def basic_lstm_model(inputs):
print "Loading basic lstm model.."
for i in range(self.config.rnn_numLayers):
with tf.variable_scope('rnnLayer' + str(i)):
lstm_cell = rnn_cell.BasicLSTMCell(self.config.hidden_size)
outputs, _ = tf.nn.dynamic_rnn(
lstm_cell,
inputs,
self.ph_seqLen, #(b_sz, tstp, h_sz)
dtype=tf.float32,
swap_memory=True,
scope='badic_lstm_model_layer-' + str(i))
inputs = outputs #b_sz, tstp, h_sz
mask = mkMask(self.ph_seqLen, tstp) # b_sz, tstp
mask = tf.expand_dims(mask, dim=2) #b_sz, tstp, 1
aggregate_state = reduce_avg(outputs,
mask,
tf.expand_dims(self.ph_seqLen, 1),
dim=-2) #b_sz, h_sz
inputs = aggregate_state
inputs = tf.reshape(inputs, [-1, self.config.hidden_size])
for i in range(self.config.fnn_numLayers):
inputs = rnn.rnn_cell._linear(inputs,
self.config.hidden_size,
bias=True,
scope='fnn_layer-' + str(i))
inputs = tf.nn.tanh(inputs)
aggregate_state = inputs
logits = rnn.rnn_cell._linear(aggregate_state,
self.config.class_num,
bias=True,
scope='fnn_softmax')
return logits
def basic_cnn_model(inputs):
in_channel = self.config.embed_size
filter_sizes = self.config.filter_sizes
out_channel = self.config.num_filters
input = inputs
for layer in range(self.config.cnn_numLayers):
with tf.name_scope("conv-layer-" + str(layer)):
conv_outputs = []
for i, filter_size in enumerate(filter_sizes):
with tf.variable_scope("conv-maxpool-%d" %
filter_size):
# Convolution Layer
filter_shape = [
filter_size, in_channel, out_channel
]
W = tf.get_variable(name='W', shape=filter_shape)
b = tf.get_variable(name='b', shape=[out_channel])
conv = tf.nn.conv1d( # size (b_sz, tstp, out_channel)
input,
W,
stride=1,
padding="SAME",
name="conv")
# Apply nonlinearity
h = tf.nn.relu(tf.nn.bias_add(conv, b),
name="relu")
conv_outputs.append(h)
input = tf.concat(
2, conv_outputs
) #b_sz, tstp, out_channel*len(filter_sizes)
in_channel = out_channel * len(filter_sizes)
# Maxpooling
# mask = tf.sequence_mask(self.ph_seqLen, tstp, dtype=tf.float32) #(b_sz, tstp)
mask = mkMask(self.ph_seqLen, tstp) # b_sz, tstp
pooled = tf.reduce_max(
input * tf.expand_dims(tf.cast(mask, dtype=tf.float32), 2),
[1]) #(b_sz, out_channel*len(filter_sizes))
#size (b_sz, out_channel*len(filter_sizes))
inputs = tf.reshape(pooled,
shape=[b_sz, out_channel * len(filter_sizes)])
for i in range(self.config.fnn_numLayers):
inputs = rnn.rnn_cell._linear(inputs,
self.config.embed_size,
bias=True,
scope='fnn_layer-' + str(i))
inputs = tf.nn.tanh(inputs)
aggregate_state = inputs
logits = rnn.rnn_cell._linear(aggregate_state,
self.config.class_num,
bias=True,
scope='fnn_softmax')
return logits
def masked_reverse_routing_iter(caps_uhat, seqLen, iter_num):
'''
Args:
caps_uhat: shape(b_sz, tstp, out_caps_num, out_caps_dim)
seqLen: shape(b_sz)
iter_num: number of iteration
Returns:
V_ret: #shape(b_sz, out_caps_num, out_caps_dim)
'''
assert iter_num > 0
b_sz = tf.shape(caps_uhat)[0]
