def f(inp, hidden_weight, hidden_bias, softmax_weight, softmax_bias):
features = nn_ops.relu(
nn_ops.xw_plus_b(inp, hidden_weight, hidden_bias), name="features")
logits = nn_ops.xw_plus_b(
features, softmax_weight, softmax_bias, name="logits")
labels = constant_op.constant(
label_data.tolist(),
shape=[batch, classes],
dtype=dtypes.float64,
name="labels")
cost = nn_ops.softmax_cross_entropy_with_logits(
labels=labels, logits=logits, name="cost")
return cost
def sequence_softmax(inputs, noutput, scope=None, name=None, linear_name=None):
"""Run a softmax layer over all the time steps of an input sequence.
Args:
inputs: (length, batch_size, depth) tensor
noutput: output depth
scope: optional scope name
name: optional name for output tensor
linear_name: name for linear (pre-softmax) output
Returns:
A tensor of size (length, batch_size, noutput).
"""
length, _, ninputs = _shape(inputs)
inputs_u = array_ops.unstack(inputs)
output_u = []
with variable_scope.variable_scope(scope, "SequenceSoftmax", [inputs]):
initial_w = random_ops.truncated_normal([0 + ninputs, noutput], stddev=0.1)
initial_b = constant_op.constant(0.1, shape=[noutput])
w = variables.model_variable("weights", initializer=initial_w)
b = variables.model_variable("biases", initializer=initial_b)
for i in xrange(length):
with variable_scope.variable_scope(scope, "SequenceSoftmaxStep",
[inputs_u[i]]):
# TODO(tmb) consider using slim.fully_connected(...,
# activation_fn=tf.nn.softmax)
linear = nn_ops.xw_plus_b(inputs_u[i], w, b, name=linear_name)
output = nn_ops.softmax(linear)
output_u += [output]
outputs = array_ops.stack(output_u, name=name)
return outputs
def extract_argmax_and_embed(prev, _):
"""Loop_function that extracts the symbol from prev and embeds it."""
if output_projection is not None:
prev = nn_ops.xw_plus_b(
prev, output_projection[0], output_projection[1])
prev_symbol = array_ops.stop_gradient(math_ops.argmax(prev, 1))
return embedding_ops.embedding_lookup(embedding, prev_symbol)
def loop_function(prev, i, log_beam_probs, beam_path, beam_symbols):
if output_projection is not None:
prev = nn_ops.xw_plus_b(
prev, output_projection[0], output_projection[1])
# prev= prev.get_shape().with_rank(2)[1]
probs = tf.log(tf.nn.softmax(prev))
if i > 1:
probs = tf.reshape(probs + log_beam_probs[-1],
[-1, beam_size * num_symbols])
best_probs, indices = tf.nn.top_k(probs, beam_size)
indices = tf.stop_gradient(tf.squeeze(tf.reshape(indices, [-1, 1])))
best_probs = tf.stop_gradient(tf.reshape(best_probs, [-1, 1]))
symbols = indices % num_symbols # Which word in vocabulary.
beam_parent = indices // num_symbols # Which hypothesis it came from.
beam_symbols.append(symbols)
beam_path.append(beam_parent)
log_beam_probs.append(best_probs)
# Note that gradients will not propagate through the second parameter of
# embedding_lookup.
emb_prev = embedding_ops.embedding_lookup(embedding, symbols)
emb_prev = tf.reshape(emb_prev,[beam_size,embedding_size])
# emb_prev = embedding_ops.embedding_lookup(embedding, symbols)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def beam_rnn_decoder(decoder_inputs, initial_state, cell, loop_function=None,
scope=None,output_projection=None, beam_size=10):
"""RNN decoder for the sequence-to-sequence model.
Args:
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
initial_state: 2D Tensor with shape [batch_size x cell.state_size].
cell: rnn_cell.RNNCell defining the cell function and size.
loop_function: If not None, this function will be applied to the i-th output
in order to generate the i+1-st input, and decoder_inputs will be ignored,
except for the first element ("GO" symbol). This can be used for decoding,
but also for training to emulate http://arxiv.org/abs/1506.03099.
Signature -- loop_function(prev, i) = next
* prev is a 2D Tensor of shape [batch_size x output_size],
* i is an integer, the step number (when advanced control is needed),
* next is a 2D Tensor of shape [batch_size x input_size].
scope: VariableScope for the created subgraph; defaults to "rnn_decoder".
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x output_size] containing generated outputs.
state: The state of each cell at the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
(Note that in some cases, like basic RNN cell or GRU cell, outputs and
states can be the same. They are different for LSTM cells though.)
"""
with variable_scope.variable_scope(scope or "rnn_decoder"):
state = initial_state
outputs = []
prev = None
log_beam_probs, beam_path, beam_symbols = [],[],[]
state_size = int(initial_state.get_shape().with_rank(2)[1])
for i, inp in enumerate(decoder_inputs):
if loop_function is not None and prev is not None:
with variable_scope.variable_scope("loop_function", reuse=True):
inp = loop_function(prev, i,log_beam_probs, beam_path, beam_symbols)
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
input_size = inp.get_shape().with_rank(2)[1]
print input_size
x = inp
output, state = cell(x, state)
if loop_function is not None:
prev = output
if i ==0:
states =[]
for kk in range(beam_size):
states.append(state)
state = tf.reshape(tf.concat(0, states), [-1, state_size])
outputs.append(tf.argmax(nn_ops.xw_plus_b(
output, output_projection[0], output_projection[1]), dimension=1))
return outputs, state, tf.reshape(tf.concat(0, beam_path),[-1,beam_size]), tf.reshape(tf.concat(0, beam_symbols),[-1,beam_size])
def loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0], output_projection[1])
prev_symbol = math_ops.argmax(prev, 1)
# Note that gradients will not propagate through the second parameter of
# embedding_lookup.
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
prev_symbol = math_ops.argmax(prev, 1)
# Note that gradients will not propagate through the second parameter of
# embedding_lookup.
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def loop_function(prev,_):
if output_projection is not None:
prev = nn_ops.xw_plus_b ( prev, output_projection[0], output_projection[1] )
tf_prev_symbol = batch_sample_with_temperature(prev)
emb_prev = embedding_ops.embedding_lookup(embedding, tf_prev_symbol)
if not update_embedding :
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
tf_prev_symbol = batch_sample_with_temperature(prev)
emb_prev = embedding_ops.embedding_lookup(embedding, tf_prev_symbol)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def to_check(logits, outputs, out_proj):
softmax_outputs = []
argmax_outputs = []
for j, outs in enumerate(logits): #length_id
softmax_outputs.append([])
argmax_outputs.append([])
for i in range(len(outs)): #batch_id
projected_out = nn_ops.xw_plus_b(outs[i], \
out_proj[0], out_proj[1])
softmax_outputs[j].append(tf.nn.softmax(projected_out))
argmax_outputs[j].append(math_ops.argmax(projected_out, axis=1))
return softmax_outputs, argmax_outputs
def project_and_apply_input_bias(logits, output_projection, input_bias):
if output_projection is not None:
logits = nn_ops.xw_plus_b(
logits, output_projection[0], output_projection[1])
# Apply softmax to ensure all tokens have a positive value.
probs = tf.nn.softmax(logits)
# Apply input bias, which is a mask of shape [batch, vocab len]
# where each token from the input in addition to all "corrective"
# tokens are set to 1.0.
return tf.mul(probs, input_bias)
def loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0], output_projection[1])
if isinstance(mc_search, bool):
prev_symbol = tf.reshape(tf.multinomial(prev, 1), [-1]) if mc_search else math_ops.argmax(prev, 1)
else:
prev_symbol = tf.cond(mc_search, lambda: tf.reshape(tf.multinomial(prev, 1), [-1]), lambda: tf.argmax(prev, 1))
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def fc(name, x, num_units_out):
num_units_in = x.shape[1]
weights_initializer = init_ops.truncated_normal_initializer(stddev=0.01)
with vs.variable_scope(name):
weights = _get_variable('weights',
shape=[num_units_in, num_units_out],
init=weights_initializer)
biases = _get_variable('biases',
shape=[num_units_out],
init=init_ops.constant_initializer(0.0))
x = nn_ops.xw_plus_b(x, weights, biases)
return x
def loop_function(prev, i, beam_symbols, beam_path, beam_log_probs):
"""Get a loop_function that extract the beam_sized previous symbols
and embeds it.
