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add first locally connected example
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‎P1B3/contrib/Fangfang/README.md‎

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```
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Input dim = 29281
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____________________________________________________________________________________________________
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Layer (type) Output Shape Param # Connected to
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====================================================================================================
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locallyconnected1d_1 (LocallyCon (None, 29281, 10) 585620 locallyconnected1d_input_1[0][0]
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____________________________________________________________________________________________________
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maxpooling1d_1 (MaxPooling1D) (None, 292, 10) 0 locallyconnected1d_1[0][0]
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____________________________________________________________________________________________________
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flatten_1 (Flatten) (None, 2920) 0 maxpooling1d_1[0][0]
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____________________________________________________________________________________________________
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dense_1 (Dense) (None, 1000) 2921000 flatten_1[0][0]
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____________________________________________________________________________________________________
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dropout_1 (Dropout) (None, 1000) 0 dense_1[0][0]
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____________________________________________________________________________________________________
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dense_2 (Dense) (None, 500) 500500 dropout_1[0][0]
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____________________________________________________________________________________________________
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dropout_2 (Dropout) (None, 500) 0 dense_2[0][0]
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____________________________________________________________________________________________________
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dense_3 (Dense) (None, 100) 50100 dropout_2[0][0]
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____________________________________________________________________________________________________
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dropout_3 (Dropout) (None, 100) 0 dense_3[0][0]
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____________________________________________________________________________________________________
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dense_4 (Dense) (None, 50) 5050 dropout_3[0][0]
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____________________________________________________________________________________________________
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dropout_4 (Dropout) (None, 50) 0 dense_4[0][0]
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____________________________________________________________________________________________________
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dense_5 (Dense) (None, 1) 51 dropout_4[0][0]
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====================================================================================================
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Total params: 4,062,321
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Trainable params: 4,062,321
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Non-trainable params: 0
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____________________________________________________________________________________________________
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Epoch 1/30
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I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties:
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name: Tesla P100-SXM2-16GB
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major: 6 minor: 0 memoryClockRate (GHz) 0.405
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pciBusID 0000:8a:00.0
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Total memory: 15.90GiB
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Free memory: 15.62GiB
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I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0
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I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: Y
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I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: Tesla P100-SXM2-16GB, pci bus id: 0000:8a:00.0)
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Epoch 1/30
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2113709/2113700 [==============================] - 17376s - loss: 0.1550 - val_loss: 0.1306
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Epoch 2/30
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2113748/2113700 [==============================] - 17110s - loss: 0.1204 - val_loss: 0.1110
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Epoch 3/30
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2113737/2113700 [==============================] - 17116s - loss: 0.1030 - val_loss: 0.0921
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Epoch 4/30
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2113712/2113700 [==============================] - 17087s - loss: 0.0890 - val_loss: 0.0797
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Epoch 5/30
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2113709/2113700 [==============================] - 17230s - loss: 0.0782 - val_loss: 0.0701
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Epoch 6/30
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2113736/2113700 [==============================] - 17198s - loss: 0.0700 - val_loss: 0.0639
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Epoch 7/30
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2113730/2113700 [==============================] - 17219s - loss: 0.0639 - val_loss: 0.0580
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Epoch 8/30
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2113728/2113700 [==============================] - 17156s - loss: 0.0592 - val_loss: 0.0539
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Epoch 9/30
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2113703/2113700 [==============================] - 17145s - loss: 0.0555 - val_loss: 0.0510
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Epoch 10/30
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2113738/2113700 [==============================] - 17103s - loss: 0.0528 - val_loss: 0.0515
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Epoch 11/30
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2113723/2113700 [==============================] - 17754s - loss: 0.0506 - val_loss: 0.0477
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Epoch 12/30
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2113711/2113700 [==============================] - 18338s - loss: 0.0489 - val_loss: 0.0472
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Epoch 13/30
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2113732/2113700 [==============================] - 18728s - loss: 0.0473 - val_loss: 0.0469
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Epoch 14/30
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2113745/2113700 [==============================] - 18740s - loss: 0.0458 - val_loss: 0.0445
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Epoch 15/30
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2113754/2113700 [==============================] - 18613s - loss: 0.0448 - val_loss: 0.0439
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Epoch 16/30
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2113711/2113700 [==============================] - 18552s - loss: 0.0437 - val_loss: 0.0434
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Epoch 17/30
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2113740/2113700 [==============================] - 18645s - loss: 0.0428 - val_loss: 0.0425
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Epoch 18/30
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2113743/2113700 [==============================] - 18649s - loss: 0.0420 - val_loss: 0.0420
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Epoch 19/30
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2113703/2113700 [==============================] - 18796s - loss: 0.0412 - val_loss: 0.0425
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Epoch 20/30
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2113748/2113700 [==============================] - 18403s - loss: 0.0406 - val_loss: 0.0423
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Epoch 21/30
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2113746/2113700 [==============================] - 18722s - loss: 0.0401 - val_loss: 0.0412
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Epoch 22/30
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2113752/2113700 [==============================] - 18599s - loss: 0.0395 - val_loss: 0.0419
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Epoch 23/30
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2113715/2113700 [==============================] - 18243s - loss: 0.0389 - val_loss: 0.0409
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Epoch 24/30
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2113710/2113700 [==============================] - 18734s - loss: 0.0384 - val_loss: 0.0419
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Epoch 25/30
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2113744/2113700 [==============================] - 18820s - loss: 0.0380 - val_loss: 0.0399
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Epoch 26/30
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2113719/2113700 [==============================] - 18118s - loss: 0.0375 - val_loss: 0.0400
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Epoch 27/30
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2113753/2113700 [==============================] - 18785s - loss: 0.0372 - val_loss: 0.0397
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Epoch 28/30
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2113706/2113700 [==============================] - 18752s - loss: 0.0368 - val_loss: 0.0398
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Epoch 29/30
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2113749/2113700 [==============================] - 18731s - loss: 0.0364 - val_loss: 0.0405
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Epoch 30/30
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2113709/2113700 [==============================] - 18683s - loss: 0.0361 - val_loss: 0.0388
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```

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