def prep_model(glove, vocab, dropout=1/2, dropout_w=0, dropout_in=4/5, l2reg=1e-4,
cnnact='tanh', cnninit='glorot_uniform', cdim={1: 1, 2: 1/2, 3: 1/2, 4: 1/2, 5: 1/2},
project=True, pdim=2.5,
ptscorer=B.mlp_ptscorer, mlpsum='sum', Ddim=1,
oact='sigmoid'):
model = Graph()
N = B.embedding(model, glove, vocab, s0pad, s1pad, dropout, dropout_w)
if dropout_in is None:
dropout_in = dropout
Nc = B.cnnsum_input(model, N, s0pad, dropout=dropout_in, l2reg=l2reg,
cnninit=cnninit, cnnact=cnnact, cdim=cdim)
# Projection
if project:
model.add_shared_node(name='proj', inputs=['e0s_', 'e1s_'], outputs=['e0p', 'e1p'],
layer=Dense(input_dim=Nc, output_dim=int(N*pdim), W_regularizer=l2(l2reg)))
# This dropout is controversial; it might be harmful to apply,
# or at least isn't a clear win.
# model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
# layer=Dropout(dropout_in, input_shape=(N,)))
# final_outputs = ['e0p_', 'e1p_']
final_outputs = ['e0p', 'e1p']
else:
final_outputs = ['e0s_', 'e1s_']
# Measurement
kwargs = dict()
if ptscorer == B.mlp_ptscorer:
kwargs['sum_mode'] = mlpsum
model.add_node(name='scoreS', input=ptscorer(model, final_outputs, Ddim, N, l2reg, **kwargs),
layer=Activation(oact))
model.add_output(name='score', input='scoreS')
return model
def prep_model(glove, vocab, dropout=1/2, dropout_w=0, dropout_in=4/5, l2reg=1e-4,
cnnact='tanh', cnninit='glorot_uniform', cdim={1: 1, 2: 1/2, 3: 1/2, 4: 1/2, 5: 1/2},
project=True, pdim=2.5,
ptscorer=B.mlp_ptscorer, mlpsum='sum', Ddim=1,
oact='sigmoid'):
model = Graph()
N = B.embedding(model, glove, vocab, s0pad, s1pad, dropout, dropout_w)
if dropout_in is None:
dropout_in = dropout
Nc = B.cnnsum_input(model, N, s0pad, dropout=dropout_in, l2reg=l2reg,
cnninit=cnninit, cnnact=cnnact, cdim=cdim)
# Projection
if project:
model.add_shared_node(name='proj', inputs=['e0s_', 'e1s_'], outputs=['e0p', 'e1p'],
layer=Dense(input_dim=Nc, output_dim=int(N*pdim), W_regularizer=l2(l2reg)))
# This dropout is controversial; it might be harmful to apply,
# or at least isn't a clear win.
# model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
# layer=Dropout(dropout_in, input_shape=(N,)))
# final_outputs = ['e0p_', 'e1p_']
final_outputs = ['e0p', 'e1p']
else:
final_outputs = ['e0s_', 'e1s_']
# Measurement
kwargs = dict()
if ptscorer == B.mlp_ptscorer:
kwargs['sum_mode'] = mlpsum
model.add_node(name='scoreS', input=ptscorer(model, final_outputs, Ddim, N, l2reg, **kwargs),
layer=Activation(oact))
model.add_output(name='score', input='scoreS')
return model
def prep_model(model, N, s0pad, s1pad, c):
Nc = B.cnnsum_input(model,
N,
s0pad,
siamese=c['cnnsiamese'],
dropout=c['dropout'],
l2reg=c['l2reg'],
cnninit=c['cnninit'],
cnnact=c['cnnact'],
cdim=c['cdim'])
# Projection
if c['project']:
model.add_shared_node(name='proj',
inputs=['e0s_', 'e1s_'],
outputs=['e0p', 'e1p'],
layer=Dense(input_dim=Nc,
output_dim=int(N * c['pdim']),
W_regularizer=l2(c['l2reg']),
activation=c['pact']))
