def buildModel(self, x, y_context, y_task, is_train, dropout, scope="multiTask"):
# Assume the input shape is (batch_size, max_length, feature_length)
#TASK = primary task, CONTEXT = secondary task
# Create lstm cell for the shared layer
body_lstm_cell, _ = createLSTMCell(self.batch_size, self.body_lstm_size, self.body_n_layer, forget_bias=0.0)
context_cost = tf.constant(0)
task_cost = tf.constant(0.0, dtype=tf.float32)
if not self.is_multi_task: context_output = tf.constant(0)
with tf.variable_scope("shared_lstm"):
body_cell_output, last_body_state = tf.nn.dynamic_rnn(cell = body_lstm_cell, dtype=tf.float32, sequence_length=self.length(x), inputs=x)
if self.is_multi_task:
with tf.variable_scope("context_branch"):
# Select the last output that is not generated by zero vectors
if self.secondary_task == "missing word":
last_body_output = self.last_relevant(body_cell_output, self.length(body_cell_output))
# feed the last output to the fc layer and make prediction
with tf.variable_scope("context_fc"):
context_fc_out = fcLayer(x=last_body_output, in_shape=self.body_lstm_size, out_shape=self.context_branch_fc, activation=self.fc_activation, dropout=dropout, is_train=is_train, scope="fc1")
with tf.variable_scope("context_pred"):
context_output, context_logits = predictionLayer(x=context_fc_out, y=y_context, in_shape=self.context_branch_fc, out_shape=y_context.get_shape()[-1].value, activation=self.context_output_activation)
context_cost = compute_cost(logit=context_logits, y=y_context, out_type="last_only", max_length=self.max_length, batch_size=self.batch_size, embed_dim=self.feature_length, activation=self.context_output_activation)
if self.secondary_task == "word generation":
context_input = tf.transpose(body_cell_output, [1, 0, 2])
context_input = tf.reshape(context_input, [-1, self.body_lstm_size])
context_input_list = tf.split(context_input, self.max_length, 0)
fc_output_list = []
with tf.variable_scope("context_fc"):
for step in range(self.max_length):
if step > 0: tf.get_variable_scope().reuse_variables()
fc_out = fcLayer(x=context_input_list[step], in_shape=self.body_lstm_size, out_shape=self.context_branch_fc, activation=self.fc_activation, dropout=dropout, is_train=is_train, scope="fc1")
fc_output_list.append(tf.expand_dims(fc_out, axis=1))
context_fc_out = tf.concat(fc_output_list, axis=1)
with tf.variable_scope("context_pred"):
context_output, context_logits = predictionLayer(x=context_fc_out, y=y_context, in_shape=self.context_branch_fc, out_shape=y_context.get_shape()[-1].value, activation=self.context_output_activation)
context_cost = compute_cost(logit=context_logits, y=y_context, out_type="sequential", max_length=self.max_length, batch_size=self.batch_size, embed_dim=self.feature_length,activation=self.context_output_activation)
print "Context cost shape: ", context_cost.get_shape()
with tf.variable_scope("task_branch"):
with tf.variable_scope("task_fc"):
# Select the last output that is not generated by zero vectors
last_body_output = self.last_relevant(body_cell_output, self.length(body_cell_output))
# feed the last output to the fc layer and make prediction
task_fc_out = fcLayer(x=last_body_output, in_shape=self.body_lstm_size, out_shape=self.task_branch_fc, activation=self.fc_activation, dropout=dropout, is_train=is_train, scope="fc2")
task_output, task_logits = predictionLayer(x=task_fc_out, y=y_task, in_shape=self.task_branch_fc, out_shape=y_task.get_shape()[-1].value, activation=self.task_output_activation)
print "Task output shape: ", task_output.get_shape()
task_cost = compute_cost(logit=task_logits, y=y_task, out_type="last_only", max_length=self.max_length, batch_size=self.batch_size, embed_dim=self.n_classes,activation=self.task_output_activation)
