"""DNA models.
Provides models trained with DNA sequence windows.
"""
from __future__ import division
from __future__ import print_function
import inspect
from keras import layers as kl
from keras import regularizers as kr
from .utils import Model
from ..utils import get_from_module
class DnaModel(Model):
"""Abstract class of a DNA model."""
def __init__(self, *args, **kwargs):
super(DnaModel, self).__init__(*args, **kwargs)
self.scope = 'dna'
def inputs(self, dna_wlen):
return [kl.Input(shape=(dna_wlen, 4), name='dna')]
class CnnL1h128(DnaModel):
"""CNN with one convolutional and one fully-connected layer with 128 units.
.. code::
Parameters: 4,100,000
Specification: conv[128@11]_mp[4]_fc[128]_do
"""
def __init__(self, nb_hidden=128, *args, **kwargs):
super(CnnL1h128, self).__init__(*args, **kwargs)
self.nb_hidden = nb_hidden
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(self.l1_decay, self.l2_decay)
x = kl.Conv1D(128, 11,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
x = kl.Flatten()(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Dense(self.nb_hidden,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class CnnL1h256(CnnL1h128):
"""CNN with one convolutional and one fully-connected layer with 256 units.
.. code::
Parameters: 8,100,000
Specification: conv[128@11]_mp[4]_fc[256]_do
"""
def __init__(self, *args, **kwargs):
super(CnnL1h256, self).__init__(*args, **kwargs)
self.nb_hidden = 256
class CnnL2h128(DnaModel):
"""CNN with two convolutional and one fully-connected layer with 128 units.
.. code::
Parameters: 4,100,000
Specification: conv[128@11]_mp[4]_conv[256@3]_mp[2]_fc[128]_do
"""
def __init__(self, nb_hidden=128, *args, **kwargs):
super(CnnL2h128, self).__init__(*args, **kwargs)
self.nb_hidden = nb_hidden
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(256, 3,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(2)(x)
x = kl.Flatten()(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Dense(self.nb_hidden,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class CnnL2h256(CnnL2h128):
"""CNN with two convolutional and one fully-connected layer with 256 units.
.. code::
Parameters: 8,100,000
Specification: conv[128@11]_mp[4]_conv[256@3]_mp[2]_fc[256]_do
"""
def __init__(self, *args, **kwargs):
super(CnnL2h256, self).__init__(*args, **kwargs)
self.nb_hidden = 256
class CnnL3h128(DnaModel):
"""CNN with three convolutional and one fully-connected layer with 128 units.
.. code::
Parameters: 4,400,000
Specification: conv[128@11]_mp[4]_conv[256@3]_mp[2]_conv[512@3]_mp[2]_
fc[128]_do
"""
def __init__(self, nb_hidden=128, *args, **kwargs):
super(CnnL3h128, self).__init__(*args, **kwargs)
self.nb_hidden = nb_hidden
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(256, 3,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(2)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(512, 3,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(2)(x)
x = kl.Flatten()(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Dense(self.nb_hidden,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class CnnL3h256(CnnL3h128):
"""CNN with three convolutional and one fully-connected layer with 256 units.
.. code::
Parameters: 8,300,000
Specification: conv[128@11]_mp[4]_conv[256@3]_mp[2]_conv[512@3]_mp[2]_
fc[256]_do
"""
def __init__(self, *args, **kwargs):
super(CnnL3h256, self).__init__(*args, **kwargs)
self.nb_hidden = 256
class CnnRnn01(DnaModel):
"""Convolutional-recurrent model.
Convolutional-recurrent model with two convolutional layers followed by a
bidirectional GRU layer.
.. code::
Parameters: 1,100,000
Specification: conv[128@11]_pool[4]_conv[256@7]_pool[4]_bgru[256]_do
"""
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(256, 7,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu')(x)
x = kl.MaxPooling1D(4)(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
gru = kl.recurrent.GRU(256, kernel_regularizer=kernel_regularizer)
x = kl.Bidirectional(gru)(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class ResNet01(DnaModel):
"""Residual network with bottleneck residual units.
.. code::
Parameters: 1,700,000
Specification: conv[128@11]_mp[2]_resb[2x128|2x256|2x512|1x1024]_gap_do
He et al., 'Identity Mappings in Deep Residual Networks.'
