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onshore_wind_model.py
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onshore_wind_model.py
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import pandas as pd
import tensorflow
from keras import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
from sklearn.impute import SimpleImputer
pd.plotting.register_matplotlib_converters()
df = pd.read_csv("data.csv")
places = ["hamburg","köln","kassel","leipzig","augsburg"]
_features = ["_temp","_wind_speed_100m"]
features = []
for city in places:
for feature in _features:
features.append(city + feature)
print(df["onshore"].describe())
train_dataset = df.sample(frac=0.8)
test_dataset = df.drop(train_dataset.index)
_xTrain = train_dataset[features]
_xTest = test_dataset[features]
yTrain = train_dataset["onshore"].to_numpy()
yTest = test_dataset["onshore"].to_numpy()
model = Sequential([
Dense(len(features),activation="relu",input_shape=[len(features)]),
Dropout(rate=0.2),
Dense(512, activation='relu'),
Dense(1)
])
def mae(guess, target):
return abs(guess - target)
def mse(guess,target):
return pow(mae(guess,target),2)
def mre(guess, target):
return guess - target
model.compile(
loss=mae,
optimizer=Adam(learning_rate=0.05),
metrics=[mae,mse,mre]
)
hist = model.fit(_xTrain, yTrain, epochs=4) #, validation_data=(xTest, yTest))
keras_file = "onshore_model.h5"
tensorflow.keras.models.save_model(model,keras_file)
converter = tensorflow.lite.TFLiteConverter.from_keras_model(model)
tfmodel = converter.convert()
open("onshore_model.tflite","wb").write(tfmodel)