return context_cost, task_cost, task_output, context_output
def buildModel(x, y_context, y_task, is_train, dropout, scope="multiTask"):
# Assume the input shape is (batch_size, n_steps, feature_length)
#TASK = primary task, CONTEXT = secondary task
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2])
print x.get_shape()
# Reshaping to (n_steps*batch_size, feature_length)
x = tf.reshape(x, [-1, feature_length])
# Split to get a list of "n_steps" tensors of shape (batch_size, feature_length)
print x.get_shape()
x = tf.split(x, n_steps, 0)
print x
# Create lstm cell for the shared layer
body_lstm_cell, body_state = createLSTMCell(batch_size, body_lstm_size, body_n_layer, forget_bias=0.0)
# Create lstm cell for branch 1
context_lstm_cell, context_state = createLSTMCell(batch_size, context_lstm_size, context_n_layer, forget_bias=0.0)
# Create lstm cells for branch 2
task_lstm_cell, task_state = createLSTMCell(batch_size, task_lstm_size, task_n_layer, forget_bias=0.0)
#combined_cost = tf.constant(0)
context_cost = tf.constant(0)
task_cost = tf.constant(0)
for time_step in range(n_steps):
with tf.variable_scope("shared_lstm"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(body_cell_output, body_state) = body_lstm_cell(x[time_step], body_state)
with tf.variable_scope("context_branch"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(context_cell_output, context_state) = context_lstm_cell(body_cell_output, context_state)
with tf.variable_scope("context_fc"):
if time_step == n_steps - 1:
context_fc_out = fcLayer(x=context_cell_output, in_shape=context_lstm_size, out_shape=context_branch_fc, activation=fc_activation, dropout=dropout, is_train=is_train, scope="fc1")
context_cost, context_output = predictionLayer(x=context_fc_out, y=y_context, in_shape=context_branch_fc, out_shape=y_context.get_shape()[-1].value, activation=output_activation)
with tf.variable_scope("task_branch"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(task_cell_output, task_state) = task_lstm_cell(body_cell_output, task_state)
with tf.variable_scope("task_fc"):
if time_step == n_steps - 1:
task_fc_out = fcLayer(x=task_cell_output, in_shape=task_lstm_size, out_shape=task_branch_fc, activation=fc_activation, dropout=dropout, is_train=is_train, scope="fc2")
task_cost, task_output = predictionLayer(x=task_fc_out, y=y_task, in_shape=context_branch_fc, out_shape=y_task.get_shape()[-1].value, activation=output_activation)
return context_cost, task_cost, task_output, context_output
def buildModel(self, x, y_context, y_task, is_train, dropout, scope="multiTask"):
# Assume the input shape is (batch_size, max_length, feature_length)
#TASK = primary task, CONTEXT = secondary task
# Create lstm cell for the shared layer
body_lstm_cell, _ = createLSTMCell(self.batch_size, self.body_lstm_size, self.body_n_layer, forget_bias=0.0)
# Create lstm cell for branch 1
context_lstm_cell, _ = createLSTMCell(self.batch_size, self.context_lstm_size, self.context_n_layer, forget_bias=0.0)
# Create lstm cells for branch 2
task_lstm_cell, _ = createLSTMCell(self.batch_size, self.task_lstm_size, self.task_n_layer, forget_bias=0.0)
context_cost = tf.constant(0)
task_cost = tf.constant(0)
with tf.variable_scope("shared_lstm"):
body_cell_output, last_body_state = tf.nn.dynamic_rnn(cell = body_lstm_cell, dtype=tf.float32, sequence_length=self.length(x), inputs=x)
with tf.variable_scope("context_branch"):
context_cell_output, last_context_state = tf.nn.dynamic_rnn(cell = context_lstm_cell, dtype=tf.float32, sequence_length=self.length(body_cell_output), inputs=body_cell_output)
# The output from LSTMs will be (batch_size, max_length, out_size)
with tf.variable_scope("context_fc"):
