def predict(directory):
maxlen = 100 # cut texts after this number of words (among top max_features most common words)
batch_size = 32
print("Loading data...")
reduced_vocab = data_processing.reduce_vocabulary(directory + "\\all.txt", 1);
max_features = len(reduced_vocab);
#[X_train,y_train] = data_processing.build_trainX(directory + "\ijcai_train.txt", reduced_vocab);
[X_test,y_test]=data_processing.build_trainX(directory + "\\test4ltsm.txt",reduced_vocab);
#print(len(X_train), 'train sequences')
savesTestData = X_test
print(len(X_test), 'test sequences')
print(min(y_test),'min_y_test')
print(max(y_test),'max_y_test')
#print(min(y_train),'min_y_train')
#print(max(y_train),'max_y_train')
print("Pad sequences (samples x time)")
#X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
#print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
model = model_from_json(open(directory + '\\my_model_architecture.json.12.25').read());
model.load_weights(directory + '\\my_model_weights.h5.12.25');
print("Load Success")
score = model.predict(X_test, batch_size=128, verbose=1);
addr = directory + "\\lstm.result.12.18.txt";
result = open(addr, "a");
test_addr = directory + "\\test4ltsm.txt"
test_file=open(test_addr,"r");
index = 0;
for line in test_file:
tmp_list=[];
line=line.strip();
qs=line.split("\t");
if(len(qs) <= 1):
word = [];
else:
word=qs[1].split(" ");
lineScore = 0;
for w in word:
if reduced_vocab.has_key(w):
tmp_list.append(reduced_vocab[w]);
if len(tmp_list)>0:
lineScore = score[index][0]
index += 1
else:
lineScore = -1;
result.write(str(lineScore) + "\n");
test_file.close();
result.close();
def train(directory):
maxlen = 100 # cut texts after this number of words (among top max_features most common words)
batch_size = 32
print("Loading data...")
reduced_vocab = data_processing.reduce_vocabulary(directory + "\\all.txt", 1);
max_features = len(reduced_vocab);
[X_train,y_train] = data_processing.build_trainX(directory + "\\train.txt", reduced_vocab);
[X_test,y_test] = data_processing.build_trainX(directory + "\\dev.txt",reduced_vocab);
print(len(X_train), 'train sequences')
print(len(X_test), 'test sequences')
print(min(y_test),'min_y_test')
print(max(y_test),'max_y_test')
print(min(y_train),'min_y_train')
print(max(y_train),'max_y_train')
print("Pad sequences (samples x time)")
X_train = sequence.pad_sequences(X_train, maxlen=maxlen)
X_test = sequence.pad_sequences(X_test, maxlen=maxlen)
print('X_train shape:', X_train.shape)
print('X_test shape:', X_test.shape)
print('Build model...')
model = Sequential()
model.add(Embedding(max_features, 256, input_length=maxlen))
model.add(LSTM(256)) # try using a GRU instead, for fun
model.add(Dropout(0.1)) #stop overfit
model.add(Dense(100))
model.add(Activation('tanh'))
model.add(Dense(1))
model.add(Activation('linear'))
# try using different optimizers and different optimizer configs
adam_tmp = Adam(lr=0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-8); #expected to be changed
model.compile(loss='mean_squared_error', optimizer='adam');
print("Train...")
model.fit(X_train, y_train, batch_size=batch_size, nb_epoch=10, validation_data=(X_test, y_test), show_accuracy=True)
score, acc = model.evaluate(X_test, y_test, batch_size=batch_size, show_accuracy=True)
json_string = model.to_json()
open(directory + '\\my_model_architecture.json.12.25', 'w').write(json_string)
model.save_weights(directory + '\\my_model_weights.h5.12.25')
print('Test score:', score)
print('Test accuracy:', acc)
