number of features: 12 , -15 < each feature < 15
number of targets: 6 , 0 < each target < 360
number of examples: 262144
my normalization: I normalized the features so that they are between 0 and 1. I normalized the targets so that they are between 1 and 10.
This is the model that I am using:
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(128, activation=tf.nn.relu),
tf.keras.layers.Dense(6, activation='linear')
])
model.compile(optimizer="rmsprop", loss='mean_squared_error', metrics=['accuracy'])
model.fit(training_x, training_y, epochs=10, batch_size=100)
This is the best result that I have got (training):
235929/235929 [==============================] - 8s 33us/step - loss: 8.9393e-04 - acc: 0.6436
testing:
loss: 0.00427692719418488
acc: 0.033187106618348276
I get almost 0% accuracy on the test set! I need a model to solve this ML problem.