我正在尝试使用skopt优化NN： https://scikit-optimize.github.io/notebooks/sklearn-gridsearchcv-replacement.html
print('...load ranges for each dimension...')
dim_num_embedding_nodes = Integer(low=32, high=512, name='embedding')
dim_hidden_layer = Integer(low=32, high=512, name='hidden_layer_size')
dim_batch_size = Integer(low=32, high=128, name='batch_size')
dim_alpha = Real(low=0,high=2,name="alpha")
dim_dropout = Real(low=0,high=1,name="dropout")
dim_recurrent_dropout = Real(low=0,high=1,name="recurrent_dropout")
dimensions = [dim_num_embedding_nodes,
dim_hidden_layer,
dim_batch_size,
dim_alpha,
dim_dropout,
dim_recurrent_dropout
]
default_parameters = [512, 64, 64, 0.01, 0.3, 0.3]
def create_model(embedding_dim, hidden_layer_size,
alpha, dropout, recurrent_dropout):
model = Sequential()
model.add(Embedding(input_dim=3000, output_dim=embedding_dim, input_length=110))
model.add(GRU(units=hidden_layer_size, recurrent_dropout=recurrent_dropout, return_sequences=False))
model.add(ELU(alpha=alpha))
model.add(Dropout(rate=dropout))
model.add(Dense(2, activation='softmax'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=["binary_accuracy"])
return model
print('...lets do a bunch of trials...')
@use_named_args(dimensions=dimensions)
def fitness(embedding_dim, hidden_layer_size, batch_size,
alpha, dropout, recurrent_dropout):
model = create_model(embedding_dim=embedding_dim,
hidden_layer_size=hidden_layer_size,
alpha=alpha,
dropout=dropout,
recurrent_dropout=recurrent_dropout)
# named blackbox becuase it represents the structure
blackbox = model.fit(x_train_sequence, y_train, batch_size=batch_size,
validation_data=(x_test_sequence, y_test),
verbose=1, shuffle=True, epochs=3)
# return the validation accuracy for the last epoch.
accuracy = blackbox.history['val_binary_accuracy'][-1]
# Print the classification accuracy.
print()
print("Accuracy: {0:.2%}".format(accuracy))
print()
return -accuracy
gp_result = gp_minimize(func=fitness,
dimensions=dimensions,
n_calls=12,
noise= 0.01,
n_jobs=-1,
kappa = 5,
x0=default_parameters)
print("best accuracy was " + str(round(gp_result.fun *-100,2))+"%.")
但是，我不断收到此错误：
回溯（最近通话最近一次）：
文件“”，第1行，在 runfile（'/ Users / mattiavicari / Desktop / Optimize_NLP / actual_skopt.py'， wdir ='/ Users / mattiavicari / Desktop / Optimize_NLP'）
文件 “ /Users/mattiavicari/anaconda3/envs/Optimize_DL/lib/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py”， 运行文件中的第827行 execfile（文件名，命名空间）
文件 “ /Users/mattiavicari/anaconda3/envs/Optimize_DL/lib/python3.7/site-packages/spyder_kernels/customize/spydercustomize.py”， 第110行，在execfile中 exec（compile（f.read（），文件名，'exec'），命名空间）
文件“ /Users/mattiavicari/Desktop/Optimize_NLP/actual_skopt.py”， 298行，在 x0 = default_parameters）
文件 “ /Users/mattiavicari/anaconda3/envs/Optimize_DL/lib/python3.7/site-packages/skopt/optimizer/gp.py”， gp_minimize中的第228行 callback = callback，n_jobs = n_jobs）
文件 “ /Users/mattiavicari/anaconda3/envs/Optimize_DL/lib/python3.7/site-packages/skopt/optimizer/base.py”， 第231行，在base_minimize中 y0 = list（map（func，x0））
文件 “ /Users/mattiavicari/anaconda3/envs/Optimize_DL/lib/python3.7/site-packages/skopt/utils.py”， 包装中的第636行 objective_value = func（** arg_dict）
TypeError：Fitness（）收到了意外的关键字参数“嵌入”
谁能解释我在做什么错？
非常感谢！