Args:
prev: previous decoder output of shape [batch_size * beam_size, num_symbols]
if i > 1 else [batch_size, num_symbols].
i: decoding step.
beam_symbols: a (i-1)-length list of tensors in shape [batch_size, beam_size],
which are symbols in the beam at each step.
beam_path: a (i-1)-length list of tensors in shape [batch_size, beam_size],
which are indices for previous symbols in the beam at each step.
beam_log_probs: a (i-1)-length list of tensors in shape [batch_size * beam_size, 1],
which are log probabilities in the beam at each step.
"""
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
log_probs = tf.log(tf.nn.softmax(prev))
if i > 1:
# broadcasting occurs in the add operation where beam_log_probs[-1]
# is in shape [batch_size * beam_size, 1].
log_probs = tf.reshape(log_probs + beam_log_probs[-1],
[-1, beam_size * num_symbols])
# Both returns are in shape [batch_size, beam_size].
best_log_probs, best_indices = tf.nn.top_k(log_probs, beam_size)
# Reshape best_indices to shape [batch_size * beam_size].
best_indices = tf.stop_gradient(
tf.squeeze(tf.reshape(best_indices, [-1, 1])))
# Reshape best_log_probs to shape [batch_size * beam_size, 1].
best_log_probs = tf.stop_gradient(tf.reshape(best_log_probs, [-1, 1]))
symbols = best_indices % num_symbols
parent_indices = best_indices // num_symbols
beam_symbols.append(tf.reshape(symbols, [-1, beam_size]))
beam_path.append(tf.reshape(parent_indices, [-1, beam_size]))
beam_log_probs.append(best_log_probs)
# emb_prev has shape [batch_size * beam_size, embedding_size].
emb_prev = embedding_ops.embedding_lookup(embedding, symbols)
if not update_embedding_for_previous:
emb_prev = tf.stop_gradient(emb_prev)
return tf.reshape(emb_prev, [-1, embedding_size])
def _fc(_input,
out_dim,
name="fc",
relu_flag=True,
stddev=0.01,
dtype=dtypes.float32):
"""
A wrapped full connection layer used in normal-fc or conv-fc(2D or 3D)
:param _input: tensor, shape's ndim must be 2(normal) or 4(conv2d) or 5(conv3d)
:param out_dim: scalar, outout dimension
:param relu_flag: bool, whether using Relu after fc operation
:param stddev: scalar, standard deviation used for params' initialization
:param dtype: tf.dtypes, data type
:return:
tensor, shape = [_input.shape[0], out_dim]
"""
with variable_scope.variable_scope(name) as scope:
input_shape = _input.get_shape()
# print 'shape-----------', input_shape
assert input_shape.ndims == 5 or input_shape.ndims == 4 or input_shape.ndims == 2
if input_shape.ndims == 2:
feed_in, dim = (_input, input_shape[-1].value)
else:
input_shape = _input.get_shape()
dim = 1
for dim_id in input_shape[1:].as_list():
dim *= dim_id
feed_in = array_ops.reshape(_input, [-1, dim])
weights = variable_scope.get_variable(
'weights',
shape=[dim, out_dim],
initializer=tf.truncated_normal_initializer(stddev=stddev,
dtype=dtype,
seed=20170705))
biases = variable_scope.get_variable(
'biases', [out_dim],
initializer=tf.constant_initializer(0., dtype=dtype))
act = nn_ops.xw_plus_b(feed_in,
weights=weights,
biases=biases,
name=scope.name)
if relu_flag:
return nn_ops.relu(act)
else:
return act
def fully_connected(inp,
inp_size,
layer_size,
name,
activation=nn_ops.relu,
dtype=dtypes.float32):
"""Helper method to create a fully connected hidden layer."""
wt = variable_scope.get_variable(
name="{}_weight".format(name), shape=[inp_size, layer_size], dtype=dtype)
bias = variable_scope.get_variable(
name="{}_bias".format(name),
shape=[layer_size],
initializer=init_ops.zeros_initializer())
output = nn_ops.xw_plus_b(inp, wt, bias)
if activation is not None:
assert callable(activation)
output = activation(output)
return output
def loop_function(prev,encoder_inputs):
if output_projection is not None:
prev = nn_ops.xw_plus_b(
prev, output_projection[0], output_projection[1])
# print("encoder_inputs",encoder_inputs)
# print("math_ops.argmax(prev, 1)", math_ops.argmax(prev, 1))
prev_symbol=[]
ind = math_ops.argmax(prev, 1)
# print(ind)
prev_symbol=[]
r=array_ops.transpose(encoder_inputs)
for i in xrange(batch):
ine=math_ops.to_int32(ind[i])
prev_symbol.append(r[i,ine])
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def loop_function(prev, _):
logit, attention_distribution = prev
vocab_size = array_ops.shape(logit)[1]
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
prev_symbol = extract_copy_augmented_argmax(logit,
attention_distribution[0])
prev_symbol_dereferenced = dereference_copy_pointers(
prev_symbol, encoder_inputs, vocab_size)
# Note that gradients will not propagate through the second parameter of
# embedding_lookup.
emb_prev = embedding_ops.embedding_lookup(embedding,
prev_symbol_dereferenced)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev
def loop_function_with_sample(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0], output_projection[1])
if is_sampling:
prev_symbol_sample = tf.squeeze(tf.multinomial(prev*opt.L,1)) #B 1 multinomial(log odds)
prev_symbol_sample = array_ops.stop_gradient(prev_symbol_sample) # important
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol_sample)
else:
if is_softargmax:
prev_symbol_one_hot = tf.nn.log_softmax(prev*opt.L) #B V
emb_prev = tf.matmul( tf.exp(prev_symbol_one_hot), embedding) # solve : Requires start <= limit when delta > 0
else:
prev_symbol = math_ops.argmax(prev, 1)
# Note that gradients will not propagate through the second parameter of
# embedding_lookup.
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
emb_prev = tf.concat([emb_prev,h], 1) if is_fed_h else emb_prev
if not update_embedding: #just update projection?
emb_prev = array_ops.stop_gradient(emb_prev)
return (emb_prev, prev_symbol_sample) if is_sampling else emb_prev
def loop_function(prev, prev_probs, beam_size, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
prev = math_ops.log(nn_ops.softmax(prev))
prev = nn_ops.bias_add(array_ops.transpose(prev),
prev_probs) # num_symbols*BEAM_SIZE
prev = array_ops.transpose(prev)
prev = array_ops.expand_dims(array_ops.reshape(prev, [-1]),
0) # 1*(BEAM_SIZE*num_symbols)
probs, prev_symbolb = nn_ops.top_k(prev, beam_size)
