nn.relu)
pool2 = tf.layers.max_pooling2d(inputs=conv2, pool_size=[2, 2], strides=2)
# Dense Layer
pool2_flat = tf.reshape(pool2, [-1, 7 * 7 * 64])
dense = tf.layers.dense(inputs=pool2_flat,
units=128,
activation=tf.nn.relu)
dropout = tf.layers.dropout(inputs=dense,
rate=0.4,
training=mode == tf.estimator.ModeKeys.TRAIN)
# Logits Layer
logits = tf.layers.dense(inputs=dropout, units=10)
#Returns logits and representer
return logits, tf.gradient(dense, input_layer)
def eval_deriv(self, bhat, b):
grads = tf.gradient(self.eval(bhat, b), bhat)
return grads[0]
def __init__(self, scope, globalAC):
# in the __init__ , we defined some important variables to make some function
# such as define loss, train_op
# define some important tensor graph ...
if scope == GLOBAL_NET_SCOPE: # let us make a global net
with tf.variable_scope(scope):
# give me some placeholders, come on !
self.s = tf.placeholder(tf.float32, [None, N_S],'S')
# the network will return para according to self.s
# para of action net and critic net
self.a_para, self.c_para = self._build_net(scope)[-2:]
else: # let us make a local worker network
with tf.variable_scope(scope):
# give me some placeholder to give the net
# this is the input of net
self.s = self.s = tf.placeholder(tf.float32, [None, N_S],'S')
# this is the action from memory
self.a_memory = tf.placeholder(tf.float32, [None, A_S],'A')
# this is the value target of q_value
self.v_target = tf.placeholder(tf.float32, [None, 1],'v_target')
# the network will return para according to self.s
# para of action net and critic net
# mu and sigma are the output about chosen action from actio_net
# mu and sigma are the parameters of a normal distribution
# self.v is the value of this statement
mu, sigma, self.v, self.a_para, self.c_para = self._build_net(scope)
# we need self,v_target and self.v to grt c_loss
td = tf.subtract(self.v_target, self.v, name ='td_error')
# this is the the loss for q_learning , for the train_operation of critic_net
with tf.variable_scope('c_loss'):
self.c_loss = tf.reduce_mean(tf.squared(td))
with tf.variable_scope('get_action_distribution'):
mu = mu*A_BOUND[1]
sigma += 1e-4
normal_dist = tf.distributions.Normal(mu, sigma)
with tf.variable_scope('a_loss'):
# we need the action from memory to get a_loss
log_prob = normal.dist.log_prob(self.a_memory)
error = log_prob*td
entropy = normal_dist.entropy() # encourage exploration
error = ENTROPY_BETA * entropy + error
self.a_loss = tf.reduce_mean(error)
with tf.variable_scope('chosen_action'):
# use the action_net of local net to choose action
self.a = tf.clip_by_value(
tf.squeeze(
normal_dist.sample(1),
axis = 0
),
A_BOUND[0],
A_BOUND[1]
)
with tf.variable_scope('local_gradient'):
# get the gradient of local net
# to train local network and update global network
self.a_grad = tf.gradient(self.a_loss, self.a_para)
self.c_grad = tf.gradient(self.c_loss, self.c_para)
with tf.variable_scope('sync'):
# todo
with tf.variable_scope('pull'):
# pull the para of global action_net to the local action_net
self.pull_a_para_op = [local_para.assign(global_para) for local_para, global_para in zip(self.a_para, globalAC.a_para)]
# pull the para of global critic_net to the local critic_net
self.pull_c_para_op = [local_para.assign(global_para) for local_para, global_para in zip(self.c_para, globalAC.c_para)]
with tf.variable_scope('push'):
# push the gradients of training to the global net
# use the gradients caculated from local net to train global net
self.update_gradient_action_op = optimizer_action.apply_gradients(zip(self.a_grad, globalAC.a_para))