# Select the last output that is not generated by zero vectors
last_context_output = self.last_relevant(context_cell_output, self.length(context_cell_output))
# feed the last output to the fc layer and make prediction
context_fc_out = fcLayer(x=last_context_output, in_shape=self.context_lstm_size, out_shape=self.context_branch_fc, activation=self.fc_activation, dropout=self.dropout, is_train=is_train, scope="fc1")
context_cost, context_output = predictionLayer(x=context_fc_out, y=y_context, in_shape=self.context_branch_fc, out_shape=y_context.get_shape()[-1].value, activation=self.output_activation)
with tf.variable_scope("task_branch"):
task_cell_output, last_task_state = tf.nn.dynamic_rnn(cell = task_lstm_cell, dtype=tf.float32, sequence_length=self.length(body_cell_output), inputs=body_cell_output)
with tf.variable_scope("task_fc"):
# Select the last output that is not generated by zero vectors
last_task_output = self.last_relevant(task_cell_output, self.length(task_cell_output))
# feed the last output to the fc layer and make prediction
task_fc_out = fcLayer(x=last_task_output, in_shape=self.task_lstm_size, out_shape=self.task_branch_fc, activation=self.fc_activation, dropout=self.dropout, is_train=is_train, scope="fc2")
task_cost, task_output = predictionLayer(x=task_fc_out, y=y_task, in_shape=self.context_branch_fc, out_shape=y_task.get_shape()[-1].value, activation=self.output_activation)
return context_cost, task_cost, task_output, context_output
def buildModel(x, y_context, y_task, is_train, scope="multiTask"):
# Assume the input shape is (batch_size, n_steps, feature_length)
#TASK = primary task, CONTEXT = secondary task
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2])
# Reshaping to (n_steps*batch_size, feature_length)
x = tf.reshape(x, [-1, feature_length])
# Split to get a list of "n_steps" tensors of shape (batch_size, feature_length)
x = tf.split(x, n_steps, 0)
# Create lstm cell for the shared layer
body_lstm_cell, body_state = createLSTMCell(batch_size,
body_lstm_size,
body_n_layer,
forget_bias=0.0)
# Create lstm cell for branch 1
context_lstm_cell, context_state = createLSTMCell(batch_size,
context_lstm_size,
context_n_layer,
forget_bias=0.0)
# Create lstm cells for branch 2
task_lstm_cell, task_state = createLSTMCell(batch_size,
task_lstm_size,
task_n_layer,
forget_bias=0.0)
#combined_cost = tf.constant(0)
context_cost = tf.constant(0)
task_cost = tf.constant(0)
#IMPLEMENTATION NOTES
#No idea how code compiles in mcrnn... indentation makes 0 sense.
#cant get context_output to next layer so have to use separate for loops...
#need to cast as float32.
#we should try both top and bottom outputs for our targets.
for time_step in range(n_steps):
# first, we construct the context lstm
print time_step
with tf.variable_scope("context_branch"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
(context_cell_output,
context_state) = context_lstm_cell(x[time_step], context_state)
with tf.variable_scope("context_fc"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
context_fc_out = fcLayer(x=context_cell_output,
in_shape=context_lstm_size,
out_shape=context_branch_fc,
activation=fc_activation,
dropout=dropout,
is_train=is_train,
scope="fc1")
context_cost, context_output = predictionLayer(
x=context_fc_out,
y=y_context,
in_shape=context_branch_fc,
out_shape=y_context.get_shape()[-1].value,
activation=output_activation)
# then make the body where the input is the concatenation of both text and context_output
with tf.variable_scope("body_lstm"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
#body_input = tf.concat([x[time_step],context_cell_output], 1)
(body_cell_output,
body_state) = body_lstm_cell(x[time_step], body_state)