probs = array_ops.squeeze(probs, [0]) # BEAM_SIZE,
prev_symbolb = array_ops.squeeze(prev_symbolb, [0]) # BEAM_SIZE,
index = prev_symbolb // num_symbols
prev_symbol = prev_symbolb % num_symbols
# Note that gradients will not propagate through the second parameter of embedding_lookup.
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
if not update_embedding:
emb_prev = array_ops.stop_gradient(emb_prev)
return emb_prev, probs, index, prev_symbol
def lstm_decoder(H, y, opt, prefix='', feed_previous=False, is_reuse=None):
#y len* batch * [0,V] H batch * h
#y = [tf.squeeze(y[:,i]) for i in xrange(y.get_shape()[1])]
y = tf.unstack(y, axis=1)
H0 = tf.squeeze(H)
H1 = (H0, tf.zeros_like(H0)) # initialize H and C
with tf.variable_scope(prefix + 'lstm_decoder', reuse=True):
cell = tf.contrib.rnn.LSTMCell(opt.n_hid)
with tf.variable_scope(prefix + 'lstm_decoder', reuse=is_reuse):
weightInit = tf.random_uniform_initializer(-0.001, 0.001)
W = tf.get_variable('W', [opt.n_hid, opt.n_words],
initializer=weightInit)
b = tf.get_variable('b', [opt.n_words],
initializer=tf.random_uniform_initializer(
-0.001, 0.001))
out_proj = (W, b) if feed_previous else None
outputs, _ = embedding_rnn_decoder(decoder_inputs=y,
initial_state=H1,
cell=cell,
feed_previous=feed_previous,
output_projection=out_proj,
num_symbols=opt.n_words,
embedding_size=opt.embed_size)
logits = [nn_ops.xw_plus_b(out, W, b) for out in outputs]
syn_sents = [math_ops.argmax(l, 1) for l in logits]
syn_sents = tf.stack(syn_sents, 1)
#outputs, _ = embedding_rnn_decoder(decoder_inputs = y, initial_state = H, cell = tf.contrib.rnn.BasicLSTMCell, num_symbols = opt.n_words, embedding_size = opt.embed_size, scope = prefix + 'lstm_decoder')
# outputs : batch * len
loss = sequence_loss(
logits[:-1], y[1:],
[tf.cast(tf.ones_like(yy), tf.float32) for yy in y[1:]])
return loss, syn_sents, logits
def simple_loop_function(prev, _):
'''Function that takes last output, and applies output projection to it'''
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
return prev
def __init__(self, feed_future_data, train, num_observation_steps,
num_prediction_steps, batch_size, rnn_size, num_layers,
learning_rate, learning_rate_decay_factor, input_size,
max_gradient_norm):
# feed_future_data: whether or not to feed the true data into the decoder instead of using a loopback
# function. If false, a loopback function is used, feeding the last generated output as the next
# decoder input.
# train: train the model (or test)
self.max_gradient_norm = max_gradient_norm
self.rnn_size = rnn_size
self.num_layers = num_layers
dtype = tf.float32
self.batch_size = batch_size
self.input_size = input_size
self.observation_steps = num_observation_steps
self.prediction_steps = num_prediction_steps
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)
if feed_future_data and not train:
print "Warning, feeding the model future sequence data (feed_forward) is not recommended when the model is not training."
# The output of the multiRNN is the size of rnn_size, and it needs to match the input size, or loopback makes
# no sense. Here a single layer without activation function is used, but it can be any number of
# non RNN layers / functions
w = tf.get_variable("proj_w", [self.rnn_size, self.input_size])
b = tf.get_variable("proj_b", [self.input_size])
output_projection = (w, b)
# define layers here
# input, linear RNN RNN linear etc
# Default should be True, but TF 0.9 was throwing a warning, implying it was false
single_cell = tf.nn.rnn_cell.BasicLSTMCell(self.rnn_size,
state_is_tuple=True)
cell = single_cell
if self.num_layers > 1:
# state_is_tuple defaults to False in TF0.9, and thus produces a warning....
cell = tf.nn.rnn_cell.MultiRNNCell([single_cell] * self.num_layers,
state_is_tuple=True)
def simple_loop_function(prev, _):
'''Function that takes last output, and applies output projection to it'''
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
return prev
# The seq2seq function: we use embedding for the input and attention.
def seq2seq_f(encoder_inputs, decoder_inputs, feed_forward):
if not feed_forward: #feed last output as next input
loopback_function = simple_loop_function
else:
loopback_function = None #feed correct input
return basic_rnn_seq2seq_with_loop_function(
encoder_inputs,
decoder_inputs,
cell,
loop_function=loopback_function,
dtype=dtype)
# Feeds for inputs.
self.observation_inputs = []
self.future_inputs = []
self.target_weights = []
self.target_inputs = []
for i in xrange(
self.observation_steps): # Last bucket is the biggest one.
self.observation_inputs.append(
tf.placeholder(tf.float32,
shape=[batch_size, self.input_size],
name="encoder{0}".format(i)))
for i in xrange(self.prediction_steps + 1):
self.future_inputs.append(
tf.placeholder(tf.float32,
shape=[batch_size, self.input_size],
name="decoder{0}".format(i)))
for i in xrange(self.prediction_steps):
self.target_weights.append(
tf.placeholder(dtype,
shape=[batch_size],
name="weight{0}".format(i)))
# Because the predictions are the future sequence inputs shifted by one and do not contain the GO symbol, some
# array manipulation must occur
#Pass observations directly to RNN encoder, no shifting neccessary
self.encoder_inputs = self.observation_inputs
targets = [
self.future_inputs[i + 1] #Skip first symbol (GO)
for i in xrange(len(self.future_inputs) - 1)
]
#remove last decoder input, but it is kept as the last target output
self.decoder_inputs = [
self.future_inputs[i] for i in xrange(len(self.future_inputs) - 1)
]
if train: #Training
self.outputs, self.internal_states = seq2seq_f(
self.encoder_inputs, self.decoder_inputs, feed_future_data)
else: #Testing
self.outputs, self.internal_states = seq2seq_f(
self.encoder_inputs, self.decoder_inputs, feed_future_data)
# self.outputs is a list of len(decoder_steps+1) containing [size batch x rnn_size]
# The output projection below reduces this to:
# a list of len(decoder_steps+1) containing [size batch x input_size]
if output_projection is not None:
self.outputs = [
nn_ops.xw_plus_b(output, output_projection[0],
output_projection[1])
for output in self.outputs
]
def rmse(x, y):
return tf.sqrt(tf.reduce_mean(tf.square(tf.sub(y, x))))
# TODO There are several types of cost functions to compare tracks. Implement many
# Mainly, average MSE over the whole track, or just at a horizon time (t+10 or something)
# There's this corner alg that Social LSTM refernces, but I haven't looked into it.
self.losses = tf.nn.seq2seq.sequence_loss(
self.outputs,
targets,
self.target_weights,
softmax_loss_function=lambda x, y: rmse(x, y))
# Gradients and SGD update operation for training the model.
params = tf.trainable_variables()
if train:
self.gradient_norms = []
self.updates = []
opt = tf.train.GradientDescentOptimizer(self.learning_rate)
gradients = tf.gradients(self.losses, params)
clipped_gradients, norm = tf.clip_by_global_norm(
gradients, self.max_gradient_norm)
self.gradient_norms.append(norm)
self.updates.append(
opt.apply_gradients(zip(clipped_gradients, params),
global_step=self.global_step))
self.saver = tf.train.Saver(tf.all_variables())
tf.scalar_summary('Loss', self.losses)
def beam_attention_decoder(decoder_inputs,
initial_state,
attention_states,
cell,
embedding,
output_size=None,
num_heads=1,
loop_function=None,
dtype=None,
scope=None,
initial_state_attention=False,
output_projection=None,
beam_size=10):
if not decoder_inputs:
raise ValueError("Must provide at least 1 input to attention decoder.")
if num_heads < 1:
raise ValueError(
"With less than 1 heads, use a non-attention decoder.")
if not attention_states.get_shape()[1:2].is_fully_defined():
raise ValueError(
"Shape[1] and [2] of attention_states must be known: %s" %
attention_states.get_shape())
if output_size is None:
output_size = cell.output_size
with variable_scope.variable_scope(scope or "attention_decoder",
dtype=dtype) as scope:
dtype = scope.dtype
# batch_size = array_ops.shape(decoder_inputs[0])[0] # Needed for reshaping.
attn_length = attention_states.get_shape()[1].value
if attn_length is None:
attn_length = array_ops.shape(attention_states)[1]
attn_size = attention_states.get_shape()[2].value
# To calculate W1 * h_t we use a 1-by-1 convolution, need to reshape before.
hidden = array_ops.reshape(attention_states,
[-1, attn_length, 1, attn_size])
hidden_features = []
v = []
attention_vec_size = attn_size # Size of query vectors for attention.
