Solve global_step issue

Zam347_scripts/generate_era5.sh

@@ -3,7 +3,7 @@
 
 python -u ../scripts/generate_transfer_learning_finetune.py \
 --input_dir /home/${USER}/preprocessedData/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/tfrecords  \
---dataset_hparams sequence_length=20 --checkpoint  /home/${USER}/models/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/vae \
+--dataset_hparams sequence_length=20 --checkpoint  /home/${USER}/models/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/convLSTM \
 --mode test --results_dir /home/${USER}/results/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500 \
 --batch_size 2 --dataset era5   > generate_era5-out.out
 

Zam347_scripts/train_era5.sh

@@ -2,5 +2,5 @@
 
 
 
-python ../scripts/train_dummy.py --input_dir  /home/${USER}/preprocessedData/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/tfrecords --dataset era5  --model vae --model_hparams_dict ../hparams/era5/vae/model_hparams.json --output_dir /home/${USER}/models/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/vae 
+python ../scripts/train_dummy.py --input_dir  /home/${USER}/preprocessedData/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/tfrecords --dataset era5  --model convLSTM  --model_hparams_dict ../hparams/era5/vae/model_hparams.json --output_dir /home/${USER}/models/era5-Y2017M01to02-128x160-74d00N71d00E-T_MSL_gph500/convLSTM 
 #srun  python scripts/train.py --input_dir data/era5 --dataset era5  --model savp --model_hparams_dict hparams/kth/ours_savp/model_hparams.json --output_dir logs/era5/ours_savp

hparams/era5/vae/model_hparams.json

 {
     "batch_size": 8,
-    "lr": 0.0002,
+    "lr": 0.001,
     "nz": 16,
-    "max_steps":20
+    "max_steps":500
 }
 
 

video_prediction/models/vanilla_convLSTM_model.py

@@ -88,7 +88,8 @@ class VanillaConvLstmVideoPredictionModel(BaseVideoPredictionModel):
 
         self.train_op = tf.train.AdamOptimizer(
             learning_rate = self.learning_rate).minimize(self.total_loss, global_step = self.global_step)
-
+        self.outputs = {}
+        self.outputs["gen_images"] = self.x_hat
         # Summary op
         self.loss_summary = tf.summary.scalar("recon_loss", self.context_frames_loss)
         self.loss_summary = tf.summary.scalar("latent_loss", self.predict_frames_loss)