82 строки
2.2 KiB
Python
82 строки
2.2 KiB
Python
from base import BaseModel
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from keras.models import Sequential
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from keras.layers import Dense, Activation, Dropout, LSTM
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from keras import optimizers
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from sklearn.metrics import mean_squared_error
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from tune_sklearn import TuneGridSearchCV, TuneSearchCV
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class KerasNeuralNetModel(BaseModel):
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def build_model(self, config=None):
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num_hidden_layers = config["num_layers"]
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optimizer = config["optimizer"]
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learning_rate = config["lr"]
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self.model = Sequential()
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self.model.add(
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Dense(
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config["num_units"],
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input_dim=self.input_dim,
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activation=config["activation"],
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kernel_initializer="glorot_normal",
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)
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)
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for i in range(0, num_hidden_layers):
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self.model.add(
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Dense(
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config["num_units"],
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activation="relu",
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kernel_initializer="glorot_normal",
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)
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)
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self.model.add(
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Dense(
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self.output_dim,
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activation=config["activation"],
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kernel_initializer="glorot_normal",
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)
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)
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# opt=optimizers.RMSprop(lr=learning_rate)
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self.model.compile(
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optimizer=optimizer,
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loss="mean_squared_error",
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metrics=["mean_squared_error"],
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)
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print(self.model.summary())
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return self.model
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def sweep(self, X, y):
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optimizers = ["rmsprop", "adam"]
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kernel_initializer = ["glorot_uniform", "normal"]
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epochs = [5, 10]
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param_grid = dict(
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optimizer=optimizers, nb_epoch=epochs, kernel_initializer=kernel_initializer
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)
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grid = TuneGridSearchCV(
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estimator=self.model,
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param_grid=param_grid,
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scoring="neg_mean_squared_error",
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)
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grid_result = grid.fit(X, y)
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return grid_result
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if __name__ == "__main__":
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config = {
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"num_layers": 10,
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"num_units": 50,
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"lr": 0.01,
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"activation": "relu",
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"optimizer": "adam",
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}
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keras_model = KerasNeuralNetModel()
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# keras_model.build_model(config=config)
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# keras_model.fit(X, y)
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# keras_model.sweep(X, y)
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