for a in xrange(num_heads):
k = variable_scope.get_variable(
"AttnW_%d" % a, [1, 1, attn_size, attention_vec_size])
hidden_features.append(
nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
v.append(
variable_scope.get_variable("AttnV_%d" % a,
[attention_vec_size]))
state = []
# 将encoder的最后一个隐层状态扩展成beam_size维，因为decoder阶段的batch_size是beam_size。
# initial_state是一个列表，RNN有多少层就有多少个元素，每个元素都是一个LSTMStateTuple，包含h,c两个隐层状态
# 所以要将其扩展成beam_size维，其实是把c和h进行扩展，最后再合成LSTMStateTuple就可以了
for layers in initial_state:
c = [layers.c] * beam_size
h = [layers.h] * beam_size
c = tf.concat(c, 0)
h = tf.concat(h, 0)
state.append(rnn_cell_impl.LSTMStateTuple(c, h))
state = tuple(state)
# state_size = int(initial_state.get_shape().with_rank(2)[1])
# states = []
# for kk in range(beam_size):
# states.append(initial_state)
# state = tf.concat(states, 0)
# state = initial_state
def attention(query):
ds = [] # Results of attention reads will be stored here.
if nest.is_sequence(query): # If the query is a tuple, flatten it.
query_list = nest.flatten(query)
for q in query_list: # Check that ndims == 2 if specified.
ndims = q.get_shape().ndims
if ndims:
assert ndims == 2
query = array_ops.concat(query_list, 1)
for a in xrange(num_heads):
with variable_scope.variable_scope("Attention_%d" % a):
y = Linear(query, attention_vec_size, True)(query)
y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
# Attention mask is a softmax of v^T * tanh(...).
s = math_ops.reduce_sum(
v[a] * math_ops.tanh(hidden_features[a] + y), [2, 3])
a = nn_ops.softmax(s)
# Now calculate the attention-weighted vector d.
d = math_ops.reduce_sum(
array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden,
[1, 2])
ds.append(array_ops.reshape(d, [-1, attn_size]))
return ds
outputs = []
prev = None
# attention也要定义成beam_size为的tensor
batch_attn_size = array_ops.stack([beam_size, attn_size])
attns = [
array_ops.zeros(batch_attn_size, dtype=dtype)
for _ in xrange(num_heads)
]
for a in attns: # Ensure the second shape of attention vectors is set.
a.set_shape([None, attn_size])
if initial_state_attention:
attns = attention(initial_state)
log_beam_probs, beam_path, beam_symbols = [], [], []
for i, inp in enumerate(decoder_inputs):
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
# If loop_function is set, we use it instead of decoder_inputs.
if i == 0:
#i=0时，输入时一个batch_szie=beam_size的tensor，且里面每个元素的值都是相同的，都是<GO>标志
inp = tf.nn.embedding_lookup(
embedding, tf.constant(1,
dtype=tf.int32,
shape=[beam_size]))
if loop_function is not None and prev is not None:
with variable_scope.variable_scope("loop_function",
reuse=True):
inp = loop_function(prev, i, log_beam_probs, beam_path,
beam_symbols)
# Merge input and previous attentions into one vector of the right size.
input_size = inp.get_shape().with_rank(2)[1]
if input_size.value is None:
raise ValueError("Could not infer input size from input: %s" %
inp.name)
inputs = [inp] + attns
x = Linear(inputs, input_size, True)(inputs)
# Run the RNN.
cell_output, state = cell(x, state)
# Run the attention mechanism.
if i == 0 and initial_state_attention:
with variable_scope.variable_scope(
variable_scope.get_variable_scope(), reuse=True):
attns = attention(state)
else:
attns = attention(state)
with variable_scope.variable_scope("AttnOutputProjection"):
inputs = [cell_output] + attns
output = Linear(inputs, output_size, True)(inputs)
if loop_function is not None:
prev = output
outputs.append(
tf.argmax(nn_ops.xw_plus_b(output, output_projection[0],
output_projection[1]),
axis=1))
return outputs, state, tf.reshape(tf.concat(beam_path, 0),
[-1, beam_size]), tf.reshape(
tf.concat(beam_symbols, 0),
[-1, beam_size])
def embedding_attention_s2s(encoder_inputs,
decoder_inputs,
cell,
num_encoder_symbols,
num_decoder_symbols,
size,
output_projection=None,
feed_previous=False,
use_lstm=False,
local_p=False,
dtype=tf.float32):
"""
Embedding with Manning, et. al. global attention model.
"""
reuse_f = lambda k: True if k > 0 else None
S = len(encoder_inputs)
with vs.variable_scope('embedding_attention_s2s') as outer_scope:
with vs.variable_scope('encoder') as enc_scope:
embedder_en, emb_en_input = _get_embedder(
'embedding_en', [num_encoder_symbols, size], encoder_inputs)
encoder_states, prev_state = [], None
for i, enc_in in enumerate(emb_en_input):
with vs.variable_scope(enc_scope, reuse=reuse_f(i)):
_, state = tf.nn.rnn(cell, [enc_in],
initial_state=prev_state,
dtype=dtype)
prev_state = state
encoder_states.append(state)
h_s_bar = tf.transpose(tf.pack(
[_get_hs(es, use_lstm) for es in encoder_states]),
perm=[1, 0, 2])
with vs.variable_scope('decoder') as dec_scope:
embedding_de, emb_de_input = _get_embedder(
'embedding_de', [num_decoder_symbols, size], decoder_inputs)
W_a = _get_weights('W_a', [size, size])
W_c = _get_weights('W_c', [2 * size, size])
if local_p:
W_p = _get_weights('W_p', [size, size])
v_p = _get_weights('v_p', [size, 1])
state = encoder_states[-1]
partial_scores = [
tf.reshape(tf.matmul(_get_hs(es, use_lstm), W_a),
[-1, 1, size]) for es in encoder_states
]
output, outputs = None, []
for i, dec_in in enumerate(emb_de_input):
with vs.variable_scope(dec_scope, reuse=reuse_f(i)):
# Loop function at test time
if feed_previous and output is not None:
w, b = output_projection
prev = nn_ops.xw_plus_b(output, w, b)
prev_symbol = math_ops.argmax(prev, 1)
dec_in = em_ops.embedding_lookup(
embedding_de, prev_symbol)
_, state = cell(dec_in, state)
h_t = _get_hs(state, use_lstm)
batch_h_t = tf.reshape(h_t, [-1, size, 1])
if local_p:
align = tf.matmul(v_p,
tf.tanh(
tf.matmul(W_p,
h_t,
transpose_b=True)),
transpose_a=True)
p_t = (S - 1) * tf.sigmoid(align)
scale = tf.exp(-4.5 * tf.square((p_t - tf.reshape(
tf.cast(tf.range(S), dtype), [-1, 1])) / S))
#scale = tf.concat(0, [tf.exp(-4.5 * tf.square((p_t - s)/S)) for s in xrange(S)])
scores = tf.nn.softmax(tf.pack([
tf.reshape(tf.batch_matmul(ps, batch_h_t), [-1])
for ps in partial_scores
]),
dim=0)
if local_p:
scores *= scale
scores /= tf.reduce_sum(scores, 0)
scores = tf.reshape(scores, [-1, 1, S])
c_t = tf.reshape(tf.batch_matmul(scores, h_s_bar),
[-1, size])
h_bar_t = tf.concat(1, [c_t, _get_hs(state, use_lstm)])
output = tf.tanh(tf.matmul(h_bar_t, W_c))
outputs.append(output)
return outputs, state
def beam_attention_decoder(decoder_inputs, initial_state, attention_states, cell,
output_size=None, num_heads=1, loop_function=None,
dtype=dtypes.float32, scope=None,
initial_state_attention=False, output_projection=None, beam_size=10):
"""RNN decoder with attention for the sequence-to-sequence model.
In this context "attention" means that, during decoding, the RNN can look up
information in the additional tensor attention_states, and it does this by
focusing on a few entries from the tensor. This model has proven to yield
especially good results in a number of sequence-to-sequence tasks. This
implementation is based on http://arxiv.org/abs/1412.7449 (see below for
details). It is recommended for complex sequence-to-sequence tasks.