self.update_gradient_critic_op = optimizer_critic.apply_gradients(zip(self.c_para, globalAC.c_para))
def _build_net(self, scope):
# to define a network structure for action_net ,critic_net in global and local network
w_init = tf.random_normal_initializer(0.0, 0.1)
with tf.variable_scope('actor'):
# we will get some normal_distributions of action, number of distributions is N_A
output_a = tf.layers.dense(
self.s,
20,
tf.nn.relu6,
kernel_initializer = w_init,
name = 'output_a'
)
mu = tf.layers.dense( # get the mu of a normal distribution of action, dim of mu is N_A
output_a,
N_A,
tf.nn.tanh,
kernel_initializer = w_init,
name = 'mu'
)
sigma = tf.layers.dense( # get the sigma of a normal distribution of action, dim of sigma is N_A
output_a,
N_A,
tf.nn.softplus,
kernel_initializer = w_init,
name = 'sigma'
)
with tf.variable_scope('critic'):
output_c = tf.layers.dense(
self.s,
20,
tf.nn.relu6,
kernel_initializer = w_init,
name = 'output_c'
)
v = tf.layers.dense( # we get the value of this statement self.s
output_c,
1,
kernel_initializer = w_init,
name = 'v'
)
a_para = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = scope+'/actor')
c_para = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope = scope+'/critic')
return mu, sigma, v, a_para, c_para
def update_global(self, feed_dict): # push the gradients to the global net to train
# to train global net using the gradiients caculated from local net
SESS.run([self.update_gradient_action_op, self.update_gradient_critic_op], feed_dict)
# some data is from placeholder
def pull_global(self): #pull the new para from global net to local net
SESS.run([self.pull_a_para_op, self.pull_c_para_op])
def choose_action(self, s):
# we need the statement of this moment to caculate a action
s = s[np.new.axis, :]
return SESS.run(self.a, {self.s:s})[0]
tf.ones([self.batch_size, self.num_steps], dtype=tf.float32),
)
self.cost = tf.reduce_sum(loss)
# self.relu_out = tf.nn.relu(tf.reshape(logits, [-1, coord_size]))
# #self.softmax_out = tf.nn.softmax(tf.reshape(logits, [-1, coord_size]))
# self.predict = tf.cast(self.relu_out, tf.int32)
correct_prediction = tf.equal(self.dense, tf.reshape(self.input_obj.targets, [-1]))
self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
if not is_training:
return
self.learning_rate = tf.Variable(0.0, trainable=False)
tvars = tf.trainable_variables()
grads, _ = tf.clip_by_global_norm(tf.gradient(self.cost, tvars), 5)
optimizer = tf.train.AdamOtimizer(self.learning_rate)
self.train_op = optimizer.apply_gradients(
zip(grads, tvars),
global_step=tf.contrib.framework.get_or_create_global_step())
)
self.new_lr = tf.placeholder(tf.float32, shape=[])
self.lr_update = tf.assign(self.learning_rate, self.new_lr)
def assign_lr(self, session, lr_value):
session.run(self.lr_update)
def train(train_data, num_epochs, num_layer, batch_size, model_save_name,
learning_rate=1.0, max_lr_epoch=10, lr_decay=0.93):
score = tf.matmul(layer1, W2)
probability = tf.nn.sigmoid(score)
tvars = tf.trainable_variables()
input_y = tf.placeholder(name='input_y', shape=[None, 1], dtype=tf.float32)
advantages = tf.placeholder(name='reward_signal', dtype=tf.float32)
W1grad = tf.placeholder(name='batch_grad1', dtype=tf.float32)
W2grad = tf.placeholder(name='batch_grad2', dtype=tf.float32)
batchGrad = [W1grad, W2grad]
loglik = tf.log(input_y*(input_y-probability) + (1-input_y)*(input_y+probability))
adam = tf.train.AdamOptimizer(learning_rate=lr)
loss = -tf.reduce_mean(loglik * advantages)
newGrads = tf.gradient(loss, tvars)
