def define_self_prediction_rew(self, convfeat, rep_size, enlargement,
scope):
#RND.
# Random target network.
for ph in self.ph_ob.values():
if len(ph.shape.as_list()) == 5: # B,T,H,W,C
logger.info("CnnTarget: using '%s' shape %s as image input" %
(ph.name, str(ph.shape)))
xr = ph[:, 1:]
xr = tf.cast(xr, tf.float32)
xr = tf.reshape(xr, (-1, *ph.shape.as_list()[-3:]))[:, :, :,
-1:]
xr = tf.clip_by_value((xr - self.ph_mean) / self.ph_std, -5.0,
5.0)
xr = tf.nn.leaky_relu(
conv(xr,
'c1r',
nf=convfeat * 1,
rf=8,
stride=4,
init_scale=np.sqrt(2)))
xr = tf.nn.leaky_relu(
conv(xr,
'c2r',
nf=convfeat * 2 * 1,
rf=4,
stride=2,
init_scale=np.sqrt(2)))
xr = tf.nn.leaky_relu(
conv(xr,
'c3r',
nf=convfeat * 2 * 1,
rf=3,
stride=1,
init_scale=np.sqrt(2)))
rgbr = [to2d(xr)]
X_r = fc(rgbr[0], 'fc1r', nh=rep_size, init_scale=np.sqrt(2))
#define expert agent observations random features
#
yes_gpu = any(get_available_gpus())
with tf.variable_scope(
tf.get_variable_scope(),
reuse=True), tf.device('/gpu:0' if yes_gpu else '/cpu:0'):
X_im = np.load(os.getcwd() + '/policies/obs.npy')
Xr_im = tf.cast(X_im, tf.float32) / 255.
Xr_im = tf.reshape(Xr_im, (-1, *ph.shape.as_list()[-3:]))[:, :, :,
-1:]
Xr_im = tf.clip_by_value((Xr_im - tf.reduce_mean(Xr_im)) /
(tf.math.reduce_std(Xr_im)**0.5), -5.0,
5.0)
Xr_im = tf.nn.leaky_relu(
conv(Xr_im,
'c1r',
nf=convfeat * 1,
rf=8,
stride=4,
init_scale=np.sqrt(2)))
Xr_im = tf.nn.leaky_relu(
conv(Xr_im,
'c2r',
nf=convfeat * 2 * 1,
rf=4,
stride=2,
init_scale=np.sqrt(2)))
Xr_im = tf.nn.leaky_relu(
conv(Xr_im,
'c3r',
nf=convfeat * 2 * 1,
rf=3,
stride=1,
init_scale=np.sqrt(2)))
Xr_im = [to2d(Xr_im)[::self.demonstration_stride]]
Xr_im = fc(Xr_im[0], 'fc1r', nh=rep_size, init_scale=np.sqrt(2))
Xr_im = tf.stop_gradient(Xr_im)
# Predictor network.
for ph in self.ph_ob.values():
if len(ph.shape.as_list()
) == 5: # B,T,H,W,C ###Batch time height width color?
logger.info("CnnTarget: using '%s' shape %s as image input" %
(ph.name, str(ph.shape)))
xrp = ph[:, 1:]
xrp = tf.cast(xrp, tf.float32)
xrp = tf.reshape(xrp, (-1, *ph.shape.as_list()[-3:]))[:, :, :,
-1:]
xrp = tf.clip_by_value((xrp - self.ph_mean) / self.ph_std,
-5.0, 5.0)
xrp = tf.nn.leaky_relu(
conv(xrp,
'c1rp_pred',
nf=convfeat,
rf=8,
stride=4,
init_scale=np.sqrt(2)))
xrp = tf.nn.leaky_relu(
conv(xrp,
'c2rp_pred',
nf=convfeat * 2,
rf=4,
stride=2,
init_scale=np.sqrt(2)))
xrp = tf.nn.leaky_relu(
conv(xrp,
'c3rp_pred',
nf=convfeat * 2,
rf=3,
stride=1,
init_scale=np.sqrt(2)))
rgbrp = to2d(xrp)
X_r_hat = tf.nn.relu(
fc(rgbrp,
'fc1r_hat1_pred',
nh=256 * enlargement,
init_scale=np.sqrt(2)))
X_r_hat = tf.nn.relu(
fc(X_r_hat,
'fc1r_hat2_pred',
nh=256 * enlargement,
init_scale=np.sqrt(2)))
X_r_hat = fc(X_r_hat,
'fc1r_hat3_pred',
nh=rep_size,
init_scale=np.sqrt(2))
self.feat_var = tf.reduce_mean(tf.nn.moments(X_r, axes=[0])[1])
self.max_feat = tf.reduce_max(tf.abs(X_r))
self.int_rew = tf.reduce_mean(
tf.square(tf.stop_gradient(X_r) - X_r_hat),
axis=-1,
keep_dims=True)
self.int_rew = tf.reshape(self.int_rew,
(self.sy_nenvs, self.sy_nsteps - 1))
####
#self.im_rew = tf.math.maximum(1 - tf.divide(tf.reduce_mean(tf.square(self.Xr_im[:(X_r).shape[0]] - X_r), axis=-1, keep_dims=True),tf.add(tf.reduce_mean(tf.square(self.Xr_im[:X_r.shape[0]]), axis=-1, keep_dims=True),tf.reduce_mean(tf.square(X_r), axis=-1, keep_dims=True))),tf.constant(0.5))
im_rew = tf.reduce_mean(tf.tensordot(tf.stop_gradient(X_r),
Xr_im,
axes=[[1], [1]]),
axis=1)
im_rew = tf.reshape(im_rew, (self.sy_nenvs, self.sy_nsteps - 1))
#self.int_rew =tf.math.maximum(self.im_rew,self.int_rew)
self.int_rew = self.int_rew * (1 + tf.math.tanh(im_rew / 100))
####
noisy_targets = tf.stop_gradient(X_r)
self.aux_loss = tf.reduce_mean(tf.square(noisy_targets - X_r_hat), -1)
mask = tf.random_uniform(shape=tf.shape(self.aux_loss),
minval=0.,
maxval=1.,
dtype=tf.float32)
mask = tf.cast(mask < self.proportion_of_exp_used_for_predictor_update,
tf.float32)
self.aux_loss = tf.reduce_sum(mask * self.aux_loss) / tf.maximum(
tf.reduce_sum(mask), 1.)
def build(self, input_shape):
if self.data_format == 'channels_last':
channel_axis = -1
input_row, input_col = input_shape[1:-1]
else:
channel_axis = 1
input_row, input_col = input_shape[2:]
if input_shape[channel_axis] is None:
raise ValueError('The channel dimension of the inputs '
'should be defined. Found `None`.')
input_filter = int(input_shape[channel_axis])
if self.data_format == 'channels_last':
input_row, input_col = input_shape[1:-1]
input_filter = input_shape[3]
else:
input_row, input_col = input_shape[2:]
input_filter = input_shape[1]
if (((input_row is None) and
((self.share_row_combining_weights,
self.share_col_combining_weights) in [(True, False),
(False, False)]))
or ((input_col is None) and
((self.share_row_combining_weights,
self.share_col_combining_weights) in [(False, True),
(False, False)]))):
raise ValueError('The spatial dimensions of the inputs to '
' a LowRankLocallyConnected2D layer '
'should be fully-defined, but layer received '
'the inputs shape ' + str(input_shape))
# Compute output shapes.
# Compute using the first filter since output will be same across filters.
kernel_size = self.kernel_size[0] if isinstance(
self.kernel_size, list) else self.kernel_size
dilations = self.dilations[0] if isinstance(self.dilations,
list) else self.dilations
output_row = conv_utils.conv_output_length(input_row,
kernel_size[0],
self.padding,
self.strides[0],
dilation=dilations)
output_col = conv_utils.conv_output_length(input_col,
kernel_size[1],
self.padding,
self.strides[1],
dilation=dilations)
if isinstance(self.kernel_size, list):
# Different filters.
self.kernel_bases = []
for i, kernel_size in enumerate(self.kernel_size):
kernel_bases_shape = (kernel_size[0], kernel_size[1],
input_filter, self.filters)
self.kernel_bases.append(
self.add_weight(shape=kernel_bases_shape,
initializer=self.kernel_initializer,
name='kernel_bases%d' % i,
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint))
else:
self.kernel_bases_shape = (self.kernel_size[0],
self.kernel_size[1], input_filter,
self.spatial_rank * self.filters)
self.kernel_shape = (output_row, output_col, self.kernel_size[0],
self.kernel_size[1], input_filter,
self.filters)
self.kernel_bases = self.add_weight(
shape=self.kernel_bases_shape,
initializer=self.kernel_initializer,
name='kernel_bases',
regularizer=self.kernel_regularizer,
constraint=self.kernel_constraint)
self.output_row = output_row
self.output_col = output_col
if not (self.share_row_combining_weights
or self.share_col_combining_weights):
if self.input_dependent:
self.combining_weights = None
else:
self.combining_weights_shape = (output_row, output_col,
self.spatial_rank)
initializer = (
tf.constant_initializer(1. / np.sqrt(self.spatial_rank))
if self.combining_weights_initializer == 'conv_init' else
self.combining_weights_initializer)
self.wts = self.add_weight(
shape=self.combining_weights_shape,
initializer=initializer,
name='combining_weights',
regularizer=self.combining_weights_regularizer,
constraint=self.combining_weights_constraint)
# If self.wts is overwritten it is removed from layer.weights.
# Thus, below assignment is necessary.
self.combining_weights = self.wts
else:
c = 1. / (float(self.share_row_combining_weights) + float(
self.share_col_combining_weights)) # Scale for init.
initializer = (tf.constant_initializer(c /
np.sqrt(self.spatial_rank))
if self.combining_weights_initializer == 'conv_init'
else self.combining_weights_initializer)
combining_weights_shape_row = (output_row, self.spatial_rank)
combining_weights_shape_col = (output_col, self.spatial_rank)
self.wts_row = tf.constant([[0.]])
self.wts_col = tf.constant([[0.]])
if self.share_row_combining_weights:
self.wts_row = self.add_weight(
shape=combining_weights_shape_row,
initializer=initializer,
name='combining_weights_row',
regularizer=self.combining_weights_regularizer,
constraint=self.combining_weights_constraint)
if self.share_col_combining_weights:
self.wts_col = self.add_weight(
shape=combining_weights_shape_col,
initializer=tf.constant_initializer(
c / np.sqrt(self.spatial_rank))
if self.combining_weights_initializer == 'conv_init' else
self.combining_weights_initializer,
name='combining_weights_col',
regularizer=self.combining_weights_regularizer,
constraint=self.combining_weights_constraint)
if self.share_row_combining_weights and self.share_col_combining_weights:
self.combining_weights = tf.math.add(self.wts_col[tf.newaxis],
self.wts_row[:,
tf.newaxis],
name='combining_weights')
self.combining_weights_shape = (output_row, output_col,
self.spatial_rank)
elif self.share_row_combining_weights:
self.combining_weights = tf.identity(self.wts_row,
name='combining_weights')
self.combining_weights_shape = combining_weights_shape_row
elif self.share_col_combining_weights:
self.combining_weights = tf.identity(self.wts_col,
name='combining_weights')
self.combining_weights_shape = combining_weights_shape_col
if not self.input_dependent:
if self.normalize_weights == 'softmax':
# Normalize the weights to sum to 1.
self.combining_weights = tf.nn.softmax(
self.combining_weights,
axis=-1,
name='normalized_combining_weights')
elif self.normalize_weights == 'norm':
# Normalize the weights to sum to preserve kernel var.
self.combining_weights = tf.math.l2_normalize(
self.combining_weights,
axis=-1,
epsilon=1e-12,
name='normalized_combining_weights')
if (self.input_dependent or isinstance(self.kernel_size, list)
or ((self.share_row_combining_weights,
self.share_col_combining_weights) in [(True, False),
(False, True)])):
# Different kernel bases can not be combined.
# Shape may not be defined for one of axes in one dimension separate wts.
self.kernel = None
else:
self.kernel = tf.tensordot(
self.combining_weights,
tf.reshape(self.kernel_bases,
(self.kernel_size[0], self.kernel_size[1],
input_filter, self.spatial_rank, self.filters)),
[[-1], [-2]],
name='kernel')
self.bias_spatial = 0.
self.bias_channels = 0.
if self.use_spatial_bias:
if not (self.share_row_combining_weights
or self.share_col_combining_weights):
self.bias_spatial = self.add_weight(
shape=(output_row, output_col, 1),
initializer=self.bias_initializer,
name='spatial_bias',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
else:
self.bias_row = 0.
self.bias_col = 0.
if self.share_row_combining_weights:
self.bias_row = self.add_weight(
shape=(output_row, 1, 1),
initializer=self.bias_initializer,
name='bias_row',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
if self.share_col_combining_weights:
self.bias_col = self.add_weight(
shape=(1, output_col, 1),
initializer=self.bias_initializer,
name='bias_col',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.bias_spatial = tf.math.add(self.bias_row,
self.bias_col,
name='spatial_bias')
if self.use_bias:
self.bias_channels = self.add_weight(
shape=(1, 1, self.filters),
initializer=self.bias_initializer,
name='bias_channels',
regularizer=self.bias_regularizer,
constraint=self.bias_constraint)
self.bias = tf.math.add(self.bias_spatial,
self.bias_channels,
name='bias')
if self.data_format == 'channels_last':
self.input_spec = InputSpec(ndim=4, axes={-1: input_filter})
else:
self.input_spec = InputSpec(ndim=4, axes={1: input_filter})
self.built = True
def _scale_expression(expr, w): """Scale a linear expression by w.""" b = tf.matmul(expr.b, w) w = tf.tensordot(expr.w, w, axes=1) return LinearExpression(w=w, b=b, lower=expr.lower, upper=expr.upper)
def compute_mel_filterbank_features(waveforms,
sample_rate=16000,
dither=1.0 / np.iinfo(np.int16).max,
preemphasis=0.97,
frame_length=25,
frame_step=10,
fft_length=None,
window_fn=functools.partial(
tf.signal.hann_window, periodic=True),
lower_edge_hertz=80.0,
upper_edge_hertz=7600.0,
num_mel_bins=80,
log_noise_floor=1e-3,
apply_mask=True):
"""Implement mel-filterbank extraction using tf ops.
