Testing Keras Sequential Neural Network - predictions totally inaccurate

Question

I am working with Keras and TensorFlow in Python for the first time and looking to use it to create a computer player for a card game. I have the following test code to prove I understand how to get a basic Neural Network setup, but the predictions are not what I expect - they have no resemblance to the outcomes in the input data. Sometimes the predictions are all 1's, sometimes all 0's.

My test code:

from keras.models import Sequential
from keras.layers import Dense

# Define the keras model
model = Sequential()
model.add(Dense(6, input_dim=3, activation='relu'))
model.add(Dense(3, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

trainingData = [[10,1900,1,1], # Features, Choice, Outcomes
                [20,1800,1,1], 
                [90,1000,1,0],
                [80,1100,1,0],
                [10,1900,0,0],
                [20,1800,0,0],
                [90,1000,0,1],
                [80,1100,0,1],
                ]

# Split training data into features and outcomes
features = []
outcomes = []
for trainingDataRow in trainingData:
    features.append(trainingDataRow[:-1])
    outcomes.append(trainingDataRow[-1])


# fit the keras model on the dataset
model.fit(features, outcomes, epochs=15, batch_size=8)

# evaluate the keras model acuracy
_, accuracy = model.evaluate(features, outcomes)
print('Accuracy: %.2f' % (accuracy*100))

# Test model predictions against training data
predictions = model.predict_classes(features)
for i in range(0,len(features)):
    print('%s => %d (expected %d)' % (features[i], predictions[i], outcomes[i]))

# Print Text decission Tree
print(model.summary())

The output:

Epoch 1/15

1/1 - ETA: 0s - loss: 265.5195 - accuracy: 0.5000
1/1 -  15s 15s/step - loss: 265.5195 - accuracy: 0.5000
Epoch 2/15

1/1 - ETA: 0s - loss: 263.9588 - accuracy: 0.5000
1/1 - 0s 16ms/step - loss: 263.9588 - accuracy: 0.5000
Epoch 3/15

1/1 - ETA: 0s - loss: 262.4041 - accuracy: 0.5000
1/1 - 0s 16ms/step - loss: 262.4041 - accuracy: 0.5000
Epoch 4/15

[etc]

1/1 - ETA: 0s - loss: 248.6939 - accuracy: 0.5000
1/1   0s 16ms/step - loss: 248.6939 - accuracy: 0.5000
Epoch 13/15

1/1 - ETA: 0s - loss: 247.2033 - accuracy: 0.5000
1/1 - 0s 16ms/step - loss: 247.2033 - accuracy: 0.5000
Epoch 14/15

1/1 - ETA: 0s - loss: 245.7196 - accuracy: 0.5000
1/1 - 0s 16ms/step - loss: 245.7196 - accuracy: 0.5000
Epoch 15/15

1/1 - ETA: 0s - loss: 244.2428 - accuracy: 0.5000
1/1 - 0s 16ms/step - loss: 244.2428 - accuracy: 0.5000

1/1 - ETA: 0s - loss: 242.7730 - accuracy: 0.5000
1/1 - 0s 445ms/step - loss: 242.7730 - accuracy: 0.5000
Accuracy: 50.00

[20, 1800, 1] => 1 (expected 1)
[90, 1000, 1] => 1 (expected 0)
[80, 1100, 1] => 1 (expected 0)
[10, 1900, 0] => 1 (expected 0)
[20, 1800, 0] => 1 (expected 0)
[90, 1000, 0] => 1 (expected 1)
[80, 1100, 0] => 1 (expected 1)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 6)                 24        
_________________________________________________________________
dense_1 (Dense)              (None, 3)                 21        
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 4         
=================================================================
Total params: 49
Trainable params: 49
Non-trainable params: 0
_________________________________________________________________
None

All help and pointers appreciated.

Following conversation below, my updated code with more input data, more epoch's and other changes. The variability of accuracy within even the last 10 epoch's is still huge though - between 56% and 83%. Advice on how I should go about improving that welcomed?

Code:

from keras.models import Sequential
from keras.layers import Dense

# Define the keras model
model = Sequential()
model.add(Dense(12, input_dim=3, activation='relu'))
model.add(Dense(6, activation='relu'))
model.add(Dense(1, activation='sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