# This dropout is controversial; it might be harmful to apply,
# or at least isn't a clear win.
# model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
# layer=Dropout(c['dropout'], input_shape=(N,)))
# return ('e0p_', 'e1p_')
return ('e0p', 'e1p')
else:
return ('e0s_', 'e1s_')
def prep_model(model, N, s0pad, s1pad, c):
Nc = B.cnnsum_input(model, N, s0pad,
dropout=c['dropout'], l2reg=c['l2reg'],
cnninit=c['cnninit'], cnnact=c['cnnact'], cdim=c['cdim'])
# Projection
if c['project']:
model.add_shared_node(name='proj', inputs=['e0s_', 'e1s_'], outputs=['e0p', 'e1p'],
layer=Dense(input_dim=Nc, output_dim=int(N*c['pdim']),
W_regularizer=l2(c['l2reg']), activation=c['pact']))
# This dropout is controversial; it might be harmful to apply,
# or at least isn't a clear win.
# model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
# layer=Dropout(c['dropout'], input_shape=(N,)))
# return ('e0p_', 'e1p_')
return ('e0p', 'e1p')
else:
return ('e0s_', 'e1s_')
def prep_model(model, N, s0pad, s1pad, c):
B.rnn_input(model,
N,
s0pad,
return_sequences=True,
dropout=c['dropout'],
dropoutfix_inp=c['dropoutfix_inp'],
dropoutfix_rec=c['dropoutfix_rec'],
sdim=c['sdim'],
rnnbidi=c['rnnbidi'],
rnn=c['rnn'],
rnnact=c['rnnact'],
rnninit=c['rnninit'],
rnnbidi_mode=c['rnnbidi_mode'],
rnnlevels=c['rnnlevels'])
Nc = B.cnnsum_input(model,
N,
s0pad,
inputs=['e0s_', 'e1s_'],
pfx='cnn',
dropout=c['dropout'],
l2reg=c['l2reg'],
cnninit=c['cnninit'],
cnnact=c['cnnact'],
cdim=c['cdim'])
# Projection
if c['project']:
model.add_shared_node(name='proj',
inputs=['cnne0s_', 'cnne1s_'],
outputs=['e0p', 'e1p'],
layer=Dense(input_dim=Nc,
output_dim=int(N * c['pdim']),
W_regularizer=l2(c['l2reg']),
activation=c['pact']))
# This dropout is controversial; it might be harmful to apply,
# or at least isn't a clear win.
# model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
# layer=Dropout(c['dropout'], input_shape=(N,)))
# return ('e0p_', 'e1p_')
return ('e0p', 'e1p')
else:
return ('cnne0s_', 'cnne1s_')
def prep_model(model, N, s0pad, s1pad, c):
B.rnn_input(model, N, s0pad, return_sequences=True,
dropout=c['dropout'], dropoutfix_inp=c['dropoutfix_inp'], dropoutfix_rec=c['dropoutfix_rec'],
sdim=c['sdim'],
rnnbidi=c['rnnbidi'], rnn=c['rnn'], rnnact=c['rnnact'], rnninit=c['rnninit'],
rnnbidi_mode=c['rnnbidi_mode'], rnnlevels=c['rnnlevels'])
Nc = B.cnnsum_input(model, N, s0pad, inputs=['e0s_', 'e1s_'], pfx='cnn',
dropout=c['dropout'], l2reg=c['l2reg'],
cnninit=c['cnninit'], cnnact=c['cnnact'], cdim=c['cdim'])
# Projection
if c['project']:
model.add_shared_node(name='proj', inputs=['cnne0s_', 'cnne1s_'], outputs=['e0p', 'e1p'],
layer=Dense(input_dim=Nc, output_dim=int(N*c['pdim']),
W_regularizer=l2(c['l2reg']), activation=c['pact']))
# This dropout is controversial; it might be harmful to apply,
# or at least isn't a clear win.
# model.add_shared_node(name='projdrop', inputs=['e0p', 'e1p'], outputs=['e0p_', 'e1p_'],
# layer=Dropout(c['dropout'], input_shape=(N,)))
# return ('e0p_', 'e1p_')
return ('e0p', 'e1p')
else:
return ('cnne0s_', 'cnne1s_')