Args:
waveforms: float32 tensor with shape [batch_size, max_len]
sample_rate: sampling rate of the waveform
dither: stddev of Gaussian noise added to waveform to prevent quantization
artefacts
preemphasis: waveform high-pass filtering constant
frame_length: frame length in ms
frame_step: frame_Step in ms
fft_length: number of fft bins
window_fn: windowing function
lower_edge_hertz: lowest frequency of the filterbank
upper_edge_hertz: highest frequency of the filterbank
num_mel_bins: filterbank size
log_noise_floor: clip small values to prevent numeric overflow in log
apply_mask: When working on a batch of samples, set padding frames to zero
Returns:
filterbanks: a float32 tensor with shape [batch_size, len, num_bins, 1]
"""
# `stfts` is a complex64 Tensor representing the short-time Fourier
# Transform of each signal in `signals`. Its shape is
# [batch_size, ?, fft_unique_bins]
# where fft_unique_bins = fft_length // 2 + 1
# Find the wave length: the largest index for which the value is !=0
# note that waveforms samples that are exactly 0.0 are quite common, so
# simply doing sum(waveforms != 0, axis=-1) will not work correctly.
wav_lens = tf.reduce_max(
tf.expand_dims(tf.range(tf.shape(waveforms)[1]), 0) *
tf.to_int32(tf.not_equal(waveforms, 0.0)),
axis=-1) + 1
if dither > 0:
waveforms += tf.random_normal(tf.shape(waveforms), stddev=dither)
if preemphasis > 0:
waveforms = waveforms[:, 1:] - preemphasis * waveforms[:, :-1]
wav_lens -= 1
frame_length = int(frame_length * sample_rate / 1e3)
frame_step = int(frame_step * sample_rate / 1e3)
if fft_length is None:
fft_length = int(2**(np.ceil(np.log2(frame_length))))
stfts = tf.contrib.signal.stft(waveforms,
frame_length=frame_length,
frame_step=frame_step,
fft_length=fft_length,
window_fn=window_fn,
pad_end=True)
stft_lens = (wav_lens + (frame_step - 1)) // frame_step
masks = tf.to_float(
tf.less_equal(tf.expand_dims(tf.range(tf.shape(stfts)[1]), 0),
tf.expand_dims(stft_lens, 1)))
# An energy spectrogram is the magnitude of the complex-valued STFT.
# A float32 Tensor of shape [batch_size, ?, 257].
magnitude_spectrograms = tf.abs(stfts)
# Warp the linear-scale, magnitude spectrograms into the mel-scale.
num_spectrogram_bins = magnitude_spectrograms.shape[-1].value
linear_to_mel_weight_matrix = (
tf.contrib.signal.linear_to_mel_weight_matrix(num_mel_bins,
num_spectrogram_bins,
sample_rate,
lower_edge_hertz,
upper_edge_hertz))
mel_spectrograms = tf.tensordot(magnitude_spectrograms,
linear_to_mel_weight_matrix, 1)
# Note: Shape inference for tensordot does not currently handle this case.
mel_spectrograms.set_shape(magnitude_spectrograms.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
log_mel_sgram = tf.log(tf.maximum(log_noise_floor, mel_spectrograms))
if apply_mask:
log_mel_sgram *= tf.expand_dims(tf.to_float(masks), -1)
return tf.expand_dims(log_mel_sgram, -1, name="mel_sgrams")
def lagrangian_optimizer_fmeasure(
train_set, epsilon, learning_rate, learning_rate_constraint, loops):
"""Implements surrogate-based Lagrangian optimizer (Algorithm 3).
Specifically solves:
max F-measure s.t. F-measure(group1) >= F-measure(group0) - epsilon.
Args:
train_set: (features, labels, groups)
epsilon: float, constraint slack.
learning_rate: float, learning rate for model parameters.
learning_rate_constraint: float, learning rate for Lagrange multipliers.
loops: int, number of iterations.
Returns:
stochastic_model containing list of models and probabilities,
deterministic_model.
"""
x_train, y_train, z_train = train_set
dimension = x_train.shape[-1]
tf.reset_default_graph()
# Data tensors.
features_tensor = tf.constant(x_train.astype("float32"), name="features")
labels_tensor = tf.constant(y_train.astype("float32"), name="labels")
# Linear model.
weights = tf.Variable(tf.zeros(dimension, dtype=tf.float32),
name="weights")
threshold = tf.Variable(0, name="threshold", dtype=tf.float32)
predictions_tensor = (tf.tensordot(features_tensor, weights, axes=(1, 0))
+ threshold)
# Contexts.
context = tfco.rate_context(predictions_tensor, labels_tensor)
context0 = context.subset(z_train < 1)
context1 = context.subset(z_train > 0)
# F-measure rates.
fm_overall = tfco.f_score(context)
fm1 = tfco.f_score(context1)
fm0 = tfco.f_score(context0)
# Rate minimization problem.
problem = tfco.RateMinimizationProblem(-fm_overall, [fm0 <= fm1 + epsilon])
# Optimizer.
optimizer = tfco.LagrangianOptimizerV1(
tf.train.AdamOptimizer(learning_rate=learning_rate),
constraint_optimizer=tf.train.AdamOptimizer(
learning_rate=learning_rate_constraint))
train_op = optimizer.minimize(problem)
# Start TF session and initialize variables.
session = tf.Session()
session.run(tf.global_variables_initializer())
# We maintain a list of objectives and model weights during training.
objectives = []
violations = []
models = []
# Perform full gradient updates.
for ii in range(loops):
# Gradient updates.
session.run(train_op)
# Checkpoint once in 10 iterations.
if ii % 10 == 0:
# Model weights.
model = [session.run(weights), session.run(threshold)]
models.append(model)
# Objective.
objective = -evaluation.expected_fmeasure(
x_train, y_train, [model], [1.0])
objectives.append(objective)
# Violation.
fmeasure0, fmeasure1 = evaluation.expected_group_fmeasures(
x_train, y_train, z_train, [model], [1.0])
violations.append([fmeasure0 - fmeasure1 - epsilon])
# Use the recorded objectives and constraints to find the best iterate.
best_iterate = tfco.find_best_candidate_index(
np.array(objectives), np.array(violations))
deterministic_model = models[best_iterate]
# Use shrinking to find a sparse distribution over iterates.
probabilities = tfco.find_best_candidate_distribution(
np.array(objectives), np.array(violations))
models_pruned = [models[i] for i in range(len(models)) if
probabilities[i] > 0.0]
probabilities_pruned = probabilities[probabilities > 0.0]
return (models_pruned, probabilities_pruned), deterministic_model
def create_model(
bert_config,
is_training,
input_ids,
input_mask,
segment_ids,
labels,
num_labels,
use_one_hot_embeddings,
num_segments,
aggregation_method,
pretrained_model='bert',
from_distilled_student=False,
):
"""Creates a classification model."""
scope = ""
if from_distilled_student:
scope = "student"
parade_model = Parade(bert_config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
num_segments=num_segments,
pretrained_model=pretrained_model,
use_one_hot_embeddings=use_one_hot_embeddings,
scope=scope)
output_layer = None
if aggregation_method == 'cls_attn':
output_layer = parade_model.reduced_by_attn()
elif aggregation_method == 'cls_avg':
output_layer = parade_model.reduced_by_avg()
elif aggregation_method == 'cls_max':
output_layer = parade_model.reduced_by_max()
elif aggregation_method == 'cls_transformer':
output_layer = parade_model.reduced_by_transformer(
is_training, num_transformer_layers=2)
else:
raise ValueError(
"Un-supported model type: {}".format(aggregation_method))
with tf.variable_scope(scope):
output_weights = tf.get_variable(
"output_weights", [num_labels, parade_model.hidden_size],
initializer=tf.truncated_normal_initializer(stddev=0.02))
output_bias = tf.get_variable("output_bias", [num_labels],
initializer=tf.zeros_initializer())
with tf.variable_scope("loss"):
if is_training:
# I.e., 0.1 dropout
output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)
logits = tf.tensordot(output_layer, output_weights, axes=[-1, -1])
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)
per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
loss = tf.reduce_mean(per_example_loss)
return (loss, per_example_loss, log_probs)
def multihead_graph_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
image_shapes=None,
attention_type="edge_vector",
name="multihead_graph_attention",
save_weights_to=None,
make_image_summary=True,
dropout_broadcast_dims=None,
adjacency_matrix=None,
num_edge_types=5,
vars_3d=False,
**kwargs):
"""Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
adjacency_matrix: an optional tensor of shape [batch, len_q, len_q]
containing edge vectors for attention
num_edge_types: number of edge types, an int
vars_3d: use 3-dimensional variables for input/output transformations
**kwargs (dict): Parameters for the attention function
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, output_depth]
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." %
(total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." %
(total_value_depth, num_heads))
vars_3d_num_heads = num_heads if vars_3d else None
with tf.variable_scope(name,
default_name="multihead_attention",
values=[query_antecedent, memory_antecedent]):
q, k, v = common_attention.compute_qkv(
query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
vars_3d_num_heads=vars_3d_num_heads)
q = common_attention.split_heads(q, num_heads)
k = common_attention.split_heads(k, num_heads)
v = common_attention.split_heads(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
if not vars_3d:
q *= key_depth_per_head**-0.5
additional_returned_value = None
if callable(
attention_type): # Generic way to extend multihead_attention
x = attention_type(q, k, v, **kwargs)
if isinstance(x, tuple):
x, additional_returned_value = x # Unpack
elif attention_type == "edge_vector":
x = graph_attention(q,
k,
v,
bias,
dropout_rate,
image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims,
adjacency_matrix=adjacency_matrix,
num_edge_types=num_edge_types)
x = common_attention.combine_heads(x)
# Set last dim specifically.
x.set_shape(x.shape.as_list()[:-1] + [total_value_depth])
if vars_3d:
o_var = tf.get_variable(
"o", [num_heads, total_value_depth // num_heads, output_depth])
o_var = tf.reshape(o_var, [total_value_depth, output_depth])
x = tf.tensordot(x, o_var, axes=1)
else:
x = common_layers.dense(x,
output_depth,
use_bias=False,
name="output_transform")
if additional_returned_value is not None:
return x, additional_returned_value
return x
def color_transform(masks):
with tf.name_scope("color_transform"):
n_components = masks.shape.as_list()[-1]
colors = tf.constant(get_mask_plot_colors(n_components),
name="mask_colors")
return tf.tensordot(masks, colors, axes=1)
def compute_attention_component(antecedent,
total_depth,
filter_width=1,
padding="VALID",
name="c",
vars_3d_num_heads=0,
sparsity_technique=None,
threshold=3.0,
training=True,
clip_alpha=None,
initial_sparsity=None,
split_heads=False,
num_heads=None):
"""Computes attention compoenent (query, key or value).
Args:
antecedent: a Tensor with shape [batch, length, channels]
total_depth: an integer
filter_width: An integer specifying how wide you want the attention
component to be.
padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
name: a string specifying scope name.
vars_3d_num_heads: an optional integer (if we want to use 3d variables)
sparsity_technique: technique used for sparsifying weights.
threshold: log alpha threshold used for evaluation with variational dropout.
training: whether model is being trained or not.
clip_alpha: alpha clipping threshold for variational dropout.
initial_sparsity: initial sparsity level for lottery ticket &
scratch experiments.
split_heads: Whether to prune each head separately.
num_heads: The number of heads in the attention module.
