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wasifhameed/Model.py Secret

Created May 21, 2019
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LSTNet Model
def LSTNetModel(init, input_shape):
# m is the number of time-series
m = input_shape[2]
# Get tensor shape except batchsize
tensor_shape = input_shape[1:]
if K.image_data_format() == 'channels_last':
ch_axis = 3
else:
ch_axis = 1
X = Input(shape = tensor_shape)
# CNN
if init.CNNFilters > 0 and init.CNNKernel > 0:
# Add an extra dimension of size 1 which is the channel dimension in Conv2D
C = Reshape((input_shape[1], input_shape[2], 1))(X)
# Apply a Conv2D that will transform it into data of dimensions (batchsize, time, 1, NumofFilters)
C = Conv2D(filters=init.CNNFilters, kernel_size=(init.CNNKernel, m), kernel_initializer=init.initialiser)(C)
C = Dropout(init.dropout)(C)
# Adjust data dimensions by removing axis=2 which is always equal to 1
c_shape = K.int_shape(C)
C = Reshape((c_shape[1], c_shape[3]))(C)
else:
# If configured not to apply CNN, copy the input
C = X
# GRU
# Apply a GRU layer (with activation set to 'relu' as per the paper) and take the returned states as result
_, R = GRU(init.GRUUnits, activation="relu", return_sequences = False, return_state = True)(C)
R = Dropout(init.dropout)(R)
# SkipGRU
if init.skip > 0:
# Calculate the number of values to use which is equal to the window divided by how many time values to skip
pt = int(init.window / init.skip)
S = PreSkipTrans(pt, int((init.window - init.CNNKernel + 1) / pt))(C)
_, S = GRU(init.SkipGRUUnits, activation="relu", return_sequences = False, return_state = True)(S)
S = PostSkipTrans(int((init.window - init.CNNKernel + 1) / pt))([S,X])
# Concatenate the outputs of GRU and SkipGRU
R = Concatenate(axis=1)([R,S])
# Dense layer
Y = Flatten()(R)
Y = Dense(m)(Y)
# AR
if init.highway > 0:
Z = PreARTrans(init.highway)(X)
Z = Flatten()(Z)
Z = Dense(1)(Z)
Z = PostARTrans(m)([Z,X])
# Generate output as the summation of the Dense layer output and the AR one
Y = Add()([Y,Z])
# Generate Model
model = Model(inputs = X, outputs = Y)
return model
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