when enabled --l_emb , then testing the model it will raise an excption.

[shwangdev]

(py3) λ python test.py -fr ktf -d data\movielns10k\ -b 512 --max_length 30 --r_l 30 --r_emb 30
Using TensorFlow backend.
2019-04-09 22:31:54.683881: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
2019-04-09 22:31:54.924866: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties:
name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate(GHz): 1.6325
pciBusID: 0000:01:00.0
totalMemory: 11.00GiB freeMemory: 9.10GiB
2019-04-09 22:31:54.943914: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2019-04-09 22:31:55.258021: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-04-09 22:31:55.271662: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] 0
2019-04-09 22:31:55.280244: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0: N
2019-04-09 22:31:55.287898: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3379 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0, compute capability: 6.1)

---

Layer (type) Output Shape Param #

embedding_1 (Embedding) (None, 30, 30) 89280

---

masking_1 (Masking) (None, 30, 30) 0

---

lstm_1 (LSTM) (None, 30) 7320

---

dense_1 (Dense) (None, 2976) 92256

---

activation_1 (Activation) (None, 2976) 0

Total params: 188,856
Trainable params: 188,856
Non-trainable params: 0

---

filename : data\movielns10k\models\ktf\rnn_cce_ml30_bs512_ne*_gc100_e30_h30_Ug_lr0.1_nt1.ktf
['data\movielns10k\models\ktf\rnn_cce_ml30_bs512_ne49.969_gc100_e30_h30_Ug_lr0.1_nt1.ktf'
'data\movielns10k\models\ktf\rnn_cce_ml30_bs512_ne149.969_gc100_e30_h30_Ug_lr0.1_nt1.ktf']
Opening file (1)
Traceback (most recent call last):
File "test.py", line 168, in
main()
File "test.py", line 161, in main
evaluator = run_tests(predictor, f, dataset, args, get_full_recommendation_list=args.save_rank, k=args.nb_of_predictions)
File "test.py", line 55, in run_tests
recommendations = predictor.top_k_recommendations(viewed, k=k)
File "D:\Users\shwang\workspace\tests\LSTM-recommendation-model\neural_networks\rnn_base.py", line 105, in top_k_recommendations
output = self.model.predict_on_batch(X)
File "d:\Users\shwang\Anaconda3\envs\py3\lib\site-packages\keras\models.py", line 1041, in predict_on_batch
return self.model.predict_on_batch(x)
File "d:\Users\shwang\Anaconda3\envs\py3\lib\site-packages\keras\engine\training.py", line 1906, in predict_on_batch
self._feed_input_shapes)
File "d:\Users\shwang\Anaconda3\envs\py3\lib\site-packages\keras\engine\training.py", line 110, in _standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking : expected embedding_1_input to have 2 dimensions, but got array with shape (1, 30, 2976)