消費時間
2015-05-15 14:03:51
2015-05-15 15:32:46
2015-06-28 18:00:17
2015-07-16 21:27:18
2015-07-16 22:04:51
2015-09-08 14:59:56
..
..

消費間隔
0
44
18
0
54
..
..

X1  X2  X3  Y
0  44  18  0
44  18  0  54
..
..  

X1  X2  X3  Y
0  44  18  0
44  18  0  4
... 

1  0  0  0  0
0  0  0  0  1
...

import pandas as pd
import numpy as np

def create_interval_dataset(dataset, look_back):
  """
  :param dataset: input array of time intervals
  :param look_back: each training set feature length
  :return: convert an array of values into a dataset matrix.
  """
  dataX, dataY = [], []
  for i in range(len(dataset) - look_back):
    dataX.append(dataset[i:i+look_back])
    dataY.append(dataset[i+look_back])
  return np.asarray(dataX), np.asarray(dataY)

df = pd.read_csv("path-to-your-time-interval-file")  
dataset_init = np.asarray(df)  # if only 1 column
dataX, dataY = create_interval_dataset(dataset, lookback=3)  # look back if the training set sequence length

import pandas as pd
import numpy as np
import random
from keras.models import Sequential, model_from_json
from keras.layers import Dense, LSTM, Dropout

class NeuralNetwork():
  def __init__(self, **kwargs):
    """
    :param **kwargs: output_dim=4: output dimension of LSTM layer; activation_lstm='tanh': activation function for LSTM layers; activation_dense='relu': activation function for Dense layer; activation_last='sigmoid': activation function for last layer; drop_out=0.2: fraction of input units to drop; np_epoch=10, the number of epoches to train the model. epoch is one forward pass and one backward pass of all the training examples; batch_size=32: number of samples per gradient update. The higher the batch size, the more memory space you'll need; loss='mean_square_error': loss function; optimizer='rmsprop'
    """
    self.output_dim = kwargs.get('output_dim', 8)
    self.activation_lstm = kwargs.get('activation_lstm', 'relu')
    self.activation_dense = kwargs.get('activation_dense', 'relu')
    self.activation_last = kwargs.get('activation_last', 'softmax')  # softmax for multiple output
    self.dense_layer = kwargs.get('dense_layer', 2)   # at least 2 layers
    self.lstm_layer = kwargs.get('lstm_layer', 2)
    self.drop_out = kwargs.get('drop_out', 0.2)
    self.nb_epoch = kwargs.get('nb_epoch', 10)
    self.batch_size = kwargs.get('batch_size', 100)
    self.loss = kwargs.get('loss', 'categorical_crossentropy')
    self.optimizer = kwargs.get('optimizer', 'rmsprop')

    def NN_model(self, trainX, trainY, testX, testY):
    """
    :param trainX: training data set
    :param trainY: expect value of training data
    :param testX: test data set
    :param testY: epect value of test data
    :return: model after training
    """
    print "Training model is LSTM network!"
    input_dim = trainX[1].shape[1]
    output_dim = trainY.shape[1] # one-hot label
    # print predefined parameters of current model:
    model = Sequential()
    # applying a LSTM layer with x dim output and y dim input. Use dropout parameter to avoid overfitting
    model.add(LSTM(output_dim=self.output_dim,
            input_dim=input_dim,
            activation=self.activation_lstm,
            dropout_U=self.drop_out,
            return_sequences=True))
    for i in range(self.lstm_layer-2):
      model.add(LSTM(output_dim=self.output_dim,
            input_dim=self.output_dim,
            activation=self.activation_lstm,
            dropout_U=self.drop_out,
            return_sequences=True))
    # argument return_sequences should be false in last lstm layer to avoid input dimension incompatibility with dense layer
    model.add(LSTM(output_dim=self.output_dim,
            input_dim=self.output_dim,
            activation=self.activation_lstm,
            dropout_U=self.drop_out))
    for i in range(self.dense_layer-1):
      model.add(Dense(output_dim=self.output_dim,
            activation=self.activation_last))
    model.add(Dense(output_dim=output_dim,
            input_dim=self.output_dim,
            activation=self.activation_last))
    # configure the learning process
    model.compile(loss=self.loss, optimizer=self.optimizer, metrics=['accuracy'])
    # train the model with fixed number of epoches
    model.fit(x=trainX, y=trainY, nb_epoch=self.nb_epoch, batch_size=self.batch_size, validation_data=(testX, testY))
    # store model to json file
    model_json = model.to_json()
    with open(model_path, "w") as json_file:
      json_file.write(model_json)
    # store model weights to hdf5 file
    if model_weight_path:
      if os.path.exists(model_weight_path):
        os.remove(model_weight_path)
      model.save_weights(model_weight_path) # eg: model_weight.h5
    return model