我正在尝试做一个小型作业项目，我正在尝试使用连体网络进行说话人识别。我正在尝试使用 talos 来寻找可能的最佳超参数。当尝试一组超参数时，代码通过 OK，但是当我给出一组超参数时，我得到了 MemoryError。
背景（不确定是否需要，但请允许我解释一下）
- 将音频分成 5 秒的块，每个块
- 计算每个音频样本的 MFCC 和 MFCC_del 和 MFCC_del_del 并将它们全部连接起来，以便将每个音频样本转换为（24x939）数据点/矩阵
- 通过有两组矩阵创建连体网络，其中一组所有“相似”配对的扬声器音频样本，另一组是“不同的扬声器”。
- 总共 6000 个测试点和 14886 个训练点（现在不使用验证，以后使用）
def yield_arrays_train(array_x_train_feat1,array_x_train_feat2,array_y_train,batch_size):
while 1:
for i in range(14886):
X_feat1_train = (array_x_train_feat1[i:i+batch_size,:,:].astype(np.float32))
X_feat2_train = (array_x_train_feat2[i:i+batch_size,:,:].astype(np.float32))
Y_train = (array_y_train[i:i+batch_size].astype(np.float32))
yield ([(np.array(X_feat1_train)),(np.array(X_feat2_train))],(np.array(Y_train)))
def yield_arrays_val(array_x_test_feat1,array_x_test_feat2,array_y_test,batch_size):
while 1:
for i in range(60):
X_feat1_test = (array_x_test_feat1[i:i+batch_size,:,:].astype(np.float32))
X_feat2_test = (array_x_test_feat2[i:i+batch_size,:,:].astype(np.float32))
Y_test = (array_y_test[i:i+batch_size].astype(np.float32))
yield ([(np.array(X_feat1_test)),(np.array(X_feat2_test))],(np.array(Y_test)))
train_generator=yield_arrays_train(xtrain_np_img1,xtrain_np_img2,y_train_numpy,6)
val_generator=yield_arrays_val(xtest_np_img1,xtest_np_img2,y_test_numpy,6)
def siamese(generator,validation_data):
W_init = tf.keras.initializers.he_normal(seed=100)
b_init = tf.keras.initializers.he_normal(seed=50)
input_shape = (24,939)
left_input = Input(input_shape)
right_input = Input(input_shape)
encoder = Sequential()
encoder.add(Conv1D(filters=8,kernel_size=6, padding='same', activation='relu',input_shape=input_shape,kernel_initializer=W_init, bias_initializer=b_init))
encoder.add(BatchNormalization())
encoder.add(Dropout(.1))
encoder.add(MaxPool1D())
encoder.add(Conv1D(filters=6,kernel_size=4, padding='same', activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Dropout(.1))
encoder.add(MaxPool1D())
encoder.add(Conv1D(filters=4,kernel_size=4, padding='same', activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Dropout(.1))
encoder.add(MaxPool1D())
encoder.add(Flatten())
encoder.add(Dense(10,activation='relu'))
encoder.add(Dropout(.1))
encoded_l = encoder(left_input)
encoded_r = encoder(right_input)
distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([encoded_l, encoded_r])
adam = optimizers.Adam(lr=.1, beta_1=0.1, beta_2=0.999,decay=.1, amsgrad=False)
earlyStopping = EarlyStopping(monitor='loss',min_delta=0,patience=3,verbose=1,restore_best_weights=False)
callback_early_stop_reduceLROnPlateau=[earlyStopping]
model = Model([left_input, right_input], distance)
model.compile(loss=contrastive_loss, optimizer=adam,metrics=[accuracy])
model.summary()
#history = model.fit([(x_train[:,:,:,0]).astype(np.float32),(x_train[:,:,:,1]).astype(np.float32)],y_train, validation_data=([(x_val[:,:,:,0]).astype(np.float32),(x_val[:,:,:,1]).astype(np.float32)], y_val) ,batch_size=params['batch_size'],epochs=params['epochs'],callbacks=callback_early_stop_reduceLROnPlateau)
history =model.fit_generator(generator=train_generator,validation_data=val_generator,steps_per_epoch=2481,epochs=5, validation_steps=1000,verbose=1,callbacks=callback_early_stop_reduceLROnPlateau,use_multiprocessing=False,workers=0)
return history,model
siamese(train_generator,val_generator)