"""
def _res_unit(self, inputs, nb_filter, size=3, stride=1, stage=1, block=1):
name = '%02d-%02d/' % (stage, block)
id_name = '%sid_' % (name)
res_name = '%sres_' % (name)
# Residual branch
# 1x1 down-sample conv
x = kl.BatchNormalization(name=res_name + 'bn1')(inputs)
x = kl.Activation('relu', name=res_name + 'act1')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter[0], 1,
name=res_name + 'conv1',
subsample_length=stride,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# LxL conv
x = kl.BatchNormalization(name=res_name + 'bn2')(x)
x = kl.Activation('relu', name=res_name + 'act2')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter[1], size,
name=res_name + 'conv2',
border_mode='same',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# 1x1 up-sample conv
x = kl.BatchNormalization(name=res_name + 'bn3')(x)
x = kl.Activation('relu', name=res_name + 'act3')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter[2], 1,
name=res_name + 'conv3',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# Identity branch
if nb_filter[-1] != inputs._keras_shape[-1] or stride > 1:
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
identity = kl.Conv1D(nb_filter[2], 1,
name=id_name + 'conv1',
subsample_length=stride,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(inputs)
else:
identity = inputs
x = kl.merge([identity, x], name=name + 'merge', mode='sum')
return x
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
name='conv1',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.BatchNormalization(name='bn1')(x)
x = kl.Activation('relu', name='act1')(x)
x = kl.MaxPooling1D(2, name='pool1')(x)
# 124
x = self._res_unit(x, [32, 32, 128], stage=1, block=1, stride=2)
x = self._res_unit(x, [32, 32, 128], stage=1, block=2)
# 64
x = self._res_unit(x, [64, 64, 256], stage=2, block=1, stride=2)
x = self._res_unit(x, [64, 64, 256], stage=2, block=2)
# 32
x = self._res_unit(x, [128, 128, 512], stage=3, block=1, stride=2)
x = self._res_unit(x, [128, 128, 512], stage=3, block=2)
# 16
x = self._res_unit(x, [256, 256, 1024], stage=4, block=1, stride=2)
x = kl.GlobalAveragePooling1D()(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class ResNet02(ResNet01):
"""Residual network with bottleneck residual units.
.. code::
Parameters: 2,000,000
Specification: conv[128@11]_mp[2]_resb[3x128|3x256|3x512|1x1024]_gap_do
He et al., 'Identity Mappings in Deep Residual Networks.'
"""
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
name='conv1',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.BatchNormalization(name='bn1')(x)
x = kl.Activation('relu', name='act1')(x)
x = kl.MaxPooling1D(2, name='pool1')(x)
# 124
x = self._res_unit(x, [32, 32, 128], stage=1, block=1, stride=2)
x = self._res_unit(x, [32, 32, 128], stage=1, block=2)
x = self._res_unit(x, [32, 32, 128], stage=1, block=3)
# 64
x = self._res_unit(x, [64, 64, 256], stage=2, block=1, stride=2)
x = self._res_unit(x, [64, 64, 256], stage=2, block=2)
x = self._res_unit(x, [64, 64, 256], stage=2, block=3)
# 32
x = self._res_unit(x, [128, 128, 512], stage=3, block=1, stride=2)
x = self._res_unit(x, [128, 128, 512], stage=3, block=2)
x = self._res_unit(x, [128, 128, 512], stage=3, block=3)
# 16
x = self._res_unit(x, [256, 256, 1024], stage=4, block=1, stride=2)
x = kl.GlobalAveragePooling1D()(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class ResConv01(ResNet01):
"""Residual network with two convolutional layers in each residual unit.
.. code::
Parameters: 2,800,000
Specification: conv[128@11]_mp[2]_resc[2x128|1x256|1x256|1x512]_gap_do
He et al., 'Identity Mappings in Deep Residual Networks.'
"""
def _res_unit(self, inputs, nb_filter, size=3, stride=1, stage=1, block=1):
name = '%02d-%02d/' % (stage, block)
id_name = '%sid_' % (name)
res_name = '%sres_' % (name)
# Residual branch
x = kl.BatchNormalization(name=res_name + 'bn1')(inputs)
x = kl.Activation('relu', name=res_name + 'act1')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter, size,
name=res_name + 'conv1',
border_mode='same',
subsample_length=stride,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.BatchNormalization(name=res_name + 'bn2')(x)
x = kl.Activation('relu', name=res_name + 'act2')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter, size,
name=res_name + 'conv2',
border_mode='same',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# Identity branch
if nb_filter != inputs._keras_shape[-1] or stride > 1:
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
identity = kl.Conv1D(nb_filter, size,
name=id_name + 'conv1',
border_mode='same',
subsample_length=stride,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(inputs)
else:
identity = inputs
x = kl.merge([identity, x], name=name + 'merge', mode='sum')
return x
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
name='conv1',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.BatchNormalization(name='bn1')(x)
x = kl.Activation('relu', name='act1')(x)
x = kl.MaxPooling1D(2, name='pool1')(x)
# 124
x = self._res_unit(x, 128, stage=1, block=1, stride=2)
x = self._res_unit(x, 128, stage=1, block=2)
# 64
x = self._res_unit(x, 256, stage=2, block=1, stride=2)
# 32
x = self._res_unit(x, 256, stage=3, block=1, stride=2)
# 32
x = self._res_unit(x, 512, stage=4, block=1, stride=2)
x = kl.GlobalAveragePooling1D()(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
class ResAtrous01(DnaModel):
"""Residual network with Atrous (dilated) convolutional layers.
