def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
python类xw_plus_b()的实例源码
def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding,
output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
gst_seq2seq.py 文件源码
项目：Deep-Reinforcement-Learning-for-Dialogue-Generation-in-tensorflow
作者: liuyuemaicha
项目源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding,
output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding,
output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
dynamic_only_m2.py 文件源码
项目：diversity_based_attention
作者: PrekshaNema25
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
dynamic_distraction_simple_hard.py 文件源码
项目：diversity_based_attention
作者: PrekshaNema25
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
dynamic_distraction_simple_soft.py 文件源码
项目：diversity_based_attention
作者: PrekshaNema25
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
dynamic_m1_eval.py 文件源码
项目：diversity_based_attention
作者: PrekshaNema25
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
dynamic_m2_eval.py 文件源码
项目：diversity_based_attention
作者: PrekshaNema25
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
seq2seq_for_MT.py 文件源码
项目：Google-Neural-Machine-Translation-GNMT
作者: shawnxu1318
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
seq2seq.py 文件源码
项目：Variational-Recurrent-Autoencoder-Tensorflow
作者: Chung-I
项目源码
文件源码
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def _extract_argmax_and_embed(embedding, output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
def _extract_argmax_and_embed(embedding,
output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
lstm1d.py 文件源码
项目：DeepLearning_VirtualReality_BigData_Project
作者: rashmitripathi
项目源码
文件源码
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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
seq2seq.py 文件源码
项目：DeepLearning_VirtualReality_BigData_Project
作者: rashmitripathi
项目源码
文件源码
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def _extract_argmax_and_embed(embedding,
output_projection=None,
update_embedding=True):
"""Get a loop_function that extracts the previous symbol and embeds it.
Args:
embedding: embedding tensor for symbols.
output_projection: None or a pair (W, B). If provided, each fed previous
output will first be multiplied by W and added B.
update_embedding: Boolean; if False, the gradients will not propagate
through the embeddings.
Returns:
A loop function.
"""
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
return loop_function