tstp = tf.shape(caps_uhat)[1]
out_caps_num = int(caps_uhat.get_shape()[2])
seqLen = tf.where(tf.equal(seqLen, 0), tf.ones_like(seqLen), seqLen)
mask = mkMask(seqLen, tstp) # shape(b_sz, tstp)
mask = tf.tile(
tf.expand_dims(mask, axis=-1), # shape(b_sz, tstp, out_caps_num)
multiples=[1, 1, out_caps_num])
# shape(b_sz, tstp, out_caps_num)
B = tf.zeros([b_sz, tstp, out_caps_num], dtype=tf.float32)
B = tf.where(mask, B, tf.ones_like(B) * _MIN_NUM)
for i in range(iter_num):
C = tf.nn.softmax(B, dim=1) # shape(b_sz, tstp, out_caps_num)
C = tf.expand_dims(C, axis=-1) # shape(b_sz, tstp, out_caps_num, 1)
weighted_uhat = C * caps_uhat # shape(b_sz, tstp, out_caps_num, out_caps_dim)
S = tf.reduce_sum(weighted_uhat,
axis=1) # shape(b_sz, out_caps_num, out_caps_dim)
V = _squash(S, axes=[2]) # shape(b_sz, out_caps_num, out_caps_dim)
V = tf.expand_dims(
V, axis=1) # shape(b_sz, 1, out_caps_num, out_caps_dim)
B = tf.reduce_sum(caps_uhat * V,
axis=-1) + B # shape(b_sz, tstp, out_caps_num)
V_ret = tf.squeeze(V, axis=[1]) # shape(b_sz, out_caps_num, out_caps_dim)
S_ret = S
return V_ret, S_ret
def masked_routing_iter(caps_uhat, seqLen, iter_num, caps_ihat=None, w_rr=None):
'''
Args:
caps_uhat: shape(b_sz, tstp, out_caps_num, out_caps_dim)
seqLen: shape(b_sz)
iter_num: number of iteration
Returns:
V_ret: #shape(b_sz, out_caps_num, out_caps_dim)
'''
assert iter_num > 0
b_sz = tf.shape(caps_uhat)[0]
tstp = tf.shape(caps_uhat)[1]
out_caps_num = int(caps_uhat.get_shape()[2])
seqLen = tf.where(tf.equal(seqLen, 0), tf.ones_like(seqLen), seqLen)
mask = mkMask(seqLen, tstp) # shape(b_sz, tstp)
floatmask = tf.cast(tf.expand_dims(mask, axis=-1), dtype=tf.float32) # shape(b_sz, tstp, 1)
B = tf.zeros([b_sz, tstp, out_caps_num], dtype=tf.float32)
C_list = list()
for i in range(iter_num):
B_logits = B
C = tf.nn.softmax(B, axis=2) # shape(b_sz, tstp, out_caps_num)
C = tf.expand_dims(C * floatmask, axis=-1) # shape(b_sz, tstp, out_caps_num, 1)
weighted_uhat = C * caps_uhat # shape(b_sz, tstp, out_caps_num, out_caps_dim)
C_list.append(C)
S = tf.reduce_sum(weighted_uhat, axis=1) # shape(b_sz, out_caps_num, out_caps_dim)
V = _squash(S, axes=[2]) # shape(b_sz, out_caps_num, out_caps_dim)
V = tf.expand_dims(V, axis=1) # shape(b_sz, 1, out_caps_num, out_caps_dim)
if caps_ihat == None:
B = tf.reduce_sum(caps_uhat * V, axis=-1) + B # shape(b_sz, tstp, out_caps_num)
else:
B = tf.reduce_sum(caps_uhat * V, axis=-1) + 0.1 * tf.squeeze(
tf.matmul(tf.matmul(caps_uhat, tf.tile(w_rr, [tf.shape(caps_uhat)[0], tf.shape(caps_uhat)[1], 1, 1])),
tf.tile(caps_ihat, [1, tf.shape(caps_uhat)[1], 1, 1])),
axis=-1) + B # shape(b_sz, tstp, out_caps_num)
V_ret = tf.squeeze(V, axis=[1]) # shape(b_sz, out_caps_num, out_caps_dim)
S_ret = S
C_ret = tf.squeeze(tf.stack(C_list), axis=[4])
return V_ret, S_ret, C_ret, B_logits
def basic_cbow_model(inputs):
mask = mkMask(self.ph_seqLen, tstp) # b_sz, tstp