trainingData = [[10,1000,0,0],
[11,1001,0,0],
[12,1002,0,0],
[13,1003,0,0],
[14,1004,0,0],
[15,1005,0,0],
[16,1006,0,0],
[17,1007,0,0],
[18,1008,0,0],
[19,1009,0,0],
[20,1010,0,0],
[21,1011,0,0],
[22,1012,0,0],
[23,1013,0,0],
[24,1014,0,0],
[25,1015,0,0],
[26,1016,0,0],
[27,1017,0,0],
[28,1018,0,0],
[29,1019,0,0],
[30,1020,0,0],
[10,1000,1,0],
[11,1001,1,0],
[12,1002,1,0],
[13,1003,1,0],
[14,1004,1,0],
[15,1005,1,0],
[16,1006,1,0],
[17,1007,1,0],
[18,1008,1,0],
[19,1009,1,0],
[20,1010,1,0],
[21,1011,1,0],
[22,1012,1,0],
[23,1013,1,0],
[24,1014,1,0],
[25,1015,1,0],
[26,1016,1,0],
[27,1017,1,0],
[28,1018,1,0],
[29,1019,1,0],
[30,1020,1,0],
[10,9000,0,0],
[11,9001,0,0],
[12,9002,0,0],
[13,9003,0,0],
[14,9004,0,0],
[15,9005,0,0],
[16,9006,0,0],
[17,9007,0,0],
[18,9008,0,0],
[19,9009,0,0],
[20,9010,0,0],
[21,9011,0,0],
[22,9012,0,0],
[23,9013,0,0],
[24,9014,0,0],
[25,9015,0,0],
[26,9016,0,0],
[27,9017,0,0],
[28,9018,0,0],
[29,9019,0,0],
[30,9020,0,0],
[10,9021,1,1],
[11,9022,1,1],
[12,9023,1,1],
[13,9024,1,1],
[14,9025,1,1],
[15,9026,1,1],
[16,9027,1,1],
[17,9028,1,1],
[18,9029,1,1],
[19,9030,1,1],
[20,9031,1,1],
[21,9032,1,1],
[22,9033,1,1],
[23,9034,1,1],
[24,9035,1,1],
[25,9036,1,1],
[26,9037,1,1],
[27,9038,1,1],
[28,9039,1,1],
[29,9040,1,1],
[30,9041,1,1],
[70,1000,0,1],
[71,1001,0,1],
[72,1002,0,1],
[73,1003,0,1],
[74,1004,0,1],
[75,1005,0,1],
[76,1006,0,1],
[77,1007,0,1],
[78,1008,0,1],
[79,1009,0,1],
[80,1010,0,1],
[81,1011,0,1],
[82,1012,0,1],
[83,1013,0,1],
[84,1014,0,1],
[85,1015,0,1],
[86,1016,0,1],
[87,1017,0,1],
[88,1018,0,1],
[89,1019,0,1],
[90,1020,0,1],
[70,1000,1,0],
[71,1001,1,0],
[72,1002,1,0],
[73,1003,1,0],
[74,1004,1,0],
[75,1005,1,0],
[76,1006,1,0],
[77,1007,1,0],
[78,1008,1,0],
[79,1009,1,0],
[80,1010,1,0],
[81,1011,1,0],
[82,1012,1,0],
[83,1013,1,0],
[84,1014,1,0],
[85,1015,1,0],
[86,1016,1,0],
[87,1017,1,0],
[88,1018,1,0],
[89,1019,1,0],
[90,1020,1,0],
[70,9000,0,1],
[71,9001,0,1],
[72,9002,0,1],
[73,9003,0,1],
[74,9004,0,1],
[75,9005,0,1],
[76,9006,0,1],
[77,9007,0,1],
[78,9008,0,1],
[79,9009,0,1],
[80,9010,0,1],
[81,9011,0,1],
[82,9012,0,1],
[83,9013,0,1],
[84,9014,0,1],
[85,9015,0,1],
[86,9016,0,1],
[87,9017,0,1],
[88,9018,0,1],
[89,9019,0,1],
[90,9020,0,1],
[70,9000,1,1],
[71,9001,1,1],
[72,9002,1,1],
[73,9003,1,1],
[74,9004,1,1],
[75,9005,1,1],
[76,9006,1,1],
[77,9007,1,1],
[78,9008,1,1],
[79,9009,1,1],
[80,9010,1,1],
[81,9011,1,1],
[82,9012,1,1],
[83,9013,1,1],
[84,9014,1,1],
[85,9015,1,1],
[86,9016,1,1],
[87,9017,1,1],
[88,9018,1,1],
[89,9019,1,1],
[90,9020,1,1]]

# Split training data into features and outcomes
features = []
outcomes = []
for trainingDataRow in trainingData:
    features.append(trainingDataRow[:-1])
    outcomes.append(trainingDataRow[-1])
#print(features)
#print(outcomes)

# fit the keras model on the dataset
model.fit(features, outcomes, epochs=500, verbose=2)

# evaluate the keras model acuracy
_, accuracy = model.evaluate(features, outcomes)
print('Accuracy: %.2f' % (accuracy*100))

# Test model predictions against training data
#predictions = model.predict_classes(features)
predictions = (model.predict(features) > 0.5).astype("int32")
for i in range(0,160,4):
    print('%s => %d (expected %d)' % (features[i], predictions[i], outcomes[i]))



# Print Text decission Tree
print(model.summary())

output:

