def __lstm_cell(self, cell_dim, num_multi_cells):
#TODO:old
# cells = [tf.contrib.rnn.core_rnn_cell.LSTMCell(cell_dim, use_peepholes=True, forget_bias=0.,
# initializer=tf.random_uniform_initializer(-0.01, 0.01))
# for _ in range(num_multi_cells)]
#
# drop_cells = [tf.contrib.rnn.core_rnn_cell.DropoutWrapper(cell,
# input_keep_prob=self.keep_prob,
# output_keep_prob=self.keep_prob) for cell in cells]
# multi_cell = tf.contrib.rnn.core_rnn_cell.MultiRNNCell(drop_cells)
# return multi_cell
#TODO:multi cell
cells = [
rnn.ConvLSTMCell([None, 7, 7, cell_dim], [5, 5],
use_peepholes=True,
forget_bias=0.,
initializer=tf.random_uniform_initializer(
-0.01, 0.01)) for _ in xrange(num_multi_cells)
]
multi_cell = rnn.MultiRNNCell(cells)
return multi_cell
# TODO:single cell
# cell = rnn.ConvLSTMCell([None, 7, 7, cell_dim], [3, 3], use_peepholes=True, forget_bias=0.,
# initializer=tf.random_uniform_initializer(-0.01, 0.01))
return cell
def __lstm_cell(self, cell_dim, num_multi_cells):
cells = [rnn.ConvLSTMCell([None, 7, 7, cell_dim], [5, 5], use_peepholes=True, forget_bias=0.,
initializer=tf.random_uniform_initializer(-0.01, 0.01))
for _ in xrange(num_multi_cells)]
multi_cell = rnn.MultiRNNCell(cells)
return multi_cell
return cell