Returns:
c : [batch, length, depth] tensor
"""
# We don't support 3d attention variables or filter_width > 1 with sparsity
# techniques
assert not sparsity_technique or (not vars_3d_num_heads
and filter_width == 1)
if vars_3d_num_heads > 0:
assert filter_width == 1
input_depth = antecedent.get_shape().as_list()[-1]
depth_per_head = total_depth // vars_3d_num_heads
initializer_stddev = input_depth**-0.5
if "q" in name:
initializer_stddev *= depth_per_head**-0.5
var = tf.get_variable(
name,
[input_depth, vars_3d_num_heads, total_depth // vars_3d_num_heads],
initializer=tf.random_normal_initializer(
stddev=initializer_stddev))
var = tf.cast(var, antecedent.dtype)
var = tf.reshape(var, [input_depth, total_depth])
return tf.tensordot(antecedent, var, axes=1)
if filter_width == 1:
if sparsity_technique:
if split_heads:
# Prune each heads weights separately so that they are free
# to have different weight magnitude distributions.
if num_heads is None:
raise ValueError(
"`num_heads` must be set for split head pruning.")
if total_depth % num_heads != 0:
raise ValueError(
"`total_depth` must be divisible by `num_heads`.")
input_depth = antecedent.get_shape().as_list()[-1]
depth_per_head = int(total_depth / num_heads)
masked_head_weights = []
for head_id in range(num_heads):
head_name = name + "_shard_{}".format(head_id)
with tf.variable_scope(head_name) as vs:
head_weights = tf.get_variable(
"kernel", [input_depth, depth_per_head])
masked_head_weights.append(
pruning.apply_mask(head_weights, vs))
component_weights = tf.concat(masked_head_weights, axis=1)
# compute the full component result
return tf.tensordot(antecedent, component_weights, axes=1)
else:
return common_sparse.dense(
antecedent,
total_depth,
use_bias=False,
sparsity_technique=sparsity_technique,
threshold=threshold,
training=training,
clip_alpha=clip_alpha,
name=name,
initial_sparsity=initial_sparsity)
else:
return common_layers.dense(antecedent,
total_depth,
use_bias=False,
name=name)
else:
return common_layers.conv1d(antecedent,
total_depth,
filter_width,
padding=padding,
name=name)
def test_correct_output(self, normalize_weights, input_dependent,
data_format, combining_weights_initializer):
spatial_rank = 2
kernel_size = 3
filters = 16
input_chs = 3
if data_format == 'channels_last':
input_shape = (1, 32, 32, input_chs)
if data_format == 'channels_first':
input_shape = (1, input_chs, 32, 32)
images = tf.constant(np.random.randn(*input_shape), dtype=tf.float32)
layer1 = tf.keras.layers.LocallyConnected2D(filters=filters,
kernel_size=(kernel_size,
kernel_size),
strides=(1, 1),
padding='valid',
data_format=data_format)
layer2 = layers.LowRankLocallyConnected2D(
filters=filters,
kernel_size=(kernel_size, kernel_size),
strides=(1, 1),
padding='valid',
spatial_rank=spatial_rank,
normalize_weights=normalize_weights,
combining_weights_initializer=combining_weights_initializer,
share_row_combining_weights=False,
share_col_combining_weights=False,
data_format=data_format,
input_dependent=input_dependent)
output1 = layer1(images)
output2 = layer2(images)
assign_ops = []
# Kernel from locally connected network.
kernel1 = layer1.kernel
combining_weights = layer2.combining_weights
if input_dependent:
combining_weights = tf.reduce_mean(combining_weights, axis=0)
# Kernel from low rank locally connected network.
kernel2 = tf.tensordot(
combining_weights,
tf.reshape(layer2.kernel_bases,
(layer2.kernel_size[0], layer2.kernel_size[1],
input_chs, layer2.spatial_rank, layer2.filters)),
[[-1], [-2]],
name='kernel')
kernel2 = kernel_low_rank_lc_to_lc(kernel2, data_format)
assign_ops.append(tf.assign(kernel1, kernel2))
# Test results consistent with keras locallyconnected2d layer.
self.evaluate(tf.global_variables_initializer())
for op in assign_ops:
self.evaluate(op)
max_error = np.max(np.abs(self.evaluate(output1 - output2)))
self.assertLess(max_error, 1e-5)
def build_model(self, args):
# auto-encoder
with tf.variable_scope('encoder_decoder'):
# word embedding
embedding = tf.get_variable('embedding',
initializer=self.word_init)
# embedding = tf.get_variable('embedding', [self.vocab_size, self.dim_emb])
enc_inputs = tf.nn.embedding_lookup(embedding, self.enc_inputs)
dec_inputs = tf.nn.embedding_lookup(embedding, self.dec_inputs)
with tf.variable_scope('projection'):
# style information
projection = {}
projection['W'] = tf.get_variable(
'W', [self.dim_h, self.vocab_size])
projection['b'] = tf.get_variable('b', [self.vocab_size])
encoder = self.create_cell(self.dim_h, args.n_layers, self.dropout,
'encoder')
decoder = self.create_cell(self.dim_h, args.n_layers, self.dropout,
'decoder')
self.loss_rec, origin_info, transfer_info = self.reconstruction(
encoder, enc_inputs, self.labels, decoder, dec_inputs,
self.targets, self.dec_mask, projection)
_, soft_tsf_ids, self.rec_ids, self.tsf_ids = self.run_decoder(
decoder, dec_inputs, embedding, projection, origin_info,
transfer_info)
# make the real sents and fake sents the same length
if args.trim_padding:
fake_probs = fake_probs[:, :1 + self.batch_len, :]
# discriminator
with tf.variable_scope('discriminator'):
classifier_embedding = tf.get_variable('embedding',
initializer=self.word_init)
# classifier_embedding = tf.get_variable('embedding', [self.vocab_size, self.dim_emb])
# remove bos, use dec_inputs to avoid noises adding into enc_inputs
real_sents = tf.nn.embedding_lookup(classifier_embedding,
self.dec_inputs[:, 1:])
fake_sents = tf.tensordot(soft_tsf_ids, classifier_embedding,
[[2], [0]])
fake_sents = fake_sents[:, :
-1, :] # make the dimension the same as real sents
# mask the sequences
mask = tf.sequence_mask(self.enc_lens,
self.max_len - 1,
dtype=tf.float32)
mask = tf.expand_dims(mask, -1)
real_sents *= mask
fake_sents *= mask
self.loss_d, self.loss_g = self.run_discriminator(
real_sents, fake_sents, self.labels, args)
##### optimizer #####
self.loss = self.loss_rec + self.rho * self.loss_g
theta_eg = retrive_var(['encoder_decoder'])
theta_d = retrive_var(['discriminator'])
opt = tf.train.AdamOptimizer(self.learning_rate, beta1=0.5)
grad, _ = zip(*opt.compute_gradients(self.loss, theta_eg))
grad, _ = tf.clip_by_global_norm(grad, 30.0)
self.optimize_tot = opt.apply_gradients(zip(grad, theta_eg))
self.optimize_rec = opt.minimize(self.loss_rec, var_list=theta_eg)
self.optimize_d = opt.minimize(self.loss_d, var_list=theta_d)
self.saver = tf.train.Saver(max_to_keep=5)
def encoder(features, mode, vocab, hps):
"""Model function.
Atttention seq2seq model, augmented with an encoder
over the targets of the nearest neighbors.
Args:
features: Dictionary of input Tensors.
mode: train or eval. Keys from tf.estimator.ModeKeys.
vocab: A list of strings of words in the vocabulary.
hps: Hyperparams.
Returns:
Encoder outputs.
"""
# [batch_size, src_len]
src_inputs = features["src_inputs"]
src_len = features["src_len"]
with tf.variable_scope("embeddings"):
scale = (3.0 / hps.emb_dim)**0.5
embeddings = tf.get_variable("embeddings", [vocab.size(), hps.emb_dim],
dtype=tf.float32,
initializer=tf.random_uniform_initializer(
minval=-scale, maxval=scale))
# [batch_size, src_len, emb_dim]
src_input_emb = tf.nn.embedding_lookup(embeddings, src_inputs)
if mode == tf_estimator.ModeKeys.TRAIN and hps.emb_drop > 0.:
src_input_emb = tf.nn.dropout(src_input_emb,
keep_prob=1.0 - hps.emb_drop)
src_att_context, neighbor_att_context = None, None
src_copy_context, neighbor_copy_context = None, None
with tf.variable_scope("src_encoder"):
# 2 * [batch_size, src_len, encoder_dim]
src_encoder_outputs, src_encoder_states = tf.nn.bidirectional_dynamic_rnn(
cell_fw=get_rnn_cell(mode=mode,
hps=hps,
input_dim=hps.emb_dim,
num_units=hps.encoder_dim,
num_layers=hps.num_encoder_layers,
dropout=hps.encoder_drop,
cell_type="lstm"),
cell_bw=get_rnn_cell(mode=mode,
hps=hps,
input_dim=hps.emb_dim,
num_units=hps.encoder_dim,
num_layers=hps.num_encoder_layers,
dropout=hps.encoder_drop,
cell_type="lstm"),
inputs=src_input_emb,
dtype=tf.float32,
sequence_length=src_len)
# [batch_size, src_len, 2*encoder_dim]
src_encoder_outputs = tf.concat(src_encoder_outputs, 2)
with tf.variable_scope("src_att_context"):
src_att_context = _build_context(
hps=hps, encoder_outputs=src_encoder_outputs)
if hps.use_copy:
with tf.variable_scope("src_copy_context"):
src_copy_context = _build_context(
hps=hps, encoder_outputs=src_encoder_outputs)
if hps.encode_neighbor or hps.att_neighbor or hps.sum_neighbor:
# [batch_size, neighbor_len]
neighbor_inputs = features["neighbor_inputs"]
neighbor_len = features["neighbor_len"]
# [batch_size, neighbor_len, emb_dim]
neighbor_input_emb = tf.nn.embedding_lookup(embeddings,
neighbor_inputs)
if mode == tf_estimator.ModeKeys.TRAIN and hps.emb_drop > 0.:
neighbor_input_emb = tf.nn.dropout(neighbor_input_emb,
keep_prob=1.0 - hps.emb_drop)
if hps.binary_neighbor:
neighbor_binary_input = features["neighbor_binary"]
if hps.binary_dim == 1:
neighbor_binary_emb = tf.to_float(neighbor_binary_input)
neighbor_binary_emb = tf.expand_dims(neighbor_binary_emb,
axis=-1)
else:
with tf.variable_scope("binary_emb"):
scale = (3.0 / hps.binary_dim)**0.5
binary_embeddings = tf.get_variable(
"binary_emb", [2, hps.binary_dim],
dtype=tf.float32,
initializer=tf.random_uniform_initializer(
minval=-scale, maxval=scale))
neighbor_binary_emb = tf.nn.embedding_lookup(
binary_embeddings, neighbor_binary_input)
neighbor_input_emb = tf.concat(
[neighbor_input_emb, neighbor_binary_emb], axis=2)
with tf.variable_scope("neighbor_encoder"):
# 2 * [batch_size, neighbor_len, encoder_dim]
input_dim = hps.emb_dim
if hps.binary_neighbor:
input_dim += hps.binary_dim
neighbor_encoder_outputs, neighbor_encoder_states = \
tf.nn.bidirectional_dynamic_rnn(
cell_fw=get_rnn_cell(
mode=mode, hps=hps,
input_dim=input_dim,
num_units=hps.neighbor_dim,
num_layers=1,
dropout=hps.encoder_drop,
cell_type="lstm"),
cell_bw=get_rnn_cell(
mode=mode, hps=hps,
input_dim=input_dim,
num_units=hps.neighbor_dim,
num_layers=1,
dropout=hps.encoder_drop,
cell_type="lstm"),
inputs=neighbor_input_emb,
dtype=tf.float32,
sequence_length=neighbor_len)
# [batch_size, neighbor_len, 2*encoder_dim]
neighbor_encoder_outputs = tf.concat(neighbor_encoder_outputs, 2)
if hps.att_neighbor:
with tf.variable_scope("neighbor_att_context"):
neighbor_att_context = _build_context(
hps=hps, encoder_outputs=neighbor_encoder_outputs)
if hps.use_copy:
with tf.variable_scope("neighbor_copy_context"):
neighbor_copy_context = _build_context(
hps=hps, encoder_outputs=neighbor_encoder_outputs)
att_context, copy_context = None, None
if hps.att_neighbor:
att_context = tf.concat([src_att_context, neighbor_att_context], 1)
if hps.use_copy:
copy_context = tf.concat([src_copy_context, neighbor_copy_context],
1)
else:
att_context = src_att_context
if hps.use_copy:
copy_context = src_copy_context
if hps.encode_neighbor:
neighbor_fw_states, neighbor_bw_states = neighbor_encoder_states
neighbor_h = tf.concat(
[neighbor_fw_states[-1].h, neighbor_bw_states[-1].h], axis=1)
if mode == tf_estimator.ModeKeys.TRAIN and hps.drop > 0.:
neighbor_h = tf.nn.dropout(neighbor_h, keep_prob=1.0 - hps.drop)
mem_input = tf.layers.dense(
neighbor_h,
units=hps.decoder_dim,
activation=tf.nn.tanh,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="mem_input")
if mode == tf_estimator.ModeKeys.TRAIN and hps.drop > 0.:
mem_input = tf.nn.dropout(mem_input, keep_prob=1.0 - hps.drop)
elif hps.sum_neighbor:
src_fw_states, src_bw_states = src_encoder_states
src_h = tf.concat([src_fw_states[-1].h, src_bw_states[-1].h], axis=1)
if mode == tf_estimator.ModeKeys.TRAIN and hps.drop > 0.:
src_h = tf.nn.dropout(src_h, keep_prob=1.0 - hps.drop)
src_h = tf.layers.dense(
src_h,
units=hps.decoder_dim,
activation=tf.nn.tanh,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="proj_src_h")
neighbor_encoder_outputs = tf.layers.dense(
neighbor_encoder_outputs,
units=hps.decoder_dim,
activation=tf.nn.tanh,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="proj_neighbor_out")
alpha = tf.tensordot(neighbor_encoder_outputs, src_h, axes=1)
alpha = tf.einsum("bij,bj->bi", neighbor_encoder_outputs, src_h)
alpha = tf.nn.softmax(alpha)
mem_input = tf.reduce_sum(
neighbor_encoder_outputs * tf.expand_dims(alpha, -1), 1)
# mem_input = tf.reduce_mean(mem_input, axis=1)
if mode == tf_estimator.ModeKeys.TRAIN and hps.drop > 0.:
mem_input = tf.nn.dropout(mem_input, keep_prob=1.0 - hps.drop)
else:
assert hps.rnn_cell != "hyper_lstm"
assert hps.att_type != "hyper"
mem_input = None
if hps.use_bridge:
with tf.variable_scope("bridge"):
out_dim = hps.num_decoder_layers * hps.decoder_dim
fw_states, bw_states = src_encoder_states
c_states, h_states = [], []
for (fw, bw) in zip(fw_states, bw_states):
c_states.append(tf.concat((fw.c, bw.c), 1))
h_states.append(tf.concat((fw.h, bw.h), 1))
cs, hs = c_states[-1], h_states[-1]
if mode == tf_estimator.ModeKeys.TRAIN and hps.drop > 0.:
hs = tf.nn.dropout(hs, keep_prob=1.0 - hps.drop)
cs = tf.nn.dropout(cs, keep_prob=1.0 - hps.drop)
h_state = tf.layers.dense(
hs,
units=out_dim,
activation=tf.nn.tanh,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="h_layer")
c_state = tf.layers.dense(
cs,
units=out_dim,
activation=tf.nn.tanh,
use_bias=True,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
name="c_layer")
else:
h_state, c_state = None, None
# print (att_context)
# dsadsa
return EncoderOutputs(embeddings=embeddings,
mem_input=mem_input,
att_context=att_context,
copy_context=copy_context,
states=(h_state, c_state))
def lagrangian_optimizer_kld(
train_set, additive_slack, learning_rate, learning_rate_constraint, loops):
"""Implements surrogate-based Lagrangian optimizer (Algorithm 2).