Epoch 1/500
6/6 - 16s - loss: 1354.8107 - accuracy: 0.5000
Epoch 2/500
6/6 - 0s - loss: 1178.2457 - accuracy: 0.5000
Epoch 3/500
6/6 - 0s - loss: 999.3210 - accuracy: 0.5000
Epoch 4/500
6/6 - 0s - loss: 821.8029 - accuracy: 0.5000
Epoch 5/500
6/6 - 0s - loss: 651.4391 - accuracy: 0.5000
Epoch 6/500
6/6 - 0s - loss: 474.1348 - accuracy: 0.5000
Epoch 7/500
6/6 - 0s - loss: 312.3422 - accuracy: 0.5000
Epoch 8/500
6/6 - 0s - loss: 136.2285 - accuracy: 0.5000
Epoch 9/500
6/6 - 0s - loss: 18.8638 - accuracy: 0.4821
Epoch 10/500
6/6 - 0s - loss: 55.3453 - accuracy: 0.5000
Epoch 11/500
6/6 - 0s - loss: 62.0218 - accuracy: 0.5000
Epoch 12/500
6/6 - 0s - loss: 48.6303 - accuracy: 0.5000
Epoch 13/500
6/6 - 0s - loss: 24.6552 - accuracy: 0.5000
Epoch 14/500
6/6 - 0s - loss: 12.4640 - accuracy: 0.4524
Epoch 15/500
6/6 - 0s - loss: 11.6526 - accuracy: 0.3988
Epoch 16/500
6/6 - 0s - loss: 10.0779 - accuracy: 0.5000
Epoch 17/500
6/6 - 0s - loss: 4.5000 - accuracy: 0.3690
Epoch 18/500
6/6 - 0s - loss: 4.4624 - accuracy: 0.4940
Epoch 19/500
6/6 - 0s - loss: 3.5397 - accuracy: 0.4702
Epoch 20/500
6/6 - 0s - loss: 3.1084 - accuracy: 0.5060
Epoch 21/500
6/6 - 0s - loss: 3.3269 - accuracy: 0.4762
Epoch 22/500
6/6 - 0s - loss: 5.0356 - accuracy: 0.5000
Epoch 23/500
6/6 - 0s - loss: 4.5903 - accuracy: 0.3988
Epoch 24/500
6/6 - 0s - loss: 4.0782 - accuracy: 0.5238
Epoch 25/500
6/6 - 0s - loss: 4.2735 - accuracy: 0.4048
Epoch 26/500
6/6 - 0s - loss: 3.4100 - accuracy: 0.4048
Epoch 27/500
6/6 - 0s - loss: 3.4263 - accuracy: 0.4702
Epoch 28/500
6/6 - 0s - loss: 3.0845 - accuracy: 0.4881
Epoch 29/500
6/6 - 0s - loss: 3.6671 - accuracy: 0.4226
Epoch 30/500
6/6 - 0s - loss: 3.2743 - accuracy: 0.4881
Epoch 31/500
6/6 - 0s - loss: 2.7294 - accuracy: 0.5060
Epoch 32/500
6/6 - 0s - loss: 2.6796 - accuracy: 0.4702
Epoch 33/500
6/6 - 0s - loss: 2.3139 - accuracy: 0.5119
Epoch 34/500
6/6 - 0s - loss: 2.2984 - accuracy: 0.4821
Epoch 35/500
6/6 - 0s - loss: 2.5401 - accuracy: 0.4881
Epoch 36/500
6/6 - 0s - loss: 2.5181 - accuracy: 0.4881
Epoch 37/500
6/6 - 0s - loss: 2.2515 - accuracy: 0.4940
Epoch 38/500
6/6 - 0s - loss: 2.1356 - accuracy: 0.4821
Epoch 39/500
6/6 - 0s - loss: 2.0135 - accuracy: 0.4643
Epoch 40/500
6/6 - 0s - loss: 1.9647 - accuracy: 0.4821
Epoch 41/500
6/6 - 0s - loss: 2.4048 - accuracy: 0.3869
Epoch 42/500
6/6 - 0s - loss: 4.4917 - accuracy: 0.4940
Epoch 43/500
6/6 - 0s - loss: 2.9583 - accuracy: 0.4286
Epoch 44/500
6/6 - 0s - loss: 2.0616 - accuracy: 0.4048
Epoch 45/500
6/6 - 0s - loss: 2.2955 - accuracy: 0.4643
Epoch 46/500
6/6 - 0s - loss: 2.6275 - accuracy: 0.4107
Epoch 47/500
6/6 - 0s - loss: 2.1440 - accuracy: 0.4762
Epoch 48/500
6/6 - 0s - loss: 2.2715 - accuracy: 0.4643
Epoch 49/500
6/6 - 0s - loss: 1.6154 - accuracy: 0.4762
Epoch 50/500
6/6 - 0s - loss: 1.3189 - accuracy: 0.5179
[etc]
Epoch 100/500
6/6 - 0s - loss: 0.9070 - accuracy: 0.7143
Epoch 101/500
6/6 - 0s - loss: 1.2207 - accuracy: 0.6786
Epoch 102/500
6/6 - 0s - loss: 1.7759 - accuracy: 0.6429
Epoch 103/500
6/6 - 0s - loss: 2.3393 - accuracy: 0.6012
Epoch 104/500
6/6 - 0s - loss: 6.1804 - accuracy: 0.5000
Epoch 105/500
6/6 - 0s - loss: 4.0825 - accuracy: 0.5833
Epoch 106/500
6/6 - 0s - loss: 2.5547 - accuracy: 0.5060
Epoch 107/500
6/6 - 0s - loss: 2.1338 - accuracy: 0.6131
Epoch 108/500
6/6 - 0s - loss: 0.8321 - accuracy: 0.7619
Epoch 109/500
6/6 - 0s - loss: 1.0405 - accuracy: 0.6964
Epoch 110/500
6/6 - 0s - loss: 1.4533 - accuracy: 0.6190
Epoch 111/500
