def __init__(self, num_units, encoder_output, scope=None):

self.hs = encoder_output

with vs.variable_scope(scope or type(self).__name__):

with vs.variable_scope("Attn1"):

hs2d = tf.reshape(self.hs, [-1, num_units])

phi_hs2d = tanh(rnn_cell.linear(hs2d, num_units, True, 1.0))

self.phi_hs = tf.reshape(phi_hs2d, tf.shape(self.hs))

super(GRUCellAttn, self).__init__(num_units)

def __call__(self, inputs, state, scope=None):

gru_out, gru_state = super(GRUCellAttn, self).__call__(inputs, state, scope)

with vs.variable_scope(scope or type(self).__name__):

with vs.variable_scope("Attn2"):

gamma_h = tanh(rnn_cell.linear(gru_out, self._num_units, True, 1.0))

weights = tf.reduce_sum(self.phi_hs * gamma_h, reduction_indices=2, keep_dims=True)

weights = tf.exp(weights - tf.reduce_max(weights, reduction_indices=0, keep_dims=True))

weights = weights / (1e-6 + tf.reduce_sum(weights, reduction_indices=0, keep_dims=True))

context = tf.reduce_sum(self.hs * weights, reduction_indices=0)

with vs.variable_scope("AttnConcat"):

out = tf.nn.relu(rnn_cell.linear([context, gru_out], self._num_units, True, 1.0))

attn_map = tf.reduce_sum(tf.slice(weights, [0, 0, 0], [-1, -1, 1]), reduction_indices=2)

def __init__(self, vocab_size, size, num_layers, max_gradient_norm, batch_size, learning_rate,

learning_rate_decay_factor, dropout, forward_only=False):

self.size = size

self.vocab_size = vocab_size

self.batch_size = batch_size

self.num_layers = num_layers

self.keep_prob = 1.0 - dropout

self.learning_rate = tf.Variable(float(learning_rate), trainable=False)

self.learning_rate_decay_op = self.learning_rate.assign(self.learning_rate * learning_rate_decay_factor)

self.global_step = tf.Variable(0, trainable=False)

self.source_tokens = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="source_tokens")

self.target_tokens = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="target_tokens")

self.source_mask = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="source_mask")

self.target_mask = tf.placeholder(tf.int32, shape=[None, self.batch_size], name="target_mask")

self.source_length = tf.reduce_sum(self.source_mask, reduction_indices=0)

self.target_length = tf.reduce_sum(self.target_mask, reduction_indices=0)

self.setup_embeddings()

self.setup_encoder()

self.setup_decoder()

self.setup_loss()

params = tf.trainable_variables()

if not forward_only:

opt = tf.train.AdamOptimizer(self.learning_rate)

gradients = tf.gradients(self.losses, params)

clipped_gradients, _ = tf.clip_by_global_norm(gradients, max_gradient_norm)

self.gradient_norm = tf.global_norm(clipped_gradients)

self.param_norm = tf.global_norm(params)

self.updates = opt.apply_gradients(

zip(clipped_gradients, params), global_step=self.global_step)

self.saver = tf.train.Saver(tf.all_variables())

def setup_embeddings(self):

with vs.variable_scope("embeddings"):

self.L_enc = tf.get_variable("L_enc", [self.vocab_size, self.size])

self.L_dec = self.L_enc #tf.get_variable("L_dec", [self.vocab_size, self.size])

self.encoder_inputs = embedding_ops.embedding_lookup(self.L_enc, self.source_tokens)

self.decoder_inputs = embedding_ops.embedding_lookup(self.L_dec, self.target_tokens)

def setup_encoder(self):

self.encoder_cell = rnn_cell.GRUCell(self.size)

with vs.variable_scope("PryamidEncoder"):

inp = self.encoder_inputs

mask = self.source_mask

out = None

for i in xrange(self.num_layers):

with vs.variable_scope("EncoderCell%d" % i) as scope:

srclen = tf.reduce_sum(mask, reduction_indices=0)

out, _ = self.bidirectional_rnn(self.encoder_cell, self.dropout(inp), srclen, scope=scope)

inp, mask = self.downscale(out, mask)

self.encoder_output = out

def setup_decoder(self):

if self.num_layers > 1:

self.decoder_cell = rnn_cell.GRUCell(self.size)

self.attn_cell = GRUCellAttn(self.size, self.encoder_output, scope="DecoderAttnCell")

