def f2(input):
cnn_fea = []
tmp = input
for i in range(nb_layers):
tmp = basic_conv2d(nb_filter=nb_filter,
nb_row=nb_row,
nb_col=nb_col,
padding=padding,
bn=bn,
dropout_rate=dropout_rate)(tmp)
tmp = BatchNormalization()(tmp)
cnn_fea.append(tmp)
# allocate weights to 3 cnn features
new_features = []
for fea in cnn_fea:
tmp1 = iLayer()(fea)
tmp2 = Reshape(([nb_filter * 16 * 8]))(tmp1)
tmp3 = Dense(units=1024, activation='tanh')(tmp2)
tmp3 = Dropout(rate=0.5)(tmp3)
new_features.append(tmp3)
# cnn_fea_flatten = Reshape(([nb_layers * nb_filter * map_height * map_width]))(cnn_fea)
# conatenate the 3 cnn layer features
res = Concatenate(axis=1)(new_features)
return res
def f3(data_in):
# main_output1 = Reshape((64 * 3 *6* map_height, map_width))(data_in)
weighted_data_in = []
for i in range(len(data_in)):
tmp = iLayer()(data_in[i])
weighted_data_in.append(tmp)
main_output1 = Concatenate(axis=1)(weighted_data_in)
main_output2 = LSTM(units=nb_flow * map_height * map_width, dropout=0.1)(main_output1)
main_output2 = Activation('tanh')(main_output2)
data_out = Reshape((nb_flow, map_height, map_width))(main_output2)
return data_out
def f2(input):
cnn_fea = []
tmp = input
for i in range(nb_layers):
tmp = basic_conv2d(nb_filter=nb_filter, nb_row=nb_row, nb_col=nb_col, padding=padding, bn=bn)(tmp)
cnn_fea.append(tmp)
# allocate weights to 3 cnn features
new_features = []
for fea in cnn_fea:
new_features.append(iLayer()(fea))
# conatenate the 3 cnn layer features
res = Concatenate(axis=1)(new_features)
return res