6/6 - 0s - loss: 2.4647 - accuracy: 0.5417
Epoch 112/500
6/6 - 0s - loss: 2.4917 - accuracy: 0.5476
Epoch 113/500
6/6 - 0s - loss: 2.2088 - accuracy: 0.5357
Epoch 114/500
6/6 - 0s - loss: 1.5922 - accuracy: 0.5952
Epoch 115/500
6/6 - 0s - loss: 0.9498 - accuracy: 0.6488
Epoch 116/500
6/6 - 0s - loss: 0.7612 - accuracy: 0.6905
Epoch 117/500
6/6 - 0s - loss: 0.9210 - accuracy: 0.7917
Epoch 118/500
6/6 - 0s - loss: 1.5319 - accuracy: 0.6012
Epoch 119/500
6/6 - 0s - loss: 2.8555 - accuracy: 0.5179
Epoch 120/500
6/6 - 0s - loss: 1.6090 - accuracy: 0.6131
Epoch 121/500
6/6 - 0s - loss: 1.9316 - accuracy: 0.5119
Epoch 122/500
6/6 - 0s - loss: 3.9357 - accuracy: 0.5060
Epoch 123/500
6/6 - 0s - loss: 1.2138 - accuracy: 0.6845
Epoch 124/500
6/6 - 0s - loss: 0.9364 - accuracy: 0.6726
Epoch 125/500
6/6 - 0s - loss: 1.7571 - accuracy: 0.6429
Epoch 126/500
6/6 - 0s - loss: 3.0547 - accuracy: 0.5298
Epoch 127/500
6/6 - 0s - loss: 0.5857 - accuracy: 0.7857
Epoch 128/500
6/6 - 0s - loss: 1.0959 - accuracy: 0.6964
Epoch 129/500
6/6 - 0s - loss: 2.6186 - accuracy: 0.4940
Epoch 130/500
6/6 - 0s - loss: 2.3914 - accuracy: 0.6250
Epoch 131/500
6/6 - 0s - loss: 1.3078 - accuracy: 0.6607
Epoch 132/500
6/6 - 0s - loss: 1.1346 - accuracy: 0.6548
Epoch 133/500
6/6 - 1s - loss: 0.9108 - accuracy: 0.7679
Epoch 134/500
6/6 - 0s - loss: 0.9480 - accuracy: 0.7024
Epoch 135/500
6/6 - 0s - loss: 2.3744 - accuracy: 0.5298
Epoch 136/500
6/6 - 0s - loss: 0.9875 - accuracy: 0.7262
Epoch 137/500
6/6 - 0s - loss: 3.0520 - accuracy: 0.5536
Epoch 138/500
6/6 - 0s - loss: 3.3804 - accuracy: 0.6250
Epoch 139/500
6/6 - 0s - loss: 2.2378 - accuracy: 0.6607
Epoch 140/500
6/6 - 0s - loss: 0.7141 - accuracy: 0.7202
Epoch 141/500
6/6 - 0s - loss: 1.0920 - accuracy: 0.6964
Epoch 142/500
6/6 - 0s - loss: 1.3448 - accuracy: 0.6905
Epoch 143/500
6/6 - 0s - loss: 1.8249 - accuracy: 0.6190
Epoch 144/500
6/6 - 0s - loss: 1.2369 - accuracy: 0.7321
Epoch 145/500
6/6 - 0s - loss: 1.9031 - accuracy: 0.6488
Epoch 146/500
6/6 - 0s - loss: 2.8954 - accuracy: 0.5774
Epoch 147/500
6/6 - 0s - loss: 3.6607 - accuracy: 0.5298
Epoch 148/500
6/6 - 0s - loss: 0.6843 - accuracy: 0.7976
Epoch 149/500
6/6 - 0s - loss: 0.4969 - accuracy: 0.8393
Epoch 150/500
6/6 - 0s - loss: 0.5488 - accuracy: 0.8333
[etc]
Epoch 200/500
6/6 - 0s - loss: 1.2898 - accuracy: 0.7619
Epoch 201/500
6/6 - 0s - loss: 0.5927 - accuracy: 0.7976
Epoch 202/500
6/6 - 0s - loss: 1.0875 - accuracy: 0.7262
Epoch 203/500
6/6 - 0s - loss: 1.1926 - accuracy: 0.7440
Epoch 204/500
6/6 - 0s - loss: 1.4880 - accuracy: 0.6845
Epoch 205/500
6/6 - 0s - loss: 1.3070 - accuracy: 0.7143
Epoch 206/500
6/6 - 0s - loss: 1.0667 - accuracy: 0.7321
Epoch 207/500
6/6 - 0s - loss: 1.7004 - accuracy: 0.6548
Epoch 208/500
6/6 - 0s - loss: 1.7348 - accuracy: 0.6964
Epoch 209/500
6/6 - 0s - loss: 1.3299 - accuracy: 0.6845
Epoch 210/500
6/6 - 0s - loss: 1.6381 - accuracy: 0.6071
Epoch 211/500
6/6 - 0s - loss: 3.2871 - accuracy: 0.5357
Epoch 212/500
6/6 - 0s - loss: 1.2281 - accuracy: 0.6786
Epoch 213/500
6/6 - 0s - loss: 0.7263 - accuracy: 0.7619
Epoch 214/500
6/6 - 0s - loss: 0.5449 - accuracy: 0.8155
Epoch 215/500
6/6 - 0s - loss: 0.8705 - accuracy: 0.7440
Epoch 216/500
6/6 - 0s - loss: 0.6476 - accuracy: 0.7738
Epoch 217/500
6/6 - 0s - loss: 0.6375 - accuracy: 0.7917
Epoch 218/500
6/6 - 0s - loss: 0.8303 - accuracy: 0.7440
Epoch 219/500
6/6 - 0s - loss: 1.6169 - accuracy: 0.7262
Epoch 220/500
6/6 - 0s - loss: 0.9138 - accuracy: 0.7321