Specifically solves:
min_{theta} sum_{G = 0, 1} KLD(p, pprG(theta))
s.t. error_rate <= additive_slack,
where p is the overall proportion of positives and pprG is the positive
prediction rate for group G.
We frame this as a constrained optimization problem:
min_{theta, xi_pos0, xi_pos1, xi_neg0, xi_neg1} {
-p log(xi_pos0) - (1-p) log(xi_neg0) - p log(xi_pos1)
-(1-p) log(xi_neg1)}
s.t.
error_rate <= additive_slack,
xi_pos0 <= ppr0(theta), xi_neg0 <= npr0(theta),
xi_pos1 <= ppr1(theta), xi_neg1 <= npr1(theta),
and formulate the Lagrangian:
max_{lambda's >= 0} min_{xi's} {
-p log(xi_pos0) - (1-p) log(xi_neg0) - p log(xi_pos1)
-(1-p) log(xi_neg1)
+ lambda_pos0 (xi_pos0 - ppr0(theta))
+ lambda_neg0 (xi_neg0 - npr0(theta))
+ lambda_pos1 (xi_pos1 - ppr1(theta))
+ lambda_neg1 (xi_neg1 - npr1(theta))}
s.t.
error_rate <= additive_slack.
We do best response for the slack variables xi:
BR for xi_pos0 = p / lambda_pos0
BR for xi_neg0 = (1 - p) / lambda_neg0
BR for xi_pos1 = p / lambda_pos1
BR for xi_neg1 = (1 - p) / lambda_neg1
We do gradient ascent on the lambda's, where
Gradient w.r.t. lambda_pos0
= BR for xi_pos0 - ppr0(theta)
= p / lambda_pos0 - ppr0(theta)
= Gradient w.r.t. lambda_pos0 of
(p log(lambda_pos0) - lambda_pos0 ppr0(theta))
Gradient w.r.t. lambda_neg0
= Gradient w.r.t. lambda_neg0 of
((1 - p) log(lambda_neg0) - lambda_neg0 npr0(theta))
Gradient w.r.t. lambda_pos1
= Gradient w.r.t. lambda_pos1 of
(p log(lambda_pos1) - lambda_pos1 ppr1(theta))
Gradient w.r.t. lambda_neg1
= Gradient w.r.t. lambda_neg1 of
((1 - p) log(lambda_neg1) - lambda_neg1 npr1(theta)).
We do gradient descent on thetas's, with ppr's and npr's replaced with hinge
surrogates. We use concave lower bounds on ppr's and npr's, so that when they
get negated in the updates, we get convex upper bounds.
See Appendix D.1 in the paper for more details.
Args:
train_set: (features, labels, groups)
additive_slack: float, additive slack on error rate constraint
learning_rate: float, learning rate for model parameters
learning_rate_constraint: float, learning rate for Lagrange multipliers
loops: int, number of iterations
Returns:
stochastic_model containing list of models and probabilities,
deterministic_model.
"""
x_train, y_train, z_train = train_set
dimension = x_train.shape[-1]
tf.reset_default_graph()
# Data tensors.
features_tensor = tf.constant(x_train.astype("float32"), name="features")
labels_tensor = tf.constant(y_train.astype("float32"), name="labels")
# Linear model.
weights = tf.Variable(tf.zeros(dimension, dtype=tf.float32),
name="weights")
threshold = tf.Variable(0, name="threshold", dtype=tf.float32)
predictions_tensor = (tf.tensordot(features_tensor, weights, axes=(1, 0))
+ threshold)
# Group-specific predictions.
predictions_group0 = tf.boolean_mask(predictions_tensor, mask=(z_train < 1))
num_examples0 = np.sum(z_train < 1)
predictions_group1 = tf.boolean_mask(predictions_tensor, mask=(z_train > 0))
num_examples1 = np.sum(z_train > 0)
# We use the TF Constrained Optimization (TFCO) library to set up the
# constrained optimization problem. The library doesn't currently support best
# responses for slack variables. So we maintain explicit Lagrange multipliers
# for the slack variables, and let the library deal with the Lagrange
# multipliers for the error rate constraint.
# Since we need to perform a gradient descent update on the model parameters,
# and an ascent update on the Lagrange multipliers on the slack variables, we
# create a single "minimization" objective using stop gradients, where a
# descent gradient update has the effect of minimizing over the model
# parameters and maximizing over the Lagrange multipliers for the slack
# variables. As noted above, the ascent update on the Lagrange multipliers for
# the error rate constraint is done by the library internally.
# Placeholders for Lagrange multipliers for the four slack variables.
lambda_pos0 = tf.Variable(0.5, dtype=tf.float32, name="lambda_pos0")
lambda_neg0 = tf.Variable(0.5, dtype=tf.float32, name="lambda_neg0")
lambda_pos1 = tf.Variable(0.5, dtype=tf.float32, name="lambda_pos1")
lambda_neg1 = tf.Variable(0.5, dtype=tf.float32, name="lambda_neg1")
# Set up prediction rates and surrogate relaxations on them.
p = np.mean(y_train) # Proportion of positives.
# Positive and negative prediction rates for group 0 and group 1.
ppr_group0 = tf.reduce_sum(tf.cast(
tf.greater(predictions_group0, tf.zeros(num_examples0, dtype="float32")),
"float32")) / num_examples0
npr_group0 = 1 - ppr_group0
ppr_group1 = tf.reduce_sum(tf.cast(
tf.greater(predictions_group1, tf.zeros(num_examples1, dtype="float32")),
"float32")) / num_examples1
npr_group1 = 1 - ppr_group1
# Hinge concave lower bounds on the positive and negative prediction rates.
# In the gradient updates, these get negated and become convex upper bounds.
# For group 0:
ppr_hinge_group0 = tf.reduce_sum(
1 - tf.nn.relu(1 - predictions_group0)) * 1.0 / num_examples0
npr_hinge_group0 = tf.reduce_sum(
1 - tf.nn.relu(1 + predictions_group0)) * 1.0 / num_examples0
# For group 1:
ppr_hinge_group1 = tf.reduce_sum(
1 - tf.nn.relu(1 - predictions_group1)) * 1.0 / num_examples1
npr_hinge_group1 = tf.reduce_sum(
1 - tf.nn.relu(1 + predictions_group1)) * 1.0 / num_examples1
# Set up KL-divergence objective for constrained optimization.
# We use stop gradients to ensure that a single descent gradient update on the
# objective has the effect of minimizing over the model parameters and
# maximizing over the Lagrange multipliers for the slack variables.
# KL-divergence for group 0.
kld_hinge_pos_group0 = (
- tf.stop_gradient(lambda_pos0) * ppr_hinge_group0
- p * tf.log(lambda_pos0) + lambda_pos0 * tf.stop_gradient(ppr_group0))
kld_hinge_neg_group0 = (
- tf.stop_gradient(lambda_neg0) * npr_hinge_group0
- (1 - p) * tf.log(lambda_neg0)
+ lambda_neg0 * tf.stop_gradient(npr_group0))
kld_hinge_group0 = kld_hinge_pos_group0 + kld_hinge_neg_group0
# KL-divergence for group 1.
kld_hinge_pos_group1 = (
- tf.stop_gradient(lambda_pos1) * ppr_hinge_group1
- p * tf.log(lambda_pos1) + lambda_pos1 * tf.stop_gradient(ppr_group1))
kld_hinge_neg_group1 = (
- tf.stop_gradient(lambda_neg1) * npr_hinge_group1
- (1 - p) * tf.log(lambda_neg1)
+ lambda_neg1 * tf.stop_gradient(npr_group1))
kld_hinge_group1 = kld_hinge_pos_group1 + kld_hinge_neg_group1
# Wrap the objective into a rate object.
objective = tfco.wrap_rate(kld_hinge_group0 + kld_hinge_group1)
# Set up error rate constraint for constrained optimization.
context = tfco.rate_context(predictions_tensor, labels_tensor)
error = tfco.error_rate(context)
constraints = [error <= additive_slack]
# Cretae rate minimization problem object.
problem = tfco.RateMinimizationProblem(objective, constraints)
# Set up optimizer.
optimizer = tfco.LagrangianOptimizerV1(
tf.train.AdamOptimizer(learning_rate=learning_rate),
constraint_optimizer=tf.train.AdamOptimizer(
learning_rate=learning_rate_constraint))
train_op = optimizer.minimize(problem)
# Start TF session and initialize variables.
session = tf.Session()
session.run(tf.global_variables_initializer())
# We maintain a list of objectives and model weights during training.
objectives = []
violations = []
models = []
# Perform full gradient updates.
for ii in range(loops):
# Gradient updates.
session.run(train_op)
# Checkpoint once in 10 iterations.
if ii % 10 == 0:
# Model weights.
model = [session.run(weights), session.run(threshold)]
models.append(model)
# Objective.
klds = evaluation.expected_group_klds(
x_train, y_train, z_train, [model], [1.0])
objectives.append(sum(klds))
# Violation.
error = evaluation.expected_error_rate(
x_train, y_train, [model], [1.0])
violations.append([error - additive_slack])
# Use the recorded objectives and constraints to find the best iterate.
best_iterate = tfco.find_best_candidate_index(
np.array(objectives), np.array(violations))
deterministic_model = models[best_iterate]
# Use shrinking to find a sparse distribution over iterates.
probabilities = tfco.find_best_candidate_distribution(
np.array(objectives), np.array(violations))
models_pruned = [models[i] for i in range(len(models)) if
probabilities[i] > 0.0]
probabilities_pruned = probabilities[probabilities > 0.0]
return (models_pruned, probabilities_pruned), deterministic_model
def prepare_processing_graph(self, model_settings):
"""Builds a TensorFlow graph to apply the input distortions.
Creates a graph that loads a WAVE file, decodes it, scales the volume,
shifts it in time, adds in background noise, calculates a spectrogram, and
then builds an MFCC fingerprint from that.
This must be called with an active TensorFlow session running, and it
creates multiple placeholder inputs, and one output:
- wav_filename_placeholder_: Filename of the WAV to load.
- foreground_volume_placeholder_: How loud the main clip should be.
- time_shift_placeholder_: How much the clip is shifted.
- background_data_placeholder_: PCM sample data for background noise.
- background_volume_placeholder_: Loudness of mixed-in background.
- mfcc_: Output 2D fingerprint of processed audio.
Args:
model_settings: Information about the current model being trained.
