def build(self, data, reuse=None):
with tf.variable_scope(self.name, reuse=reuse):
wave = data["wave"]
mu_wav = mu_law(wave)
conditions = tf.cast(mu_wav + self.central_class, dtype=tf.int32)
inputs = tf.to_int64(data["labels"])
# 1st. build the initial causal layer.
with tf.variable_scope("init_causal_layer"):
# 1st. right shift inputs to get labels.
labels = tf.pad(inputs[:, :-1], [[0, 0], [1, 0]], constant_values=self.label_pad_value)
# 2nd. to one-hot representation
conditions_one_hot = tf.one_hot(conditions, depth=self.sample_classes, dtype=tf.float32)
inputs_one_hot = tf.one_hot(inputs, depth=self.label_classes, dtype=tf.float32)
# labels_one_hot = tf.one_hot(labels, depth=self.label_classes, dtype=tf.float32)
# 3rd. calculate
init_causal_out = dilated_causal_conv1d(inputs=inputs_one_hot, dilation_rate=1,
filters=self.residual_units, width=self.conv_width)
# 2nd. build the dilated causal blocks.
with tf.variable_scope("dilated_causal_blocks"):
resi_out = init_causal_out
skip_out = 0.
for idx in range(self.blks):
with tf.variable_scope("blk_{}".format(idx)):
for idy in range(self.layers_per_blk):
with tf.variable_scope("layer_{}".format(idy)):
conv_out = dilated_causal_conv1d(inputs=resi_out,
dilation_rate=self.dilation_base ** idy,
filters=2*self.residual_units,
width=self.conv_width)
conditions_conv = tf.layers.dense(inputs=conditions_one_hot,
units=2*self.residual_units,
activation=None, name="conditions_conv")
acti_out = self_gated_layer(inputs=tf.add(conv_out, conditions_conv))
resi_out += tf.layers.dense(inputs=acti_out, units=self.residual_units,
activation=None, name="residual_out")
skip_out += tf.layers.dense(inputs=acti_out, units=self.residual_units,
activation=None, name="skip_out")
# 3rd. calculate probabilities, and get loss.
with tf.variable_scope("softmax"):
# 1st. relu
skip_out = tf.nn.relu(skip_out)
# 2nd. dense -> relu
skip_out = tf.layers.dense(inputs=skip_out, units=self.hidden_out, activation=tf.nn.relu)
# 3rd. dense -> softmax
logits = tf.layers.dense(inputs=skip_out, units=self.label_classes, activation=None)
# 4th. get modes
modes = tf.argmax(logits, axis=-1)
# 5th. get loss.
loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits)
return {"wave": wave, "output": tf.to_float(modes), "labels": tf.to_float(labels), "loss": loss}
def build(self, data, reuse=None):
with tf.variable_scope(self.name, reuse=reuse):
wave = data["wave"]
label = data["labels"]
mu_wav = mu_law(wave)
wav_indices = tf.cast(mu_wav + self.central_class, dtype=tf.int32)
# 1st. build the initial causal layer.
with tf.variable_scope("init_causal_layer"):
# 1st. right shift.
right_shift_wav_indices = tf.pad(wav_indices[:, :-1], [[0, 0], [1, 0]], constant_values=self.central_class)
# 2nd. to one-hot representation
wav_one_hot = tf.one_hot(right_shift_wav_indices, depth=self.sample_classes, dtype=tf.float32)
# 3rd. calculate
init_causal_out = dilated_causal_conv1d(inputs=wav_one_hot, dilation_rate=1, filters=self.residual_units, width=self.conv_width)
# 2nd. build the dilated causal blocks.
with tf.variable_scope("dilated_causal_blocks"):
resi_out = init_causal_out
skip_out = 0.