Epoch 221/500
6/6 - 0s - loss: 0.6194 - accuracy: 0.8036
Epoch 222/500
6/6 - 0s - loss: 1.0605 - accuracy: 0.6905
Epoch 223/500
6/6 - 0s - loss: 2.4083 - accuracy: 0.5893
Epoch 224/500
6/6 - 0s - loss: 1.3246 - accuracy: 0.7440
Epoch 225/500
6/6 - 0s - loss: 2.6301 - accuracy: 0.5179
Epoch 226/500
6/6 - 0s - loss: 2.8703 - accuracy: 0.6429
Epoch 227/500
6/6 - 0s - loss: 2.3044 - accuracy: 0.6131
Epoch 228/500
6/6 - 0s - loss: 1.3211 - accuracy: 0.7262
Epoch 229/500
6/6 - 0s - loss: 1.6195 - accuracy: 0.7440
Epoch 230/500
6/6 - 0s - loss: 2.7555 - accuracy: 0.5357
Epoch 231/500
6/6 - 0s - loss: 1.5666 - accuracy: 0.6667
Epoch 232/500
6/6 - 0s - loss: 1.1756 - accuracy: 0.7083
Epoch 233/500
6/6 - 0s - loss: 1.1853 - accuracy: 0.7143
Epoch 234/500
6/6 - 0s - loss: 1.6407 - accuracy: 0.7024
Epoch 235/500
6/6 - 0s - loss: 2.0056 - accuracy: 0.6369
Epoch 236/500
6/6 - 0s - loss: 1.3144 - accuracy: 0.6905
Epoch 237/500
6/6 - 0s - loss: 1.4989 - accuracy: 0.6667
Epoch 238/500
6/6 - 0s - loss: 1.8646 - accuracy: 0.6845
Epoch 239/500
6/6 - 0s - loss: 2.2994 - accuracy: 0.6369
Epoch 240/500
6/6 - 0s - loss: 1.6436 - accuracy: 0.6429
Epoch 241/500
6/6 - 0s - loss: 1.0493 - accuracy: 0.7321
Epoch 242/500
6/6 - 0s - loss: 1.2539 - accuracy: 0.6607
Epoch 243/500
6/6 - 0s - loss: 0.9008 - accuracy: 0.7321
Epoch 244/500
6/6 - 0s - loss: 1.7432 - accuracy: 0.6488
Epoch 245/500
6/6 - 0s - loss: 0.5311 - accuracy: 0.8095
Epoch 246/500
6/6 - 0s - loss: 0.6543 - accuracy: 0.8155
Epoch 247/500
6/6 - 0s - loss: 1.8998 - accuracy: 0.6726
Epoch 248/500
6/6 - 0s - loss: 2.6555 - accuracy: 0.6429
Epoch 249/500
6/6 - 0s - loss: 1.2809 - accuracy: 0.7083
Epoch 250/500
6/6 - 0s - loss: 1.3445 - accuracy: 0.7262
[etc]
Epoch 300/500
6/6 - 0s - loss: 1.1862 - accuracy: 0.7560
Epoch 301/500
6/6 - 0s - loss: 1.5770 - accuracy: 0.7143
Epoch 302/500
6/6 - 0s - loss: 1.4353 - accuracy: 0.7083
Epoch 303/500
6/6 - 0s - loss: 1.4100 - accuracy: 0.6786
Epoch 304/500
6/6 - 0s - loss: 2.4307 - accuracy: 0.6488
Epoch 305/500
6/6 - 0s - loss: 2.6659 - accuracy: 0.6310
Epoch 306/500
6/6 - 0s - loss: 1.9433 - accuracy: 0.6905
Epoch 307/500
6/6 - 0s - loss: 1.5349 - accuracy: 0.6250
Epoch 308/500
6/6 - 0s - loss: 1.6226 - accuracy: 0.6250
Epoch 309/500
6/6 - 0s - loss: 2.4500 - accuracy: 0.7202
Epoch 310/500
6/6 - 0s - loss: 1.4430 - accuracy: 0.7381
Epoch 311/500
6/6 - 0s - loss: 0.4379 - accuracy: 0.7976
Epoch 312/500
6/6 - 0s - loss: 0.6183 - accuracy: 0.7917
Epoch 313/500
6/6 - 0s - loss: 2.0482 - accuracy: 0.6310
Epoch 314/500
6/6 - 0s - loss: 3.3638 - accuracy: 0.5774
Epoch 315/500
6/6 - 0s - loss: 3.8912 - accuracy: 0.5655
Epoch 316/500
6/6 - 0s - loss: 3.9136 - accuracy: 0.6488
Epoch 317/500
6/6 - 0s - loss: 2.2531 - accuracy: 0.6905
Epoch 318/500
6/6 - 0s - loss: 1.3046 - accuracy: 0.6488
Epoch 319/500
6/6 - 0s - loss: 1.2162 - accuracy: 0.7202
Epoch 320/500
6/6 - 0s - loss: 1.0580 - accuracy: 0.7440
Epoch 321/500
6/6 - 0s - loss: 1.8373 - accuracy: 0.6786
Epoch 322/500
6/6 - 0s - loss: 1.8425 - accuracy: 0.6905
Epoch 323/500
6/6 - 0s - loss: 2.7649 - accuracy: 0.6607
Epoch 324/500
6/6 - 0s - loss: 3.4705 - accuracy: 0.5833
Epoch 325/500
6/6 - 0s - loss: 3.1626 - accuracy: 0.5595
Epoch 326/500
6/6 - 0s - loss: 2.6431 - accuracy: 0.6310
Epoch 327/500
6/6 - 0s - loss: 3.6114 - accuracy: 0.5833
Epoch 328/500
6/6 - 0s - loss: 0.9896 - accuracy: 0.7321
Epoch 329/500
6/6 - 0s - loss: 0.6469 - accuracy: 0.7976
Epoch 330/500
