def C2W2C(batch_size, maxlen, d_C, d_Wi, d_W, d_L, d_D, V_C):
# inputs
context = Input(batch_shape=(batch_size, maxlen), name='context', dtype='int32')
y_tm1 = Input(batch_shape=(batch_size, maxlen), name='y_tm1', dtype='int32')
c = Input(batch_shape=(batch_size, d_L), name='c', dtype='floatX')
# sub-models
c2w = C2W(maxlen, d_C, d_W, d_Wi, V_C)
lm = LanguageModel(batch_size, d_W, d_L)
w2c = W2C(maxlen, d_L, d_C, d_D, V_C)
# the actual c2w2c model
ctx_mask = ContextMask()(context)
ctx_emb = Dropout(.5)(c2w(context))
C = Dropout(.5)(lm([ctx_emb, ctx_mask]))
y_logit = w2c([C, y_tm1])
y = Activation('softmax')(y_logit)
c2w2c = Model(input=[context, y_tm1], output=y)
# separe W2C for text generation
w2c_logit = w2c([c, y_tm1])
w2c_model = Model(input=[c, y_tm1], output=Activation('softmax')(w2c_logit))
lm_model = Model(input=context, output=C)
def predict_ctx(gen, n_samples):
return lm_model.predict_generator(gen, n_samples)
def predict_chars(c_dat, y_tm1_dat):
assert len(c_dat.shape) == len(y_tm1_dat.shape), (c_dat.shape, y_tm1_dat.shape)
assert len(c_dat.shape) in [1, 2]
assert c_dat.shape[-1] == d_L, c_dat.shape
assert y_tm1_dat.shape[-1] == maxlen, y_tm1_dat.shape
actual_size, n_samples = batch_size, 1
if len(c_dat.shape) == 2:
n_samples = c_dat.shape[0]
actual_size = max(batch_size, n_samples)
# stateful LSTMs require fixed-size batch so let's just pad other
# samples with zeros
C_dat = np.zeros((actual_size, d_L), dtype=np.float32)
Y_tm1_dat = np.zeros((actual_size, maxlen), dtype=np.float32)
if len(c_dat.shape) == 1:
c_dat = np.reshape(c_dat, (1,) + c_dat.shape)
y_tm1_dat = np.reshape(y_tm1_dat, (1,) + y_tm1_dat.shape)
np.copyto(C_dat[0:n_samples], c_dat)
np.copyto(Y_tm1_dat[0:n_samples], y_tm1_dat)
return w2c_model.predict({'c': C_dat, 'y_tm1': Y_tm1_dat}, batch_size=batch_size)
c2w2c.get_hyperparams = lambda: (batch_size, d_C, d_Wi, d_W, d_L, d_D, V_C)
c2w2c.get_c2w = lambda: c2w
c2w2c.get_lm = lambda: lm
c2w2c.get_w2c = lambda: w2c
c2w2c.predict_ctx = predict_ctx
c2w2c.predict_chars = predict_chars
c2w2c.save_states = lambda: lm.save_states()
c2w2c.restore_states = lambda: lm.restore_states()
return c2w2c