Args:
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
initial_state: 2D Tensor [batch_size x cell.state_size].
attention_states: 3D Tensor [batch_size x attn_length x attn_size].
cell: rnn_cell.RNNCell defining the cell function and size.
output_size: Size of the output vectors; if None, we use cell.output_size.
num_heads: Number of attention heads that read from attention_states.
loop_function: If not None, this function will be applied to i-th output
in order to generate i+1-th input, and decoder_inputs will be ignored,
except for the first element ("GO" symbol). This can be used for decoding,
but also for training to emulate http://arxiv.org/abs/1506.03099.
Signature -- loop_function(prev, i) = next
* prev is a 2D Tensor of shape [batch_size x output_size],
* i is an integer, the step number (when advanced control is needed),
* next is a 2D Tensor of shape [batch_size x input_size].
dtype: The dtype to use for the RNN initial state (default: tf.float32).
scope: VariableScope for the created subgraph; default: "attention_decoder".
initial_state_attention: If False (default), initial attentions are zero.
If True, initialize the attentions from the initial state and attention
states -- useful when we wish to resume decoding from a previously
stored decoder state and attention states.
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors of
shape [batch_size x output_size]. These represent the generated outputs.
Output i is computed from input i (which is either the i-th element
of decoder_inputs or loop_function(output {i-1}, i)) as follows.
First, we run the cell on a combination of the input and previous
attention masks:
cell_output, new_state = cell(linear(input, prev_attn), prev_state).
Then, we calculate new attention masks:
new_attn = softmax(V^T * tanh(W * attention_states + U * new_state))
and then we calculate the output:
output = linear(cell_output, new_attn).
state: The state of each decoder cell the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
Raises:
ValueError: when num_heads is not positive, there are no inputs, shapes
of attention_states are not set, or input size cannot be inferred
from the input.
"""
if not decoder_inputs:
raise ValueError("Must provide at least 1 input to attention decoder.")
if num_heads < 1:
raise ValueError("With less than 1 heads, use a non-attention decoder.")
if not attention_states.get_shape()[1:2].is_fully_defined():
raise ValueError("Shape[1] and [2] of attention_states must be known: %s"
% attention_states.get_shape())
if output_size is None:
output_size = cell.output_size
with variable_scope.variable_scope(scope or "attention_decoder"):
batch_size = array_ops.shape(decoder_inputs[0])[0] # Needed for reshaping.
attn_length = attention_states.get_shape()[1].value
attn_size = attention_states.get_shape()[2].value
# To calculate W1 * h_t we use a 1-by-1 convolution, need to reshape before.
hidden = array_ops.reshape(
attention_states, [-1, attn_length, 1, attn_size])
hidden_features = []
v = []
attention_vec_size = attn_size # Size of query vectors for attention.
for a in xrange(num_heads):
k = variable_scope.get_variable("AttnW_%d" % a,
[1, 1, attn_size, attention_vec_size])
hidden_features.append(nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
v.append(variable_scope.get_variable("AttnV_%d" % a,
[attention_vec_size]))
print("Initial_state")
state = initial_state
def attention(query):
"""Put attention masks on hidden using hidden_features and query."""
ds = [] # Results of attention reads will be stored here.
for a in xrange(num_heads):
with variable_scope.variable_scope("Attention_%d" % a):
y = linear(query, attention_vec_size, True)
y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
# Attention mask is a softmax of v^T * tanh(...).
s = math_ops.reduce_sum(
v[a] * math_ops.tanh(hidden_features[a] + y), [2, 3])
a = nn_ops.softmax(s)
# Now calculate the attention-weighted vector d.
d = math_ops.reduce_sum(
array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden,
[1, 2])
# for c in range(ct):
ds.append(array_ops.reshape(d, [-1, attn_size]))
return ds
outputs = []
prev = None
batch_attn_size = tf.stack([batch_size, attn_size])
attns = [array_ops.zeros(batch_attn_size, dtype=dtype)
for _ in xrange(num_heads)]
for a in attns: # Ensure the second shape of attention vectors is set.
a.set_shape([None, attn_size])
if initial_state_attention:
attns = []
attns.append(attention(initial_state))
tmp = tf.reshape(tf.concat(axis=0, values=attns), [-1, attn_size])
attns = []
attns.append(tmp)
log_beam_probs, beam_path, beam_symbols = [],[],[]
for i, inp in enumerate(decoder_inputs):
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
# If loop_function is set, we use it instead of decoder_inputs.
if loop_function is not None :
with variable_scope.variable_scope("loop_function", reuse=True):
if prev is not None:
inp = loop_function(prev, i,log_beam_probs, beam_path, beam_symbols)
input_size = inp.get_shape().with_rank(2)[1]
x = linear([inp] + attns, input_size, True)
cell_output, state = cell(x, state)
# Run the attention mechanism.
if i == 0 and initial_state_attention:
with variable_scope.variable_scope(variable_scope.get_variable_scope(),
reuse=True):
attns = attention(state)
else:
attns = attention(state)
with variable_scope.variable_scope("AttnOutputProjection"):
output = linear([cell_output] + attns, output_size, True)
if loop_function is not None:
prev = output
if i ==0:
with variable_scope.variable_scope(variable_scope.get_variable_scope(), reuse=True):
attns = attention(state)
outputs.append(tf.argmax(nn_ops.xw_plus_b(
output, output_projection[0], output_projection[1]), axis=1))
return outputs, state, tf.reshape(tf.concat(axis=0, values=beam_path),[-1,beam_size]), tf.reshape(tf.concat(axis=0, values=beam_symbols),[-1,beam_size])
def loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
prev_symbol = math_ops.argmax(prev, 1)
return _embed_decoder(prev_symbol, embedding, attention_states)
def lstm_decoder_embedding(H,
y,
W_emb,
opt,
prefix='',
add_go=False,
feed_previous=False,
is_reuse=None,
is_fed_h=True,
is_sampling=False,
is_softargmax=False,
beam_width=None):
biasInit = bias_init
if add_go:
y = tf.concat([tf.ones([opt.batch_size, 1], dtype=tf.int32), y], 1)
y = tf.unstack(y, axis=1) # 1, . , .
if hasattr(opt, 'global_feature') and opt.global_feature:
H = layers.fully_connected(H,
num_outputs=opt.n_hid,
biases_initializer=biasInit,
activation_fn=None,
scope=prefix + 'lstm_decoder',
reuse=is_reuse)
H0 = tf.squeeze(H)
H1 = (H0, tf.zeros_like(H0)) # tf.zeros_like(H0) # initialize C and H#
y_input = [tf.concat([tf.nn.embedding_lookup(W_emb, features),H0],1) for features in y] if is_fed_h \
else [tf.nn.embedding_lookup(W_emb, features) for features in y]
with tf.variable_scope(prefix + 'lstm_decoder', reuse=True):
cell = tf.contrib.rnn.LSTMCell(opt.n_hid)
with tf.variable_scope(prefix + 'lstm_decoder', reuse=is_reuse):
weightInit = weight_init
W = tf.get_variable('W', [opt.n_hid, opt.embed_size],
initializer=weightInit)
b = tf.get_variable('b', [opt.n_words], initializer=bias_init)
W_new = tf.matmul(W, W_emb, transpose_b=True) # h* V
out_proj = (W_new, b) if feed_previous else None
decoder_res = rnn_decoder_custom_embedding(emb_inp=y_input,
initial_state=H1,
cell=cell,
embedding=W_emb,
opt=opt,
feed_previous=feed_previous,
output_projection=out_proj,
num_symbols=opt.n_words,
is_fed_h=is_fed_h,
is_softargmax=is_softargmax,
is_sampling=is_sampling)
outputs = decoder_res[0]
logits = [nn_ops.xw_plus_b(out, W_new, b) for out in outputs
] # hidden units to prob logits: out B*h W: h*E Wemb V*E
if is_sampling:
syn_sents = decoder_res[2]
loss = sequence_loss(
logits[:-1], syn_sents,
[tf.cast(tf.ones_like(yy), tf.float32) for yy in syn_sents])
syn_sents = tf.stack(syn_sents, 1)
else:
syn_sents = [math_ops.argmax(l, 1) for l in logits[:-1]]
syn_sents = tf.stack(syn_sents, 1)
ones = tf.ones([opt.batch_size], dtype=tf.float32)
mask = [ones, ones] + [
tf.cast(tf.not_equal(yy, dp.PAD_ID), tf.float32) for yy in y[1:-2]
]
loss_all = sequence_loss_by_example(logits[:-1], y[1:], mask)
loss = tf.reduce_mean(loss_all)
return loss, syn_sents, logits, loss_all
def pad_output_function(output):
return nn_ops.xw_plus_b(output,
pad_output_projection[0],
pad_output_projection[1],
name="pad_output_projection")
def MDN_output_function(output):
return nn_ops.xw_plus_b(output,
MDN_output_projection[0],
MDN_output_projection[1],
name="MDN_output_projection")
def gru_decoder_embedding(H,
y,
W_emb,
opt,
prefix='',
add_go=False,
feed_previous=False,
is_reuse=None,
is_fed_h=True,
is_sampling=False,
is_softargmax=False,
beam_width=None,
res=None):
#y len* batch * [0,V] H batch * h
biasInit = tf.constant_initializer(0.001, dtype=tf.float32)
#y = [tf.squeeze(y[:,i]) for i in xrange(y.get_shape()[1])]
if add_go:
y = tf.concat([tf.ones([opt.batch_size, 1], dtype=tf.int32), y], 1)
y = tf.unstack(y, axis=1) # 1, . , .