输出：
Model: "model_6"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_11 (InputLayer) (None, 24, 939) 0
__________________________________________________________________________________________________
input_12 (InputLayer) (None, 24, 939) 0
__________________________________________________________________________________________________
sequential_6 (Sequential) (None, 10) 45580 input_11[0][0]
input_12[0][0]
__________________________________________________________________________________________________
lambda_6 (Lambda) (None, 1) 0 sequential_6[1][0]
sequential_6[2][0]
==================================================================================================
Total params: 45,580
Trainable params: 45,544
Non-trainable params: 36
__________________________________________________________________________________________________
Epoch 1/5
2481/2481 [==============================] - 29s 12ms/step - loss: 0.0056 - accuracy: 0.9986 - val_loss: 0.8333 - val_accuracy: 0.1667
Epoch 2/5
2481/2481 [==============================] - 28s 11ms/step - loss: nan - accuracy: 0.9993 - val_loss: nan - val_accuracy: 0.8333
Epoch 3/5
2481/2481 [==============================] - 28s 11ms/step - loss: nan - accuracy: 1.0000 - val_loss: nan - val_accuracy: 0.8333
Epoch 4/5
2481/2481 [==============================] - 28s 11ms/step - loss: nan - accuracy: 1.0000 - val_loss: nan - val_accuracy: 0.8333
Epoch 00004: early stopping
(<keras.callbacks.callbacks.History at 0x2969fa3fd88>,
<keras.engine.training.Model at 0x2969f2a17c8>)
所以没有任何 Talos 或者它正在使用一组随机超参数。现在我第一次使用 Talos
p = {
'filter1':[1,2,4,6,8,12,16,24],
'kernel_size1':[2,4,6,8,12],
'filter3' : [1,2,4,6,8],
'kernel_size3' : [1,2,4,6,8,12],
'decay' :[.1,0.01,.001,.0001,.00001],
'droprate1' :[.1,.2,.3],
'filter2':[1,2,4,6,8],
'kernel_size2':[2,4,6,8,12],
'droprate4' : [.1,.2,.3],
'droprate2' :[.1,.2,.3],
'unit1': [10,24,36,64,128,256],
'droprate3': [.1,.2,.3],
'lr' :[(.1,0.01,.001,.0001,.00001)],
'batch_size' : [1,2],
'epochs': [4,8,10] }
def siamese(generator,validation_data,params):
W_init = tf.keras.initializers.he_normal(seed=100)
b_init = tf.keras.initializers.he_normal(seed=50)
input_shape = (24,939)
left_input = Input(input_shape)
right_input = Input(input_shape)
encoder = Sequential()
encoder.add(Conv1D(filters=(params['filter1']),kernel_size=(params['kernel_size1']), padding='same', activation='relu',input_shape=input_shape,kernel_initializer=W_init, bias_initializer=b_init))
encoder.add(BatchNormalization())
encoder.add(Dropout((params["droprate1"])))
encoder.add(MaxPool1D())
encoder.add(Conv1D(filters=(params["filter2"]),kernel_size=(params['kernel_size2']), padding='same', activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Dropout((params["droprate2"])))
encoder.add(MaxPool1D())
encoder.add(Conv1D(filters=(params["filter3"]),kernel_size=(params['kernel_size3']), padding='same', activation='relu'))
encoder.add(BatchNormalization())
encoder.add(Dropout((params['droprate3'])))
encoder.add(MaxPool1D())
encoder.add(Flatten())
encoder.add(Dense((params['unit1']),activation='relu'))
encoder.add(Dropout((params['droprate4'])))
encoded_l = encoder(left_input)
encoded_r = encoder(right_input)
distance = Lambda(euclidean_distance, output_shape=eucl_dist_output_shape)([encoded_l, encoded_r])