for idx in range(self.blks):
with tf.variable_scope("blk_{}".format(idx)):
for idy in range(self.layers_per_blk):
with tf.variable_scope("layer_{}".format(idy)):
conv_out = dilated_causal_conv1d(inputs=resi_out, dilation_rate=self.dilation_base ** idy,
filters=2*self.residual_units, width=self.conv_width)
acti_out = self_gated_layer(inputs=conv_out)
resi_out += tf.layers.dense(inputs=acti_out, units=self.residual_units, activation=None, name="residual_out")
skip_out += tf.layers.dense(inputs=acti_out, units=self.residual_units, activation=None, name="skip_out")
# 3rd. calculate probabilities, and get loss.
with tf.variable_scope("softmax"):
# 1st. relu
skip_out = tf.nn.relu(skip_out)
# 2nd. dense -> relu
skip_out = tf.layers.dense(inputs=skip_out, units=self.hidden_out, activation=tf.nn.relu)
# 3rd. dense, get energy(logits).
logits = tf.layers.dense(inputs=skip_out, units=self.label_classes, activation=None)
# 4th. get scores, and modes, then recover to waveform := [-1, 1).
scores = tf.nn.softmax(logits=logits, dim=-1)
modes = tf.argmax(scores, axis=-1)
# 5th. get loss.
loss = focal_loss(labels=wav_indices, logits=logits)
return {"wave": wave, "output": tf.to_float(modes), "labels": tf.to_float(label), "loss": loss}
def build(self, data, reuse=None):
with tf.variable_scope(self.name, reuse=reuse):
wave = data["wave"]
assert wave.get_shape().with_rank(2)
assert wave.get_shape()[1].value == 1
mu_wav = mu_law(wave)
conditions = tf.cast(mu_wav + self.central_class, dtype=tf.int32)
# TODO construct inputs by the shape of conditions.
inputs = tf.to_int64(data["inputs"])
assert inputs.get_shape().with_rank(2)
assert inputs.get_shape()[1].value == 1
init_op_lst = []
# 1st. build the initial causal layer.
with tf.variable_scope("init_causal_layer"):
# 1st. to one-hot representation
conditions_one_hot = tf.one_hot(conditions, depth=self.sample_classes, dtype=tf.float32)
inputs_one_hot = tf.one_hot(inputs, depth=self.label_classes, dtype=tf.float32)
# 3rd. calculate
init_causal_out = fast_gen_dilated_causal_conv1d(inputs=inputs_one_hot, dilation_rate=1,
filters=self.residual_units, width=self.conv_width)
init_op_lst.append(init_causal_out["init_op"])
init_causal_out = init_causal_out["out"]
# 2nd. build the dilated causal blocks.
with tf.variable_scope("dilated_causal_blocks"):
resi_out = init_causal_out
skip_out = 0.
for idx in range(self.blks):
with tf.variable_scope("blk_{}".format(idx)):
for idy in range(self.layers_per_blk):
with tf.variable_scope("layer_{}".format(idy)):
conv_out = fast_gen_dilated_causal_conv1d(inputs=resi_out,
dilation_rate=self.dilation_base ** idy,
filters=2*self.residual_units,
width=self.conv_width)
conditions_conv = tf.layers.dense(inputs=conditions_one_hot,
units=2*self.residual_units,
activation=None, name="conditions_conv")
init_op_lst.append(conv_out["init_op"])
conv_out = conv_out["out"]
added = tf.add(conv_out, conditions_conv)
acti_out = self_gated_layer(inputs=added)
resi_out += tf.layers.dense(inputs=acti_out, units=self.residual_units,
activation=None, name="residual_out")
skip_out += tf.layers.dense(inputs=acti_out, units=self.residual_units,
activation=None, name="skip_out")
# 3rd. calculate probabilities, and get loss.
with tf.variable_scope("softmax"):
# 1st. relu
skip_out = tf.nn.relu(skip_out)
# 2nd. dense -> relu
skip_out = tf.layers.dense(inputs=skip_out, units=self.hidden_out, activation=tf.nn.relu)
# 3rd. dense -> softmax
logits = tf.layers.dense(inputs=skip_out, units=self.label_classes, activation=tf.nn.softmax)
# 4th. get modes
modes = tf.argmax(logits, axis=-1)
# # 3rd. dense, get energy(logits).
# logits = tf.layers.dense(inputs=skip_out, units=self.label_classes, activation=None)
# # 4th. sample from the logits, then recover to waveform := [-1, 1).
# logits = tf.squeeze(logits, axis=1)
# synthesized_samples = tf.multinomial(logits=logits, num_samples=1) - self.central_class
# synthesized_samples = inv_mu_law(synthesized_samples)
return {"init_op": init_op_lst, "detected_gci": modes}