I am trying to implement Asynchronous Methods for Deep Reinforcement Learning and one of the steps requires to accumulate the gradient over different steps and then apply it. What is the best way to achieve this in tensorflow? I got so far as to accumulate the gradient and I don't think is the fastest way to achieve it (lots of transfers from tensorflow to python and back). Any suggestions are welcome. This is my code of a toy NN. It does not model or compute anything it just exercise the operations that I want to use.
import tensorflow as tf
from model import *
graph = tf.Graph()
with graph.as_default():
state = tf.placeholder(tf.float32, shape=[None, 80,80,1])
with tf.variable_scope('layer1'):
W = weight_variable([8, 8, 1, 32])
variable_summaries(W, "layer1/W")
b = bias_variable([32])
variable_summaries(b, "layer1/b")
h = conv2d(state, W, 4) + b
activation = tf.nn.relu(h)
pool1 = max_pool_2x2(activation)
print(pool1.get_shape())
pool1 = tf.reshape(pool1, [-1, 3200])
with tf.variable_scope('readout'):
W = weight_variable([3200, 3])
b = bias_variable([3])
logits = tf.matmul(pool1, W) + b
variable_summaries(h, "y")
action_indexes = tf.placeholder(tf.int32, shape=[None], name="action_indexes")
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, action_indexes)
starter_learning_rate = 1e-6
global_step = tf.Variable(0, trainable=False)
# decay every 1000 steps with a base of 0.96:
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
10000, 0.96, staircase=True)
optimizer = tf.train.RMSPropOptimizer(learning_rate)
gradients_and_variables = optimizer.compute_gradients(loss, tf.trainable_variables())
discounted_values = tf.placeholder(tf.float32, shape=[None, 1])
with tf.Session(graph=graph) as s:
for v in tf.trainable_variables():
print(v.name, v.dtype, v.get_shape())
s.run(tf.initialize_all_variables())
feed_dict= {
state : np.zeros([1, 80, 80, 1]),
action_indexes: [1],
}
var_to_grad = dict((var.name, grad) for grad, var in gradients_and_variables)
keys = sorted(var_to_grad.keys())
print(keys)
name_to_var = dict((var.name, var) for _, var in gradients_and_variables)
for i in range(10):
gradients = s.run([ var_to_grad[k] for k in keys], feed_dict=feed_dict)
for k,v in zip(keys, gradients):
var_to_grad[k] += v
for k in keys:
print(var_to_grad[k])
s.run( optimizer.apply_gradients( (g, name_to_var[v]) for v,g in var_to_grad.iteritems()), feed_dict=feed_dict)
Updated code after @yaroslave suggestion:
import tensorflow as tf
from model import *
graph = tf.Graph()
with graph.as_default():
minibatch = 32
state = tf.placeholder(tf.float32, shape=[minibatch, 80,80,1], name="input")
with tf.variable_scope('layer1'):
W = weight_variable([8, 8, 1, 32])
variable_summaries(W, "layer1/W")
b = bias_variable([32])
variable_summaries(b, "layer1/b")
h = conv2d(state, W, 4) + b
activation = tf.nn.relu(h)
pool1 = max_pool_2x2(activation)
print(pool1.get_shape())
pool1 = tf.reshape(pool1, [-1, 3200])
with tf.variable_scope('readout'):
W = weight_variable([3200, 3])
b = bias_variable([3])
logits = tf.matmul(pool1, W) + b
variable_summaries(h, "y")
action_indexes = tf.placeholder(tf.int32, shape=[minibatch], name="action_indexes")
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(logits, action_indexes)
starter_learning_rate = 1e-6
global_step = tf.Variable(0, trainable=False)
# decay every 1000 steps with a base of 0.96:
learning_rate = tf.train.exponential_decay(starter_learning_rate,
global_step,
10000, 0.96, staircase=True)
optimizer = tf.train.RMSPropOptimizer(learning_rate)
trainable_variables = tf.trainable_variables()
varname_to_var = dict( (v.name, v) for v in trainable_variables )
keys = sorted(varname_to_var.keys())
gradients_and_variables = optimizer.compute_gradients(loss, [ varname_to_var[k] for k in keys])
var_to_grad = dict((var.name, grad) for grad, var in gradients_and_variables)
name_to_var = dict((var.name, var) for _, var in gradients_and_variables)
# save the gradients in memory
var_to_ref_grad = {}
for k in keys:
grad = var_to_grad[k]
print(k, grad.get_shape())
ref = tf.Variable(tf.zeros_like(grad))
ref = ref.assign_add(grad)
var_to_ref_grad[k] = ref
discounted_values = tf.placeholder(tf.float32, shape=[None, 1], name='discounted_values')
# control when to apply gradients
compute_gradients_flag = tf.placeholder(tf.int32, name="compute_gradients")
def fn1():
var_grad_list = []
for k in keys:
grad = var_to_ref_grad[k]
var = varname_to_var[k]
var_grad_list.append((grad,var))
optimizer.apply_gradients(var_grad_list)
return tf.no_op()
fn2 = lambda : tf.no_op()
last_op = tf.cond(tf.equal(compute_gradients_flag, 1), fn1, fn2)
with tf.Session(graph=graph) as s:
feed_dict= {
state : np.zeros([minibatch, 80, 80, 1]),
action_indexes: [1],
compute_gradients_flag: False,
}
s.run(tf.initialize_all_variables())
for i in range(10):
# accumulate gradients
s.run(last_op, feed_dict=feed_dict)
You don't really have to manually accumulate gradients. You can have Tensorflow accumulate them for you by applying the rollout update as a batch.
s_list = list_of_states_visited
a_list = list_of_actions_taken
R_list = list_of_value_targets
sess.run(local_net.update, feed_dict={
local_net.input: s_list,
local_net.a: a_list,
local_net.R: R_list
})
Something like this might work to create ops for accumulating gradients, resetting the accumulated gradients, and applying the accumulated gradients (untested!):
def build_gradient_accumulators(optimizer, gradients_and_variables):
accum_grads_and_vars = []
accumulators = []
resetters = []
for grad, var in gradients_and_variables:
accum = tf.Variable(tf.zeros_like(grad))
accum = accum.assign_add(grad)
accumulators.append(accum)
accum_grads_and_vars.append((accum, var))
resetters.append(tf.assign(accum, tf.zeros_like(accum)))
reset_op = tf.group(*resetters)
accum_op = tf.group(*accumulators)
apply_op = optimizer.apply_gradients(accum_grads_and_vars)
return reset_op, accum_op, apply_op
来源：https://stackoverflow.com/questions/37710974/how-to-accumulate-and-appy-gradients-for-async-n-step-dqnetwork-update-in-tensor