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networktrainer.py
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114 lines (92 loc) · 4.44 KB
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import pandas as pd
from tensorflow import keras
from keras import layers, initializers
# network trainer
# builds (or should I say attempts to build) a keras model from training data
# basically it loads the data, we build a model and add layers, train it, validate it and save it
# the 7 models in this file are some of the models we tried (we did make some variations on these that are not listed)
# number 5 was the most successful
if __name__ == "__main__":
trainingdata = pd.read_csv("trainingdata.csv")
trainingdata.head()
td_in = trainingdata.iloc[:, 0 : 25].values
td_out = trainingdata.iloc[:, 25 : 27].values
# attempt 1 - 10 epochs
# result: failure
#model = keras.Sequential()
#model.add(layers.Dense(32, input_dim=25, activation="linear"))
#model.add(layers.Dense(32, activation="linear"))
#model.add(layers.Dense(2, activation="linear"))
# attempt 2 - 10 epochs
# result: failure
#model = keras.Sequential()
#model.add(layers.Dense(32, input_dim=25, activation="linear"))
#model.add(layers.Dense(32, activation="relu"))
#model.add(layers.Dense(32, activation="relu"))
#model.add(layers.Dense(2, activation="linear"))
# attempt 3 - 10 epochs
# result: failure
#model = keras.Sequential()
#model.add(layers.Dense(32, input_dim=25, activation="linear"))
#model.add(layers.Dense(32, kernel_initializer=initializers.Ones(), activation="relu"))
#model.add(layers.Dense(32, kernel_initializer=initializers.Ones(), activation="relu"))
#model.add(layers.Dense(2, activation="linear"))
# attempt 4 - 30 epochs
# result: failure
#model = keras.Sequential()
#model.add(layers.Dense(32, input_dim=25, activation="linear"))
#model.add(layers.Dense(32, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="sigmoid"))
#model.add(layers.Dense(32, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="sigmoid"))
#model.add(layers.Dense(2, activation="sigmoid"))
# attempt 5 - 30 epochs
# result: ok
model = keras.Sequential()
model.add(layers.Dense(32, input_dim=25, activation="linear"))
model.add(layers.Dense(256, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="relu"))
model.add(layers.Dense(512, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="tanh"))
model.add(layers.Dense(256, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="sigmoid"))
model.add(layers.Dense(32, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="relu"))
model.add(layers.Dense(2, activation="sigmoid"))
# attempt 6 - 30 epochs
# result: failure
#model = keras.Sequential()
#model.add(layers.Dense(64, input_dim=25))
#model.add(layers.Dense(64))
#model.add(layers.Dense(64))
#model.add(layers.Dense(64))
#model.add(layers.Dense(64))
#model.add(layers.Dense(2, activation="sigmoid"))
# attempt 7 - 250 epochs
# result: failure
#model = keras.Sequential()
#model.add(layers.Dense(128, input_dim=25, activation="linear"))
#model.add(layers.Dense(256, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="relu"))
#model.add(layers.Dense(512, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="tanh"))
#model.add(layers.Dense(256, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="sigmoid"))
#model.add(layers.Dense(128, kernel_initializer=initializers.RandomNormal(mean=0.0, stddev=0.5, seed=None), activation="relu"))
#model.add(layers.Dense(2, activation="sigmoid"))
model.compile(optimizer="adam", loss="mae", metrics=["accuracy"])
losses = model.fit(
x=td_in,
y=td_out,
epochs=250,
validation_split=0.1
)
# attempt 1
#model.save("keras_model")
# attempt 2
#model.save("keras_model2")
# attempt 3
#model.save("keras_model3")
# attempt 4
#model.save("keras_model4")
# attempt 5
model.save("keras_model5")
# attempt 6
#model.save("keras_model6")
# attempt 7
#model.save("keras_model7")
print(losses.history["accuracy"])
model.summary()
loss_dataframe = pd.DataFrame(losses.history)
loss_dataframe.loc[:,["loss", "val_loss"]].plot()