def build_model(self):
with tf.device("/cpu:0"):
with tf.variable_scope(self.scope):
self.build_graph()
self.all_variables = tf.trainable_variables(scope=self.scope)
self._getflat = U.GetFlat(self.all_variables)
self.placeholders = [
tf.placeholder(v.value().dtype,
v.get_shape().as_list())
for v in self.all_variables
]
self.sess = tf.InteractiveSession() if tf.get_default_session(
) == None else tf.get_default_session()
self.sess.run(tf.global_variables_initializer())
self.vars, self.assigns = self.init_attr()
self.saver = tf.train.Saver()
def __init__(self, *args, **kwargs):
self.args, self.kwargs = args, kwargs
self.scope = self._initialize(*args, **kwargs)
self.all_variables = tf.get_collection(tf.GraphKeys.VARIABLES, self.scope.name)
self.trainable_variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, self.scope.name)
self.num_params = sum(int(np.prod(v.get_shape().as_list())) for v in self.trainable_variables)
self._setfromflat = U.SetFromFlat(self.trainable_variables)
self._getflat = U.GetFlat(self.trainable_variables)
logger.info('Trainable variables ({} parameters)'.format(self.num_params))
for v in self.trainable_variables:
shp = v.get_shape().as_list()
logger.info('- {} shape:{} size:{}'.format(v.name, shp, np.prod(shp)))
logger.info('All variables')
for v in self.all_variables:
shp = v.get_shape().as_list()
logger.info('- {} shape:{} size:{}'.format(v.name, shp, np.prod(shp)))
placeholders = [tf.placeholder(v.value().dtype, v.get_shape().as_list()) for v in self.all_variables]
self.set_all_vars = U.function(
inputs=placeholders,
outputs=[],
updates=[tf.group(*[v.assign(p) for v, p in zip(self.all_variables, placeholders)])]
)
def __init__(self, var_list, beta1=0.9, beta2=0.999, epsilon=1e-08):
self.var_list = var_list
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
# self.scale_grad_by_procs = scale_grad_by_procs
size = sum(U.numel(v) for v in var_list)
self.m = np.zeros(size, 'float32')
self.v = np.zeros(size, 'float32')
self.t = 0
self.setfromflat = U.SetFromFlat(var_list)
self.getflat = U.GetFlat(var_list)
def __init__(self, var_list, *, beta1=0.9, beta2=0.999, epsilon=1e-08, scale_grad_by_procs=True, comm=None):
self.var_list = var_list
self.beta1 = beta1
self.beta2 = beta2
self.epsilon = epsilon
self.scale_grad_by_procs = scale_grad_by_procs
size = sum(U.numel(v) for v in var_list)
self.m = np.zeros(size, 'float32')
self.v = np.zeros(size, 'float32')
self.t = 0
self.setfromflat = U.SetFromFlat(var_list)
self.getflat = U.GetFlat(var_list)
self.comm = MPI.COMM_WORLD if comm is None else comm