def inferenceD(dna_seq_placeholder,
keep_prob_placeholder,
num_classes,
concat_revcom=False):
with tf.variable_scope("inference_d", reuse=None) as scope:
print "Running inferenceD"
dna_conv_filter_width = 20
num_dna_filters = 16
pad_size = 0
x_nuc = dtl.NucInput(dna_seq_placeholder, pad_size, 'dna_input',
concat_revcom)
cl1 = dtl.Conv2d(
x_nuc,
filter_shape=[1, dna_conv_filter_width, 4, num_dna_filters],
strides=[1, 1, 1, 1],
padding='SAME',
name='dna_conv1')
r1 = dtl.Relu(cl1)
p1 = dtl.AvgPool(r1, [1, 4], 'dna_avg_pool1')
flat = dtl.Flatten(p1)
l1 = dtl.Linear(flat, 32, 'linear1')
r2 = dtl.Relu(l1)
readout = dtl.Linear(r2, num_classes, 'readout')
dropout = dtl.Dropout(readout, keep_prob_placeholder, name="dropout")
nn = dtl.Network(x_nuc, [dropout],
bounds=[0., 1.],
use_zbeta_first=False)
logits = nn.forward()
return logits, nn
def test_nuc_input(nucs):
with tf.Session() as sess:
l1 = dtl.NucInput(nucs,pad_size=0,name='nuc_input',concat_revcom=True)
out = sess.run(l1.output)
print "Outshape",out.shape
#print np.squeeze(out,axis=1).shape
print "New seqs", dbt.onehot_4d_to_nuc(out)
def inferenceA(dna_seq_placeholder,
keep_prob_placeholder,
num_classes,
concat_revcom=False):
with tf.variable_scope("inference_a", reuse=None) as scope:
print "Running inferenceA"
dna_conv_filter_width = 24
num_dna_filters = 48
pad_size = 0
#Reshape and pad nucleotide input
x_nuc = dtl.NucInput(dna_seq_placeholder, pad_size, 'dna_input',
concat_revcom)
cl1 = dtl.Conv2d(
x_nuc,
filter_shape=[1, dna_conv_filter_width, 4, num_dna_filters],
strides=[1, 1, 1, 1],
padding='SAME',
name='dna_conv1')
r1 = dtl.Relu(cl1)
p1 = dtl.AvgPool(r1, [1, 4], 'dna_avg_pool1')
cl2 = dtl.Conv2d(p1,
filter_shape=[
1, dna_conv_filter_width, num_dna_filters,
num_dna_filters
],
strides=[1, 1, 1, 1],
padding='SAME',
name='dna_conv2')
r2 = dtl.Relu(cl2)
p2 = dtl.AvgPool(r2, [1, 4], 'dna_avg_pool2')
flat = dtl.Flatten(p2)
l1 = dtl.Linear(flat, 100, 'linear1')
r3 = dtl.Relu(l1)
l2 = dtl.Linear(r3, 50, 'linear2')
r4 = dtl.Relu(l2)
l3 = dtl.Linear(r4, num_classes, 'linear3')
drop_out = dtl.Dropout(l3, keep_prob_placeholder, name="dropout")
#nn = dtl.Network(x_nuc,[cl1,r1,p1, cl2,r2,p2,flat,l1,r3,l2,r4,l3],bounds=[0.,1.])
nn = dtl.Network(x_nuc, [drop_out],
bounds=[0., 1.],
use_zbeta_first=False)
logits = nn.forward()
return logits, nn
def inferenceB(dna_seq_placeholder,
keep_prob_placeholder,
num_classes,
concat_revcom=False):
with tf.variable_scope("inference_b", reuse=None) as scope:
print "Running inference B"
print "Simple 2 layer linear network with no dropout"
x_nuc = dtl.NucInput(dna_seq_placeholder, pad_size, 'dna_input',
concat_revcom)
flat = dtl.Flatten(x_nuc)
l1 = dtl.Linear(flat, 1024, 'linear1')
r1 = dtl.Relu(l1)
l2 = dtl.Linear(r1, 1024, 'linear2')
r2 = dtl.Relu(l2)
nn = dtl.Network(x_nuc, [r2], bounds=[0., 1.], use_zbeta_first=False)
logits = nn.forward()
return logits, nn
def inferenceF(dna_seq_placeholder, keep_prob_placeholder, num_classes):
"""2-strided convolutions with small filters (size 3) """
with tf.variable_scope("inference_f") as scope:
print "Utilizing inferencef_600bp"
pad_size = 0
seq_len = dna_seq_placeholder.get_shape().as_list()[2]
if seq_len != 600:
print "Sequence length does not equal 600"
print "Possible errors"
nf1 = 24
nf2 = 48
stride = 2
fw1 = 3
fw2 = 6
num_classes = 2
x_nuc = dtl.NucInput(dna_seq_placeholder, pad_size, 'dna_input')
cl1 = dtl.Conv2d(x_nuc,
filter_shape=[1, fw1, 4, nf1],
strides=[1, 1, 1, 1],
padding='SAME',
name='dna_conv1')
r1 = dtl.Relu(cl1)
cl2 = dtl.Conv2d(r1,
filter_shape=[1, fw1, nf1, nf1],
strides=[1, 1, stride, 1],
padding='SAME',
name='dna_conv2')
r2 = dtl.Relu(cl2)
cl3 = dtl.Conv2d(r2,
filter_shape=[1, fw1, nf1, nf1],
strides=[1, 1, stride, 1],
padding='SAME',
name='dna_conv3')
r3 = dtl.Relu(cl3)
cl4 = dtl.Conv2d(r3,
filter_shape=[1, fw1, nf1, nf2],
strides=[1, 1, 1, 1],
padding='SAME',
name='dna_conv4')
r4 = dtl.Relu(cl4)
cl5 = dtl.Conv2d(r4,
filter_shape=[1, fw1, nf2, nf2],
strides=[1, 1, stride, 1],
padding='SAME',
name='dna_conv5')
r5 = dtl.Relu(cl5)
cl6 = dtl.Conv2d(r5,
filter_shape=[1, fw1, nf2, nf2],
strides=[1, 1, stride, 1],
padding='SAME',
name='dna_conv6')
r6 = dtl.Relu(cl6)
flat = dtl.Flatten(r6)
l2 = dtl.Linear(flat, nf2, 'linear2')
r4 = dtl.Relu(l2)
l3 = dtl.Linear(r4, num_classes, 'linear3')
drop_out = dtl.Dropout(l3, keep_prob_placeholder, name="dropout")
#Passing the last layer to the Network constructor
#will automatically initialize Network with every preceding layer
nn = dtl.Network(x_nuc, [drop_out],
bounds=[0., 1.],
use_zbeta_first=False)
logits = nn.forward()
return logits, nn