diff options
| -rwxr-xr-x | prepare.sh | 10 |
1 files changed, 5 insertions, 5 deletions
@@ -94,25 +94,25 @@ md5_check metaData_taxistandsID_name_GPSlocation.csv 724805b0b1385eb3efc02e8bdfe echo -e "\n$BLUE# Conversion of training set to HDF5" echo "${YELLOW}This might take some time$RESET" -data/csv_to_hdf5.py "$TAXI_PATH" "$TAXI_PATH/data.hdf5" +python2 data/csv_to_hdf5.py "$TAXI_PATH" "$TAXI_PATH/data.hdf5" echo -e "\n$BLUE# Generation of validation set" echo "${YELLOW}This might take some time$RESET" echo -n "${YELLOW}initialization... $RESET" -data/init_valid.py +python2 data/init_valid.py echo "${GREEN}ok" echo -n "${YELLOW}cutting... $RESET" -data/make_valid_cut.py test_times_0 +python2 data/make_valid_cut.py test_times_0 echo "${GREEN}ok" echo -e "\n$BLUE# Generation of destination cluster" echo "${YELLOW}This might take some time$RESET" echo -n "${YELLOW}generating... $RESET" -data_analysis/cluster_arrival.py +python2 data_analysis/cluster_arrival.py echo "${GREEN}ok" @@ -122,4 +122,4 @@ echo -n "${YELLOW}mkdir output... $RESET"; mkdir output; echo "${GREEN}ok" echo -e "\n$GREEN${BOLD}The data was successfully prepared" echo "${YELLOW}To train the winning model on gpu, you can now run the following command:" -echo "${YELLOW}THEANO_FLAGS=floatX=float32,device=gpu,optimizer=FAST_RUN ./train.py dest_mlp_tgtcls_1_cswdtx_alexandre" +echo "${YELLOW}THEANO_FLAGS=floatX=float32,device=gpu,optimizer=FAST_RUN python2 train.py dest_mlp_tgtcls_1_cswdtx_alexandre" |