from keras.layers.core import Dense, Activation, Dropout
from keras.layers.recurrent import LSTM
from keras.models import Sequential
import lstm, time #helper libraries
Using TensorFlow backend.
#Step 1 Load Data
X_train, y_train, X_test, y_test = lstm.load_data('sp500.csv', 50, True)
#Step 2 Build Model
model = Sequential()
model.add(LSTM(
input_dim=1,
output_dim=50,
return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(
100,
return_sequences=False))
model.add(Dropout(0.2))
model.add(Dense(
output_dim=1))
model.add(Activation('linear'))
start = time.time()
model.compile(loss='mse', optimizer='rmsprop')
print 'compilation time : ', time.time() - start
compilation time : 0.0409510135651
#Step 3 Train the model
model.fit(
X_train,
y_train,
batch_size=512,
nb_epoch=1,
validation_split=0.05)
Train on 3523 samples, validate on 186 samples Epoch 1/1 3523/3523 [==============================] - 8s - loss: 0.0098 - val_loss: 0.0011
<keras.callbacks.History at 0x10f8b9b90>
#Step 4 - Plot the predictions!
predictions = lstm.predict_sequences_multiple(model, X_test, 50, 50)
lstm.plot_results_multiple(predictions, y_test, 50)
yo