mask = tf.expand_dims(mask, dim=2) #b_sz, tstp, 1
aggregate_state = reduce_avg(inputs,
mask,
tf.expand_dims(self.ph_seqLen, 1),
dim=-2) #b_sz, emb_sz
inputs = aggregate_state
inputs = tf.reshape(inputs, [-1, self.config.embed_size])
for i in range(self.config.fnn_numLayers):
inputs = rnn.rnn_cell._linear(inputs,
self.config.embed_size,
bias=True,
scope='fnn_layer-' + str(i))
inputs = tf.nn.tanh(inputs)
aggregate_state = inputs
logits = rnn.rnn_cell._linear(aggregate_state,
self.config.class_num,
bias=True,
scope='fnn_softmax')
return logits
def basic_Centality(in_x, xLen, config, is_train, scope=None):
'''
:param in_x: shape(b_sz, xlen, h_sz)
:param xLen:
:return:
'''
def PowerEigen(matrix, max_iter, eta):
'''
:param matrix: shape(b_sz, dim, dim)
:return out: shape(b_sz, dim)
Power method with out gradient, this way it wont't store intermediate states and saves memory.
'''
b_sz = tf.shape(matrix)[0]
dim = tf.shape(matrix)[1]
def body(time, _, y):
'''
:param y: shape(b_sz, dim, 1)
:return out: shape(b_sz, dim, 1)
'''
v = y / (tf.sqrt(tf.reduce_sum(y**2, axis=1, keep_dims=True)) +
EPSILON)
y = tf.matmul(matrix, v) # shape(b_sz, dim, 1)
theta = tf.matmul(v, y, transpose_a=True) # shape(b_sz, 1, 1)
stop = tf.less(tf.reduce_sum((y - theta * v)**2, axis=1),
eta *
tf.squeeze(theta, axis=1)**2) # shape(b_sz, 1)
stop = tf.squeeze(stop, axis=1) # shape(b_sz)
acc = tf.cast(tf.logical_not(stop), dtype=time.dtype)
assert_inf = tf.Assert(tf.reduce_all(tf.is_finite(y)),
['y inf print v', v],
summarize=100000)
assert_nan = tf.Assert(tf.logical_not(
tf.reduce_any(tf.is_nan(y))), ['y Nan print v', v],
summarize=100000)
with tf.control_dependencies([assert_inf, assert_nan]):
y = tf.identity(y)
return time + acc, stop, y,
def stop_cond(time, stop, _):
'''
:param time: shape(b_sz) int32
:param stop: shape(b_sz,) bool
:param _:
:return:
'''
return tf.logical_and(tf.reduce_all(tf.less(time, max_iter)),
tf.reduce_any(tf.logical_not(stop)))
zero_step = tf.zeros([b_sz], dtype=tf.int32)
stop = tf.zeros(shape=[b_sz], dtype=tf.bool) # shape(b_sz,)
init_eig = tf.random_normal([b_sz, dim, 1],
dtype=matrix.dtype)**2 + EPSILON
# init_eig = tf.ones([b_sz, dim, 1], dtype=matrix.dtype)
time, stop, y = tf.while_loop(
stop_cond,
body=body,
loop_vars=[zero_step, stop, init_eig],
back_prop=False,
swap_memory=True) # shape(b_sz, dim, 1)
v = y / (tf.sqrt(tf.reduce_sum(y**2, axis=1, keep_dims=True)) +
EPSILON)
eigenValue = tf.reduce_mean(tf.matmul(matrix, v) / v,
axis=1) # shape(b_sz, 1)
tf.summary.histogram("converge-time", time)
return time, stop, eigenValue, v # shape(b_sz, dim, 1)
def PowerEigen_grad_step(matrix, max_iter, eigenVector):
'''
:param matrix: shape(b_sz, dim, dim)
:param max_iter: gradient iteration number
:param eigenVector: the converged eigen vector
:return out: shape(b_sz, dim)
Power method with gradient, small number of steps is sufficient to get a good approximation of gradient.
