def __init__(self, args):
self.args = args
if args.gen_model == 'rnn':
cell_fn = rnn_cell.BasicRNNCell
elif args.gen_model == 'gru':
cell_fn = rnn_cell.GRUCell
elif args.gen_model == 'lstm':
cell_fn = rnn_cell.BasicLSTMCell
else:
raise Exception("model type not supported: {}".format(args.model))
with tf.variable_scope('GEN') as scope:
cell = cell_fn(args.rnn_size)
self.cell = cell = rnn_cell.MultiRNNCell([cell] * args.num_layers)
# sequence of word tokens taken as input
self.input_data = tf.placeholder(tf.int32, [args.batch_size, args.seq_length], name='input_data')
self.latent_state = tf.placeholder(tf.float32, [args.batch_size, args.latent_size])
# weights to map the latent state into the (usually) bigger initial state
# right now this only works for rnn (other more complex models have more than
# one initial state which needs to be given a value)
# Right now we support up to two layers (state1 and state2)
self.latent_to_initial_state1 = tf.Variable(tf.random_normal([args.latent_size, args.rnn_size], stddev=0.35, dtype=tf.float32), name='latent_to_intial_state1')
self.latent_to_initial_state2 = tf.Variable(tf.random_normal([args.latent_size, args.rnn_size], stddev=0.35, dtype=tf.float32), name='latent_to_intial_state2')
self.initial_state1 = tf.matmul(self.latent_state, self.latent_to_initial_state1)
self.initial_state2 = tf.matmul(self.latent_state, self.latent_to_initial_state2)
# these are the actual approximate word vectors generated by the model
self.outputs = tf.placeholder(tf.float32, [args.seq_length, args.batch_size, args.rnn_size])
self.lr = tf.Variable(0.0, trainable=False, name='learning_rate')
self.has_init_seq2seq = False
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