# finally make the output task cell.
with tf.variable_scope("task_branch"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
task_input = tf.concat([body_cell_output, context_cell_output], 1)
(task_cell_output,
task_state) = task_lstm_cell(task_input, task_state)
with tf.variable_scope("task_fc"):
if time_step == n_steps - 1:
task_fc_out = fcLayer(x=task_cell_output,
in_shape=task_lstm_size,
out_shape=task_branch_fc,
activation=fc_activation,
dropout=dropout,
is_train=is_train,
scope="fc2")
task_cost, task_output = predictionLayer(
x=task_fc_out,
y=y_task,
in_shape=context_branch_fc,
out_shape=y_task.get_shape()[-1].value,
activation=output_activation)
return context_cost, task_cost, task_output, context_output
def buildModel(x, y_context, y_task, is_train, scope="multiTask"):
# Assume the input shape is (batch_size, n_steps, feature_length)
# Permuting batch_size and n_steps
x = tf.transpose(x, [1, 0, 2])
print x.get_shape()
# Reshaping to (n_steps*batch_size, feature_length)
x = tf.reshape(x, [-1, feature_length])
# Split to get a list of "n_steps" tensors of shape (batch_size, feature_length)
print x.get_shape()
x = tf.split(x, n_steps, 0)
# Create lstm cell for the shared layer
lstm_cell_0, state_0 = createLSTMCell(batch_size,
lstm_size_0,
n_layer_0,
forget_bias=0.0)
# Create lstm cell for branch 1
lstm_cell_1, state_1 = createLSTMCell(batch_size,
lstm_size_1,
n_layer_1,
forget_bias=0.0)
# Create lstm cells for branch 2
task_lstm_cell, task_state = createLSTMCell(batch_size,
lstm_size_2,
n_layer_2,
forget_bias=0.0)
combined_cost = tf.constant(0)
cost1 = tf.constant(0)
cost2 = tf.constant(0)
for time_step in range(n_steps):
with tf.variable_scope("SharedLSTM"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
shared_lstm_bn = tf.contrib.layers.batch_norm(x[time_step],
center=True,
scale=True,
is_training=True,
scope='bn1')
(cell_output_0, state_0) = lstm_cell_0(shared_lstm_bn, state_0)
with tf.variable_scope("task_branch"):
if time_step > 0:
tf.get_variable_scope().reuse_variables()
task_lstm_bn = tf.contrib.layers.batch_norm(cell_output_0,
center=True,
scale=True,
is_training=True,
scope='bn2')
(task_cell_output,
task_state) = task_lstm_cell(task_lstm_bn, task_state)
with tf.variable_scope("Branch_task_fc"):
if time_step == n_steps - 1:
task_fc_bn = tf.contrib.layers.batch_norm(task_cell_output,
center=True,
scale=True,
is_training=True,
scope='bn3')
fc_out2 = fcLayer(x=task_fc_bn,
in_shape=lstm_size_2,
out_shape=branch2_fc,
activation=fc_activation,
dropout=dropout,
is_train=is_train,
scope="fc2")
task_pred_bn = tf.contrib.layers.batch_norm(fc_out2,
center=True,
scale=True,
is_training=True,
scope='bn4')
cost2, output2 = predictionLayer(
x=task_pred_bn,
y=y_task,
in_shape=branch1_fc,
out_shape=y_task.get_shape()[-1].value,
activation=output_activation)
return cost2, output2
def buildModel(self,
x,
y_context,
y_task,
is_train,
dropout,
scope="multiTask"):
context_cost = tf.constant(0)
task_cost = tf.constant(0.0, dtype=tf.float32)
# Assume the input shape is (batch_size, max_length, feature_length)
# TASK = primary task, CONTEXT = secondary task
# Create the forward cell for all LSTMs
body_lstm_cell, _ = createLSTMCell(self.batch_size,
self.body_lstm_size,
self.body_n_layer,
forget_bias=0.0)
context_lstm_cell, _ = createLSTMCell(self.batch_size,
self.context_lstm_size,
self.context_n_layer,
forget_bias=0.0)
task_lstm_cell, _ = createLSTMCell(self.batch_size,
self.task_lstm_size,
self.task_n_layer,
forget_bias=0.0)
if self.is_bidirectional:
# Create the backward cell for LSTM
body_lstm_cell_bw, _ = createLSTMCell(self.batch_size,
self.body_lstm_size,
self.body_n_layer,
forget_bias=0.0)
context_lstm_cell_bw, _ = createLSTMCell(self.batch_size,
self.context_lstm_size,
self.context_n_layer,
forget_bias=0.0)
task_lstm_cell_bw, _ = createLSTMCell(self.batch_size,
self.task_lstm_size,
self.task_n_layer,
forget_bias=0.0)
if not self.is_multi_task: context_output = tf.constant(0)
with tf.variable_scope("shared_lstm"):
body_cell_output, last_body_state = self.rnn_layer(
fw_cell=body_lstm_cell,
bw_cell=body_lstm_cell_bw,
rnn_inputs=x,