'''
b_sz = tf.shape(matrix)[0]
dim = tf.shape(matrix)[1]
def body(time, y):
'''
:param y: shape(b_sz, dim, 1)
:return out: shape(b_sz, dim, 1)
'''
v = y / (tf.sqrt(tf.reduce_sum(y**2, axis=1, keep_dims=True)) +
EPSILON)
y = tf.matmul(matrix, v) # shape(b_sz, dim, 1)
return time + 1, y
def stop_cond(time, _):
'''
:param time: shape(b_sz) int32
:param stop: shape(b_sz,) bool
:param _:
:return:
'''
return tf.less(time, max_iter)
zero_step = tf.constant(0, dtype=tf.int32)
time, y = tf.while_loop(stop_cond,
body=body,
loop_vars=[zero_step, eigenVector],
swap_memory=True) # shape(b_sz, dim, 1)
v = y / (tf.sqrt(tf.reduce_sum(y**2, axis=1, keep_dims=True)) +
EPSILON)
eigenValue = tf.reduce_mean(tf.matmul(matrix, v) / v,
axis=1) # shape(b_sz, 1)
return time, eigenValue, tf.transpose(
v, perm=[0, 2, 1]) # (shape(b_sz, 1), shape(b_sz, 1, dim))
def connect_fnn(in_x):
'''
:param in_x: shape(b_sz, xLen, h_sz)
:return:
'''
h_sz = int(in_x.get_shape()[-1])
units = config['units']
left = tf.layers.dense(in_x, units=units,
name='left-fnn') #shape(b_sz, xLen, h_sz)
right = tf.layers.dense(
in_x, units=units, name='right-fnn') # shape(b_sz, xLen, h_sz)
fnn_concat_mlp = tf.nn.tanh(
tf.expand_dims(left, axis=1) + tf.expand_dims(right, axis=2))
fnn_concat_mlp = tf.layers.dense(fnn_concat_mlp,
units=1,
name='concat-mlp')
connectivity = fnn_concat_mlp
connectivity = tf.squeeze(connectivity, axis=-1)
return connectivity
h_sz = int(in_x.get_shape()[-1])
maxLen = tf.shape(in_x)[1]
b_sz = tf.shape(in_x)[0]
mask = tf.expand_dims(mkMask(xLen, maxLen),
axis=2) # shape(b_sz, xlen, 1)
mask = tf.logical_and(mask,
tf.transpose(mask,
perm=[0, 2, 1
])) # shape(b_sz, xlen, xlen)
with tf.variable_scope(scope or 'Centrality'):
if config['center-mode'] == 'center':
in_x_cent = in_x - tf.reduce_mean(in_x, axis=2, keepdims=True)
elif config['center-mode'] == 'ln':
in_x_cent = layers.layer_norm(in_x, begin_norm_axis=-1)
elif config['center-mode'] == 'none':
in_x_cent = in_x
else:
raise ValueError('center-mode: %s' % config['center-mode'])
connectivity = connect_fnn(in_x_cent)
masked_connected = tf.where(mask, connectivity,
tf.ones_like(connectivity) * NINF)
masked_connected = tf.nn.softmax(masked_connected, axis=1)
masked_connected = tf.where(mask, masked_connected,
tf.zeros_like(connectivity))
time, stop, _, eigenVec = PowerEigen(
masked_connected,
tf.cond(is_train, lambda: config['max-iter'], lambda: 5000),
eta=config['power-eta'])
eigenVec = tf.stop_gradient(eigenVec)
# eigenVec = tf.transpose(eigenVec, perm=[0, 2, 1])
_, eigenValue, eigenVec = PowerEigen_grad_step(
masked_connected,
max_iter=config['grad-iter'],
eigenVector=eigenVec)