updateGrads = adam.apply_gradients(zip(batchGrad, tvars))
""" Model Network """
h_model = 256
input_data = tf.placeholder(name='input_data', shape=[None, 5], dtype=tf.float32)
with tf.variable_scope('rnnlm'):
# weights and biases
softmax_w = tf.get_variable('softmax_w', shape=[h_model, 50])
softmax_b = tf.get_variable('softmax_b', shape=[50])
previous_state = tf.placeholder(name='previous_state', shape=[None, 5], dtype=tf.float32)
W1M = tf.get_variable(name='W1M', shape=[5, h_model],
initializer=tf.contrib.layers.xavier_initializer())
def trainModel(self, on_policy=False, target=False):
# sampling
if on_policy:
mini_batch = [self.memory[-1]]
else:
mini_batch = random.sample(self.memory, self.batch_size)
states = [x[0] for x in mini_batch]
actions = [[x[1]] for x in mini_batch]
rewards = [x[2] for x in mini_batch]
#next_states = [x[3] for x in mini_batch]
mus = [x[3] for x in mini_batch]
dones = [int(x[4]) for x in mini_batch]
states = np.asarray(states)
actions = np.asarray(actions)
mus = np.asarray(mus)
states = tf.convert_to_tensor(states, dtype=tf.float32)
actions = tf.convert_to_tensor(actions, dtype=tf.int32)
mus = tf.convert_to_tensor(mus, dtype=tf.float32)
q = self.critic(states)
pi = self.actor(states)
pi_avg = self.polyak(states)
#q_a = get_by_index(q, actions) # get_by_index might not work
# might need to implement tf.gather another way
#pi_a = get_by_index(pi, actions)
a_index = tf.stack([tf.range(tf.shape(actions)[0]), actions[:, 0]],
axis=-1)
q_a = tf.gather_nd(q, a_index)
pi_a = tf.gather_nd(pi, a_index)
v = tf.reduce_sum(q * pi, axis=-1) # might need ,axis = -1
rho = pi / (mu + 1e-6)
rho_a = tf.gather_nd(rho, a_index)
rho_bar = tf.minimum(1.0, rho_a)
print(v, '\n ############################################## \n', dones)
q_ret = v[-1] * dones[-1]
q_rets = []
for i in reversed(range(len(rewards))):
q_ret = rewards[i] + self.gamma * q_ret
q_rets.append(q_ret)
q_ret = (rho_bar[i] * (q_ret - q_a[i])) + v[i]
# (edit1?) need correction for when new sequence is beginning ??
q_rets.reverse()
#q_ret = tf.reshape(tf.stack(values=q_rets, axis=1), [-1]) # (edit1) in reference to seq_to_batch
# OpenAI baseline a2c.utils
print(q_ret)
print('#############################################')
print(q_ret.shape)
q_ret = tf.expand_dims(tf.convert_to_tensor(q_ret, dtype=tf.float32),
axis=1)
# adv = q_ret - v
loss_f = -rho_bar * tf.log(pi_a + 1e-6) * (q_ret - v)
#loss_f = tf.reduce_mean(loss_f)
loss_bc = -tf.maximum((1 - c / rho), 0.0) * pi * tf.log(pi) * (
q - v) # note that tf.____ functions might need to be
# tf.math.____
# might need to reshape either q or v
#loss_bc = tf.reduce_mean(loss_bc)
loss_q = tf.reduce_mean(tf.square(tf.stop_gradient(q_ret) - q_a) * 0.5)
# (edit1) in reference t
g = tf.gradients(-(loss_f + loss_bc), pi)
k = pi_avg / (pi + 1e-6)
#k_dot_g = tf.reduce_sum(k*g, axis=-1)
grad_pi = tf.maximum(0.0,
(tf.reduce_sum(k * g, axis=-1) - self.delta) /
(tf.reduce_sum(tf.square(k), axis=-1) + 1e-6))
grad_pi = tf.gradient(grad_pi, self.actor.trainable_variables)
grad_v = tf.gradients(loss_q, self.critic.trainable_variables)
trainer_pi = tf.train.Adam(learning_rate=self.learning_rate)
trainer_v = tf.train.Adam(learning_rate=self.learning_rate)
trainer_pi.apply_gradients(grad_pi)
trainer_v.apply_gradients(grad_v)
self.update_polyak()
def __init__(self, scope, globalAC):
# in the __init__ , we defined some important variables to make some function
# such as define loss, train_op
# define some important tensor graph ...
if scope == GLOBAL_NET_SCOPE: # let us make a global net
with tf.variable_scope(scope):