adam = optimizers.Adam(lr=params['lr'], beta_1=0.1, beta_2=0.999,decay=params['decay'], amsgrad=False)
earlyStopping = EarlyStopping(monitor='loss',min_delta=0,patience=3,verbose=1,restore_best_weights=False)
callback_early_stop_reduceLROnPlateau=[earlyStopping]
model = Model([left_input, right_input], distance)
model.compile(loss=contrastive_loss, optimizer=adam,metrics=[accuracy])
model.summary()
#history = model.fit([(x_train[:,:,:,0]).astype(np.float32),(x_train[:,:,:,1]).astype(np.float32)],y_train, validation_data=([(x_val[:,:,:,0]).astype(np.float32),(x_val[:,:,:,1]).astype(np.float32)], y_val) ,batch_size=params['batch_size'],epochs=params['epochs'],callbacks=callback_early_stop_reduceLROnPlateau)
history=model.fit_generator(generator=train_generator,validation_data=val_generator,steps_per_epoch=2481,epochs=5,validation_steps=1000,verbose=1,callbacks=callback_early_stop_reduceLROnPlateau,use_multiprocessing=False,workers=0)
return history,model
t=ta.Scan(x=[xtrain_np_img1,xtrain_np_img2],y=y_train_numpy,x_val=[xtest_np_img1,xtest_np_img2],y_val=y_test_numpy,model=siamese,params=p,experiment_name='exp_1')
我得到的错误是：
---------------------------------------------------------------------------
MemoryError Traceback (most recent call last)
<ipython-input-9-df856388a4bb> in <module>
1 #t=ta.Scan(x=xtrain_np_img1_img2,y=y_train_numpy,x_val=xtest_np_img1_img2,y_val=y_test_numpy,model=siamese,params=p,experiment_name='exp_1')
2
----> 3 t=ta.Scan(x=[xtrain_np_img1,xtrain_np_img2],y=y_train_numpy,x_val=[xtest_np_img1,xtest_np_img2],y_val=y_test_numpy,model=siamese,params=p,experiment_name='exp_1')
~\anaconda3\envs\MyEnv\lib\site-packages\talos\scan\Scan.py in __init__(self, x, y, params, model, experiment_name, x_val, y_val, val_split, random_method, seed, performance_target, fraction_limit, round_limit, time_limit, boolean_limit, reduction_method, reduction_interval, reduction_window, reduction_threshold, reduction_metric, minimize_loss, disable_progress_bar, print_params, clear_session, save_weights)
194 # start runtime
195 from .scan_run import scan_run
--> 196 scan_run(self)
~\anaconda3\envs\MyEnv\lib\site-packages\talos\scan\scan_run.py in scan_run(self)
7
8 from .scan_prepare import scan_prepare
----> 9 self = scan_prepare(self)
10
11 # initiate the progress bar
~\anaconda3\envs\MyEnv\lib\site-packages\talos\scan\scan_prepare.py in scan_prepare(self)
28 round_limit=self.round_limit,
29 time_limit=self.time_limit,
---> 30 boolean_limit=self.boolean_limit
31 )
32
~\anaconda3\envs\MyEnv\lib\site-packages\talos\parameters\ParamSpace.py in __init__(self, params, param_keys, random_method, fraction_limit, round_limit, time_limit, boolean_limit)
42
43 # create the parameter space
---> 44 self.param_space = self._param_space_creation()
45
46 # handle the boolean limits separately
~\anaconda3\envs\MyEnv\lib\site-packages\talos\parameters\ParamSpace.py in _param_space_creation(self)
133 if len(self.param_index) > 100000:
134
--> 135 final_grid = list(it.product(*self._params_temp))
136 out = np.array(final_grid, dtype='object')
137
MemoryError:
我的问题是因为我是 python/机器学习等的新手
- 如何正确使用 fit_generator（之前我尝试使用仅 fit 功能，但由于内存错误，我的窗口会出现死屏，我切换到尝试 fit_generator 但仍然出现相同的错误）。我认为我没有正确编写代码？
- 我的笔记本电脑规格太差，无法运行此代码吗？我的规格 RAM：16 GB windows 10 64 位 CPU：intel core i5（四核） GPU：它内置了 GTX1050，但我没有像之前尝试过的那样配置它，但运气不佳