"""
desired_samples = model_settings['desired_samples']
self.wav_filename_placeholder_ = tf.placeholder(tf.string, [],
name='filename')
wav_loader = io_ops.read_file(self.wav_filename_placeholder_)
wav_decoder = contrib_audio.decode_wav(wav_loader,
desired_channels=1,
desired_samples=desired_samples)
# Allow the audio sample's volume to be adjusted.
self.foreground_volume_placeholder_ = tf.placeholder(
tf.float32, [], name='foreground_volme')
scaled_foreground = tf.multiply(wav_decoder.audio,
self.foreground_volume_placeholder_)
# Shift the sample's start position, and pad any gaps with zeros.
self.time_shift_placeholder_ = tf.placeholder(tf.int32,
name='timeshift')
# TODO(see--): Write test with np.roll
shifted_foreground = tf_roll(scaled_foreground,
self.time_shift_placeholder_)
# Mix in background noise.
self.background_data_placeholder_ = tf.placeholder(
tf.float32, [desired_samples, 1], name='background_data')
self.background_volume_placeholder_ = tf.placeholder(
tf.float32, [], name='background_volume')
background_mul = tf.multiply(self.background_data_placeholder_,
self.background_volume_placeholder_)
background_add = tf.add(background_mul, shifted_foreground)
# removed clipping: tf.clip_by_value(background_add, -1.0, 1.0)
self.background_clamp_ = background_add
self.background_clamp_ = tf.reshape(
self.background_clamp_, (1, model_settings['desired_samples']))
# Run the spectrogram and MFCC ops to get a 2D 'fingerprint' of the audio.
#stfts = tf.contrib.signal.stft(
stfts = tf.signal.stft(
self.background_clamp_,
frame_length=model_settings['window_size_samples'],
frame_step=model_settings['window_stride_samples'],
fft_length=None)
self.spectrogram_ = tf.abs(stfts)
num_spectrogram_bins = self.spectrogram_.shape[-1].value
lower_edge_hertz, upper_edge_hertz = 80.0, 7600.0
linear_to_mel_weight_matrix = \
tf.signal.linear_to_mel_weight_matrix(
model_settings['dct_coefficient_count'],
num_spectrogram_bins, model_settings['sample_rate'],
lower_edge_hertz, upper_edge_hertz)
mel_spectrograms = tf.tensordot(self.spectrogram_,
linear_to_mel_weight_matrix, 1)
mel_spectrograms.set_shape(self.spectrogram_.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
log_mel_spectrograms = tf.log(mel_spectrograms + 1e-6)
self.mfcc_ = tf.signal.mfccs_from_log_mel_spectrograms(
log_mel_spectrograms
)[:, :, :model_settings['num_log_mel_features']] # :13
def multihead_attention(query_antecedent,
memory_antecedent,
bias,
total_key_depth,
total_value_depth,
output_depth,
num_heads,
dropout_rate,
shared_rel=False,
max_relative_position=None,
image_shapes=None,
attention_type="dot_product",
block_length=128,
block_width=128,
q_filter_width=1,
kv_filter_width=1,
q_padding="VALID",
kv_padding="VALID",
cache=None,
gap_size=0,
num_memory_blocks=2,
name="multihead_attention",
save_weights_to=None,
make_image_summary=True,
dropout_broadcast_dims=None,
max_length=None,
vars_3d=False,
scale_dotproduct=True,
**kwargs):
"""Multihead scaled-dot-product attention with input/output transformations.
Args:
query_antecedent: a Tensor with shape [batch, length_q, channels]
memory_antecedent: a Tensor with shape [batch, length_m, channels] or None
bias: bias Tensor (see attention_bias())
total_key_depth: an integer
total_value_depth: an integer
output_depth: an integer
num_heads: an integer dividing total_key_depth and total_value_depth
dropout_rate: a floating point number
shared_rel: boolean to share relative embeddings
max_relative_position: Maximum distance between inputs to generate
unique relation embeddings for. Only relevant
when using "dot_product_relative" attention.
image_shapes: optional tuple of integer scalars.
see comments for attention_image_summary()
attention_type: a string, either "dot_product", "dot_product_relative",
"local_mask_right", "local_unmasked", "masked_dilated_1d",
"unmasked_dilated_1d", graph, or any attention function
with the signature (query, key, value, **kwargs)
block_length: an integer - relevant for "local_mask_right"
block_width: an integer - relevant for "local_unmasked"
q_filter_width: An integer specifying how wide you want the query to be.
kv_filter_width: An integer specifying how wide you want the keys and values
to be.
q_padding: One of "VALID", "SAME" or "LEFT". Default is VALID: No padding.
kv_padding: One of "VALID", "SAME" or "LEFT". Default is "VALID":
no padding.
cache: dict containing Tensors which are the results of previous
attentions, used for fast decoding. Expects the dict to contrain two
keys ('k' and 'v'), for the initial call the values for these keys
should be empty Tensors of the appropriate shape.
'k' [batch_size, 0, key_channels]
'v' [batch_size, 0, value_channels]
gap_size: Integer option for dilated attention to indicate spacing between
memory blocks.
num_memory_blocks: Integer option to indicate how many memory blocks to look
at.
name: an optional string.
save_weights_to: an optional dictionary to capture attention weights
for vizualization; the weights tensor will be appended there under
a string key created from the variable scope (including name).
make_image_summary: Whether to make an attention image summary.
dropout_broadcast_dims: an optional list of integers less than 4
specifying in which dimensions to broadcast the dropout decisions.
saves memory.
max_length: an integer - needed by relative attention
vars_3d: use 3-dimensional variables for input/output transformations
scale_dotproduct: whether to normalize the attention product.
**kwargs (dict): Parameters for the attention function
Caching:
WARNING: For decoder self-attention, i.e. when memory_antecedent == None,
the caching assumes that the bias contains future masking.
The caching works by saving all the previous key and value values so that
you are able to send just the last query location to this attention
function. I.e. if the cache dict is provided it assumes the query is of the
shape [batch_size, 1, hidden_dim] rather than the full memory.
Returns:
The result of the attention transformation. The output shape is
[batch_size, length_q, hidden_dim]
unless the cache dict is provided in which case only the last memory
position is calculated and the output shape is [batch_size, 1, hidden_dim]
Optionally returns an additional loss parameters (ex: load balance loss for
the experts) returned by the attention_type function.
Raises:
ValueError: if the key depth or value depth are not divisible by the
number of attention heads.
"""
if total_key_depth % num_heads != 0:
raise ValueError("Key depth (%d) must be divisible by the number of "
"attention heads (%d)." %
(total_key_depth, num_heads))
if total_value_depth % num_heads != 0:
raise ValueError("Value depth (%d) must be divisible by the number of "
"attention heads (%d)." %
(total_value_depth, num_heads))
vars_3d_num_heads = num_heads if vars_3d else 0
with tf.variable_scope(name,
default_name="multihead_attention",
values=[query_antecedent, memory_antecedent]):
if cache is None or memory_antecedent is None:
q, k, v = common_attention.compute_qkv(
query_antecedent,
memory_antecedent,
total_key_depth,
total_value_depth,
q_filter_width,
kv_filter_width,
q_padding,
kv_padding,
vars_3d_num_heads=vars_3d_num_heads)
if cache is not None:
if attention_type != "dot_product":
# TODO(petershaw): Support caching when using relative position
# representations, i.e. "dot_product_relative" attention.
raise NotImplementedError(
"Caching is not guaranteed to work with attention types other than"
" dot_product.")
if bias is None:
raise ValueError(
"Bias required for caching. See function docstring "
"for details.")
if memory_antecedent is not None:
# Encoder-Decoder Attention Cache
q = common_attention.compute_attention_component(
query_antecedent,
total_key_depth,
q_filter_width,
q_padding,
"q",
vars_3d_num_heads=vars_3d_num_heads)
k = cache["k_encdec"]
v = cache["v_encdec"]
else:
k = common_attention.split_heads(k, num_heads)
v = common_attention.split_heads(v, num_heads)
decode_loop_step = kwargs.get("decode_loop_step")
if decode_loop_step is None:
k = cache["k"] = tf.concat([cache["k"], k], axis=2)
v = cache["v"] = tf.concat([cache["v"], v], axis=2)
else:
# Inplace update is required for inference on TPU.
# Inplace_ops only supports inplace_update on the first dimension.
# The performance of current implementation is better than updating
# the tensor by adding the result of matmul(one_hot,
# update_in_current_step)
tmp_k = tf.transpose(cache["k"], perm=[2, 0, 1, 3])
tmp_k = inplace_ops.alias_inplace_update(
tmp_k, decode_loop_step, tf.squeeze(k, axis=2))
k = cache["k"] = tf.transpose(tmp_k, perm=[1, 2, 0, 3])
tmp_v = tf.transpose(cache["v"], perm=[2, 0, 1, 3])
tmp_v = inplace_ops.alias_inplace_update(
tmp_v, decode_loop_step, tf.squeeze(v, axis=2))
v = cache["v"] = tf.transpose(tmp_v, perm=[1, 2, 0, 3])
q = common_attention.split_heads(q, num_heads)
if cache is None:
k = common_attention.split_heads(k, num_heads)
v = common_attention.split_heads(v, num_heads)
key_depth_per_head = total_key_depth // num_heads
if not vars_3d:
if scale_dotproduct:
q *= key_depth_per_head**-0.5
additional_returned_value = None
if callable(
attention_type): # Generic way to extend multihead_attention
x = attention_type(q, k, v, **kwargs)
if isinstance(x, tuple):
x, additional_returned_value = x # Unpack
elif attention_type == "dot_product":
x = common_attention.dot_product_attention(
q,
k,
v,
bias,
dropout_rate,
image_shapes,
save_weights_to=save_weights_to,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims)
elif attention_type == "dot_product_relative":
x = common_attention.dot_product_attention_relative(
q,
k,
v,
bias,
max_relative_position,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary)
elif attention_type == "dot_product_relative_v2":
x = common_attention.dot_product_self_attention_relative_v2(
q,
k,
v,
bias,
max_length,
dropout_rate,
image_shapes,
make_image_summary=make_image_summary,
dropout_broadcast_dims=dropout_broadcast_dims)
elif attention_type == "local_within_block_mask_right":
x = common_attention.masked_within_block_local_attention_1d(
q, k, v, block_length=block_length)
elif attention_type == "rel_local_mask_right":
x = common_attention.masked_rel_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary,
dropout_rate=dropout_rate,
share_rel_embed=shared_rel)
elif attention_type == "local_mask_right":
x = common_attention.masked_local_attention_1d(
q,
k,
v,
block_length=block_length,
make_image_summary=make_image_summary)
elif attention_type == "local_unmasked":
x = common_attention.local_attention_1d(q,
k,
v,
block_length=block_length,
filter_width=block_width)
elif attention_type == "masked_dilated_1d":
x = common_attention.masked_dilated_self_attention_1d(
q, k, v, block_length, block_width, gap_size,
num_memory_blocks)
else:
assert attention_type == "unmasked_dilated_1d"
x = common_attention.dilated_self_attention_1d(
q, k, v, block_length, block_width, gap_size,
num_memory_blocks)
x = common_attention.combine_heads(x)
# Set last dim specifically.
x.set_shape(x.shape.as_list()[:-1] + [total_value_depth])
if vars_3d:
o_var = tf.get_variable(
"o", [num_heads, total_value_depth // num_heads, output_depth])
o_var = tf.cast(o_var, x.dtype)
o_var = tf.reshape(o_var, [total_value_depth, output_depth])
x = tf.tensordot(x, o_var, axes=1)
else:
x = common_layers.dense(x,
output_depth,
use_bias=False,
name="output_transform")
if additional_returned_value is not None:
return x, additional_returned_value
return x
def lagrangian_optimizer(train_set,
epsilon=epsilon,
learning_rate=0.01,
learning_rate_constraint=0.01,
loops=2000):
tf.reset_default_graph()
x_train, y_train, z_train = train_set
num_examples = x_train.shape[0]
dimension = x_train.shape[-1]
# Data tensors.
features_tensor = tf.constant(x_train.astype("float32"),
name="features")
labels_tensor = tf.constant(y_train.astype("float32"), name="labels")
# Linear model.
weights = tf.Variable(tf.zeros(dimension, dtype=tf.float32),
name="weights")
threshold = tf.Variable(0, name="threshold", dtype=tf.float32)
predictions_tensor = (
tf.tensordot(features_tensor, weights, axes=(1, 0)) + threshold)
predictions_group0 = tf.boolean_mask(predictions_tensor,
mask=(z_train < 1))
num0 = np.sum(z_train < 1)
predictions_group1 = tf.boolean_mask(predictions_tensor,
mask=(z_train > 0))
num1 = np.sum(z_train > 0)
# Set up rates.
context = tfco.rate_context(predictions_tensor, labels_tensor)
true_positive_rate = tfco.true_positive_rate(context)
true_negative_rate = tfco.true_negative_rate(context)
context0 = context.subset(z_train < 1)
true_positive_rate0 = tfco.true_positive_rate(context0)
context1 = context.subset(z_train > 0)
true_positive_rate1 = tfco.true_positive_rate(context1)
# Set up slack variables.
slack_tpr = tf.Variable(0.5, dtype=tf.float32)
slack_tnr = tf.Variable(0.5, dtype=tf.float32)
# Projection ops for slacks.
projection_ops = []
projection_ops.append(
tf.assign(slack_tpr, tf.clip_by_value(slack_tpr, 0.001, 0.999)))
projection_ops.append(
tf.assign(slack_tnr, tf.clip_by_value(slack_tnr, 0.001, 0.999)))
# Set up 1 - G-mean objective.
objective = tfco.wrap_rate(1.0 - tf.sqrt(slack_tpr * slack_tnr))
# Set up slack constraints.
constraints = []
constraints.append(tfco.wrap_rate(slack_tpr) <= true_positive_rate)
constraints.append(tfco.wrap_rate(slack_tnr) <= true_negative_rate)
# Set up fairness equal-opportunity constraints.
constraints.append(
true_positive_rate0 <= true_positive_rate1 + epsilon)
constraints.append(
true_positive_rate1 <= true_positive_rate0 + epsilon)
# Set up constraint optimization problem.
problem = tfco.RateMinimizationProblem(objective, constraints)
# Set up solver.
optimizer = tf.train.AdamOptimizer(learning_rate)
constraint_optimizer = tf.train.AdamOptimizer(learning_rate_constraint)
lagrangian_optimizer = tfco.ProxyLagrangianOptimizerV1(
optimizer=optimizer, constraint_optimizer=constraint_optimizer)
train_op = lagrangian_optimizer.minimize(problem)
# Start TF session and initialize variables.
session = tf.Session()
tf.set_random_seed(654321) # Set random seed for reproducibility.
