tflearn conv2d_transpose 모양이 호환되지 않음

Question

크기 [256 256 3]의 이미지를 입력으로 제공하고 같은 크기의 출력을 원하지만 아래에 언급 된 오류가 표시됩니다. 나는 셰이프, 필터, 스트라이드를 변경하려고 시도했지만 아무것도 작동하지 않습니다. 동일한 것을 성취하는 다른 방법은 상기와 동일하게 인식되거나 링크 될 것이며, 입력과 출력은 모두 이미지입니다.

enter code here 
import tensorflow as tf 
import tflearn 
import os 

import numpy as np 
from tflearn.layers.conv import conv_2d, max_pool_2d,conv_2d_transpose 
from tflearn.layers.core import input_data, dropout, fully_connected 
from tflearn.layers.estimator import regression 
import loaddata 

LR = 1e-3 
MODEL_NAME = 'deblurr-{}-{}.model'.format(LR, '2conv-basic') 
IMG_SIZE = 256 
strides = [1,2,2,1] 


convnet = input_data(shape=[None, IMG_SIZE, IMG_SIZE, 3],dtype=tf.float32, 
name='input') 
print ("convnet :" , convnet.shape) 


convnet = conv_2d(convnet, 6, 5,strides=strides, activation='LeakyReLU') 
print ("convnet :" , convnet.shape) 

convnet = conv_2d(convnet,12, 5,strides=strides, activation='LeakyReLU') 
print ("convnet :" , convnet.shape) 

convnet = conv_2d(convnet, 24, 5,strides=strides, activation='LeakyReLU') 
print ("convnet :" , convnet.shape) 


convnet = conv_2d(convnet, 48, 5,strides=strides, activation='LeakyReLU') 
print ("convnet :" , convnet.shape) 

convnet = conv_2d(convnet, 24, 5,strides=strides, activation='LeakyReLU') 
print ("convnet :" , convnet.shape) 

convnet = conv_2d_transpose(convnet, 12, 5,output_shape= 
[64,64,12],strides=strides, activation='tanh') 
print ("convnet :" , convnet.shape) 
convnet = dropout(convnet, 0.8) 
print ("convnet :" , convnet.shape) 

convnet = conv_2d_transpose(convnet, 6, 5,output_shape= 
[128,128,6],strides=strides, activation='tanh') 
print ("convnet :" , convnet.shape) 
convnet = dropout(convnet, 0.8) 
print ("convnet :" , convnet.shape) 

convnet = conv_2d_transpose(convnet, 3, 5,output_shape= 
[256,256,3],strides=strides, activation='tanh') 
print ("convnet :" , convnet.shape) 
convnet = dropout(convnet, 0.8) 
print ("convnet :" , convnet.shape) 

convnet = regression(convnet, optimizer='adam', learning_rate=LR, 
loss='categorical_crossentropy', name='targets') 
print ("convnet :" , convnet.shape) 

model = tflearn.models.dnn.DNN(convnet) 

if os.path.exists('{}.meta'.format(MODEL_NAME)): 
model.load(MODEL_NAME) 
print('model loaded!') 

y_train, x_train = loaddata.load_data(data_type='train') 


X = x_train 
Y = y_train 

y_test, x_test = loaddata.load_data(data_type='test') 

test_x = y_test 
test_y = x_test 

model.fit({'input': X}, {'targets': Y}, n_epoch=10, validation_set=({'input': 
test_x}, {'targets': test_y}), batch_size=2, 
snapshot_step=500, show_metric=True, run_id=MODEL_NAME) 

이로 나오는 오류 : 당신이 당신의 전환 레이어에서 모양 출력을 보면

ValueError        Traceback (most recent call last) 
C:\Users\USER\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py in merge_with(self, other) 
    562   for i, dim in enumerate(self._dims): 
--> 563   new_dims.append(dim.merge_with(other[i])) 
    564   return TensorShape(new_dims) 

C:\Users\USER\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py in merge_with(self, other) 
    137  other = as_dimension(other) 
--> 138  self.assert_is_compatible_with(other) 
    139  if self._value is None: 

C:\Users\USER\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py in assert_is_compatible_with(self, other) 
    110  raise ValueError("Dimensions %s and %s are not compatible" % (self, 
--> 111                  other)) 
    112 

