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| import time | |
| from keras.models import Sequential | |
| from keras.layers.core import Dense, Dropout, Activation, Lambda, TimeDistributedDense | |
| from keras.layers.recurrent import LSTM | |
| from keras.layers.embeddings import Embedding | |
| from keras.optimizers import RMSprop | |
| from keras.utils import np_utils | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| x_input = np.arange(0.,50.,.01) | |
| y_input = map(lambda x : 1.337 * np.sin(2.*np.pi*x*1.),x_input) | |
| print 'x_input shape=' + str(len(x_input)) + ', y_input shape=' + str(len(y_input)) | |
| lenOfTime = 30 # number of periods in the past to base the predictions on | |
| stepsInFuture = 20 # number of periods in the future to predict | |
| for i in range(3000): | |
| _mx = np.hstack((y_input[i:(i+lenOfTime)], np.repeat(y_input[i+lenOfTime-1],stepsInFuture))) | |
| _target = y_input[(i+lenOfTime):(i+lenOfTime+stepsInFuture)] | |
| if i == 0: | |
| mx = _mx | |
| target = _target | |
| #print 'mx 0 = ' + str(mx) + ', target 0 = ' + str(target) | |
| else: | |
| mx = np.vstack((mx,_mx)) | |
| target = np.vstack((target,_target)) | |
| print 'mx shape=' + str(mx.shape) + ', target shape=' + str(target.shape) | |
| X = mx.reshape(3000,lenOfTime+stepsInFuture,1) | |
| y = target.reshape(3000,stepsInFuture,1) | |
| print 'X shape=' + str(X.shape) + ', y shape=' + str(y.shape) | |
| X_train = X[0:2000,:] | |
| y_train = y[0:2000,:] | |
| X_test = X[2000:,:] | |
| y_test = y[2000:,:] | |
| print 'X_train shape=' + str(X_train.shape) + ', y_train shape=' + str(y_train.shape) | |
| print 'X_test shape=' + str(X_test.shape) + ', y_test shape=' + str(y_test.shape) | |
| #print 'X_train[0:3,:,0] = ' + str(X_train[0:3,:,0]) + ', y_train[0:3,:,0] = ' + str(y_train[0:3,:,0]) | |
| hidden_neurons = 100 | |
| def time_slice(output): | |
| return output[:,-20:,:] #todo: how to use stepsInFuture here? | |
| model = Sequential() | |
| model.add(LSTM(output_dim=hidden_neurons, input_dim = 1, return_sequences=True, activation='tanh')) | |
| model.add(LSTM(output_dim=hidden_neurons, input_dim = hidden_neurons, return_sequences=True, activation='tanh')) | |
| model.add(LSTM(output_dim=hidden_neurons, input_dim = hidden_neurons, return_sequences=True, activation='tanh')) | |
| model.add(Lambda(time_slice, output_shape=(stepsInFuture, hidden_neurons))) | |
| model.add(TimeDistributedDense(output_dim=1, activation = 'linear', input_dim=hidden_neurons, input_length=stepsInFuture)) | |
| start = time.time() | |
| model.compile(loss="mse", optimizer="rmsprop") | |
| print 'model compiled in ' + str(time.time() - start) + ' seconds' | |
| model.fit(X_train, y_train, batch_size=128, nb_epoch=30,validation_data=(X_test, y_test), show_accuracy=True) | |
| plt.plot(model.predict(X_test)[0,:,0], label='predicted') | |
| plt.plot(y_test[0,:,0], label = 'actual') | |
| plt.legend() | |
| plt.show() | |
| print "MAE: {0:.6f}".format(np.mean(abs(y_test - model.predict(X_test)))) | |
| print "MSE: {0:.6f}".format(np.mean((y_test - model.predict(X_test)) ** 2.)) |
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