Residual network with Atrous (dilated) convolutional layer in bottleneck
units. Atrous convolutional layers allow to increase the receptive field and
hence better model long-range dependencies.
.. code::
Parameters: 2,000,000
Specification: conv[128@11]_mp[2]_resa[3x128|3x256|3x512|1x1024]_gap_do
He et al., 'Identity Mappings in Deep Residual Networks.'
Yu and Koltun, 'Multi-Scale Context Aggregation by Dilated Convolutions.'
"""
def _res_unit(self, inputs, nb_filter, size=3, stride=1, atrous=1,
stage=1, block=1):
name = '%02d-%02d/' % (stage, block)
id_name = '%sid_' % (name)
res_name = '%sres_' % (name)
# Residual branch
# 1x1 down-sample conv
x = kl.BatchNormalization(name=res_name + 'bn1')(inputs)
x = kl.Activation('relu', name=res_name + 'act1')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter[0], 1,
name=res_name + 'conv1',
subsample_length=stride,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# LxL conv
x = kl.BatchNormalization(name=res_name + 'bn2')(x)
x = kl.Activation('relu', name=res_name + 'act2')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.AtrousConv1D(nb_filter[1], size,
atrous_rate=atrous,
name=res_name + 'conv2',
border_mode='same',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# 1x1 up-sample conv
x = kl.BatchNormalization(name=res_name + 'bn3')(x)
x = kl.Activation('relu', name=res_name + 'act3')(x)
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(nb_filter[2], 1,
name=res_name + 'conv3',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
# Identity branch
if nb_filter[-1] != inputs._keras_shape[-1] or stride > 1:
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
identity = kl.Conv1D(nb_filter[2], 1,
name=id_name + 'conv1',
subsample_length=stride,
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(inputs)
else:
identity = inputs
x = kl.merge([identity, x], name=name + 'merge', mode='sum')
return x
def __call__(self, inputs):
x = inputs[0]
kernel_regularizer = kr.L1L2(l1=self.l1_decay, l2=self.l2_decay)
x = kl.Conv1D(128, 11,
name='conv1',
kernel_initializer=self.init,
kernel_regularizer=kernel_regularizer)(x)
x = kl.Activation('relu', name='act1')(x)
x = kl.MaxPooling1D(2, name='pool1')(x)
# 124
x = self._res_unit(x, [32, 32, 128], stage=1, block=1, stride=2)
x = self._res_unit(x, [32, 32, 128], atrous=2, stage=1, block=2)
x = self._res_unit(x, [32, 32, 128], atrous=4, stage=1, block=3)
# 64
x = self._res_unit(x, [64, 64, 256], stage=2, block=1, stride=2)
x = self._res_unit(x, [64, 64, 256], atrous=2, stage=2, block=2)
x = self._res_unit(x, [64, 64, 256], atrous=4, stage=2, block=3)
# 32
x = self._res_unit(x, [128, 128, 512], stage=3, block=1, stride=2)
x = self._res_unit(x, [128, 128, 512], atrous=2, stage=3, block=2)
x = self._res_unit(x, [128, 128, 512], atrous=4, stage=3, block=3)
# 16
x = self._res_unit(x, [256, 256, 1024], stage=4, block=1, stride=2)
x = kl.GlobalAveragePooling1D()(x)
x = kl.Dropout(self.dropout)(x)
return self._build(inputs, x)
def list_models():
"""Return the name of models in the module."""
models = dict()
for name, value in globals().items():
if inspect.isclass(value) and name.lower().find('model') == -1:
models[name] = value
return models
def get(name):
"""Return object from module by its name."""
return get_from_module(name, globals())