session.run(tf.global_variables_initializer())
# We maintain a list of objectives and model weights during training.
objectives = []
violations = []
models = []
# Perform full gradient updates.
for ii in xrange(loops):
# Gradient update.
session.run(train_op)
# Projection.
session.run(projection_ops)
# Checkpoint once in 100 iterations.
if ii % 100 == 0:
# Model weights.
model = [session.run(weights), session.run(threshold)]
models.append(model)
# Snapshot performace
error, tpr0, tpr1 = evaluate_expected_results(
train_set, [model], [1.0])
objectives.append(error)
violations.append(
[tpr0 - tpr1 - epsilon, tpr1 - tpr0 - epsilon])
# Use the recorded objectives and constraints to find the best iterate.
# Best model
best_iterate = tfco.find_best_candidate_index(np.array(objectives),
np.array(violations))
best_model = models[best_iterate]
# Stochastic model over a subset of classifiers.
probabilities = tfco.find_best_candidate_distribution(
np.array(objectives), np.array(violations))
models_pruned = [
models[i] for i in range(len(models)) if probabilities[i] > 0.0
]
probabilities_pruned = probabilities[probabilities > 0.0]
# Stochastic model over all classifiers.
probabilities_all = probabilities * 0.0 + 1.0 / len(probabilities)
# Return Pruned models, Avg models, Best model
results = {
'stochastic': (models, probabilities_all),
'pruned': (models_pruned, probabilities_pruned),
'best': best_model,
'objectives': objectives,
'violations': violations
}
return results
def smpl_model_batched(model_path, betas, pose, trans, simplify=False):
"""
Construct a compute graph that takes in parameters and outputs a tensor as
model vertices. Face indices are also returned as a numpy ndarray.
Parameters:
---------
pose: Also known as 'theta', a [24,3] tensor indicating child joint rotation
relative to parent joint. For root joint it's global orientation.
Represented in a axis-angle format.
betas: Parameter for model shape. A tensor of shape [10] as coefficients of
PCA components. Only 10 components were released by SMPL author.
trans: Global translation tensor of shape [3].
Return:
------
A tensor for vertices, and a numpy ndarray as face indices.
"""
# For detailed comments see smpl_np.py
with open(model_path, 'rb') as f:
params = pickle.load(f)
J_regressor = tf.constant(
np.array(params['J_regressor'].todense(), dtype=np.float64))
weights = tf.constant(params['weights'], dtype=np.float64)
posedirs = tf.constant(params['posedirs'], dtype=np.float64)
v_template = tf.constant(params['v_template'], dtype=np.float64)
shapedirs = tf.constant(params['shapedirs'], dtype=np.float64)
f = params['f']
kintree_table = params['kintree_table']
id_to_col = {kintree_table[1, i]: i for i in range(kintree_table.shape[1])}
parent = {
i: id_to_col[kintree_table[0, i]]
for i in range(1, kintree_table.shape[1])
}
v_shaped = tf.tensordot(betas, shapedirs, axes=[[1], [2]]) + v_template
J = tf.matmul(J_regressor, v_shaped)
pose_cube = tf.reshape(pose, (-1, 1, 3))
R_cube_big = rodrigues(pose_cube)
if simplify:
v_posed = v_shaped
else:
R_cube = R_cube_big[1:]
I_cube = tf.expand_dims(tf.eye(3, dtype=tf.float64), axis=0) + \
tf.zeros((R_cube.get_shape()[0], 3, 3), dtype=tf.float64)
lrotmin = tf.squeeze(tf.reshape((R_cube - I_cube), (-1, 1)))
v_posed = v_shaped + tf.tensordot(lrotmin, posedirs, axes=[[1], [2]])
results = []
results.append(
with_zeros(
tf.concat((R_cube_big[0], tf.reshape(J[0, :], (3, 1))), axis=1)))
for i in range(1, kintree_table.shape[1]):
results.append(
tf.matmul(
results[parent[i]],
with_zeros(
tf.concat((R_cube_big[i],
tf.reshape(J[i, :] - J[parent[i], :], (3, 1))),
axis=1))))
stacked = tf.stack(results, axis=0)
results = stacked - \
pack(
tf.matmul(
stacked,
tf.reshape(
tf.concat((J, tf.zeros((24, 1), dtype=tf.float64)), axis=1),
(24, 4, 1)
)
)
)
T = tf.tensordot(weights, results, axes=((1), (0)))
rest_shape_h = tf.concat(
(v_posed,
tf.ones((v_posed.get_shape().as_list()[0], 1), dtype=tf.float64)),
axis=1)
v = tf.matmul(T, tf.reshape(rest_shape_h, (-1, 4, 1)))
v = tf.reshape(v, (-1, 4))[:, :3]
result = v + tf.reshape(trans, (1, 3))
return result
def step_state(state):
return state + tf.reduce_sum(
input_tensor=tf.tensordot(data, state, ([1], [1])))
def runMoreRnn(path=None, epochs=10, saveResult=True):
trainData, validData, testData, wordId = loadWordIdsFromFiles()
trainData = np.array(trainData, np.float32)
# validData = np.array(validData, np.float32)
testData = np.array(testData, np.float32)
vocabSz = len(wordId)
info = loadInfo('rnn', path)
learnRate = info['learning rate']
batchSz = info['batch size']
embedSz = info['embed size']
rnnSz = info['rnn size']
winSz = info['win size']
numWin = (trainData.shape[0] - 1) // (batchSz * winSz)
# each batch has winSz * numWin words
batchLen = winSz * numWin
testNumWin = (testData.shape[0] - 1) // (batchSz * winSz)
testBatchLen = winSz * testNumWin
inp = tf.placeholder(tf.int32, shape=[batchSz, winSz])
# ans = tf.placeholder(tf.int32, shape=[batchSz * winSz])
ans = tf.placeholder(tf.int32, shape=[batchSz, winSz])
E = tf.Variable(tf.random_normal([vocabSz, embedSz], stddev=0.1))
embed = tf.nn.embedding_lookup(E, inp)
rnn = BasicRNNCell(rnnSz, activation='relu')
initialState = rnn.zero_state(batchSz, tf.float32)
output, nextState = tf.nn.dynamic_rnn(rnn, embed, initial_state=initialState)
# output = tf.reshape(output, [batchSz * winSz, rnnSz])
W = tf.Variable(tf.random_normal([rnnSz, vocabSz], stddev=.1))
B = tf.Variable(tf.random_normal([vocabSz], stddev=.1))
# logits = tf.matmul(output, W) + B
logits = tf.tensordot(output, W, [[2], [0]]) + B
ents = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=ans)
loss = tf.reduce_sum(ents)
train = tf.train.GradientDescentOptimizer(learnRate).minimize(loss)
trainPerp = np.zeros(epochs + 1, dtype=np.float32)
trainPerp[0] = info['train perplexity']
testPerp = np.zeros(epochs + 1, dtype=np.float32)
testPerp[0] = info['test perplexity']
with tf.Session() as sess:
loadSession(sess, 'rnn', path)
startTime = time.time()
epoch = 0
print('epoch:', end=' ')
while epoch < epochs:
epoch += 1
win = 0
state = sess.run(initialState)
testState = sess.run(initialState)
# print(state, testState)
winStart, winEnd = 0, winSz
while win < numWin:
inInp = np.array([trainData[i * batchLen + winStart:i * batchLen + winEnd] for i in range(batchSz)])
# inAns = np.reshape(np.array([trainData[i * batchLen + winStart + 1: i * batchLen + winEnd + 1] for i in range(batchSz)]), batchSz * winSz)
inAns = np.array([trainData[i * batchLen + winStart + 1: i * batchLen + winEnd + 1] for i in range(batchSz)])
_, state, outLoss = sess.run([train, nextState, loss], {inp: inInp, ans: inAns, nextState: state})
trainPerp[epoch] += outLoss
if win < testNumWin:
inInp = np.array([testData[i * testBatchLen + winStart:i * testBatchLen + winEnd] for i in range(batchSz)])
# inAns = np.reshape(np.array([testData[i * testBatchLen + winStart + 1: i * testBatchLen + winEnd + 1] for i in range(batchSz)]), batchSz * winSz)
inAns = np.array([testData[i * testBatchLen + winStart + 1: i * testBatchLen + winEnd + 1] for i in range(batchSz)])
testState, testOutLoss = sess.run([nextState, loss], {inp: inInp, ans: inAns, nextState: testState})
testPerp[epoch] += testOutLoss
winStart, winEnd = winEnd, winEnd + winSz
win += 1
print(epoch + info['epochs'], end=' ')
trainPerp[1:] = np.exp(trainPerp[1:] / (trainData.shape[0] // (batchSz * batchLen) * (batchSz * batchLen)))
testPerp[1:] = np.exp(testPerp[1:] / (testData.shape[0] // (batchSz * testBatchLen) * (batchSz * testBatchLen)))
print(f'\nelapsed: {time.time() - startTime}')
print('train perplexity:', trainPerp[-1])
print('test perplexity:', testPerp[-1])
info['epochs'] += epochs
info['train perplexity'] = trainPerp[-1]
info['test perplexity'] = testPerp[-1]
if saveResult:
save(sess, info)
drawPerplexity(trainPerp, testPerp, info['epochs'] - epochs)
for l in range(num_hidden_layers):
current_layer = tf.layers.dense(previous_layer,
num_hidden_neurons[l],
activation=tf.nn.sigmoid)
previous_layer = current_layer
dnn_output = tf.layers.dense(previous_layer, matrix_size)
with tf.name_scope('loss'):
print("dnn_output = ", dnn_output)
x_trial = tf.transpose(dnn_output)
print("x_trial = ", x_trial)
temp1 = (tf.tensordot(tf.transpose(x_trial), x_trial, axes=1) * A)
temp2 = (1 - tf.tensordot(tf.transpose(x_trial),
tf.tensordot(A, x_trial, axes=1),
axes=1)) * np.eye(matrix_size)
func = tf.tensordot((temp1 - temp2), x_trial, axes=1)
print(temp1)
print(temp2)
print(func)
func = tf.transpose(func)
x_trial = tf.transpose(x_trial)
loss = tf.losses.mean_squared_error(func, x_trial)
learning_rate = 0.001
def option_values(values, policy):
return tf.tensordot(
values[:, policy, Ellipsis], self._policy_weights[policy], axes=[1, 0])
def testPCgradBasic(self,
denylist,
allowlist,
pcgrad_var_idx):
tf.disable_eager_execution()
for dtype in [tf.dtypes.float32, tf.dtypes.float64]:
with self.session(graph=tf.Graph()):
var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype)
var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype)
const0_np = np.array([1., 0.], dtype=dtype.as_numpy_dtype)
const1_np = np.array([-1., -1.], dtype=dtype.as_numpy_dtype)
const2_np = np.array([-1., 1.], dtype=dtype.as_numpy_dtype)
var0 = tf.Variable(var0_np, dtype=dtype, name='first_var/var0')
var1 = tf.Variable(var1_np, dtype=dtype, name='second_var/var1')
const0 = tf.constant(const0_np)
const1 = tf.constant(const1_np)
const2 = tf.constant(const2_np)
loss0 = tf.tensordot(var0, const0, 1) + tf.tensordot(var1, const2, 1)
loss1 = tf.tensordot(var0, const1, 1) + tf.tensordot(var1, const0, 1)
learning_rate = lambda: 0.001
opt = tf.train.GradientDescentOptimizer(learning_rate)
losses = loss0 + loss1
opt_grads = opt.compute_gradients(losses, var_list=[var0, var1])
pcgrad_opt = pcgrad.PCGrad(
tf.train.GradientDescentOptimizer(learning_rate),
denylist=denylist,
allowlist=allowlist)
pcgrad_col_opt = pcgrad.PCGrad(
tf.train.GradientDescentOptimizer(learning_rate),
use_collection_losses=True,
denylist=denylist,
allowlist=allowlist)
losses = [loss0, loss1]
pcgrad_grads = pcgrad_opt.compute_gradients(
losses, var_list=[var0, var1])
tf.add_to_collection(pcgrad.PCGRAD_LOSSES_COLLECTION, loss0)
tf.add_to_collection(pcgrad.PCGRAD_LOSSES_COLLECTION, loss1)
pcgrad_grads_collection = pcgrad_col_opt.compute_gradients(
None, var_list=[var0, var1])
with tf.Graph().as_default():
