问题描述
我打算使用fit_generator和Talos（用于超参数调整）。早些时候，当我使用fit方法时，我收到了MemoryError，因此在这里搜索时，人们说我应该尝试使用fit_generator。早些时候，我给了很多参数，所以即使使用fit_generator，我也得到了MemoryError，现在我减少了参数的数量，现在却得到了不同的错误。请在下面找到代码和错误。
CODE ::::::
def yield_arrays_train(array_x_train_feat1=xtrain_np_img1,array_x_train_feat2=xtrain_np_img2,array_y_train=y_train_numpy,batch_size=6):
while 1:
for i in range(14886):
X_feat1_train = (array_x_train_feat1[i:i+batch_size,:,:].astype(np.float16))
X_feat2_train = (array_x_train_feat2[i:i+batch_size,:].astype(np.float16))
Y_train = (array_y_train[i:i+batch_size].astype(np.float16))
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.float16))
X_feat2_test = (array_x_test_feat2[i:i+batch_size,:].astype(np.float16))
Y_test = (array_y_test[i:i+batch_size].astype(np.float16))
yield ([(np.array(X_feat1_test)),(np.array(X_feat2_test))],(np.array(Y_test)))
def siamese (array_x_train_feat1=xtrain_np_img1,array_x_test_feat1=xtest_np_img1,array_x_test_feat2=xtest_np_img2,array_y_test=y_test_numpy):
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,activation='relu'))
encoder.add(Batchnormalization())
encoder.add(Dropout(.1))
encoder.add(MaxPool1D())
encoder.add(Conv1D(filters=4,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[:,(x_val[:,y_val),batch_size=params['batch_size'],epochs=params['epochs'],callbacks=callback_early_stop_reduceLROnPlateau)
history=model.fit_generator(generator=yield_arrays_train(array_x_train_feat1,array_x_train_feat2,array_y_train,6),validation_data=yield_arrays_val(array_x_test_feat1,steps_per_epoch=2481,epochs=5,validation_steps=1000,callbacks=callback_early_stop_reduceLROnPlateau,use_multiprocessing=False,workers=0)
return history,model
siamese (xtrain_np_img1,xtrain_np_img2,y_train_numpy,xtest_np_img1,xtest_np_img2,y_test_numpy)
OUTPUT ::::::::
WARNING:tensorflow:From C:\Users\DELL\AppData\Roaming\Python\python37\site-packages\keras\backend\tensorflow_backend.py:4070: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.
Model: "model_1"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None,24,939) 0
__________________________________________________________________________________________________
input_2 (InputLayer) (None,939) 0
__________________________________________________________________________________________________
sequential_1 (Sequential) (None,10) 45580 input_1[0][0]
input_2[0][0]
__________________________________________________________________________________________________
lambda_1 (Lambda) (None,1) 0 sequential_1[1][0]
sequential_1[2][0]
==================================================================================================
Total params: 45,580
Trainable params: 45,544
Non-trainable params: 36
__________________________________________________________________________________________________
WARNING:tensorflow:From C:\Users\DELL\anaconda3\envs\MyEnv\lib\site-packages\tensorflow\python\ops\math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0,which has the same broadcast rule as np.where
WARNING:tensorflow:From C:\Users\DELL\AppData\Roaming\Python\python37\site-packages\keras\backend\tensorflow_backend.py:422: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.
Epoch 1/5
2481/2481 [==============================] - 30s 12ms/step - loss: 0.0024 - accuracy: 0.9992 - val_loss: 0.8333 - val_accuracy: 0.1667
Epoch 2/5
2481/2481 [==============================] - 28s 11ms/step - loss: 6.9194e-05 - accuracy: 0.9999 - val_loss: 0.8333 - val_accuracy: 0.1667
Epoch 3/5
2481/2481 [==============================] - 28s 11ms/step - loss: nan - accuracy: 0.9993 - 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 5/5
2481/2481 [==============================] - 28s 11ms/step - loss: nan - accuracy: 1.0000 - val_loss: nan - val_accuracy: 0.8333oss: nan - acc
Epoch 00005: early stopping
(<keras.callbacks.callbacks.History at 0x26cf45c6ec8>,<keras.engine.training.Model at 0x26cf3e364c8>)
所以当我使用一组硬编码的参数时，以上代码在没有Talos的情况下工作了
如果我使用
p = {
'filter1':[4,6,8],'kernel_size1':[2,4,6],'filter3' : [2,],'kernel_size3' : [4,'decay' :[.1,0.01,.001],'droprate1' :[.1,.2,.3],'filter2':[4,'kernel_size2':[8,12],'droprate4' : [.1,.2],'droprate2' :[.1,'unit1': [10,36,64],'droprate3': [.1,'lr' :[.1,.001]
}
def siamese (array_x_train_feat1=xtrain_np_img1,array_y_test=y_test_numpy,params=p):
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']),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']),activation='relu'))
encoder.add(Batchnormalization())
encoder.add(Dropout((params['droprate2'])))
encoder.add(MaxPool1D())
encoder.add(Conv1D(filters=(params['filter3']),kernel_size=(params['kernel_size3']),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,encoded_r])
adam = optimizers.Adam(lr=params['lr'],metrics=[accuracy])
model.summary()
history=model.fit_generator(generator=yield_arrays_train(array_x_train_feat1,workers=0)
return history,model
t=ta.Scan(x=[xtrain_np_img1.astype(np.float16),xtrain_np_img2.astype(np.float16)],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')
----> 3 t=ta.Scan(x=[xtrain_np_img1.astype(np.float16),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)
24 # otherwise proceed with next permutation
25 from .scan_round import scan_round
---> 26 self = scan_round(self)
27 self.pbar.update(1)
28
~\anaconda3\envs\MyEnv\lib\site-packages\talos\scan\scan_round.py in scan_round(self)
17 # fit the model
18 from ..model.ingest_model import ingest_model
---> 19 self.model_history,self.round_model = ingest_model(self)
20 self.round_history.append(self.model_history.history)
21
~\anaconda3\envs\MyEnv\lib\site-packages\talos\model\ingest_model.py in ingest_model(self)
8 self.x_val,9 self.y_val,---> 10 self.round_params)
<ipython-input-27-fe409e1ff506> in siamese(array_x_train_feat1,array_x_test_feat1,params)
11
12 encoder = Sequential()
---> 13 encoder.add(Conv1D(filters=(params['filter1']),bias_initializer=b_init))
14 encoder.add(Batchnormalization())
15 encoder.add(Dropout((params['droprate1'])))
~\AppData\Roaming\Python\python37\site-packages\keras\legacy\interfaces.py in wrapper(*args,**kwargs)
89 warnings.warn('Update your `' + object_name + '` call to the ' +
90 'Keras 2 API: ' + signature,stacklevel=2)
---> 91 return func(*args,**kwargs)
92 wrapper._original_function = func
93 return wrapper
~\AppData\Roaming\Python\python37\site-packages\keras\layers\convolutional.py in __init__(self,filters,kernel_size,strides,padding,data_format,dilation_rate,activation,use_bias,kernel_initializer,bias_initializer,kernel_regularizer,bias_regularizer,activity_regularizer,kernel_constraint,bias_constraint,**kwargs)
351 kernel_constraint=kernel_constraint,352 bias_constraint=bias_constraint,--> 353 **kwargs)
354
355 def get_config(self):
~\AppData\Roaming\Python\python37\site-packages\keras\layers\convolutional.py in __init__(self,rank,**kwargs)
107 self.filters = filters
108 self.kernel_size = conv_utils.normalize_tuple(kernel_size,--> 109 'kernel_size')
110 self.strides = conv_utils.normalize_tuple(strides,'strides')
111 self.padding = conv_utils.normalize_padding(padding)
~\AppData\Roaming\Python\python37\site-packages\keras\utils\conv_utils.py in normalize_tuple(value,n,name)
37 if len(value_tuple) != n:
38 raise ValueError('The `' + name + '` argument must be a tuple of ' +
---> 39 str(n) + ' integers. Received: ' + str(value))
40 for single_value in value_tuple:
41 try:
ValueError: The `kernel_size` argument must be a tuple of 1 integers. Received: [2,6]
如果我只给出一组超参数只是为了检查我得到不同的错误还是相同的错误
p = {
'filter1':[6],'kernel_size1':[4],'filter3' : [4],'kernel_size3' : [6],'decay' :[.1],'droprate1' :[.1],'filter2':[4],'kernel_size2':[8],'droprate4' : [.1],'droprate2' :[.1],'unit1': [10],'droprate3': [.1],'lr' :[.1]
}
def siamese (array_x_train_feat1=xtrain_np_img1,model
t=ta.Scan(x=[xtrain_np_img1.astype(np.float16),experiment_name='exp_1')
错误:::::::
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-31-4c923301ede6> in <module>
1 #t=ta.Scan(x=xtrain_np_img1_img2,x_val=xtest_np_img1_img2,experiment_name='exp_1')
2
----> 3 t=ta.Scan(x=[xtrain_np_img1.astype(np.float16),---> 10 self.round_params)
<ipython-input-30-fe409e1ff506> in siamese(array_x_train_feat1,bias_initializer=b_init))
14 encoder.add(Batchnormalization())
15 encoder.add(Dropout((params['droprate1'])))
~\AppData\Roaming\Python\python37\site-packages\keras\engine\sequential.py in add(self,layer)
164 # and create the node connecting the current layer
165 # to the input layer we just created.
--> 166 layer(x)
167 set_inputs = True
168 else:
~\AppData\Roaming\Python\python37\site-packages\keras\engine\base_layer.py in __call__(self,inputs,**kwargs)
461 'You can build it manually via: '
462 '`layer.build(batch_input_shape)`')
--> 463 self.build(unpack_singleton(input_shapes))
464 self.built = True
465
~\AppData\Roaming\Python\python37\site-packages\keras\layers\convolutional.py in build(self,input_shape)
139 name='kernel',140 regularizer=self.kernel_regularizer,--> 141 constraint=self.kernel_constraint)
142 if self.use_bias:
143 self.bias = self.add_weight(shape=(self.filters,),~\AppData\Roaming\Python\python37\site-packages\keras\engine\base_layer.py in add_weight(self,name,shape,dtype,initializer,regularizer,trainable,constraint)
277 if dtype is None:
278 dtype = self.dtype
--> 279 weight = K.variable(initializer(shape,dtype=dtype),280 dtype=dtype,281 name=name,~\anaconda3\envs\MyEnv\lib\site-packages\tensorflow\python\ops\init_ops.py in __call__(self,partition_info)
513 if partition_info is not None:
514 scale_shape = partition_info.full_shape
--> 515 fan_in,fan_out = _compute_fans(scale_shape)
516 if self.mode == "fan_in":
517 scale /= max(1.,fan_in)
~\anaconda3\envs\MyEnv\lib\site-packages\tensorflow\python\ops\init_ops.py in _compute_fans(shape)
1445 fan_in = shape[-2] * receptive_field_size
1446 fan_out = shape[-1] * receptive_field_size
-> 1447 return int(fan_in),int(fan_out)
1448
1449
TypeError: int() argument must be a string,a bytes-like object or a number,not 'list'
对我来说，所有这些似乎都是python错误，与talos（超参数优化工具）无关。
我的问题是
我有另一个查询，但是为此，我将启动另一个线程来理解我正在通过MFCC + MFCC_del + MFCC_del_del来识别扬声器并使用暹罗网络解决的问题，它将起作用吗？ 可以从此链接访问完整代码，但我尚未将数据文件上传到Google驱动器，因此它无法正常工作，但有人可能会看到完整代码的主要部分
https://colab.research.google.com/drive/1Twssq_MiQ4RxMZ69A595SFIgs4JhG18u?usp=sharing ############################# 编辑：我只是检查一个链接https://www.kaggle.com/sohaibanwaar1203/talos-hyper-parameter-optimization 在此代码是
params = {'lr': (0.1,1 ),'epochs': [10,5,15],'dropout': (0,0.40,0.8),'optimizer': ["Adam","Adagrad","sgd"],'loss': ["binary_crossentropy","mean_squared_error","mean_absolute_error"],'last_activation': ["softmax","sigmoid"],'activation' :["relu","selu","linear"],'clipnorm':(0.0,0.5,1),'decay':(1e-6,1e-4,1e-2),'momentum':(0.9,0.2),'l1': (0.01,0.001,0.0001),'l2': (0.01,'No_of_CONV_and_Maxpool_layers':[1,2],'No_of_Dense_Layers': [2,3,4],'No_of_Units_in_dense_layers':[64,32],'Kernal_Size':[(3,3),(5,5)],'Conv2d_filters':[60,40,80,120],'pool_size':[(3,'padding':["valid","same"]
}
lr = params['lr']
epochs=params['epochs']
dropout_rate=params['dropout']
optimizer=params['optimizer']
loss=params['loss']
last_activation=params['last_activation']
activation=params['activation']
clipnorm=params['clipnorm']
decay=params['decay']
momentum=params['momentum']
l1=params['l1']
l2=params['l2']
No_of_CONV_and_Maxpool_layers=params['No_of_CONV_and_Maxpool_layers']
No_of_Dense_Layers =params['No_of_Dense_Layers']
No_of_Units_in_dense_layers=params['No_of_Units_in_dense_layers']
Kernal_Size=params['Kernal_Size']
Conv2d_filters=params['Conv2d_filters']
pool_size_p=params['pool_size']
padding_p=params['padding']
print (type((params['epochs'])))
<class 'list'>
因此，即使在此博客中，我也看到输出是列表，所以我不明白为什么会出错。
解决方法
暂无找到可以解决该程序问题的有效方法，小编努力寻找整理中！
如果你已经找到好的解决方法，欢迎将解决方案带上本链接一起发送给小编。
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