pyspark/bigdl/examples/keras/imdb_cnn_lstm.py
def build_keras_model():
    """
    Define a recurrent convolutional model in Keras 1.2.2
    """
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation
    from keras.layers import Embedding
    from keras.layers import LSTM
    from keras.layers import Convolution1D, MaxPooling1D
    keras_model = Sequential()
    keras_model.add(Embedding(20000, 128, input_length=100))
    keras_model.add(Dropout(0.25))
    keras_model.add(Convolution1D(nb_filter=64,
                                  filter_length=5,
                                  border_mode='valid',
                                  activation='relu',
                                  subsample_length=1))
    keras_model.add(MaxPooling1D(pool_length=4))
    keras_model.add(LSTM(70))
    keras_model.add(Dense(1))
    keras_model.add(Activation('sigmoid'))
    return keras_model
def build_keras_model():
    from keras.models import Sequential
    from keras.layers import Dense, Dropout, Activation
    from keras.layers import Embedding
    from keras.layers import LSTM
    from keras.layers import Convolution1D, MaxPooling1D
    keras_model = Sequential()
    keras_model.add(Embedding(20000, 128, input_length=100))
    keras_model.add(Dropout(0.25))
    keras_model.add(Convolution1D(nb_filter=64,
                                  filter_length=5,
                                  border_mode='valid',
                                  activation='relu',
                                  subsample_length=1))
    keras_model.add(MaxPooling1D(pool_length=4))
    keras_model.add(LSTM(70))
    keras_model.add(Dense(1))
    keras_model.add(Activation('sigmoid'))
    return keras_model
def build_keras_model ( ) : from keras . models import Sequential from keras . layers import Dense , Dropout , Activation from keras . layers import Embedding from keras . layers import LSTM from keras . layers import Convolution1D , MaxPooling1D keras_model = Sequential ( ) keras_model . add ( Embedding ( 20000 , 128 , input_length = 100 ) ) keras_model . add ( Dropout ( 0.25 ) ) keras_model . add ( Convolution1D ( nb_filter = 64 , filter_length = 5 , border_mode = 'valid' , activation = 'relu' , subsample_length = 1 ) ) keras_model . add ( MaxPooling1D ( pool_length = 4 ) ) keras_model . add ( LSTM ( 70 ) ) keras_model . add ( Dense ( 1 ) ) keras_model . add ( Activation ( 'sigmoid' ) ) return keras_model
Define a recurrent convolutional model in Keras 1.2.2
Define a recurrent convolutional model in Keras 1 . 2 . 2