def __init__(self,
dim,
batch_norm,
dropout,
rec_dropout,
header,
mode,
partition,
ihm_pos,
target_repl=False,
depth=1,
input_dim=76,
size_coef=4,
**kwargs):
print "==> not used params in network class:", kwargs.keys()
self.dim = dim
self.batch_norm = batch_norm
self.dropout = dropout
self.rec_dropout = rec_dropout
self.depth = depth
self.size_coef = size_coef
# Parse channels
channel_names = set()
for ch in header:
if ch.find("mask->") != -1:
continue
pos = ch.find("->")
if pos != -1:
channel_names.add(ch[:pos])
else:
channel_names.add(ch)
channel_names = sorted(list(channel_names))
print "==> found {} channels: {}".format(len(channel_names),
channel_names)
channels = [] # each channel is a list of columns
for ch in channel_names:
indices = range(len(header))
indices = filter(lambda i: header[i].find(ch) != -1, indices)
channels.append(indices)
# Input layers and masking
X = Input(shape=(None, input_dim), name='X')
mX = Masking()(X)
# Masks
ihm_M = Input(shape=(1, ), name='ihm_M')
decomp_M = Input(shape=(None, ), name='decomp_M')
los_M = Input(shape=(None, ), name='los_M')
inputs = [X, ihm_M, decomp_M, los_M]
# Preprocess each channel
cX = []
for ch in channels:
cX.append(Slice(ch)(mX))
pX = [] # LSTM processed version of cX
for x in cX:
p = x
for i in range(depth):
p = LSTM(units=dim,
activation='tanh',
return_sequences=True,
dropout=dropout,
recurrent_dropout=rec_dropout)(p)
pX.append(p)
# Concatenate processed channels
Z = Concatenate(axis=2)(pX)
# Main part of the network
for i in range(depth):
Z = LSTM(units=int(size_coef * dim),
activation='tanh',
return_sequences=True,
dropout=dropout,
recurrent_dropout=rec_dropout)(Z)
L = Z
if dropout > 0:
L = Dropout(dropout)(L)
# Output modules
outputs = []
## ihm output
# NOTE: masking for ihm prediction works this way:
# if ihm_M = 1 then we will calculate an error term
# if ihm_M = 0, our prediction will be 0 and as the label
# will also be 0 then error_term will be 0.
if target_repl:
ihm_seq = TimeDistributed(Dense(1, activation='sigmoid'),
name='ihm_seq')(L)
ihm_y = GetTimestep(ihm_pos)(ihm_seq)
ihm_y = Multiply(name='ihm_single')([ihm_y, ihm_M])
outputs += [ihm_y, ihm_seq]
else:
ihm_seq = TimeDistributed(Dense(1, activation='sigmoid'))(L)
ihm_y = GetTimestep(ihm_pos)(ihm_seq)
ihm_y = Multiply(name='ihm')([ihm_y, ihm_M])
outputs += [ihm_y]
## decomp output
decomp_y = TimeDistributed(Dense(1, activation='sigmoid'))(L)
decomp_y = ExtendMask(name='decomp',
add_epsilon=True)([decomp_y, decomp_M])
outputs += [decomp_y]
## los output
if partition == 'none':
los_y = TimeDistributed(Dense(1, activation='relu'))(L)
else:
los_y = TimeDistributed(Dense(10, activation='softmax'))(L)
los_y = ExtendMask(name='los', add_epsilon=True)([los_y, los_M])
outputs += [los_y]
## pheno output
if target_repl:
pheno_seq = TimeDistributed(Dense(25, activation='sigmoid'),
name='pheno_seq')(L)
pheno_y = LastTimestep(name='pheno_single')(pheno_seq)
outputs += [pheno_y, pheno_seq]
else:
pheno_seq = TimeDistributed(Dense(25, activation='sigmoid'))(L)
pheno_y = LastTimestep(name='pheno')(pheno_seq)
outputs += [pheno_y]
return super(Network, self).__init__(inputs=inputs, outputs=outputs)
def __init__(self,
dim,
batch_norm,
dropout,
rec_dropout,
header,
task,
target_repl=False,
deep_supervision=False,
num_classes=1,
depth=1,
input_dim=76,
size_coef=4,
**kwargs):
self.dim = dim
self.batch_norm = batch_norm
self.dropout = dropout
self.rec_dropout = rec_dropout
self.depth = depth
self.size_coef = size_coef
if task in ['decomp', 'ihm', 'ph']:
final_activation = 'sigmoid'
elif task in ['los']:
if num_classes == 1:
final_activation = 'relu'
else:
final_activation = 'softmax'
else:
raise ValueError("Wrong value for task")
print("==> not used params in network class:", kwargs.keys())
# Parse channels
channel_names = set()
for ch in header:
if ch.find("mask->") != -1:
continue
pos = ch.find("->")
if pos != -1:
channel_names.add(ch[:pos])
else:
channel_names.add(ch)
channel_names = sorted(list(channel_names))
print("==> found {} channels: {}".format(len(channel_names),
channel_names))
channels = [] # each channel is a list of columns
for ch in channel_names:
indices = range(len(header))
indices = list(filter(lambda i: header[i].find(ch) != -1, indices))
channels.append(indices)
# Input layers and masking
X = Input(shape=(None, input_dim), name='X')
inputs = [X]
mX = Masking()(X)
if deep_supervision:
M = Input(shape=(None, ), name='M')
inputs.append(M)
# Configurations
is_bidirectional = True
if deep_supervision:
is_bidirectional = False
# Preprocess each channel
cX = []
for ch in channels:
cX.append(Slice(ch)(mX))
pX = [] # LSTM processed version of cX
for x in cX:
p = x
for i in range(depth):
num_units = dim
if is_bidirectional:
num_units = num_units // 2
lstm = LSTM(units=num_units,
activation='tanh',
return_sequences=True,
dropout=dropout,
recurrent_dropout=rec_dropout)
if is_bidirectional:
p = Bidirectional(lstm)(p)
else:
p = lstm(p)
pX.append(p)
# Concatenate processed channels
Z = Concatenate(axis=2)(pX)
# Main part of the network
for i in range(depth - 1):
num_units = int(size_coef * dim)
if is_bidirectional:
num_units = num_units // 2
lstm = LSTM(units=num_units,
activation='tanh',
return_sequences=True,
dropout=dropout,
recurrent_dropout=rec_dropout)
if is_bidirectional:
Z = Bidirectional(lstm)(Z)
else:
Z = lstm(Z)
# Output module of the network
return_sequences = (target_repl or deep_supervision)
L = LSTM(units=int(size_coef * dim),
activation='tanh',
return_sequences=return_sequences,
dropout=dropout,
recurrent_dropout=rec_dropout)(Z)
if dropout > 0:
L = Dropout(dropout)(L)
if target_repl:
y = TimeDistributed(Dense(num_classes,
activation=final_activation),
name='seq')(L)
y_last = LastTimestep(name='single')(y)
outputs = [y_last, y]
elif deep_supervision:
y = TimeDistributed(Dense(num_classes,
activation=final_activation))(L)
y = ExtendMask()([y, M]) # this way we extend mask of y to M
outputs = [y]
else:
y = Dense(num_classes, activation=final_activation)(L)
outputs = [y]
super(Network, self).__init__(inputs=inputs, outputs=outputs)