6/6 - 0s - loss: 1.2563 - accuracy: 0.7202
Epoch 331/500
6/6 - 0s - loss: 0.5271 - accuracy: 0.8274
Epoch 332/500
6/6 - 0s - loss: 1.1875 - accuracy: 0.6369
Epoch 333/500
6/6 - 0s - loss: 2.5915 - accuracy: 0.6607
Epoch 334/500
6/6 - 0s - loss: 3.2393 - accuracy: 0.6012
Epoch 335/500
6/6 - 0s - loss: 1.5667 - accuracy: 0.6726
Epoch 336/500
6/6 - 0s - loss: 3.3724 - accuracy: 0.5893
Epoch 337/500
6/6 - 0s - loss: 4.6063 - accuracy: 0.5595
Epoch 338/500
6/6 - 0s - loss: 4.2892 - accuracy: 0.5655
Epoch 339/500
6/6 - 0s - loss: 3.1177 - accuracy: 0.6429
Epoch 340/500
6/6 - 0s - loss: 1.5357 - accuracy: 0.6726
Epoch 341/500
6/6 - 0s - loss: 1.7998 - accuracy: 0.7321
Epoch 342/500
6/6 - 0s - loss: 3.4708 - accuracy: 0.5952
Epoch 343/500
6/6 - 0s - loss: 1.6397 - accuracy: 0.6369
Epoch 344/500
6/6 - 0s - loss: 1.0144 - accuracy: 0.7143
Epoch 345/500
6/6 - 0s - loss: 0.5396 - accuracy: 0.8095
Epoch 346/500
6/6 - 0s - loss: 0.4824 - accuracy: 0.8333
Epoch 347/500
6/6 - 0s - loss: 0.6122 - accuracy: 0.8095
Epoch 348/500
6/6 - 0s - loss: 0.9176 - accuracy: 0.7262
Epoch 349/500
6/6 - 0s - loss: 1.0516 - accuracy: 0.7083
Epoch 350/500
6/6 - 0s - loss: 3.0169 - accuracy: 0.6964
[etc]
Epoch 400/500
6/6 - 0s - loss: 1.4745 - accuracy: 0.6905
Epoch 401/500
6/6 - 0s - loss: 1.0852 - accuracy: 0.7560
Epoch 402/500
6/6 - 0s - loss: 1.3118 - accuracy: 0.7440
Epoch 403/500
6/6 - 0s - loss: 1.1269 - accuracy: 0.7143
Epoch 404/500
6/6 - 0s - loss: 0.7287 - accuracy: 0.7917
Epoch 405/500
6/6 - 0s - loss: 0.4720 - accuracy: 0.8155
Epoch 406/500
6/6 - 0s - loss: 0.6519 - accuracy: 0.7917
Epoch 407/500
6/6 - 0s - loss: 1.4163 - accuracy: 0.7202
Epoch 408/500
6/6 - 0s - loss: 1.5731 - accuracy: 0.6964
Epoch 409/500
6/6 - 0s - loss: 2.1105 - accuracy: 0.6905
Epoch 410/500
6/6 - 0s - loss: 2.0785 - accuracy: 0.6190
Epoch 411/500
6/6 - 0s - loss: 2.7981 - accuracy: 0.6012
Epoch 412/500
6/6 - 0s - loss: 2.8780 - accuracy: 0.6607
Epoch 413/500
6/6 - 0s - loss: 2.8910 - accuracy: 0.6369
Epoch 414/500
6/6 - 0s - loss: 2.9615 - accuracy: 0.6369
Epoch 415/500
6/6 - 0s - loss: 4.4401 - accuracy: 0.5536
Epoch 416/500
6/6 - 0s - loss: 3.5945 - accuracy: 0.6012
Epoch 417/500
6/6 - 0s - loss: 2.9201 - accuracy: 0.7024
Epoch 418/500
6/6 - 0s - loss: 0.6884 - accuracy: 0.7917
Epoch 419/500
6/6 - 0s - loss: 0.6324 - accuracy: 0.7798
Epoch 420/500
6/6 - 0s - loss: 0.4122 - accuracy: 0.8571
Epoch 421/500
6/6 - 0s - loss: 0.5619 - accuracy: 0.8095
Epoch 422/500
6/6 - 0s - loss: 0.7549 - accuracy: 0.7798
Epoch 423/500
6/6 - 0s - loss: 1.7936 - accuracy: 0.6667
Epoch 424/500
6/6 - 0s - loss: 1.0680 - accuracy: 0.7024
Epoch 425/500
6/6 - 0s - loss: 1.1780 - accuracy: 0.7202
Epoch 426/500
6/6 - 0s - loss: 1.4397 - accuracy: 0.6845
Epoch 427/500
6/6 - 0s - loss: 2.2156 - accuracy: 0.6905
Epoch 428/500
6/6 - 0s - loss: 3.7839 - accuracy: 0.5774
Epoch 429/500
6/6 - 0s - loss: 4.7977 - accuracy: 0.6012
Epoch 430/500
6/6 - 0s - loss: 2.8695 - accuracy: 0.6488
Epoch 431/500
6/6 - 0s - loss: 2.0530 - accuracy: 0.6667
Epoch 432/500
6/6 - 0s - loss: 1.2545 - accuracy: 0.7381
Epoch 433/500
6/6 - 0s - loss: 0.6428 - accuracy: 0.7619
Epoch 434/500
6/6 - 0s - loss: 0.5938 - accuracy: 0.7560
Epoch 435/500
6/6 - 0s - loss: 0.6733 - accuracy: 0.8274
Epoch 436/500
6/6 - 0s - loss: 0.6819 - accuracy: 0.7798
Epoch 437/500
6/6 - 0s - loss: 0.9188 - accuracy: 0.7738
Epoch 438/500
6/6 - 0s - loss: 0.7398 - accuracy: 0.7560
Epoch 439/500
6/6 - 0s - loss: 0.5840 - accuracy: 0.7857
Epoch 440/500
