def make_embedding(self, x):
with self.graph.as_default():
x = players.Convolution1D(128,
5,
border_mode='valid',
subsample_length=2,
activation='relu')(x)
x = players.Convolution1D(self.embedding_size,
5,
border_mode='valid',
subsample_length=1,
activation='relu')(x)
x = tf.reduce_max(x, reduction_indices=1)
return x
def make_embedding(self, x):
with self.graph.as_default():
x = model_utils.pad_up_to(x, size=29, axis=1)
x = players.Convolution1D(128,
5,
border_mode='valid',
subsample_length=2,
activation='relu')(x)
x = players.Convolution1D(512,
5,
border_mode='valid',
subsample_length=2,
activation='relu')(x)
x = players.Convolution1D(self.embedding_size,
5,
border_mode='valid',
activation='relu')(x)
initializer = tf.truncated_normal_initializer(stddev=0.01,
seed=1337)
cell = tf.contrib.rnn.LSTMCell(self.embedding_size,
use_peepholes=False,
initializer=initializer,
num_proj=None,
num_unit_shards=1,
num_proj_shards=1,
forget_bias=1.0,
state_is_tuple=False)
x, _ = tf.nn.dynamic_rnn(cell,
x,
sequence_length=None,
initial_state=None,
dtype='float32',
parallel_iterations=32,
swap_memory=False)
last_timestep = tf.shape(x)[1]
indices = tf.stack([0, last_timestep - 1, 0])
indices = tf.cast(indices, 'int32')
embedded = tf.slice(x, indices, [-1, 1, -1])
embedded = tf.squeeze(embedded, [1])
embedded.set_shape((None, self.embedding_size))
return embedded