out = self.decoder_inputs

with vs.variable_scope("Decoder"):

inp = self.decoder_inputs

for i in xrange(self.num_layers - 1):

with vs.variable_scope("DecoderCell%d" % i) as scope:

out, _ = rnn.dynamic_rnn(self.decoder_cell, self.dropout(inp), time_major=True,

dtype=dtypes.float32, sequence_length=self.target_length,

scope=scope)

inp = out

with vs.variable_scope("DecoderAttnCell") as scope:

out, _ = rnn.dynamic_rnn(self.attn_cell, self.dropout(inp), time_major=True,

dtype=dtypes.float32, sequence_length=self.target_length,

scope=scope)

self.decoder_output = out

def setup_loss(self):

with vs.variable_scope("Logistic"):

do2d = tf.reshape(self.decoder_output, [-1, self.size])

logits2d = rnn_cell.linear(do2d, self.vocab_size, True, 1.0)

outputs2d = tf.nn.softmax(logits2d)

self.outputs = tf.reshape(outputs2d, [-1, self.batch_size, self.vocab_size])

targets_no_GO = tf.slice(self.target_tokens, [1, 0], [-1, -1])

masks_no_GO = tf.slice(self.target_mask, [1, 0], [-1, -1])

# easier to pad target/mask than to split decoder input since tensorflow does not support negative indexing

labels1d = tf.reshape(tf.pad(targets_no_GO, [[0, 1], [0, 0]]), [-1])

mask1d = tf.reshape(tf.pad(masks_no_GO, [[0, 1], [0, 0]]), [-1])

losses1d = tf.nn.sparse_softmax_cross_entropy_with_logits(logits2d, labels1d) * tf.to_float(mask1d)

losses2d = tf.reshape(losses1d, [-1, self.batch_size])

self.losses = tf.reduce_sum(losses2d) / self.batch_size

def dropout(self, inp):

return tf.nn.dropout(inp, self.keep_prob)

def downscale(self, inp, mask):

with vs.variable_scope("Downscale"):

inp2d = tf.reshape(tf.transpose(inp, perm=[1, 0, 2]), [-1, 2 * self.size])

out2d = rnn_cell.linear(inp2d, self.size, True, 1.0)

out3d = tf.reshape(out2d, [self.batch_size, -1, self.size])

out3d = tf.transpose(out3d, perm=[1, 0, 2])

out = tanh(out3d)

mask = tf.transpose(mask)

mask = tf.reshape(mask, [-1, 2])

mask = tf.cast(mask, tf.bool)

mask = tf.reduce_any(mask, reduction_indices=1)

mask = tf.to_int32(mask)

mask = tf.reshape(mask, [self.batch_size, -1])

mask = tf.transpose(mask)

return out, mask

def bidirectional_rnn(self, cell, inputs, lengths, scope=None):

name = scope.name or "BiRNN"

# Forward direction

with vs.variable_scope(name + "_FW") as fw_scope:

output_fw, output_state_fw = rnn.dynamic_rnn(cell, inputs, time_major=True, dtype=dtypes.float32,

sequence_length=lengths, scope=fw_scope)

# Backward direction

with vs.variable_scope(name + "_BW") as bw_scope:

output_bw, output_state_bw = rnn.dynamic_rnn(cell, inputs, time_major=True, dtype=dtypes.float32,

sequence_length=lengths, scope=bw_scope)

output_bw = tf.reverse_sequence(output_bw, tf.to_int64(lengths), seq_dim=0, batch_dim=1)

outputs = output_fw + output_bw

output_state = output_state_fw + output_state_bw

return (outputs, output_state)

def train(self, session, source_tokens, source_mask, target_tokens, target_mask):

input_feed = {}

input_feed[self.source_tokens] = source_tokens

input_feed[self.target_tokens] = target_tokens

input_feed[self.source_mask] = source_mask

input_feed[self.target_mask] = target_mask

output_feed = [self.updates, self.gradient_norm, self.losses, self.param_norm]

outputs = session.run(output_feed, input_feed)

return outputs[1], outputs[2], outputs[3]

def test(self, session, source_tokens, source_mask, target_tokens, target_mask):

input_feed = {}

input_feed[self.source_tokens] = source_tokens

input_feed[self.target_tokens] = target_tokens

input_feed[self.source_mask] = source_mask

input_feed[self.target_mask] = target_mask

output_feed = [self.losses, self.outputs]

outputs = session.run(output_feed, input_feed)