6/6 - 0s - loss: 0.5400 - accuracy: 0.7976
Epoch 441/500
6/6 - 0s - loss: 0.4590 - accuracy: 0.8095
Epoch 442/500
6/6 - 0s - loss: 0.5159 - accuracy: 0.7917
Epoch 443/500
6/6 - 0s - loss: 1.0400 - accuracy: 0.7321
Epoch 444/500
6/6 - 0s - loss: 0.8215 - accuracy: 0.7679
Epoch 445/500
6/6 - 0s - loss: 0.4529 - accuracy: 0.8571
Epoch 446/500
6/6 - 0s - loss: 0.4551 - accuracy: 0.8214
Epoch 447/500
6/6 - 0s - loss: 1.3074 - accuracy: 0.7619
Epoch 448/500
6/6 - 0s - loss: 1.7789 - accuracy: 0.7024
Epoch 449/500
6/6 - 0s - loss: 1.5543 - accuracy: 0.7440
Epoch 450/500
6/6 - 0s - loss: 0.7703 - accuracy: 0.7679
Epoch 451/500
6/6 - 0s - loss: 0.6668 - accuracy: 0.7857
Epoch 452/500
6/6 - 0s - loss: 0.4969 - accuracy: 0.7917
Epoch 453/500
6/6 - 0s - loss: 0.7541 - accuracy: 0.7560
Epoch 454/500
6/6 - 0s - loss: 0.6923 - accuracy: 0.7619
Epoch 455/500
6/6 - 0s - loss: 0.4974 - accuracy: 0.7798
Epoch 456/500
6/6 - 0s - loss: 0.9360 - accuracy: 0.7202
Epoch 457/500
6/6 - 0s - loss: 0.8772 - accuracy: 0.7262
Epoch 458/500
6/6 - 0s - loss: 0.5241 - accuracy: 0.7976
Epoch 459/500
6/6 - 0s - loss: 0.6441 - accuracy: 0.8452
Epoch 460/500
6/6 - 0s - loss: 0.8062 - accuracy: 0.7798
Epoch 461/500
6/6 - 0s - loss: 0.5682 - accuracy: 0.7738
Epoch 462/500
6/6 - 0s - loss: 1.2154 - accuracy: 0.7083
Epoch 463/500
6/6 - 0s - loss: 0.9338 - accuracy: 0.7440
Epoch 464/500
6/6 - 0s - loss: 0.5318 - accuracy: 0.7798
Epoch 465/500
6/6 - 0s - loss: 0.4244 - accuracy: 0.7679
Epoch 466/500
6/6 - 0s - loss: 0.7675 - accuracy: 0.7857
Epoch 467/500
6/6 - 0s - loss: 1.0929 - accuracy: 0.7024
Epoch 468/500
6/6 - 0s - loss: 2.7063 - accuracy: 0.6071
Epoch 469/500
6/6 - 0s - loss: 3.1285 - accuracy: 0.5714
Epoch 470/500
6/6 - 0s - loss: 1.4364 - accuracy: 0.7262
Epoch 471/500
6/6 - 0s - loss: 1.3276 - accuracy: 0.7143
Epoch 472/500
6/6 - 0s - loss: 0.5895 - accuracy: 0.8274
Epoch 473/500
6/6 - 0s - loss: 0.7874 - accuracy: 0.7202
Epoch 474/500
6/6 - 0s - loss: 0.8847 - accuracy: 0.7560
Epoch 475/500
6/6 - 0s - loss: 2.4059 - accuracy: 0.6190
Epoch 476/500
6/6 - 0s - loss: 0.5856 - accuracy: 0.7976
Epoch 477/500
6/6 - 0s - loss: 1.6138 - accuracy: 0.6726
Epoch 478/500
6/6 - 0s - loss: 3.6635 - accuracy: 0.6190
Epoch 479/500
6/6 - 0s - loss: 1.5387 - accuracy: 0.6786
Epoch 480/500
6/6 - 0s - loss: 1.5804 - accuracy: 0.7202
Epoch 481/500
6/6 - 0s - loss: 1.1936 - accuracy: 0.7500
Epoch 482/500
6/6 - 0s - loss: 0.5385 - accuracy: 0.8274
Epoch 483/500
6/6 - 0s - loss: 0.5614 - accuracy: 0.7679
Epoch 484/500
6/6 - 0s - loss: 0.8756 - accuracy: 0.7500
Epoch 485/500
6/6 - 0s - loss: 0.6093 - accuracy: 0.7560
Epoch 486/500
6/6 - 0s - loss: 1.9588 - accuracy: 0.6310
Epoch 487/500
6/6 - 0s - loss: 1.7198 - accuracy: 0.7083
Epoch 488/500
6/6 - 0s - loss: 0.9304 - accuracy: 0.7321
Epoch 489/500
6/6 - 0s - loss: 0.5345 - accuracy: 0.8095
Epoch 490/500
6/6 - 0s - loss: 2.6957 - accuracy: 0.5893
Epoch 491/500
6/6 - 0s - loss: 2.6077 - accuracy: 0.6607
Epoch 492/500
6/6 - 0s - loss: 4.0575 - accuracy: 0.5774
Epoch 493/500
6/6 - 0s - loss: 3.4324 - accuracy: 0.5655
Epoch 494/500
6/6 - 0s - loss: 3.7296 - accuracy: 0.6131
Epoch 495/500
6/6 - 0s - loss: 2.2588 - accuracy: 0.7262
Epoch 496/500
6/6 - 0s - loss: 3.1585 - accuracy: 0.6250
Epoch 497/500
6/6 - 0s - loss: 2.1257 - accuracy: 0.6905
Epoch 498/500
6/6 - 0s - loss: 1.3597 - accuracy: 0.7381
Epoch 499/500
6/6 - 0s - loss: 0.5437 - accuracy: 0.8155
Epoch 500/500
6/6 - 0s - loss: 0.4785 - accuracy: 0.7976