# make the size of hidden unit to be n_hid
if not opt.additive_noise_lambda:
H = layers.fully_connected(H,
num_outputs=opt.n_hid,
biases_initializer=biasInit,
activation_fn=None,
scope=prefix + 'gru_decoder',
reuse=is_reuse)
H0 = tf.squeeze(H)
# H1 = (H0, tf.zeros_like(H0)) # initialize H and C #
H1 = H0
y_input = [tf.concat([tf.nn.embedding_lookup(W_emb, features),H0],1) for features in y] if is_fed_h \
else [tf.nn.embedding_lookup(W_emb, features) for features in y]
with tf.variable_scope(prefix + 'gru_decoder', reuse=True):
cell = tf.contrib.rnn.GRUCell(opt.n_hid)
# cell = tf.contrib.rnn.GRUCell(opt.maxlen)
with tf.variable_scope(prefix + 'gru_decoder', reuse=is_reuse):
weightInit = tf.random_uniform_initializer(-0.001, 0.001)
W = tf.get_variable('W', [opt.n_hid, opt.embed_size],
initializer=weightInit)
b = tf.get_variable('b', [opt.vocab_size],
initializer=tf.random_uniform_initializer(
-0.001, 0.001))
W_new = tf.matmul(W, W_emb, transpose_b=True) # h* V
out_proj = (W_new, b) if feed_previous else None
decoder_res = rnn_decoder_custom_embedding_gru(
emb_inp=y_input,
initial_state=H1,
cell=cell,
embedding=W_emb,
opt=opt,
feed_previous=feed_previous,
output_projection=out_proj,
num_symbols=opt.vocab_size,
is_fed_h=is_fed_h,
is_softargmax=is_softargmax,
is_sampling=is_sampling)
outputs = decoder_res[0]
if beam_width:
#cell = rnn_cell.LSTMCell(cell_depth)
#batch_size_tensor = constant_op.constant(opt.batch_size)
initial_state = cell.zero_state(
opt.batch_size * beam_width, tf.float32
) #beam_search_decoder.tile_batch(H0, multiplier=beam_width)
output_layer = layers_core.Dense(opt.vocab_size,
use_bias=True,
kernel_initializer=W_new,
bias_initializer=b,
activation=None)
bsd = beam_search_decoder.BeamSearchDecoder(
cell=cell,
embedding=W_emb,
start_tokens=array_ops.fill([opt.batch_size],
dp.GO_ID), # go is 1
end_token=dp.EOS_ID,
initial_state=initial_state,
beam_width=beam_width,
output_layer=output_layer,
length_penalty_weight=0.0)
#pdb.set_trace()
final_outputs, final_state, final_sequence_lengths = (
decoder.dynamic_decode(bsd,
output_time_major=False,
maximum_iterations=opt.maxlen))
beam_search_decoder_output = final_outputs.beam_search_decoder_output
#print beam_search_decoder_output.get_shape()
logits = [nn_ops.xw_plus_b(out, W_new, b) for out in outputs
] # hidden units to prob logits: out B*h W: h*E Wemb V*E
if is_sampling:
syn_sents = decoder_res[2]
loss = sequence_loss(
logits[:-1], syn_sents,
[tf.cast(tf.ones_like(yy), tf.float32) for yy in syn_sents])
#loss = sequence_loss(logits[:-1], syn_sents, [tf.cast(tf.not_equal(yy,dp.PAD_ID),tf.float32) for yy in syn_sents])
#loss = sequence_loss(logits[:-1], syn_sents, [tf.concat([tf.ones([1]), tf.cast(tf.not_equal(yy,dp.PAD_ID),tf.float32)],0) for yy in syn_sents[:-1]]) # use one more pad after EOS
syn_sents = tf.stack(syn_sents, 1)
else:
syn_sents = [math_ops.argmax(l, 1) for l in logits]
syn_sents = tf.stack(syn_sents, 1)
loss = sequence_loss(
logits[:-1], y[1:],
[tf.cast(tf.ones_like(yy), tf.float32) for yy in y[1:]])
#loss = sequence_loss(logits[:-1], y[1:], [tf.cast(tf.not_equal(yy,dp.PAD_ID),tf.float32) for yy in y[:-1]]) # use one more pad after EOS
#outputs, _ = embedding_rnn_decoder(decoder_inputs = y, initial_state = H, cell = tf.contrib.rnn.BasicLSTMCell, num_symbols = opt.vocab_size, embedding_size = opt.embed_size, scope = prefix + 'lstm_decoder')
# outputs : batch * len
# save the res
if res is not None:
res['outputs'] = [tf.multiply(out, W) for out in outputs]
return loss, syn_sents, logits
def beam_attention_decoder(decoder_inputs, initial_state, attention_states, cell,
output_size=None, num_heads=1, loop_function=None,
dtype=dtypes.float32, scope=None,
initial_state_attention=False, output_projection=None, beam_size=10):
"""RNN decoder with attention for the sequence-to-sequence model.
In this context "attention" means that, during decoding, the RNN can look up
information in the additional tensor attention_states, and it does this by
focusing on a few entries from the tensor. This model has proven to yield
especially good results in a number of sequence-to-sequence tasks. This
implementation is based on http://arxiv.org/abs/1412.7449 (see below for
details). It is recommended for complex sequence-to-sequence tasks.
Args:
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
initial_state: 2D Tensor [batch_size x cell.state_size].
attention_states: 3D Tensor [batch_size x attn_length x attn_size].
cell: rnn_cell.RNNCell defining the cell function and size.
output_size: Size of the output vectors; if None, we use cell.output_size.
num_heads: Number of attention heads that read from attention_states.
loop_function: If not None, this function will be applied to i-th output
in order to generate i+1-th input, and decoder_inputs will be ignored,
except for the first element ("GO" symbol). This can be used for decoding,
but also for training to emulate http://arxiv.org/abs/1506.03099.
Signature -- loop_function(prev, i) = next
* prev is a 2D Tensor of shape [batch_size x output_size],
* i is an integer, the step number (when advanced control is needed),
* next is a 2D Tensor of shape [batch_size x input_size].
dtype: The dtype to use for the RNN initial state (default: tf.float32).
scope: VariableScope for the created subgraph; default: "attention_decoder".
initial_state_attention: If False (default), initial attentions are zero.
If True, initialize the attentions from the initial state and attention
states -- useful when we wish to resume decoding from a previously
stored decoder state and attention states.
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors of
shape [batch_size x output_size]. These represent the generated outputs.
Output i is computed from input i (which is either the i-th element
of decoder_inputs or loop_function(output {i-1}, i)) as follows.
First, we run the cell on a combination of the input and previous
attention masks:
cell_output, new_state = cell(linear(input, prev_attn), prev_state).
