x, t, accuracy, loss = build_self_attention_model(train=True)
solver = S.Adam()
solver.set_parameters(nn.get_parameters())
x, t, accuracy, loss = build_self_attention_model(train=True)
trainer = Trainer(inputs=[x, t],
loss=loss,
metrics={
'cross entropy': loss,
'accuracy': accuracy
},
solver=solver)
for epoch in range(max_epoch):
x, t, accuracy, loss = build_self_attention_model(train=True)
trainer.update_variables(inputs=[x, t],
loss=loss,
metrics={
'cross entropy': loss,
'accuracy': accuracy
})
trainer.run(train_data_iter, None, epochs=1, verbose=1)
x, t, accuracy, loss = build_self_attention_model(train=False)
trainer.update_variables(inputs=[x, t],
loss=loss,
metrics={
'cross entropy': loss,
'accuracy': accuracy
})
trainer.evaluate(dev_data_iter, verbose=1)
# Create solver.
solver = S.Momentum(1e-2, momentum=0.9)
solver.set_parameters(nn.get_parameters())
x, t, loss = build_model(train=True)
trainer = Trainer(inputs=[x, t], loss=loss, metrics={'PPL': np.e**loss}, solver=solver, save_path='char-cnn-lstmlm')
trainer.run(train_data_iter, valid_data_iter, epochs=max_epoch)
for epoch in range(max_epoch):
x, t, loss = build_model(train=True)
trainer.update_variables(inputs=[x, t], loss=loss, metrics={'PPL': np.e**loss})
trainer.run(train_data_iter, None, epochs=1, verbose=1)
x, t, loss = build_model(train=False)
trainer.update_variables(inputs=[x, t], loss=loss, metrics={'PPL': np.e**loss})
trainer.evaluate(valid_data_iter, verbose=1)
# nn.load_parameters('char-cnn-lstm_best.h5')
# batch_size = 1
# sentence_length = 1
# x, embeddings = build_model(get_embeddings=True)
# W = np.zeros((len(w2i), sum(filters)))
# for i, word in enumerate(w2i):
# vec = wordseq2charseq([[w2i[word]]])
# x.d = vec
# embeddings.forward(clear_no_need_grad=True)
# W[w2i[word], :] = embeddings.d[0][0]
# def get_word_from_id(id):
