class Model:
def __init__(self):
pass
def build(self, dataset, hidden_size=128):
self.l = Language(dataset)
inputs = layers.Input(shape=(self.l.max_encoder_seq_length,
self.l.num_encoder_tokens))
x = layers.LSTM(hidden_size, return_sequences=True)(inputs)
x = layers.Flatten()(x)
x = layers.RepeatVector(self.l.max_decoder_seq_length)(x)
x = layers.Dense(hidden_size, activation='tanh')(x)
x = layers.LSTM(hidden_size, return_sequences=True)(x)
x = layers.Dense(self.l.num_decoder_tokens, activation='softmax')(x)
#print(x.shape)
self.model = keras.Model(inputs, x)
self.model.compile(optimizer='adam',
loss=losses.categorical_crossentropy,
metrics=['accuracy'])
def train(self, epochs=100, bsize=100):
self.model.fit(self.l.encoder_input_data,
self.l.decoder_input_data,
epochs=epochs,
batch_size=bsize)
def predict(self, s):
e = self.l.encode_input(s)
prediction = self.model.predict(e)
ans = ""
for p in prediction[0]:
out = self.l.reverse_target_word_index[np.argmax(p)]
if out != "_end" and out != "_start":
if out[1:].isnumeric() and (int(out[1:]) - 1) < len(s.split()):
#print(out)
ans += s.split()[int(out[1:]) - 1] + " "
else:
ans += str(out) + " "
# ans += str(out)+" "
# print(s,"\noutput: ",ans,"\n")
return ans
def save(self, name=""):
self.model.save("model" + str(name) + ".h5")
import pickle
with open("language" + str(name) + ".pickle", "wb") as fp:
pickle.dump(self.l, fp)
def load(self, name=""):
import pickle
self.model = models.load_model("model" + str(name) + ".h5")
with open("language" + str(name) + ".pickle", "rb") as fb:
self.l = pickle.load(fb)
