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STConvolution.py
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STConvolution.py
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from __future__ import print_function
from keras.models import Sequential
from keras.layers.core import Dense
from keras.layers.core import Reshape, Merge
from keras.layers.core import Activation
from keras.layers.advanced_activations import LeakyReLU
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import ZeroPadding3D
from keras.layers.convolutional import Convolution2D, Convolution3D
def seqCNN(n_flow=4, seq_len=3, map_height=32, map_width=32):
model = Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(n_flow*seq_len, map_height, map_width), border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(n_flow, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model
def seqCNNBase(conf=(4, 3, 32, 32)):
n_flow, seq_len, map_height, map_width = conf
model = Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(n_flow*seq_len, map_height, map_width), border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(128, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(n_flow, 3, 3, border_mode='same'))
# model.add(Activation('tanh'))
return model
def seqCNNBaseLayer1(conf=(4, 3, 32, 32)):
# 1 layer CNN for early fusion
n_flow, seq_len, map_height, map_width = conf
model = Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(n_flow * seq_len, map_height, map_width), border_mode='same'))
model.add(Activation('relu'))
return model
def seqCNN_CPT(c_conf=(4, 3, 32, 32), p_conf=(4, 3, 32, 32), t_conf=(4, 3, 32, 32)):
'''
C - Temporal Closeness
P - Period
T - Trend
conf = (nb_flow, seq_len, map_height, map_width)
'''
model = Sequential()
components = []
for conf in [c_conf, p_conf, t_conf]:
if conf is not None:
components.append(seqCNNBaseLayer1(conf))
nb_flow = conf[0]
model.add(Merge(components, mode='concat', concat_axis=1)) # concat
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(nb_flow, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model
def seqCNNBaseLayer1_2(conf=(4, 3, 32, 32)):
# 1 layer CNN for early fusion
n_flow, seq_len, map_height, map_width = conf
model = Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(n_flow * seq_len, map_height, map_width), border_mode='same'))
# model.add(Activation('relu'))
return model
def seqCNN_CPT2(c_conf=(4, 3, 32, 32), p_conf=(4, 3, 32, 32), t_conf=(4, 3, 32, 32)):
'''
C - Temporal Closeness
P - Period
T - Trend
conf = (nb_flow, seq_len, map_height, map_width)
'''
model = Sequential()
components = []
for conf in [c_conf, p_conf, t_conf]:
if conf is not None:
components.append(seqCNNBaseLayer1_2(conf))
nb_flow = conf[0]
# model.add(Merge(components, mode='concat', concat_axis=1)) # concat
if len(components) > 1:
model.add(Merge(components, mode='sum'))
else:
model = components[0]
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(nb_flow, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model
def seqCNN_CPTM(c_conf=(4, 3, 32, 32), p_conf=(4, 3, 32, 32), t_conf=(4, 3, 32, 32), metadata_dim=None):
'''
C - Temporal Closeness
P - Period
T - Trend
conf = (nb_flow, seq_len, map_height, map_width)
metadata_dim
'''
model = Sequential()
components = []
for conf in [c_conf, p_conf, t_conf]:
if conf is not None:
components.append(seqCNNBaseLayer1_2(conf))
# nb_flow = conf[0]
nb_flow, _, map_height, map_width = conf
# model.add(Merge(components, mode='concat', concat_axis=1)) # concat
if len(components) > 1:
model.add(Merge(components, mode='sum'))
else:
model = components[0]
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution2D(nb_flow, 3, 3, border_mode='same'))
metadata_processor = Sequential()
# metadata_processor.add(Dense(output_dim=nb_flow * map_height * map_width, input_dim=metadata_dim))
metadata_processor.add(Dense(output_dim=10, input_dim=metadata_dim))
metadata_processor.add(Activation('relu'))
metadata_processor.add(Dense(output_dim=nb_flow * map_height * map_width))
metadata_processor.add(Activation('relu'))
metadata_processor.add(Reshape((nb_flow, map_height, map_width)))
model_final=Sequential()
model_final.add(Merge([model, metadata_processor], mode='sum'))
model_final.add(Activation('tanh'))
return model_final
def lateFusion(metadata_dim, n_flow=2, seq_len=3, map_height=32, map_width=32):
model=Sequential()
mat_model=seqCNNBase(n_flow, seq_len, map_height, map_width)
metadata_processor=Sequential()
metadata_processor.add(Dense(output_dim=n_flow * map_height * map_width, input_dim=metadata_dim))
metadata_processor.add(Reshape((n_flow, map_height, map_width)))
# metadata_processor.add(Activation('relu'))
model=Sequential()
model.add(Merge([mat_model, metadata_processor], mode='sum'))
model.add(Activation('tanh'))
return model
def seqCNN_BN(n_flow=4, seq_len=3, map_height=32, map_width=32):
model=Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(n_flow*seq_len, map_height, map_width), border_mode='same'))
model.add(LeakyReLU(0.2))
model.add(BatchNormalization())
model.add(Convolution2D(128, 3, 3, border_mode='same'))
model.add(LeakyReLU(0.2))
model.add(BatchNormalization())
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(LeakyReLU(0.2))
model.add(BatchNormalization())
model.add(Convolution2D(n_flow, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model
def seqCNN_LReLU(n_flow=4, seq_len=3, map_height=32, map_width=32):
model=Sequential()
model.add(Convolution2D(64, 3, 3, input_shape=(n_flow*seq_len, map_height, map_width), border_mode='same'))
model.add(LeakyReLU(0.2))
# model.add(BatchNormalization())
model.add(Convolution2D(128, 3, 3, border_mode='same'))
model.add(LeakyReLU(0.2))
# model.add(BatchNormalization())
model.add(Convolution2D(64, 3, 3, border_mode='same'))
model.add(LeakyReLU(0.2))
# model.add(BatchNormalization())
model.add(Convolution2D(n_flow, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model
def seq3DCNN(n_flow=4, seq_len=3, map_height=32, map_width=32):
model=Sequential()
# model.add(ZeroPadding3D(padding=(0, 1, 1), input_shape=(n_flow, seq_len, map_height, map_width)))
# model.add(Convolution3D(64, 2, 3, 3, border_mode='valid'))
model.add(Convolution3D(64, 2, 3, 3, border_mode='same', input_shape=(n_flow, seq_len, map_height, map_width)))
model.add(Activation('relu'))
model.add(Convolution3D(128, 2, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(Convolution3D(64, 2, 3, 3, border_mode='same'))
model.add(Activation('relu'))
model.add(ZeroPadding3D(padding=(0, 1, 1)))
model.add(Convolution3D(n_flow, seq_len, 3, 3, border_mode='valid'))
# model.add(Convolution3D(n_flow, seq_len-2, 3, 3, border_mode='same'))
model.add(Activation('tanh'))
return model