attn_weights = tf.abs(eigenVec) / (tf.reduce_sum(
tf.abs(eigenVec), axis=-1, keep_dims=True) + EPSILON)
aggregated = tf.matmul(attn_weights,
in_x_cent) # shape(b_sz, 1, h_sz)
aggregated = tf.squeeze(aggregated, axis=1)
aggregated = layers.layer_norm(aggregated, begin_norm_axis=-1)
return aggregated, time, stop
def feed_back_lstm(inputs):
def feed_back_net(inputs, seq_len, feed_back_steps):
'''
Args:
inputs: shape(b_sz, tstp, emb_sz)
'''
shape_of_input = tf.shape(inputs)
b_sz = shape_of_input[0]
h_sz = self.config.hidden_size
tstp = shape_of_input[1]
emb_sz = self.config.embed_size
def body(time, prev_output, state_ta):
'''
Args:
prev_output: previous output shape(b_sz, tstp, hidden_size)
'''
prev_output = tf.reshape(prev_output,
shape=[-1, h_sz
]) #shape(b_sz*tstp, h_sz)
output_linear = tf.nn.rnn_cell._linear(
prev_output,
output_size=h_sz, #shape(b_sz*tstp, h_sz)
bias=False,
scope='output_transformer')
output_linear = tf.reshape(
output_linear, shape=[b_sz, tstp,
h_sz]) #shape(b_sz, tstp, h_sz)
output_linear = tf.tanh(
output_linear) #shape(b_sz, tstp, h_sz)
rnn_input = tf.concat(2, [output_linear, inputs],
name='concat_output_input'
) #shape(b_sz, tstp, h_sz+emb_sz)
cell = tf.nn.rnn_cell.BasicLSTMCell(h_sz)
cur_outputs, state = tf.nn.dynamic_rnn(cell,
rnn_input,
seq_len,
dtype=tf.float32,
swap_memory=True,
time_major=False,
scope='encoder')
state = tf.concat(1, state)
state_ta = state_ta.write(time, state)
return time + 1, cur_outputs, state_ta #shape(b_sz, tstp, h_sz)
def condition(time, *_):
return time < feed_back_steps
state_ta = tf.TensorArray(dtype=tf.float32,
dynamic_size=True,
clear_after_read=True,
size=0)
initial_output = tf.zeros(shape=[b_sz, tstp, h_sz],
dtype=inputs.dtype,
name='initial_output')
time = tf.constant(0, dtype=tf.int32)
_, outputs, state_ta = tf.while_loop(
condition,
body, [time, initial_output, state_ta],
swap_memory=True)
final_state = state_ta.read(state_ta.size() - 1)
return final_state, outputs
_, outputs = feed_back_net(inputs,
self.ph_seqLen,
feed_back_steps=10)
mask = mkMask(self.ph_seqLen, tstp) # b_sz, tstp
mask = tf.expand_dims(mask, dim=2) #b_sz, tstp, 1
aggregate_state = reduce_avg(outputs,
mask,
tf.expand_dims(self.ph_seqLen, 1),
dim=-2) #b_sz, h_sz
inputs = aggregate_state
inputs = tf.reshape(inputs, [-1, self.config.hidden_size])
for i in range(self.config.fnn_numLayers):
inputs = rnn.rnn_cell._linear(inputs,
self.config.hidden_size,
bias=True,
scope='fnn_layer-' + str(i))
inputs = tf.nn.tanh(inputs)
aggregate_state = inputs
logits = rnn.rnn_cell._linear(aggregate_state,
self.config.class_num,
bias=True,
scope='fnn_softmax')
return logits