# give me some placeholders, come on !
self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
# the network will return para according to self.s
# para of action net and critic net
self.a_para, self.c_para = self._build_net(scope)[-2:]
else: # let us make a local worker network
with tf.variable_scope(scope):
# give me some placeholder to give the net
# this is the input of net
self.s = self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
# this is the action from memory
self.a_memory = tf.placeholder(tf.float32, [None, A_S], 'A')
# this is the value target of q_value
self.v_target = tf.placeholder(tf.float32, [None, 1],
'v_target')
# the network will return para according to self.s
# para of action net and critic net
# mu and sigma are the output about chosen action from actio_net
# mu and sigma are the parameters of a normal distribution
# self.v is the value of this statement
mu, sigma, self.v, self.a_para, self.c_para = self._build_net(
scope)
# we need self,v_target and self.v to grt c_loss
td = tf.subtract(self.v_target, self.v, name='td_error')
# this is the the loss for q_learning , for the train_operation of critic_net
with tf.variable_scope('c_loss'):
self.c_loss = tf.reduce_mean(tf.squared(td))
with tf.variable_scope('get_action_distribution'):
mu = mu * A_BOUND[1]
sigma += 1e-4
normal_dist = tf.distributions.Normal(mu, sigma)
with tf.variable_scope('a_loss'):
# we need the action from memory to get a_loss
log_prob = normal.dist.log_prob(self.a_memory)
error = log_prob * td
entropy = normal_dist.entropy() # encourage exploration
error = ENTROPY_BETA * entropy + error
self.a_loss = tf.reduce_mean(error)
with tf.variable_scope('chosen_action'):
# use the action_net of local net to choose action
self.a = tf.clip_by_value(
tf.squeeze(normal_dist.sample(1), axis=0), A_BOUND[0],
A_BOUND[1])
with tf.variable_scope('local_gradient'):
# get the gradient of local net
# to train local network and update global network
self.a_grad = tf.gradient(self.a_loss, self.a_para)
self.c_grad = tf.gradient(self.c_loss, self.c_para)
with tf.variable_scope('sync'):
# todo
pass
with tf.variable_scope('pull'):
# pull the para of global action_net to the local action_net
self.pull_a_para_op = [
local_para.assign(global_para)
for local_para, global_para in zip(
self.a_para, globalAC.a_para)
]
# pull the para of global critic_net to the local critic_net
self.pull_c_para_op = [
local_para.assign(global_para)
for local_para, global_para in zip(
self.c_para, globalAC.c_para)
]
with tf.variable_scope('push'):
# push the gradients of training to the global net
# use the gradients caculated from local net to train global net
self.update_gradient_action_op = optimizer_action.apply_gradients(
zip(self.a_grad, globalAC.a_para))
self.update_gradient_critic_op = optimizer_critic.apply_gradients(
zip(self.c_para, globalAC.c_para))
def backward(self, input, *args, **kwargs):
if self.identity: return input
if self.mas:
return input * tf.cast(self.mask, tf.float32)
else:
return tf.gradient(self, self.input, input)[0]
def backward(self, input):
return tf.gradient(self, self.input, input)[0]
logits = tf.matmul(h_fc1,W_fc2) + b_fc2
#var = [noise]
var = [x_noise]
with tf.name_scope("cross_entropy"):
#cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits = logits,labels = y))
cost = tf.reduce_sum(tf.multiply(logits,y))
print cost
tf.summary.scalar('cross entropy',cost)
with tf.name_scope("train"):
#k = tf.Variable()
grad = tf.gradient(cost,x_noise)
mean, var = tf.nn.moments(grad, axes=[0])
learning_rate = tf.reciprocal(tf.sqrt(var))
train_step = tf.train.AdamOptimizer(learning_rate).minimize(-cost,var_list=var)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits,1),tf.argmax(y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
tf.summary.scalar('accuracy',accuracy)
#with tf.name_scope("test_accuracy"):
# correct_prediction = tf.equal(tf.argmax(logits,1),tf.argmax(y,1))
# test_accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
# tf.summary.scalar('test_accuracy',test_accuracy)
def mle_gradient(loss, W, mu, tau, P0):
return tf.gradient(loss, [W, mu, tau, P0])