def _RunLSTMLayer(self, basename, init_weights, m_init_scalar,
c_init_scalar, pad_scalar):
with self.test_session() as sess:
num_inputs = 1
num_nodes = 1
seq_length = 3
weights = init_weights(
lstm.LSTMCellWeightsShape(num_inputs, num_nodes))
m_init = constant_op.constant([[m_init_scalar]] * self._batch_size)
c_init = constant_op.constant([[c_init_scalar]] * self._batch_size)
x_seq = [constant_op.constant(self._inputs)] * seq_length
pad_seq = [
constant_op.constant([[pad_scalar]] * self._batch_size)
] * seq_length
out_seq = lstm.LSTMLayer('lstm', weights, m_init, c_init, x_seq,
pad_seq)
_DumpGraph(
sess.graph, 'lstm_layer_%s_%d_%d_%d' %
(basename, m_init_scalar, c_init_scalar, pad_scalar))
# Initialize variables and run the unrolled LSTM layer.
sess.run(variables.global_variables_initializer())
return sess.run(out_seq)
def testLSTMLayerErrors(self):
num_inputs = 1
num_nodes = 1
seq_length = 3
weights = array_ops.zeros(lstm.LSTMCellWeightsShape(num_inputs, num_nodes))
m = constant_op.constant([[0.]] * self._batch_size)
c = constant_op.constant([[0.]] * self._batch_size)
x_seq = [constant_op.constant(self._inputs)] * seq_length
pad = constant_op.constant([[0.]] * self._batch_size)
with self.assertRaisesWithPredicateMatch(ValueError, 'length of x_seq'):
lstm.LSTMLayer('lstm', weights, m, c, x_seq, [pad])
with self.assertRaisesWithPredicateMatch(ValueError, 'length of x_seq'):
lstm.LSTMLayer('lstm', weights, m, c, x_seq, [pad] * 2)
with self.assertRaisesWithPredicateMatch(ValueError, 'length of x_seq'):
lstm.LSTMLayer('lstm', weights, m, c, x_seq, [pad] * 4)
def _RunLSTMCell(self, basename, init_weights, m_prev_scalar, c_prev_scalar,
pad_scalar):
with self.cached_session() as sess:
num_inputs = 1
num_nodes = 1
weights = init_weights(lstm.LSTMCellWeightsShape(num_inputs, num_nodes))
m_prev = constant_op.constant([[m_prev_scalar]] * self._batch_size)
c_prev = constant_op.constant([[c_prev_scalar]] * self._batch_size)
x = constant_op.constant(self._inputs)
pad = constant_op.constant([[pad_scalar]] * self._batch_size)
m, c = lstm.LSTMCell(weights, m_prev, c_prev, x, pad)
_DumpGraph(sess.graph, 'lstm_cell_%s_%d_%d_%d' %
(basename, m_prev_scalar, c_prev_scalar, pad_scalar))
# Initialize variables and run the unrolled LSTM step.
self.evaluate(variables.global_variables_initializer())
return self.evaluate([m, c])