is_bidirectional=self.is_bidirectional)
if self.is_multi_task:
with tf.variable_scope("context_branch"):
# The output from bidirectional LSTM is a list = [fw_output, bw_output], each of size (batch_size, max_length, out_size)
if self.is_bidirectional:
# Concatenate the input of both directions along the feature dimension axis 2(300 -> 600)
context_input = tf.concat(body_cell_output, axis=2)
else:
context_input = body_cell_output
context_cell_output, last_context_state = self.rnn_layer(
fw_cell=context_lstm_cell,
bw_cell=None,
rnn_inputs=context_input)
# The output from LSTMs will be (batch_size, max_length, out_size)
# Select the last output that is not generated by zero vectors
if self.secondary_task == "missing word":
last_context_output = self.last_relevant(
context_cell_output, self.length(context_cell_output))
# feed the last output to the fc layer and make prediction
with tf.variable_scope("context_fc"):
context_fc_out = fcLayer(
x=last_context_output,
in_shape=self.contxt_fc_input_size,
out_shape=self.context_branch_fc,
activation=self.fc_activation,
dropout=self.dropout,
is_train=is_train,
scope="fc1")
with tf.variable_scope("context_pred"):
context_output, context_logits = predictionLayer(
x=context_fc_out,
y=y_context,
in_shape=self.context_branch_fc,
out_shape=y_context.get_shape()[-1].value,
activation=self.context_output_activation)
context_cost = compute_cost(
logit=context_logits,
y=y_context,
out_type="last_only",
max_length=self.max_length,
batch_size=self.batch_size,
embed_dim=self.feature_length,
activation=self.context_output_activation)
if self.secondary_task == "word generation":
context_cell_output = tf.transpose(context_cell_output,
[1, 0, 2])
context_cell_output = tf.reshape(
context_cell_output, [-1, self.context_lstm_size])
context_output_list = tf.split(context_cell_output,
self.max_length, 0)
fc_output_list = []
with tf.variable_scope("context_fc"):
for step in range(self.max_length):
if step > 0:
tf.get_variable_scope().reuse_variables()
fc_out = fcLayer(
x=context_output_list[step],
in_shape=self.context_fc_input_size,
out_shape=self.context_branch_fc,
activation=self.fc_activation,
dropout=self.dropout,
is_train=is_train,
scope="fc1")
fc_output_list.append(
tf.expand_dims(fc_out, axis=1))
context_fc_out = tf.concat(fc_output_list, axis=1)
print "context fc output shape before transpose: ", context_fc_out.get_shape(
)
with tf.variable_scope("context_pred"):
context_output, context_logits = predictionLayer(
x=context_fc_out,
y=y_context,
in_shape=self.context_branch_fc,
out_shape=y_context.get_shape()[-1].value,
activation=self.context_output_activation)
print "Context prediction output shape: ", context_output.get_shape(
)
context_cost = compute_cost(
logit=context_logits,
y=y_context,
out_type="sequential",
max_length=self.max_length,
batch_size=self.batch_size,
embed_dim=self.feature_length,
activation=self.context_output_activation)
print "Context cost shape: ", context_cost.get_shape()
with tf.variable_scope("task_branch"):
if self.is_bidirectional:
task_input = tf.concat(body_cell_output, axis=2)
else:
task_input = body_cell_output
task_cell_output, last_task_state = self.rnn_layer(
fw_cell=task_lstm_cell, bw_cell=None, rnn_inputs=task_input)
with tf.variable_scope("task_fc"):
# Select the last output that is not generated by zero vectors
last_task_output = self.last_relevant(
task_cell_output, self.length(task_cell_output))
# feed the last output to the fc layer and make prediction
task_fc_out = fcLayer(x=last_task_output,
in_shape=self.task_fc_input_size,
out_shape=self.task_branch_fc,
activation=self.fc_activation,
dropout=self.dropout,
is_train=is_train,
scope="fc2")
task_output, task_logits = predictionLayer(
x=task_fc_out,
y=y_task,
in_shape=self.context_branch_fc,
out_shape=y_task.get_shape()[-1].value,
activation=self.task_output_activation)
task_cost = compute_cost(
logit=task_logits,
y=y_task,
out_type="last_only",
max_length=self.max_length,
batch_size=self.batch_size,
embed_dim=self.feature_length,
activation=self.task_output_activation)
return context_cost, task_cost, task_output, context_output