def build_mod9(opt=adam()):
n = int(2.2 * 1024)
in1 = Input((128,), name='x1')
x1 = fc_block1(in1, n, d=0.3)
x1 = fc_identity(x1, n, d=0.3)
x1 = fc_identity(x1, n, d=0.3)
in2 = Input((1024,), name='x2')
x2 = fc_block1(in2, n, d=0.3)
x2 = fc_identity(x2, n, d=0.3)
x2 = fc_identity(x2, n, d=0.3)
x = merge([x1, x2], mode='concat', concat_axis=1)
x = fc_identity(x, n, d=0.3)
x = fc_identity(x, n, d=0.3)
x = fc_block1(x, n)
out = Dense(4716, activation='sigmoid', name='output')(x)
model = Model(input=[in1, in2], output=out)
model.compile(optimizer=opt, loss='categorical_crossentropy')
# model.summary()
# plot(model=model, show_shapes=True)
return model
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