Then, we calculate new attention masks:
new_attn = softmax(V^T * tanh(W * attention_states + U * new_state))
and then we calculate the output:
output = linear(cell_output, new_attn).
state: The state of each decoder cell the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
Raises:
ValueError: when num_heads is not positive, there are no inputs, shapes
of attention_states are not set, or input size cannot be inferred
from the input.
"""
if not decoder_inputs:
raise ValueError("Must provide at least 1 input to attention decoder.")
if num_heads < 1:
raise ValueError("With less than 1 heads, use a non-attention decoder.")
if not attention_states.get_shape()[1:2].is_fully_defined():
raise ValueError("Shape[1] and [2] of attention_states must be known: %s"
% attention_states.get_shape())
if output_size is None:
output_size = cell.output_size
with variable_scope.variable_scope(scope or "attention_decoder"):
batch_size = array_ops.shape(decoder_inputs[0])[0] # Needed for reshaping.
attn_length = attention_states.get_shape()[1].value
attn_size = attention_states.get_shape()[2].value
# To calculate W1 * h_t we use a 1-by-1 convolution, need to reshape before.
hidden = array_ops.reshape(
attention_states, [-1, attn_length, 1, attn_size])
hidden_features = []
v = []
attention_vec_size = attn_size # Size of query vectors for attention.
for a in xrange(num_heads):
k = variable_scope.get_variable("AttnW_%d" % a,
[1, 1, attn_size, attention_vec_size])
hidden_features.append(nn_ops.conv2d(hidden, k, [1, 1, 1, 1], "SAME"))
v.append(variable_scope.get_variable("AttnV_%d" % a,
[attention_vec_size]))
print "Initial_state"
state_size = int(initial_state.get_shape().with_rank(2)[1])
states =[]
for kk in range(1):
states.append(initial_state)
state = tf.reshape(tf.concat(0, states), [-1, state_size])
def attention(query):
"""Put attention masks on hidden using hidden_features and query."""
ds = [] # Results of attention reads will be stored here.
for a in xrange(num_heads):
with variable_scope.variable_scope("Attention_%d" % a):
y = linear(query, attention_vec_size, True)
y = array_ops.reshape(y, [-1, 1, 1, attention_vec_size])
# Attention mask is a softmax of v^T * tanh(...).
s = math_ops.reduce_sum(
v[a] * math_ops.tanh(hidden_features[a] + y), [2, 3])
a = nn_ops.softmax(s)
# Now calculate the attention-weighted vector d.
d = math_ops.reduce_sum(
array_ops.reshape(a, [-1, attn_length, 1, 1]) * hidden,
[1, 2])
# for c in range(ct):
ds.append(array_ops.reshape(d, [-1, attn_size]))
return ds
outputs = []
prev = None
batch_attn_size = array_ops.pack([batch_size, attn_size])
attns = [array_ops.zeros(batch_attn_size, dtype=dtype)
for _ in xrange(num_heads)]
for a in attns: # Ensure the second shape of attention vectors is set.
a.set_shape([None, attn_size])
if initial_state_attention:
attns = []
attns.append(attention(initial_state))
tmp = tf.reshape(tf.concat(0, attns), [-1, attn_size])
attns = []
attns.append(tmp)
log_beam_probs, beam_path, beam_symbols = [],[],[]
for i, inp in enumerate(decoder_inputs):
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
# If loop_function is set, we use it instead of decoder_inputs.
if loop_function is not None :
with variable_scope.variable_scope("loop_function", reuse=True):
if prev is not None:
inp = loop_function(prev, i,log_beam_probs, beam_path, beam_symbols)
input_size = inp.get_shape().with_rank(2)[1]
x = linear([inp] + attns, input_size, True)
cell_output, state = cell(x, state)
# Run the attention mechanism.
if i == 0 and initial_state_attention:
with variable_scope.variable_scope(variable_scope.get_variable_scope(),
reuse=True):
attns = attention(state)
else:
attns = attention(state)
with variable_scope.variable_scope("AttnOutputProjection"):
output = linear([cell_output] + attns, output_size, True)
if loop_function is not None:
prev = output
if i ==0:
states =[]
for kk in range(beam_size):
states.append(state)
state = tf.reshape(tf.concat(0, states), [-1, state_size])
with variable_scope.variable_scope(variable_scope.get_variable_scope(), reuse=True):
attns = attention(state)
outputs.append(tf.argmax(nn_ops.xw_plus_b(
output, output_projection[0], output_projection[1]), dimension=1))
return outputs, state, tf.reshape(tf.concat(0, beam_path),[-1,beam_size]), tf.reshape(tf.concat(0, beam_symbols),[-1,beam_size])
def argmax_loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
prev_symbol = math_ops.argmax(prev, 1)
return prev_symbol
def __call__(self, inputs, state, scope=None):
"""Run the input projection and then the cell."""
dtype = inputs.dtype
memory = array_ops.identity(self.memory)
# array_ops.ref_identity()
# deep_copy(self.memory)
with vs.variable_scope("memory_projection"):
c_t, h_t = state
v = math_ops.tanh(nn_ops.xw_plus_b(h_t, self.w, self.b))
if v.get_shape()[0] != self.batch_size:
raise Exception("Beam Search Not supported now!")
else:
similarity = math_ops.matmul(
array_ops.expand_dims(v,
1), # batch_size, 1 , embedding_size
array_ops.transpose(memory, [0, 2, 1]))
weight = nn_ops.softmax(
array_ops.squeeze(similarity) # batch_size, topic_num
)
weight_tile = gen_array_ops.tile(array_ops.expand_dims(
weight, -1), [1, 1, self.embedding_size],
name="weight")
mt = math_ops.reduce_sum(memory * weight_tile, axis=1)
# update memory
if self.update_mem:
gate = math_ops.matmul(memory,
array_ops.expand_dims(
inputs,
axis=2)) # [batch_size, num, 1]
gate = math_ops.sigmoid(
gen_array_ops.squeeze(gate)) # batch_size x num
inputs_expand = gen_array_ops.tile(
array_ops.expand_dims(inputs, axis=1),
[1, self.mem_num, 1]) # batch_size x num x embedding
uu_tile = gen_array_ops.tile(
array_ops.expand_dims(self.uu, axis=0),
[self.batch_size, 1, 1
]) # batch_size x embedding x embedding
vv_tile = gen_array_ops.tile(
array_ops.expand_dims(self.uv, axis=0),
[self.batch_size, 1, 1
]) # batch_size x embedding x embedding
candidate = math_ops.add(
math_ops.matmul(inputs_expand, uu_tile),
math_ops.matmul(memory,
vv_tile)) # batch_size x num x embedding
# print(gate)
gate_tile = gen_array_ops.tile(array_ops.expand_dims(gate, 2),
[1, 1, self.embedding_size])
updated_mem = (1 - gate_tile) * memory + gate_tile * candidate
self.memory = updated_mem
with vs.variable_scope("attention_mechanism"):
encoder_processed = self.memory_layer(
self.encoder_outputs) # map to attention size
# [batch_size, hidden_size] -> [batch_size, 1, attention_size]
query_processed = array_ops.expand_dims(self.query_layer(c_t), 1)
scores = math_ops.reduce_sum(
self.attention_v *
math_ops.tanh(encoder_processed + query_processed), [2])
alpha = nn_ops.softmax(scores, axis=1)
output_hidden_size = self.encoder_outputs.shape[2].value
alpha_tile = gen_array_ops.tile(array_ops.expand_dims(alpha, -1),
[1, 1, output_hidden_size],
name="weight")
# print(weight_tile) # batch_size x num x embedding_size
weighted_sum = math_ops.reduce_sum(self.encoder_outputs *
alpha_tile,
axis=1)
return self._cell(tf.concat([inputs, weighted_sum, mt], axis=1), state)
def beam_rnn_decoder(decoder_inputs,
initial_state,
cell,
loop_function=None,
scope=None,
output_projection=None,
beam_size=1):
"""RNN decoder for the sequence-to-sequence model.
