Question

我正在使用Tensorflow.js来预测我在Keras训练的模型。但是，当我输入4维张量时，会出现以下错误：

UnhandledPromiseRejectionWarning: Unhandled promise rejection (rejection id: 1): Error: dot support for x of rank 4 is not yet implemented: x shape = 32,1,1,100

我在网上找不到有关此错误的任何内容-我怀疑它与Tensorflow.js尚不具备此功能有关，但我不确定。知道我可以在哪里获得更多信息吗？

编辑1

这是我的代码，引发错误的行是model.predict(noise_tensor)。大部分与之无关的代码：

  noise_tensor.print(true)

  generated_images = model.predict(noise_tensor) //error occours here

这是我的4d张量的打印输出：

Tensor
  dtype: float32
  rank: 4
  shape: [64,1,1,100]
  values:
    [ [ [[0.3799773 , -0.0252707, 0.0118336 , ..., 0.1703698 , -0.0649208, 0.2152225 ],]],


      [ [[0.219656  , 0.2850143 , -0.1078744, ..., 0.1627689 , -0.0838831, -0.1112608],]],


      [ [[-0.1295149, -0.08308  , 0.1872116 , ..., -0.2033772, -0.4184959, -0.3357461],]],


     ...
      [ [[0.0029674 , 0.0422036 , 0.067896  , ..., 0.1368463 , 0.1122015 , -0.0395375],]],


      [ [[0.043546  , -0.0281712, 0.0898769 , ..., 0.205565  , 0.1444133 , 0.0067788 ],]],


      [ [[-0.1089588, -0.0161969, -0.0724337, ..., 0.1427118 , -0.2577117, 0.0013836 ],]]]

以下是Keras模型的摘要：

_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
dense_1 (Dense)              (None, 1, 1, 32768)       3309568
_________________________________________________________________
reshape_1 (Reshape)          (None, 8, 8, 512)         0
_________________________________________________________________
batch_normalization_1 (Batch (None, 8, 8, 512)         2048
_________________________________________________________________
activation_1 (Activation)    (None, 8, 8, 512)         0
_________________________________________________________________
conv2d_transpose_1 (Conv2DTr (None, 16, 16, 256)       3277056
_________________________________________________________________
batch_normalization_2 (Batch (None, 16, 16, 256)       1024
_________________________________________________________________
activation_2 (Activation)    (None, 16, 16, 256)       0
_________________________________________________________________
conv2d_transpose_2 (Conv2DTr (None, 32, 32, 128)       819328
_________________________________________________________________
batch_normalization_3 (Batch (None, 32, 32, 128)       512
_________________________________________________________________
activation_3 (Activation)    (None, 32, 32, 128)       0
_________________________________________________________________
conv2d_transpose_3 (Conv2DTr (None, 64, 64, 64)        204864
_________________________________________________________________
batch_normalization_4 (Batch (None, 64, 64, 64)        256
_________________________________________________________________
activation_4 (Activation)    (None, 64, 64, 64)        0
_________________________________________________________________
conv2d_transpose_4 (Conv2DTr (None, 128, 128, 1)       1601
_________________________________________________________________
activation_5 (Activation)    (None, 128, 128, 1)       0
=================================================================
Total params: 7,616,257
Trainable params: 7,614,337
Non-trainable params: 1,920
_________________________________________________________________

以及Python中的相应代码：

def construct_generator():

    generator = Sequential()

    generator.add(Dense(units=8 * 8 * 512,
                        kernel_initializer='glorot_uniform',
                        input_shape=(1, 1, 100)))
    generator.add(Reshape(target_shape=(8, 8, 512)))
    generator.add(BatchNormalization(momentum=0.5))
    generator.add(Activation('relu'))

    generator.add(Conv2DTranspose(filters=256, kernel_size=(5, 5),
                                  strides=(2, 2), padding='same',
                                  data_format='channels_last',
                                  kernel_initializer='glorot_uniform'))
    generator.add(BatchNormalization(momentum=0.5))
    generator.add(Activation('relu'))

    generator.add(Conv2DTranspose(filters=128, kernel_size=(5, 5),
                                  strides=(2, 2), padding='same',
                                  data_format='channels_last',
                                  kernel_initializer='glorot_uniform'))
    generator.add(BatchNormalization(momentum=0.5))
    generator.add(Activation('relu'))

    generator.add(Conv2DTranspose(filters=64, kernel_size=(5, 5),
                                  strides=(2, 2), padding='same',
                                  data_format='channels_last',
                                  kernel_initializer='glorot_uniform'))
    generator.add(BatchNormalization(momentum=0.5))
    generator.add(Activation('relu'))

    generator.add(Conv2DTranspose(filters=1, kernel_size=(5, 5),
                                  strides=(2, 2), padding='same',
                                  data_format='channels_last',
                                  kernel_initializer='glorot_uniform'))
    generator.add(Activation('tanh'))

    optimizer = Adam(lr=0.00015, beta_1=0.5)
    generator.compile(loss='binary_crossentropy',
                      optimizer=optimizer,
                      metrics=None)

    print('generator')
    generator.summary()

    return generator

编辑2

这是tensorflow.js中的错误。对于未来的访问者，请查看GitHub线程here。

Answer 1

目前，输入tf.dot应该处于1或2级

错误：尚未实现对等级4的x的点支持

编辑1

编辑2

1 个答案: