OK
input dataset a record like this:
===========INPUT===================
[[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
=================OUTPUT==================
[[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
then model predict like this:
Epoch 0 Score: 99.87674774169922
input:
[[ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.3333, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
predict:
[[ 0.5013, 0.4983, 0.5016, 0.5005, 0.5014, 0.5012, 0.5000, 0.5005, 0.5012, 0.5001, 0.4994, 0.5001, 0.4986, 0.5003, 0.5003, 0.5024, 0.4972, 0.5008, 0.4972, 0.5026, 0.4986, 0.5011, 0.4976, 0.5005, 0.5007, 0.5007, 0.4995, 0.4998, 0.4983, 0.5018, 0.5002, 0.5003, 0.4980, 0.4987, 0.5002, 0.5028, 0.5035, 0.4961, 0.4989, 0.4990, 0.5011, 0.4993, 0.5012, 0.5002, 0.5015, 0.5022, 0.5028, 0.5019, 0.5000, 0.4993, 0.4995, 0.5007, 0.5005, 0.5021, 0.4992, 0.4993, 0.5007, 0.5008, 0.4997, 0.4970, 0.4984, 0.4981, 0.4993, 0.4991, 0.5017, 0.4995, 0.5001, 0.4996, 0.5016, 0.4996, 0.4982, 0.5014, 0.5005, 0.4999, 0.5010, 0.5009, 0.5001, 0.4995, 0.4997, 0.4992, 0.4986, 0.5009, 0.5014, 0.5023, 0.5034, 0.4999, 0.5035, 0.5011, 0.5007, 0.4997, 0.5020, 0.5021, 0.4997, 0.5008, 0.5044, 0.4999, 0.5001, 0.5019, 0.5006, 0.4976, 0.4985, 0.4980, 0.4981, 0.5000, 0.4988, 0.5014, 0.4986, 0.5003, 0.5018, 0.4986, 0.4964, 0.5005, 0.4982, 0.4984, 0.5008, 0.5033, 0.4964, 0.5000, 0.4983, 0.4960, 0.4991, 0.4967, 0.5001, 0.4970, 0.5002, 0.4997, 0.5013, 0.5008, 0.5018, 0.4990, 0.5005, 0.5014, 0.4982, 0.5011, 0.4996, 0.5021, 0.4992, 0.5011, 0.4995, 0.5018, 0.5004, 0.5022, 0.4991, 0.5001]]
Euclidean Distance: 5.982511043548584
The following is about keras
input data:
model training:
autoencoder.compile(Adam(), loss='binary_crossentropy')
autoencoder.fit(train_data[:train_nums], train_data[:train_nums], batch_size= 256, epochs=50,
validation_data=(train_data[train_nums:], train_data[train_nums:]))