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python - LSTM 的网格搜索超参数调整

问题描述

尝试在我的 LSTM 模型中实现网格搜索时出现不同的错误。我正在尝试与此非常相似的东西。

# train the model
def build_model(train, n_back=1, n_predict=1, epochs=10, batch_size=10, neurons=100, activation='relu', optimizer='adam'):
    # define model
    model = Sequential()
    model.add(LSTM(neurons, activation=activation, input_shape=(n_timesteps, n_features)))
    model.add(RepeatVector(n_outputs))
    model.add(LSTM(neurons, activation=activation, return_sequences=True))
    model.add(TimeDistributed(Dense(neurons)))
    model.add(TimeDistributed(Dense(1)))
    model.compile(loss='mse', optimizer=optimizer)
    # fit network
    model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=1)
    return model

#### Epochs and Batch Size
batch_size = [10, 20]
epochs = [1, 10]

# Optimizer: Select!
#### Optimizer
optimizer = ['Adam', 'Adamax'] #'SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam'

#### Learning Rate and Momentum
learn_rate = [0.01, 0.2] #0.001, 0.01, 0.1, 0.2, 0.3
momentum = [0.0, 0.2, 0.9] #0.0, 0.2, 0.4, 0.6, 0.8, 0.9
lr_optimizer = SGD(lr=learn_rate, momentum=momentum)

#### Tune Network Weight Initialization
init_mode = ['lecun_uniform','zero', 'he_normal'] #'uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform'

#### Neuron Activation Function
activation = ['relu', 'softmax'] #'softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear'

#### Tune Dropout Regularization
weight_constraint = [2, 3] #1, 2, 3, 4, 5
dropout_rate = [0.0, 0.1, 0.5, 0.9] #0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9

#### Tune the Number of Neurons in the Hidden Layer
neurons = [100, 200] #10, 50, 100, 200

# create model
model = KerasClassifier(build_fn=build_model(train, n_back, n_predict, epochs, batch_size, neurons, activation, optimizer), verbose=1)
param_grid = dict(batch_size=batch_size, epochs=epochs, optimizer=optimizer,
                  activation=activation,neurons=neurons)

grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)
grid_result = grid.fit(train_x, train_y)

例如，一个错误是：

('Could not interpret activation function identifier:', ['relu', 'softmax'])

我究竟做错了什么？

有没有更好的方法来“调整”我的 LSTM？

标签： pythonperformancelstmhyperparameters

解决方案

简短：GridSearchCV 只是在 2D 而非 3D 中工作，或者换句话说，只是 3D 而不是 4D（随着时间的推移）。在这种情况下，您必须设置自己的网格搜索。

如果您对此有任何疑问，可以联系我。希望这有帮助。

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