# Shouldn't return non-slot variables from other graphs.
self.assertEmpty(opt.variables())
self.evaluate(tf.global_variables_initializer())
grad_vec, pcgrad_vec, pcgrad_col_vec = self.evaluate(
[opt_grads, pcgrad_grads, pcgrad_grads_collection])
# Make sure that both methods take grads of the same vars.
self.assertAllCloseAccordingToType(pcgrad_vec, pcgrad_col_vec)
results = [{
'var': var0,
'pcgrad_vec': [0.5, -1.5],
'result': [0.9995, 2.0015]
}, {
'var': var1,
'pcgrad_vec': [0.5, 1.5],
'result': [2.9995, 3.9985]
}]
grad_var_idx = {0, 1}.difference(pcgrad_var_idx)
self.assertAllCloseAccordingToType(
grad_vec[0][0], [0.0, -1.0], atol=1e-5)
self.assertAllCloseAccordingToType(
grad_vec[1][0], [0.0, 1.0], atol=1e-5)
pcgrad_vec_idx = 0
for var_idx in pcgrad_var_idx:
self.assertAllCloseAccordingToType(
pcgrad_vec[pcgrad_vec_idx][0],
results[var_idx]['pcgrad_vec'],
atol=1e-5)
pcgrad_vec_idx += 1
for var_idx in grad_var_idx:
self.assertAllCloseAccordingToType(
pcgrad_vec[pcgrad_vec_idx][0], grad_vec[var_idx][0], atol=1e-5)
pcgrad_vec_idx += 1
self.evaluate(opt.apply_gradients(pcgrad_grads))
self.assertAllCloseAccordingToType(
self.evaluate([results[idx]['var'] for idx in pcgrad_var_idx]),
[results[idx]['result'] for idx in pcgrad_var_idx])
def apply_ccm(image, ccm):
"""Applies a color correction matrix."""
shape = tf.shape(image)
image = tf.reshape(image, [-1, 3])
image = tf.tensordot(image, ccm, axes=[[-1], [-1]])
return tf.reshape(image, shape)
def broadcast_matmul_train(x,
variational_params,
clip_alpha=None,
eps=common.EPSILON):
R"""Training computation for VD matrix multiplication with N input matrices.
Multiplies a 3D tensor `x` with a set of 2D parameters. Each 2D matrix
`x[i, :, :]` in the input tensor is multiplied indendently with the
parameters, resulting in a 3D output tensor with shape
`x.shape[:2] + weight_parameters[0].shape[1]`.
Args:
x: 3D Tensor representing the input batch.
variational_params: 2-tuple of Tensors, where the first tensor is the
unscaled weight values and the second is the log of the alpha values
for the hard concrete distribution.
clip_alpha: Int or None. If integer, we clip the log \alpha values to
[-clip_alpha, clip_alpha]. If None, don't clip the values.
eps: Small constant value to use in log and sqrt operations to avoid NaNs.
Returns:
Output Tensor of the batched matmul operation.
Raises:
RuntimeError: If the variational_params argument is not a 2-tuple.
"""
theta, log_sigma2 = _verify_variational_params(variational_params)
theta.get_shape().assert_has_rank(2)
log_sigma2.get_shape().assert_has_rank(2)
# The input data must have be rank 2 or greater
assert x.get_shape().ndims >= 2
input_rank = x.get_shape().ndims
if clip_alpha is not None:
# Compute the log_alphas and then compute the
# log_sigma2 again so that we can clip on the
# log alpha magnitudes
log_alpha = common.compute_log_alpha(log_sigma2, theta, eps,
clip_alpha)
log_sigma2 = common.compute_log_sigma2(log_alpha, theta, eps)
# Compute the mean and standard deviation of the distributions over the
# activations
mu_activation = tf.tensordot(x, theta, [[input_rank - 1], [0]])
var_activation = tf.tensordot(tf.square(x), tf.exp(log_sigma2),
[[input_rank - 1], [0]])
std_activation = tf.sqrt(var_activation + eps)
# Reshape the output back to the rank of the input
input_shape = x.get_shape().as_list()
weight_shape = theta.get_shape().as_list()
output_shape = input_shape[:-1] + [weight_shape[1]]
mu_activation.set_shape(output_shape)
std_activation.set_shape(output_shape)
# NOTE: We sample noise for each weight in theta, which will be shared by
# each matrix product that was done. This is equivalent to sampling the same
# set of weights for all matrix products done by this op in an iteration.
# The element-wise multiply below broadcasts.
num_pad_dims = len(output_shape) - 2
padding = [tf.constant(1, dtype=tf.int32) for _ in range(num_pad_dims)]
# NOTE: On GPU, the first dim may not be defined w/ the Transformer. Create
# a tf.Tensor from the list shape and TF should match the first dim
# appropriately
batch_size = tf.shape(x)[0]
data_dim = tf.shape(theta)[-1]
noise_shape = tf.stack([batch_size] + padding + [data_dim], axis=0)
output = mu_activation + std_activation * tf.random_normal(noise_shape)
return output
def _static_subsample(self, indicator, batch_size, labels):
"""Returns subsampled minibatch.
Args:
indicator: boolean tensor of shape [N] whose True entries can be sampled.
N should be a complie time constant.
batch_size: desired batch size. This scalar cannot be None.
labels: boolean tensor of shape [N] denoting positive(=True) and negative
(=False) examples. N should be a complie time constant.
Returns:
sampled_idx_indicator: boolean tensor of shape [N], True for entries which
are sampled. It ensures the length of output of the subsample is always
batch_size, even when number of examples set to True in indicator is
less than batch_size.
Raises:
ValueError: if labels and indicator are not 1D boolean tensors.
"""
# Check if indicator and labels have a static size.
if not indicator.shape.is_fully_defined():
raise ValueError(
'indicator must be static in shape when is_static is'
'True')
if not labels.shape.is_fully_defined():
raise ValueError('labels must be static in shape when is_static is'
'True')
if not isinstance(batch_size, int):
raise ValueError(
'batch_size has to be an integer when is_static is'
'True.')
input_length = tf.shape(indicator)[0]
# Set the number of examples set True in indicator to be at least
# batch_size.
num_true_sampled = tf.reduce_sum(tf.cast(indicator, tf.float32))
additional_false_sample = tf.less_equal(
tf.cumsum(tf.cast(tf.logical_not(indicator), tf.float32)),
batch_size - num_true_sampled)
indicator = tf.logical_or(indicator, additional_false_sample)
# Shuffle indicator and label. Need to store the permutation to restore the
# order post sampling.
permutation = tf.random_shuffle(tf.range(input_length))
indicator = ops.matmul_gather_on_zeroth_axis(
tf.cast(indicator, tf.float32), permutation)
labels = ops.matmul_gather_on_zeroth_axis(tf.cast(labels, tf.float32),
permutation)
# index (starting from 1) when indicator is True, 0 when False
indicator_idx = tf.where(tf.cast(indicator, tf.bool),
tf.range(1, input_length + 1),
tf.zeros(input_length, tf.int32))
# Replace -1 for negative, +1 for positive labels
signed_label = tf.where(
tf.cast(labels, tf.bool), tf.ones(input_length, tf.int32),
tf.scalar_mul(-1, tf.ones(input_length, tf.int32)))
# negative of index for negative label, positive index for positive label,
# 0 when indicator is False.
signed_indicator_idx = tf.multiply(indicator_idx, signed_label)
sorted_signed_indicator_idx = tf.nn.top_k(signed_indicator_idx,
input_length,
sorted=True).values
[num_positive_samples, num_negative_samples
] = self._get_num_pos_neg_samples(sorted_signed_indicator_idx,
batch_size)
sampled_idx = self._get_values_from_start_and_end(
sorted_signed_indicator_idx, num_positive_samples,
num_negative_samples, batch_size)
# Shift the indices to start from 0 and remove any samples that are set as
# False.
sampled_idx = tf.abs(sampled_idx) - tf.ones(batch_size, tf.int32)
sampled_idx = tf.multiply(
tf.cast(tf.greater_equal(sampled_idx, tf.constant(0)), tf.int32),
sampled_idx)
sampled_idx_indicator = tf.cast(
tf.reduce_sum(tf.one_hot(sampled_idx, depth=input_length), axis=0),
tf.bool)
# project back the order based on stored permutations
reprojections = tf.one_hot(permutation,
depth=input_length,
dtype=tf.float32)
return tf.cast(
tf.tensordot(tf.cast(sampled_idx_indicator, tf.float32),
reprojections,
axes=[0, 0]), tf.bool)
def attention(ratelayer, inputs, tag, attention_size=32):
ratelayer.attention_size = attention_size
ratelayer.tag = tag
if isinstance(inputs, tuple):
print("Attention layer - inputs is tuple, concat")
# In case of Bi-RNN, concatenate the forward and the backward RNN outputs.
inputs = tf.concat(inputs, 2)
if ratelayer.time_major:
# (T,B,D) => (B,T,D)
inputs = tf.transpose(inputs, [1, 0, 2])
hidden_size = inputs.shape[
2].value # D value - hidden size of the RNN layer
print("hidden_size in attention layer", hidden_size)
print("Att input shape", inputs.shape)
# Trainable parameters
with tf.variable_scope('v_' + ratelayer.tag):
w_omega = tf.get_variable(initializer=tf.random_normal(
[hidden_size + FLAGS.latent_dim, ratelayer.attention_size],
stddev=0.1),
name='w_omega')
ratelayer.vars['w_omega'] = w_omega
# b_omega = tf.get_variable(initializer=tf.random_normal(
# [ratelayer.attention_size], stddev=0.1), name='b_omega')
# ratelayer.vars['b_omega'] = b_omega
u_omega = tf.get_variable(initializer=tf.random_normal(
[ratelayer.attention_size], stddev=0.1),
name='u_omega')
ratelayer.vars['u_omega'] = u_omega
b_v = tf.get_variable(initializer=tf.random_normal([1],
stddev=0.1),
name='b_v')
ratelayer.vars['b_v'] = b_v
# init for projection vars
ratelayer.vars['project_' + self.tag] = tf.get_variable(
initializer=tf.random_normal(
[FLAGS.latent_dim, FLAGS.latent_dim], stddev=0.1),
name='project_' + ratelayer.tag + '_matrix')
ratelayer.vars['project_bias_' +
ratelayer.tag] = tf.get_variable(
initializer=tf.random_normal(
[FLAGS.latent_dim], stddev=0.1),
name='b_projection_' + ratelayer.tag)
# transform and tile
ratelayer.vars['projected_' + ratelayer.tag + '_latent'] = \
dot(ratelayer.vars[ratelayer.tag + '_latent'], ratelayer.vars['project_' + ratelayer.tag]) \
+ ratelayer.vars['project_bias_' + ratelayer.tag]
ratelayer.vars['projected_' + ratelayer.tag + '_latent'] = \
tf.nn.sigmoid(ratelayer.vars['projected_'+ratelayer.tag+'_latent'])
projected_latent = tf.tile(
tf.expand_dims(ratelayer.vars['projected_' + ratelayer.tag +
'_latent'],
axis=0), [inputs.shape[0], 1, 1])
# concat and non-linear attention additive one like
# in https://lilianweng.github.io/lil-log/2018/06/24/attention-attention.html
v1 = tf.concat([inputs, projected_latent], axis=2)
v = tf.tanh(tf.tensordot(v1, w_omega, axes=1))
vu = tf.tensordot(v, u_omega, axes=1, name='vu')
# For each of the timestamps its vector of size A from `v` is reduced with `u` vector
print("vu shape", vu.shape) # vu shape (4, 2005)
alphas = tf.nn.softmax(vu, name='alphas', axis=0) # (B,T) shape
# Output of (Bi-)RNN is reduced with attention vector; the result has (B,D) shape
output = tf.reduce_sum(inputs * tf.expand_dims(alphas, -1), 0)
return output, alphas
def body(self, features):
hparams = self._hparams
ps_devices = self._ps_devices
single_device = (len(ps_devices) == 1)
assert hparams.num_model_shards % len(ps_devices) == 0
shards_per_device = hparams.num_model_shards // len(ps_devices)
model_devices = [
ps_devices[i // shards_per_device]
for i in range(hparams.num_model_shards)
]
print("model_devices = %s" % model_devices)
mp = expert_utils.Parallelism(model_devices, reuse=False)
targets_vocab_size = self._problem_hparams.vocabulary[
"targets"].vocab_size
# squeeze out channels, heights
targets = tf.squeeze(features["targets_raw"], [2, 3])
targets_embedding_var = mp(
tf.get_variable,
"embedding", [[targets_vocab_size, hparams.model_d]] * mp.n,
initializer=tf.random_normal_initializer(0.0,
hparams.model_d**-0.5))
shifted_targets = common_layers.shift_right_2d(targets)