Args:
decoder_inputs: A list of 2D Tensors [batch_size x input_size].
initial_state: 2D Tensor with shape [batch_size x cell.state_size].
cell: rnn_cell.RNNCell defining the cell function and size.
loop_function: If not None, this function will be applied to the i-th output
in order to generate the i+1-st input, and decoder_inputs will be ignored,
except for the first element ("GO" symbol). This can be used for decoding,
but also for training to emulate http://arxiv.org/abs/1506.03099.
Signature -- loop_function(prev, i) = next
* prev is a 2D Tensor of shape [batch_size x output_size],
* i is an integer, the step number (when advanced control is needed),
* next is a 2D Tensor of shape [batch_size x input_size].
scope: VariableScope for the created subgraph; defaults to "rnn_decoder".
Returns:
A tuple of the form (outputs, state), where:
outputs: A list of the same length as decoder_inputs of 2D Tensors with
shape [batch_size x output_size] containing generated outputs.
state: The state of each cell at the final time-step.
It is a 2D Tensor of shape [batch_size x cell.state_size].
(Note that in some cases, like basic RNN cell or GRU cell, outputs and
states can be the same. They are different for LSTM cells though.)
"""
with variable_scope.variable_scope(scope or "rnn_decoder"):
state = initial_state
outputs = []
prev = None
log_beam_probs, beam_path, beam_symbols = [], [], []
state_size = int(initial_state.get_shape().with_rank(2)[1])
for i, inp in enumerate(decoder_inputs):
if loop_function is not None and prev is not None:
with variable_scope.variable_scope("loop_function",
reuse=True):
inp = loop_function(prev, i, log_beam_probs, beam_path,
beam_symbols)
if i > 0:
variable_scope.get_variable_scope().reuse_variables()
input_size = inp.get_shape().with_rank(2)[1]
x = inp
output, state = cell(x, state)
if loop_function is not None:
prev = output
if i == 0:
states = []
for kk in range(beam_size):
states.append(state)
state = tf.reshape(tf.concat(0, states), [-1, state_size])
outputs.append(
tf.argmax(nn_ops.xw_plus_b(output, output_projection[0],
output_projection[1]),
dimension=1))
return outputs, state, tf.reshape(tf.concat(0, beam_path),
[-1, beam_size]), tf.reshape(
tf.concat(0, beam_symbols),
[-1, beam_size])
def loop_function(prev, _):
if output_projection is not None:
prev = nn_ops.xw_plus_b(prev, output_projection[0],
output_projection[1])
return prev
def loop_function(prev, out_proj, embedding):
prev = nn_ops.xw_plus_b(prev, out_proj[0], out_proj[1])
prev_symbol = math_ops.argmax(prev, axis=1)
emb_prev = embedding_ops.embedding_lookup(embedding, prev_symbol)
return [emb_prev, prev_symbol]
def _BuildAndTestMiniMNIST(self, param_index, tag):
# Fix seed to avoid occasional flakiness
np.random.seed(6)
# Hyperparameters
batch = 3
inputs = 16
features = 32
classes = 10
# Define the parameters
inp_data = np.random.random_sample(inputs * batch)
hidden_weight_data = np.random.randn(
inputs * features) / np.sqrt(inputs)
hidden_bias_data = np.random.random_sample(features)
sm_weight_data = np.random.randn(
features * classes) / np.sqrt(features)
sm_bias_data = np.random.random_sample(classes)
# special care for labels since they need to be normalized per batch
label_data = np.random.random(batch * classes).reshape(
(batch, classes))
s = label_data.sum(axis=1)
label_data /= s[:, None]
with self.session(use_gpu=True):
# We treat the inputs as "parameters" here
inp = constant_op.constant(inp_data.tolist(),
shape=[batch, inputs],
dtype=dtypes.float64,
name="inp")
hidden_weight = constant_op.constant(hidden_weight_data.tolist(),
shape=[inputs, features],
dtype=dtypes.float64,
name="hidden_weight")
hidden_bias = constant_op.constant(hidden_bias_data.tolist(),
shape=[features],
dtype=dtypes.float64,
name="hidden_bias")
softmax_weight = constant_op.constant(sm_weight_data.tolist(),
shape=[features, classes],
dtype=dtypes.float64,
name="softmax_weight")
softmax_bias = constant_op.constant(sm_bias_data.tolist(),
shape=[classes],
dtype=dtypes.float64,
name="softmax_bias")
# List all the parameter so that we can test them one at a time
all_params = [
inp, hidden_weight, hidden_bias, softmax_weight, softmax_bias
]
param_sizes = [
[batch, inputs], # inp
[inputs, features], # hidden_weight,
[features], # hidden_bias
[features, classes], # softmax_weight,
[classes]
] # softmax_bias
# Now, Building MNIST
features = nn_ops.relu(nn_ops.xw_plus_b(inp, hidden_weight,
hidden_bias),
name="features")
logits = nn_ops.xw_plus_b(features,
softmax_weight,
softmax_bias,
name="logits")
labels = constant_op.constant(label_data.tolist(),
shape=[batch, classes],
dtype=dtypes.float64,
name="labels")
cost = nn_ops.softmax_cross_entropy_with_logits(labels=labels,
logits=logits,
name="cost")
# Test the gradients.
err = gradient_checker.compute_gradient_error(
all_params[param_index],
param_sizes[param_index],
cost, [batch],
delta=1e-5)
tf_logging.info("Mini MNIST: %s gradient error = %g", tag, err)
return err
def _BuildAndTestMiniMNIST(self, param_index, tag):
# Fix seed to avoid occasional flakiness
np.random.seed(6)
# Hyperparameters
batch = 3
inputs = 16
features = 32
classes = 10
# Define the parameters
inp_data = np.random.random_sample(inputs * batch)
hidden_weight_data = np.random.randn(inputs * features) / np.sqrt(inputs)
hidden_bias_data = np.random.random_sample(features)
sm_weight_data = np.random.randn(features * classes) / np.sqrt(features)
sm_bias_data = np.random.random_sample(classes)
# special care for labels since they need to be normalized per batch
label_data = np.random.random(batch * classes).reshape((batch, classes))
s = label_data.sum(axis=1)
label_data /= s[:, None]
with self.session(use_gpu=True):
# We treat the inputs as "parameters" here
inp = constant_op.constant(
inp_data.tolist(),
shape=[batch, inputs],
dtype=dtypes.float64,
name="inp")
hidden_weight = constant_op.constant(
hidden_weight_data.tolist(),
shape=[inputs, features],
dtype=dtypes.float64,
name="hidden_weight")
hidden_bias = constant_op.constant(
hidden_bias_data.tolist(),
shape=[features],
dtype=dtypes.float64,
name="hidden_bias")
softmax_weight = constant_op.constant(
sm_weight_data.tolist(),
shape=[features, classes],
dtype=dtypes.float64,
name="softmax_weight")
softmax_bias = constant_op.constant(
sm_bias_data.tolist(),
shape=[classes],
dtype=dtypes.float64,
name="softmax_bias")
# List all the parameter so that we can test them one at a time
all_params = [
inp, hidden_weight, hidden_bias, softmax_weight, softmax_bias
]
param_sizes = [
[batch, inputs], # inp
[inputs, features], # hidden_weight,
[features], # hidden_bias
[features, classes], # softmax_weight,
[classes]
] # softmax_bias
# Now, Building MNIST
features = nn_ops.relu(
nn_ops.xw_plus_b(inp, hidden_weight, hidden_bias), name="features")
logits = nn_ops.xw_plus_b(
features, softmax_weight, softmax_bias, name="logits")
labels = constant_op.constant(
label_data.tolist(),
shape=[batch, classes],
dtype=dtypes.float64,
name="labels")
cost = nn_ops.softmax_cross_entropy_with_logits(
labels=labels, logits=logits, name="cost")
# Test the gradients.
err = gradient_checker.compute_gradient_error(
all_params[param_index],
param_sizes[param_index],
cost, [batch],
delta=1e-5)
tf_logging.info("Mini MNIST: %s gradient error = %g", tag, err)
return err