Почему я получаю этот график отключен ошибка?

Question

Я пытаюсь создать логово enet, но когда я пытаюсь скомпилировать модель, я получаю это сообщение об ошибке. Вот моя модель:

from tensorflow import keras
from keras.utils import plot_model
dropoutRate = 0.2

def globalAvgPooling(x):
  height = np.shape(x)[2]
  width = np.shape(x)[1]
  poolSize = [width, height]
  return tf.keras.layers.AveragePooling2D(pool_size=poolSize, strides=1)(x)

def concatenation(layers):
  return tf.keras.layers.concatenate(layers, axis=3)

class DenseNet():
  def __init__(self, filters, numBlocks, numClasses, training):
    self.filters = filters
    self.numBlocks = numBlocks
    self.training = training
    self.numClasses = numClasses
    self.model = self.denseNet()

  def bottleneckLayer(self, inputX):
    x = tf.keras.layers.BatchNormalization()(inputs=inputX, training=self.training)
    x = tf.keras.activations.relu(x)
    x = tf.keras.layers.Conv2D(use_bias=False, filters=self.filters, kernel_size=1, strides=1, padding='same')(x)
    x = tf.layers.dropout(inputs=x, rate=dropoutRate, training=self.training)
    x = tf.keras.layers.BatchNormalization()(inputs=inputX, training=self.training)
    x = tf.keras.activations.relu(x)
    x = tf.keras.layers.Conv2D(use_bias=False, filters=self.filters, kernel_size=3, strides=1, padding='same')(x)
    x = tf.layers.dropout(inputs=x, rate=dropoutRate, training=self.training)
    return x


  def denseBlock(self, inputX, numLayers):
    concatLayers = list()
    concatLayers.append(inputX)
    x = self.bottleneckLayer(inputX=inputX)
    concatLayers.append(x)
    for i in range(self.numBlocks - 1):
      x = concatenation(concatLayers)
      x = self.bottleneckLayer(inputX=x)
      concatLayers.append(x)

    x = concatenation(concatLayers)
    return x


  def transitionLayer(self, inputX):
    x = tf.keras.layers.BatchNormalization()(inputs=inputX, training=self.training)
    x = tf.keras.activations.relu(x)
    x = tf.keras.layers.Conv2D(use_bias=False, filters=self.filters, kernel_size=1, strides=1, padding='same')(x)
    x = tf.layers.dropout(inputs=x, rate=dropoutRate, training=self.training)
    x = tf.keras.layers.AveragePooling2D(pool_size=[2,2], strides=2, padding='valid')(x)
    return x

  def denseNet(self):
    inputs = keras.Input(shape=(32,32,3))
    x = tf.keras.layers.Conv2D(use_bias=False, filters=self.filters, kernel_size=7, strides=1, padding='same')(inputs)
    x = self.denseBlock(inputX=x, numLayers=6) #Dense block 1 with 6 layers
    x = self.transitionLayer(inputX=x)
    x = self.denseBlock(inputX=x, numLayers=12) #Dense block 2 with 12 layers
    x = self.transitionLayer(inputX=x)
    x = self.denseBlock(inputX=x, numLayers= 48) #Dense block 3 with 48 layers
    x = self.transitionLayer(inputX=x)
    x = self.denseBlock(inputX=x, numLayers=32) #Dense block 4 with 32 layers (final block)
    x = globalAvgPooling(x=x)
    x = tf.keras.layers.Softmax()(x)
    outputs = tf.keras.layers.Dense(units=self.numClasses)(x)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return x

tf.compat.v1.disable_eager_execution()
growthK = 24
numBlock = 2

cameraModel = DenseNet(filters=growthK, numBlocks=numBlock, numClasses=4, training=True).model

Вот сообщение об ошибке, которое я получаю:

ValueError: Graph disconnected: cannot obtain value for tensor 
    Tensor("dropout_109/dropout/mul_1:0", shape=(?, 32, 32, 24), dtype=float32)
    at layer "concatenate_48". The following previous layers were accessed without issue:
    ['input_6', 'conv2d_120', 'batch_normalization_115', 'tf_op_layer_Relu_115', 'conv2d_122', 'dropout_108']

Что я делаю не так?

Answer 1

Ошибка исходит из строки:

x = tf.keras.layers.BatchNormalization()(inputs=inputX, training=self.training)

, так как я думаю, что в ней указан неверный параметр inputs. Это должно выглядеть так:

x = tf.keras.layers.BatchNormalization()(inputs=x, training=self.training)

Именно поэтому значение График отключено.