# Bypass the symbol modality and use a different embedding on each shard.
if single_device:
targets_embedding_var_combined = tf.concat(targets_embedding_var,
1)
decoder_input_combined = common_layers.embedding(
shifted_targets,
targets_vocab_size,
hparams.model_d * mp.n,
multiplier=hparams.model_d**0.5,
embedding_var=targets_embedding_var_combined,
)
decoder_input = tf.split(decoder_input_combined, mp.n, axis=2)
else:
targets_embedding_var_combined = None
decoder_input = mp(
common_layers.embedding,
shifted_targets,
targets_vocab_size,
hparams.model_d,
multiplier=hparams.model_d**0.5,
embedding_var=targets_embedding_var,
)
decoder_self_attention_bias = mp(
common_attention.attention_bias_lower_triangle,
tf.shape(targets)[1])
if "targets_segmentation" in features:
# "Packed" dataset - keep the examples from seeing each other.
targets_segmentation = features["targets_segmentation"]
targets_position = features["targets_position"]
decoder_self_attention_bias = mp(
tf.add, decoder_self_attention_bias,
mp(common_attention.attention_bias_same_segment,
targets_segmentation, targets_segmentation))
decoder_input = mp(
common_attention.add_timing_signal_1d_given_position,
decoder_input, targets_position)
else:
targets_position = None
decoder_self_attention_bias = mp(
common_attention.attention_bias_lower_triangle,
tf.shape(targets)[1])
decoder_input = mp(common_attention.add_timing_signal_1d,
decoder_input)
if self.has_input:
inputs = tf.squeeze(features["inputs_raw"], [2, 3])
inputs_vocab_size = self._problem_hparams.vocabulary[
"inputs"].vocab_size
# share everything for now
share_inputs_and_targets_embedding = True
if share_inputs_and_targets_embedding:
assert inputs_vocab_size == targets_vocab_size
inputs_embedding_var = targets_embedding_var
inputs_embedding_var_combined = targets_embedding_var_combined
if single_device:
encoder_input_combined = common_layers.embedding(
inputs,
inputs_vocab_size,
hparams.model_d * mp.n,
multiplier=hparams.model_d**0.5,
embedding_var=inputs_embedding_var_combined,
)
encoder_input = tf.split(encoder_input_combined, mp.n, axis=2)
else:
encoder_input = mp(
common_layers.embedding,
inputs,
inputs_vocab_size,
hparams.model_d,
multiplier=hparams.model_d**0.5,
embedding_var=inputs_embedding_var,
)
if "inputs_segmentation" in features:
# "Packed" dataset - keep the examples from seeing each other.
inputs_segmentation = features["inputs_segmentation"]
inputs_position = features["inputs_position"]
encoder_self_attention_bias = mp(
common_attention.attention_bias_same_segment,
inputs_segmentation, inputs_segmentation)
encoder_decoder_attention_bias = mp(
common_attention.attention_bias_same_segment,
targets_segmentation, inputs_segmentation)
encoder_input = mp(
common_attention.add_timing_signal_1d_given_position,
encoder_input, inputs_position)
else:
encoder_padding = tf.to_float(tf.equal(inputs, 0))
ignore_padding = common_attention.attention_bias_ignore_padding(
encoder_padding)
encoder_self_attention_bias = ignore_padding
encoder_decoder_attention_bias = ignore_padding
inputs_position = None
encoder_input = mp(common_attention.add_timing_signal_1d,
encoder_input)
# encoder stack here
with tf.variable_scope("encoder"):
encoder_input = mp(tf.nn.dropout, encoder_input,
1.0 - hparams.layer_prepostprocess_dropout)
encoder_output = _layer_stack(mp, encoder_input,
encoder_self_attention_bias,
hparams.encoder_layers, hparams)
else:
encoder_decoder_attention_bias = None
encoder_output = None
with tf.variable_scope("decoder"):
decoder_input = mp(tf.nn.dropout, decoder_input,
1.0 - hparams.layer_prepostprocess_dropout)
decoder_output = _layer_stack(
mp,
decoder_input,
decoder_self_attention_bias,
layers=hparams.decoder_layers,
hparams=hparams,
encoder_output=encoder_output,
encoder_decoder_attention_bias=encoder_decoder_attention_bias)
# Bypass the symbol modality and compute logits directly.
# We compute a different set of logits on each shard, and sum them.
# Share the weights with the target embedding.
output_var = targets_embedding_var
output_var_combined = targets_embedding_var_combined
if single_device:
decoder_output = tf.concat(decoder_output, 2)
logits = tf.tensordot(decoder_output, output_var_combined,
[[2], [1]])
num, denom = common_layers.padded_cross_entropy(
logits, targets, hparams.label_smoothing)
training_loss = num / denom
else:
logits = mp(tf.tensordot, decoder_output, output_var,
[[[2], [1]]] * mp.n)
logits = expert_utils.all_reduce_ring(logits, mp)
# On each device, we compute the loss for a part of the batch.
# This is faster than computing the whole loss on one shard.
mp, logits = expert_utils.reduce_by_device(mp, logits,
lambda l: l[0])
def _loss_for_shard(logits, targets, shard):
logits = common_layers.approximate_split(logits, mp.n,
0)[shard]
targets = common_layers.approximate_split(targets, mp.n,
0)[shard]
return common_layers.padded_cross_entropy(
logits, targets, hparams.label_smoothing)
num, denom = mp(_loss_for_shard, logits, targets, range(mp.n))
training_loss = tf.add_n(num) / tf.add_n(denom)
logits = logits[0]
logits = tf.expand_dims(tf.expand_dims(logits, 2), 3)
# override training loss so that it is not computed externally.
losses = {"training": training_loss}
return logits, losses
def graph_fn(tensora, tensorb):
return tf.tensordot(tensora, tensorb, axes=1)
def boolean_mask(boxlist,
indicator,
fields=None,
scope=None,
use_static_shapes=False,
indicator_sum=None):
"""Select boxes from BoxList according to indicator and return new BoxList.
`boolean_mask` returns the subset of boxes that are marked as "True" by the
indicator tensor. By default, `boolean_mask` returns boxes corresponding to
the input index list, as well as all additional fields stored in the boxlist
(indexing into the first dimension). However one can optionally only draw
from a subset of fields.
Args:
boxlist: BoxList holding N boxes
indicator: a rank-1 boolean tensor
fields: (optional) list of fields to also gather from. If None (default),
all fields are gathered from. Pass an empty fields list to only gather
the box coordinates.
scope: name scope.
use_static_shapes: Whether to use an implementation with static shape
gurantees.
indicator_sum: An integer containing the sum of `indicator` vector. Only
required if `use_static_shape` is True.
Returns:
subboxlist: a BoxList corresponding to the subset of the input BoxList
specified by indicator
Raises:
ValueError: if `indicator` is not a rank-1 boolean tensor.
"""
with tf.name_scope(scope, 'BooleanMask'):
if indicator.shape.ndims != 1:
raise ValueError('indicator should have rank 1')
if indicator.dtype != tf.bool:
raise ValueError('indicator should be a boolean tensor')
if use_static_shapes:
if not (indicator_sum and isinstance(indicator_sum, int)):
raise ValueError('`indicator_sum` must be a of type int')
selected_positions = tf.cast(indicator, dtype=tf.float32)
indexed_positions = tf.cast(tf.multiply(
tf.cumsum(selected_positions), selected_positions),
dtype=tf.int32)
one_hot_selector = tf.one_hot(indexed_positions - 1,
indicator_sum,
dtype=tf.float32)
sampled_indices = tf.cast(tf.tensordot(tf.cast(tf.range(
tf.shape(indicator)[0]),
dtype=tf.float32),
one_hot_selector,
axes=[0, 0]),
dtype=tf.int32)
return gather(boxlist, sampled_indices, use_static_shapes=True)
else:
subboxlist = box_list.BoxList(
tf.boolean_mask(boxlist.get(), indicator))
if fields is None:
fields = boxlist.get_extra_fields()
for field in fields:
if not boxlist.has_field(field):
raise ValueError(
'boxlist must contain all specified fields')
subfieldlist = tf.boolean_mask(boxlist.get_field(field),
indicator)
subboxlist.add_field(field, subfieldlist)
return subboxlist
def startLstm(epochs=10, saveResult=True):
trainData, validData, testData, wordId = loadWordIdsFromFiles()
trainData = np.array(trainData, np.float32)
# validData = np.array(validData, np.float32)
testData = np.array(testData, np.float32)
vocabSz = len(wordId)
learnRate = 0.001
embedSz = 128
rnnSz, batchSz, winSz = 512, 10, 5
numWin = (trainData.shape[0] - 1) // (batchSz * winSz)
# each batch has winSz * numWin words
batchLen = winSz * numWin
testNumWin = (testData.shape[0] - 1) // (batchSz * winSz)
testBatchLen = winSz * testNumWin
inp = tf.placeholder(tf.int32, shape=[batchSz, winSz])
# ans = tf.placeholder(tf.int32, shape=[batchSz * winSz])
ans = tf.placeholder(tf.int32, shape=[batchSz, winSz])
E = tf.Variable(tf.random_normal([vocabSz, embedSz], stddev=0.1))
embed = tf.nn.embedding_lookup(E, inp)
rnn = LSTMCell(rnnSz)
initialState = rnn.zero_state(batchSz, tf.float32)
output, nextState = tf.nn.dynamic_rnn(rnn, embed, initial_state=initialState)
# output = tf.reshape(output, [batchSz * winSz, rnnSz])
W = tf.Variable(tf.random_normal([rnnSz, vocabSz], stddev=.1))
B = tf.Variable(tf.random_normal([vocabSz], stddev=.1))
# logits = tf.matmul(output, W) + B
logits = tf.tensordot(output, W, [[2], [0]]) + B
ents = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=ans)
loss = tf.reduce_sum(ents)
train = tf.train.GradientDescentOptimizer(learnRate).minimize(loss)
trainPerp = np.zeros(epochs, dtype=np.float32)
testPerp = np.zeros(epochs, dtype=np.float32)
with tf.Session() as sess:
startTime = time.time()
sess.run(tf.global_variables_initializer())
epoch = 0
print('epoch:', end=' ')
while epoch < epochs:
win = 0
inState = sess.run(initialState)
testState = sess.run(initialState)
# print(inState, testState)
winStart, winEnd = 0, winSz
while win < numWin:
inInp = np.array([trainData[i * batchLen + winStart:i * batchLen + winEnd] for i in range(batchSz)])
# inAns = np.reshape(np.array([trainData[i * batchLen + winStart + 1: i * batchLen + winEnd + 1] for i in range(batchSz)]), batchSz * winSz)
inAns = np.array([trainData[i * batchLen + winStart + 1: i * batchLen + winEnd + 1] for i in range(batchSz)])
_, inState, outLoss = sess.run([train, nextState, loss], {inp: inInp, ans: inAns, nextState: inState})
trainPerp[epoch] += outLoss
if win < testNumWin:
inInp = np.array([testData[i * testBatchLen + winStart:i * testBatchLen + winEnd] for i in range(batchSz)])
# inAns = np.reshape(np.array([testData[i * testBatchLen + winStart + 1: i * testBatchLen + winEnd + 1] for i in range(batchSz)]), batchSz * winSz)
inAns = np.array([testData[i * testBatchLen + winStart + 1: i * testBatchLen + winEnd + 1] for i in range(batchSz)])
testState, testOutLoss = sess.run([nextState, loss], {inp: inInp, ans: inAns, nextState: testState})
testPerp[epoch] += testOutLoss
winStart, winEnd = winEnd, winEnd + winSz
win += 1
epoch += 1
print(epoch, end=' ')
trainPerp = np.exp(trainPerp / (trainData.shape[0] // (batchSz * batchLen) * (batchSz * batchLen)))
testPerp = np.exp(testPerp / (testData.shape[0] // (batchSz * testBatchLen) * (batchSz * testBatchLen)))
print(f'\nelapsed: {time.time() - startTime}')
print('train perplexity:', trainPerp[-1])
print('test perplexity:', testPerp[-1])
info = {'style': 'lstm', 'batch size': batchSz, 'embed size': embedSz, 'rnn size': rnnSz, 'win size': winSz,
'learning rate': learnRate, 'epochs': epochs, 'train perplexity': trainPerp[-1], 'test perplexity': testPerp[-1]}
if saveResult:
save(sess, info)
drawPerplexity(trainPerp, testPerp)
