如何使用tf.data.Dataset和tf.keras进行多输入和多输出？

Question

我遇到一个有关使用tf.keras构建模型以及还将tf.data.Dataset作为输入管道的多输出的问题。 这是我的代码如下：

  a = tf.keras.layers.Input(shape=(368, 368, 3))
  conv1 = tf.keras.layers.Conv2D(64, 3, 1)(a)
  conv2 = tf.keras.layers.Conv2D(64, 3, 1)(conv1)
  maxpool = tf.keras.layers.MaxPooling2D(pool_size=8, strides=8, 
   padding='same')(conv2)
  conv3 = tf.keras.layers.Conv2D(5, 1, 1)(maxpool)
  conv4 = tf.keras.layers.Conv2D(6, 1, 1)(maxpool)

  inputs = a
  outputs = [conv3, conv4]

  model = tf.keras.models.Model(inputs=inputs, outputs=outputs)


  model.compile(optimizer=tf.keras.optimizers.SGD(),
          loss=tf.keras.losses.mean_squared_error)


  import numpy as np
  data = np.random.rand(10, 368, 368, 3)
  cpm  = np.random.rand(10, 46, 46, 5)
  paf  = np.random.rand(10, 46, 46, 6)

  dataset1 = tf.data.Dataset.from_tensor_slices((data))
  dataset2 = tf.data.Dataset.from_tensor_slices((cpm, paf))
  dataset1 = dataset1.batch(10).repeat()
  dataset2 = dataset2.batch(10).repeat()

  dataset  = tf.data.Dataset.zip((dataset1, dataset2))

  model.fit(dataset, epochs=200, steps_per_epoch=30)

我正在使用tensorflow == 1.10.1并且出现如下错误：

 File "/home/ulsee/work/tensorflow-HalfBodyPose/learnkeras.py", line 123, in <module>
model.fit(dataset, epochs=200, steps_per_epoch=30)
 File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training.py", line 1278, in fit
validation_split=validation_split)
 File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training.py", line 917, in _standardize_user_data
exception_prefix='target')
 File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 143, in standardize_input_data
data = [standardize_single_array(x) for x in data]
 File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 143, in <listcomp>
data = [standardize_single_array(x) for x in data]
 File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/keras/engine/training_utils.py", line 81, in standardize_single_array
elif x.ndim == 1:
AttributeError: 'tuple' object has no attribute 'ndim'

更新 ：升级tf == 1.11.0后，我已使此代码起作用。 所以也许我认为这是一个版本错误。

Answer 1

您可以尝试的一件事是连接输出，然后对目标numpy数组执行相同的操作。 我不确定这对您的应用程序逻辑是否有意义。

def conc_op(tensors):
    return K.concatenate(tensors) # K refers to Keras backend

def conc_op_shape(input_shapes):
    shape1 = list(input_shapes[0])
    shape2 = list(input_shapes[1])
    return tuple(shape1[:-1], shape1[-1]+shape2[-1])

a = tf.keras.layers.Input(shape=(368, 368, 3))
conv1 = tf.keras.layers.Conv2D(64, 3, 1)(a)
conv2 = tf.keras.layers.Conv2D(64, 3, 1)(conv1)
maxpool = tf.keras.layers.MaxPooling2D(pool_size=8, strides=8, padding='same')(conv2)
conv3 = tf.keras.layers.Conv2D(5, 1, 1)(maxpool)
conv4 = tf.keras.layers.Conv2D(6, 1, 1)(maxpool)

inputs = a
outputs = [conv3, conv4]
conc_outputs = Lambda(conc_op, output_shape=conc_op_shape)(outputs) # This is a keras layer
model = tf.keras.models.Model(inputs=inputs, outputs=conc_outputs)

model.compile(optimizer=tf.keras.optimizers.SGD(), loss=keras.losses.mean_squared_error)
model.summary()
data = np.random.rand(10, 368, 368, 3)
cpm  = np.random.rand(10, 46, 46, 5)
paf  = np.random.rand(10, 46, 46, 6)
label = np.concatenate((cpm, paf), axis=-1)

dataset = tf.data.Dataset.from_tensor_slices((data, label))
dataset = dataset.batch(2).repeat()
model.fit(dataset.make_one_shot_iterator(), epochs=2, steps_per_epoch=5) # Just to check if it runs

返回结果：

Epoch 1/2
5/5 [==============================] - 15s 3s/step - loss: 0.4057
Epoch 2/2
5/5 [==============================] - 0s 32ms/step - loss: 0.2282