ValueError: Dimensions 32 and 8 are not compatible 

During handling of the above exception, another exception occurred: 

ValueError        Traceback (most recent call last) 
E:\extra\notes\New folder\source\models.py in <module>() 
    53 print ("convnet :" , convnet.shape) 
    54 
---> 55 model = tflearn.models.dnn.DNN(convnet) 
    56 
    57 if os.path.exists('{}.meta'.format(MODEL_NAME)): 

C:\Users\USER\Anaconda3\lib\site-packages\tflearn\models\dnn.py in __init__(self, network, clip_gradients, tensorboard_verbose, tensorboard_dir, checkpoint_path, best_checkpoint_path, max_checkpoints, session, best_val_accuracy) 
    63        max_checkpoints=max_checkpoints, 
    64        session=session, 
---> 65        best_val_accuracy=best_val_accuracy) 
    66   self.session = self.trainer.session 
    67 

C:\Users\USER\Anaconda3\lib\site-packages\tflearn\helpers\trainer.py in __init__(self, train_ops, graph, clip_gradients, tensorboard_dir, tensorboard_verbose, checkpoint_path, best_checkpoint_path, max_checkpoints, keep_checkpoint_every_n_hours, random_seed, session, best_val_accuracy) 
    129     train_op.initialize_training_ops(i, self.session, 
    130             tensorboard_verbose, 
--> 131             clip_gradients) 
    132 
    133    # Saver for saving a model 

C:\Users\USER\Anaconda3\lib\site-packages\tflearn\helpers\trainer.py in initialize_training_ops(self, i, session, tensorboard_verbose, clip_gradients) 
    695    # Compute gradients operations 
    696    with tf.control_dependencies([loss_avg_op, acc_avg_op]): 
--> 697     self.grad = tf.gradients(total_loss, self.train_vars) 
    698     if clip_gradients > 0.0: 
    699      self.grad, self.grad_norm = \ 

C:\Users\USER\Anaconda3\lib\site-packages\tensorflow\python\ops\gradients_impl.py in gradients(ys, xs, grad_ys, name, colocate_gradients_with_ops, gate_gradients, aggregation_method) 
    560    if (isinstance(in_grad, ops.Tensor) and 
    561     t_in.dtype != dtypes.resource): 
--> 562    in_grad.set_shape(t_in.get_shape()) 
    563    _SetGrad(grads, t_in, in_grad) 
    564   if loop_state: 

C:\Users\USER\Anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in set_shape(self, shape) 
    376   this tensor. 
    377  """ 
--> 378  self._shape = self._shape.merge_with(shape) 
    379 
    380 @property 

C:\Users\USER\Anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py in merge_with(self, other) 
    564   return TensorShape(new_dims) 
    565  except ValueError: 
--> 566   raise ValueError("Shapes %s and %s are not compatible" % (self, other)) 
    567 
    568 def concatenate(self, other): 

ValueError: Shapes (?, 32, 32, 24) and (?, 8, 8, 24) are not compatible 

Answer 1

하기 : convnet5에서

convnet1: (?, 256, 256, 3) 
convnet2: (?, 128, 128, 6) 
convnet3: (?, 64, 64, 12) 
convnet4: (?, 32, 32, 24) 
convnet5: (?, 16, 16, 48) 
convnet6: (?, 8, 8, 24) 
convnet7: (?, 64, 64, 12) 
convnet8: (?, 128, 128, 6) 
convnet9: (?, 256, 256, 3) 
convnet10: (?, 256, 256, 3) 

이 convnet6에 이 내 코드입니다 당신은 당신의 차원을 계수 8로 상향 조정하려고합니다. 그러나 당신의 코드는 당신의 진보에서 2의 계수로만 상향 조정합니다 :

strides = [1,2,2,1] 
convnet = conv_2d_transpose(convnet, 12, 5,output_shape= 
      [64,64,12],strides=strides, activation='tanh') 

역 전파중인 그래디언트 모양이이 레이어와 호환되지 않습니다. 그러나이 행을 다음으로 변경하면

convnet = conv_2d_transpose(convnet, 12, 5,output_shape= 
      [64,64,12],strides=[1,8,8,1], activation='tanh') 

코드가 작동합니다.