def add_placeholders_op(self):
"""
Adds placeholders to the graph
These placeholders are used as inputs by the rest of the model building and will be fed
data during training.
"""
# here, typically, a state shape is (5,3,22)
state_shape = list([
self.env.args.visible_radius_unit_front + 1,
2 * self.env.args.visible_radius_unit_side + 1,
len(self.env.state.xmap.item_class_id) + 1
])
self.s = tf.placeholder(tf.bool,
shape=(None, None, state_shape[0],
state_shape[1], state_shape[2]))
self.hs = DNC.state_placeholder(self.config)
self.slen = tf.placeholder(tf.int32, shape=(None))
self.sp = tf.placeholder(tf.bool,
shape=(None, None, state_shape[0],
state_shape[1], state_shape[2]))
self.hsp = DNC.state_placeholder(self.config)
self.splen = tf.placeholder(tf.int32, shape=(None))
self.a = tf.placeholder(tf.int32, shape=(None)) # (nb*state_history,)
self.past_a = tf.placeholder(tf.int32,
shape=(None)) # (nb*state_history,)
self.r = tf.placeholder(tf.float32,
shape=(None)) # (nb*state_history,)
self.done_mask = tf.placeholder(tf.bool,
shape=(None)) # (nb*state_history,)
self.seq_mask = tf.placeholder(tf.bool,
shape=(None)) # (nb*state_history,)
self.lr = tf.placeholder(tf.float32, shape=(None))
def add_placeholders_op(self):
"""
Adds placeholders to the graph
These placeholders are used as inputs by the rest of the model building and will be fed
data during training.
"""
# here, typically, a state shape is (2*4*3+2)
num_classes = len(self.env.state.xmap.item_class_id)
ndigits = self.config.ndigits
nway = self.config.nway
self.s = tf.placeholder(tf.float32, shape=(None, None, 2*(num_classes-1)*ndigits*nway+2))
self.hs = DNC.state_placeholder(self.config)
self.slen = tf.placeholder(tf.int32, shape=(None))
self.pred_flag = tf.placeholder(tf.float32, shape=(None, None)) # (nb, state_history)
self.target_action = tf.placeholder(tf.float32, shape=(None, None, 2*(num_classes-1)*ndigits*nway)) # (nb, state_history, num_actions)
self.lr = tf.placeholder(tf.float32, shape=(None))