Question

我正在使用NodaTime。当我测试整个网络时，我会收到

“ ValueError：新数组的总大小必须保持不变。

可能是什么原因？任何帮助将不胜感激。

我已经测试了作者提供的数据集样本。我拍摄了约700张图像，然后尝试了。作者提供了7890个图像数据集。

'''

def get_optimizer(name = 'adagrad', l_rate = 0.0001, dec = 0.0, b_1 = 0.9, b_2 = 0.999, mom = 0.5, rh = 0.9):
    eps = 1e-8

    adam = Adam(lr = l_rate, beta_1 = b_1, beta_2 = b_2, epsilon = eps, decay = dec)
    sgd = SGD(lr = l_rate, momentum = mom, decay = dec, nesterov = True)
    rmsp = RMSprop(lr = l_rate, rho = rh, epsilon = eps, decay = dec)
    adagrad = Adagrad(lr = l_rate, epsilon = eps, decay = dec)

    optimizers = {'adam': adam, 'sgd':sgd, 'rmsp': rmsp, 'adagrad': adagrad}

    return optimizers[name]

def build_autoencoder():
    #define network architecture for encoder
    model = Sequential()

    #6 convoltuion layers
    model.add(Convolution2D(16, 3, 3, border_mode='same', subsample = (2,2), activation = 'relu',
            input_shape=(224, 224, 3)))
    model.add(Convolution2D(32, 3, 3, border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Convolution2D(64, 3, 3, border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Convolution2D(128, 3, 3, border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Convolution2D(256, 3, 3, border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Convolution2D(512, 3, 3, border_mode='same', subsample = (2,2), activation = 'relu'))

    #Flatten 
    model.add(Flatten())

    #2 fully connected layers
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(p=0))
    model.add(Dense(4096, activation='relu'))
    model.add(Dropout(p=0))


    #define network architecture for decoder

    #2 fully connected layers
    model.add(Dense(4096, activation='relu'))
    model.add(Dense(4096, activation='relu'))

    #reshape to 2D
    model.add(Reshape((8, 8, 64)))

    #5 upconv layers
    model.add(Deconvolution2D(256, 3, 3, (None, 15, 15,256), border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Deconvolution2D(128, 3, 3, (None, 29, 29, 128), border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Deconvolution2D(64, 3, 3, (None, 57, 57, 64), border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Deconvolution2D(32, 3, 3, (None, 113, 113, 32),border_mode='same', subsample = (2,2), activation = 'relu'))
    model.add(Deconvolution2D(16, 3, 3, (None, 225, 225, 16), border_mode='same', subsample = (2,2), activation = 'relu'))

    #output layer, add 3 RGB channels for reconstructed output view
    model.add(Deconvolution2D(3, 3, 3, (None, 225, 225, 3), border_mode='same', subsample = (1,1), activation = 'relu'))

    #add a resize layer to resize (225, 225) to (224, 224)
    model.add(Reshape((225*225,3)))
    model.add(Lambda(lambda x: x[:,:50176,])) # throw away some
    model.add(Reshape((224,224,3)))

'''

build_autoencoder
    model.add(Reshape((225*225,3)))

  File "C:\ProgramData\Anaconda3\envs\myenv\lib\site-packages\keras\engine\sequential.py", line 181, in add
    output_tensor = layer(self.outputs[0])

  File "C:\ProgramData\Anaconda3\envs\myenv\lib\site-packages\keras\engine\base_layer.py", line 474, in __call__
    output_shape = self.compute_output_shape(input_shape)

  File "C:\ProgramData\Anaconda3\envs\myenv\lib\site-packages\keras\layers\core.py", line 398, in compute_output_shape
    input_shape[1:], self.target_shape)

  File "C:\ProgramData\Anaconda3\envs\myenv\lib\site-packages\keras\layers\core.py", line 386, in _fix_unknown_dimension
    raise ValueError(msg)

ValueError: total size of new array must be unchanged

错误在第reshape ((225*225,3))行

ValueError：新数组的总大小必须保持不变（https://github.com/ajgupta93/enhanced-view-synthesis/blob/master/code/viewsyn_model.py）

0 个答案: