def curvature_range(self):
# set up the curvature window
self._curv_win = tf.Variable(np.zeros([
self._curv_win_width,
]),
dtype=tf.float32,
name="curv_win",
trainable=False)
# we can use log smoothing for curvature range to follow trend faster
# self._curv_win = tf.scatter_update(
# self._curv_win, self._global_step % self._curv_win_width,
# tf.log(self._grad_norm_squared + EPS))
self._curv_win = tf.scatter_update(
self._curv_win, self._global_step % self._curv_win_width,
self._grad_norm_squared + EPS)
# note here the iterations start from iteration 0
valid_window = tf.slice(
self._curv_win, tf.constant([
0,
]),
tf.expand_dims(tf.minimum(tf.constant(self._curv_win_width),
self._global_step + 1),
dim=0))
if self._h_min_log_smooth:
self._h_min_t = tf.log(tf.reduce_min(valid_window) + EPS)
else:
self._h_min_t = tf.reduce_min(valid_window)
if self._h_max_log_smooth:
self._h_max_t = tf.log(tf.reduce_max(valid_window) + EPS)
else:
self._h_max_t = tf.reduce_max(valid_window)
curv_range_ops = []
with tf.control_dependencies([self._h_min_t, self._h_max_t]):
avg_op = self._moving_averager.apply(
[self._h_min_t, self._h_max_t])
with tf.control_dependencies([avg_op]):
if self._h_min_log_smooth:
self._h_min = tf.exp(
tf.identity(
self._moving_averager.average(self._h_min_t)))
else:
self._h_min = \
tf.identity(self._moving_averager.average(self._h_min_t))
if self._h_max_log_smooth:
self._h_max = tf.exp(
tf.identity(
self._moving_averager.average(self._h_max_t)))
else:
self._h_max = \
tf.identity(self._moving_averager.average(self._h_max_t))
if self._sparsity_debias:
self._h_min = self._h_min * self._sparsity_avg
self._h_max = self._h_max * self._sparsity_avg
curv_range_ops.append(avg_op)
return curv_range_ops
def focal_loss(logits, labels, alpha, gamma=2, name='focal_loss'):
"""
Focal loss for multi classification
:param logits: A float32 tensor of shape [batch_size num_class].
:param labels: A int32 tensor of shape [batch_size, num_class] or [batch_size].
:param alpha: A 1D float32 tensor for focal loss alpha hyper-parameter
:param gamma: A scalar for focal loss gamma hyper-parameter.
Returns: A tensor of the same shape as `lables`
"""
if len(labels.shape) == 1:
labels = tf.one_hot(labels, logits.shape[-1])
else:
labels = labels
labels = tf.to_float(labels)
y_pred = tf.nn.softmax(logits, dim=-1)
L = -labels * tf.log(y_pred)
L *= alpha * ((1 - y_pred)**gamma)
loss = tf.reduce_sum(L)
if tf.executing_eagerly():
tf.contrib.summary.scalar(name, loss)
else:
tf.summary.scalar(name, loss)
return loss
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]
"""
# is a complex64 Tensor representing the short-time Fourier
# Transform of each signal in . 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.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.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")