1/6 [====>.........................] - ETA: 2s - loss: 0.0759 - accuracy: 1.0000
6/6 [==============================] - 1s 3ms/step - loss: 0.5537 - accuracy: 0.8333

Accuracy: 83.33

[10, 1000, 0] => 0 (expected 0)
[14, 1004, 0] => 0 (expected 0)
[18, 1008, 0] => 0 (expected 0)
[22, 1012, 0] => 0 (expected 0)
[26, 1016, 0] => 0 (expected 0)
[30, 1020, 0] => 0 (expected 0)
[13, 1003, 1] => 0 (expected 0)
[17, 1007, 1] => 0 (expected 0)
[21, 1011, 1] => 0 (expected 0)
[25, 1015, 1] => 0 (expected 0)
[29, 1019, 1] => 0 (expected 0)
[12, 9002, 0] => 1 (expected 0)
[16, 9006, 0] => 1 (expected 0)
[20, 9010, 0] => 1 (expected 0)
[24, 9014, 0] => 1 (expected 0)
[28, 9018, 0] => 1 (expected 0)
[11, 9022, 1] => 1 (expected 1)
[15, 9026, 1] => 1 (expected 1)
[19, 9030, 1] => 1 (expected 1)
[23, 9034, 1] => 1 (expected 1)
[27, 9038, 1] => 1 (expected 1)
[70, 1000, 0] => 1 (expected 1)
[74, 1004, 0] => 1 (expected 1)
[78, 1008, 0] => 1 (expected 1)
[82, 1012, 0] => 1 (expected 1)
[86, 1016, 0] => 1 (expected 1)
[90, 1020, 0] => 1 (expected 1)
[73, 1003, 1] => 0 (expected 0)
[77, 1007, 1] => 0 (expected 0)
[81, 1011, 1] => 0 (expected 0)
[85, 1015, 1] => 1 (expected 0)
[89, 1019, 1] => 1 (expected 0)
[72, 9002, 0] => 1 (expected 1)
[76, 9006, 0] => 1 (expected 1)
[80, 9010, 0] => 1 (expected 1)
[84, 9014, 0] => 1 (expected 1)
[88, 9018, 0] => 1 (expected 1)
[71, 9001, 1] => 1 (expected 1)
[75, 9005, 1] => 1 (expected 1)
[79, 9009, 1] => 1 (expected 1)
Model: "sequential"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense (Dense)                (None, 12)                48        
_________________________________________________________________
dense_1 (Dense)              (None, 6)                 78        
_________________________________________________________________
dense_2 (Dense)              (None, 1)                 7         
=================================================================
Total params: 133
Trainable params: 133
Non-trainable params: 0
_________________________________________________________________
None

Thanks very much Kevin

Answer 1

Could it be that you just don't have enough training data? Usually you train on 1000s of data points, not just 8. If this was the case, the network weights post-training would be very similar to what they were pre-training.

It does look like your loss is dropping from epoch to epoch though, so I think you should be good if you just train for more epochs. That would effectively do the same thing as adding more training data, assuming you can't come up with more combinations of features and actions. If you can, definitely do that before just cranking up the number of epochs.