使用GridsearchCV优化CNN导致错误的原因是什么？

Question

我目前正努力在CNN上取得好成绩，我决定使用网格搜索来优化参数。我目前正在尝试使用scikit-learn GridSearchCV。

def create_model(init_mode='uniform',activation_mode='linear',optimizer_mode="adam", activation_mode_conv = 'linear'):
        model = Sequential()


        model.add(ZeroPadding2D((6,4),input_shape=(6,3,3)))
        model.add(Convolution2D(32,3,3 , activation=activation_mode_conv))
        print model.output_shape
        model.add(Convolution2D(32, 3,3, activation=activation_mode_conv))
        print model.output_shape
        model.add(MaxPooling2D(pool_size=(2,2),strides=(2,1)))
        print model.output_shape
        model.add(Convolution2D(64, 3,3 , activation=activation_mode_conv))
        print model.output_shape
        model.add(Convolution2D(64, 3,3 , activation=activation_mode_conv))
        print model.output_shape
        model.add(MaxPooling2D(pool_size=(2,2),strides=(2,1)))
        model.add(Flatten())
        print model.output_shape
        model.add(Dense(output_dim=32, input_dim=64, init=init_mode,activation=activation_mode))
        model.add(Dense(output_dim=13, input_dim=50, init=init_mode,activation=activation_mode))
        model.add(Dense(output_dim=1, input_dim=13, init=init_mode,activation=activation_mode))
        model.add(Dense(output_dim=1,  init=init_mode, activation=activation_mode))
        #print model.summary()
        model.compile(loss='mean_squared_error',optimizer=optimizer_mode)

        return model
        #reduce_lr=ReduceLROnPlateau(monitor='val_loss', factor=0.01, patience=3, verbose=1, mode='auto', epsilon=0.1, cooldown=0, min_lr=0.000000000000000001)
        #stop  = EarlyStopping(monitor='val_loss', min_delta=0, patience=5, verbose=1, mode='auto')

        #log=csv_logger = CSVLogger('training_'+str(i)+'.csv')
        #print "Model Train"

        #hist_current = model.fit(np.array(data_train_input),
        #                    np.array(data_train_output),
        #                    shuffle=False,
        #                    validation_data=(np.array(data_test_input),np.array(data_test_output)),
        #                    validation_split=0.1,
        #                    nb_epoch=150000,
        #                    verbose=1,
        #                    callbacks=[reduce_lr,log,stop])

        #print()
        #print model.summary()
        #print "Model stored"
        #model.save(spectogram_path+"Model"+str(feature)+".h5")
        #model.save_weights(spectogram_path+"Model"+str(feature)+"_weights.h5")
        #del model



## Make it work for other feature ranges
## Add the CNN part and test it
## Try with gabor kernels as suggested by the other paper..

input_train, input_test, output_train, output_test =  model(0,train_input_data_interweawed_normalized[:-(len(train_input_data_interweawed_normalized)-1000)],output_data_train[:-(len(output_data_train)-1000)],test_input_data_interweawed_normalized[:-(len(test_input_data_interweawed_normalized)-1000)],output_data_test[:-(len(output_data_test)-1000)])

del test_input_data
del test_name
del test_input_data_normalized
del test_name_normalized
del test_input_data_interweawed
del test_name_interweawed
del test_input_data_interweawed_normalized
del test_name_interweawed_normalized

del train_input_data
del train_name
del train_input_data_normalized
del train_name_normalized
del train_input_data_interweawed
del train_name_interweawed
del train_input_data_interweawed_normalized
del train_name_interweawed_normalized


seed = 7
np.random.seed(seed)
print "Regressor"
model = KerasRegressor(build_fn = create_model, verbose = 10)
init_mode_list = ['uniform', 'lecun_uniform', 'normal', 'zero', 'glorot_normal', 'glorot_uniform', 'he_normal', 'he_uniform']
activation_mode_list = ['softmax', 'softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']
activation_mode_list_conv =  ['softplus', 'softsign', 'relu', 'tanh', 'sigmoid', 'hard_sigmoid', 'linear']
optimizer_mode_list = ['SGD', 'RMSprop', 'Adagrad', 'Adadelta', 'Adam', 'Adamax', 'Nadam']
batch_size_list = [10, 20, 40, 60, 80, 100]
epochs = [10, 50, 100]
param_grid = dict(init_mode=init_mode_list, batch_size=batch_size_list, nb_epoch=epochs, activation_mode=activation_mode_list, optimizer_mode = optimizer_mode_list, activation_mode_conv =  activation_mode_list_conv)
grid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=1)
print "Grid fit"
grid_result = grid.fit(np.asarray(input_train), np.array(output_train))

# summarize results
print("Best: %f using %s" % (grid_result.best_score_, grid_result.best_params_))
means = grid_result.cv_results_['mean_test_score']
stds = grid_result.cv_results_['std_test_score']
params = grid_result.cv_results_['params']
for mean, stdev, param in zip(means, stds, params):
    print("%f (%f) with: %r" % (mean, stdev, param))

这运行..但它的问题是它最后只提供了一个结果。我运行了一次代码，然后它崩溃了这个错误消息：

cannot allocate memory for thread-local data: ABORT

我不确定会导致这个问题的原因是什么？