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Commit 06e7a80e authored by b.gong's avatar b.gong
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Solve global_step issue

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