tensorflow - 运行预训练的 CNN 时不可调用非类型对象

Question

我正在尝试使用一些用于二进制分类的数据来运行预训练网络。不幸的是，我收到了标题中提到的错误。顺便说一句，我正在使用我自己的数据。

这是我的代码：

conv_base <- application_vgg16(
  weights = "imagenet",
  include_top = FALSE,
  input_shape = c(150, 150, 3)
)
model <- keras_model_sequential() %>% 
  conv_base %>% 
  layer_flatten() %>% 
  layer_dense(units = 256, activation = "relu") %>% 
  layer_dense(units = 1, activation = "sigmoid")
train_datagen = image_data_generator(
  rescale = 1/255,
  rotation_range = 40,
  width_shift_range = 0.2,
  height_shift_range = 0.2,
  shear_range = 0.2,
  zoom_range = 0.2,
  horizontal_flip = TRUE,
  fill_mode = "nearest"
)

# Note that the validation data shouldn't be augmented!
test_datagen <- image_data_generator(rescale = 1/255)  

train_generator <- flow_images_from_directory(
  train_dir,                  # Target directory  
  train_datagen,              # Data generator
  target_size = c(150, 150),  # Resizes all images to 150 × 150
  batch_size = 20,
  class_mode = "binary"       # binary_crossentropy loss for binary labels
)

validation_generator <- flow_images_from_directory(
  validation_dir,
  test_datagen,
  target_size = c(150, 150),
  batch_size = 20,
  class_mode = "binary"
)

model %>% compile(
  loss = "binary_crossentropy",
  optimizer = optimizer_rmsprop(lr = 2e-5),
  metrics = c("accuracy")
)

history <- model %>% fit_generator(
  train_generator,
  steps_per_epoch = 100,
  epochs = 30,
  validation_data = validation_generator,
  validation_steps = 50
)

上面显示了我加载预训练网络，然后将 2 个额外的密集层加载到模型中

错误定义如下

Detailed traceback: 
  File "/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 1296, in fit_generator
    steps_name='steps_per_epoch')
  File "/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/keras/engine/training_generator.py", line 265, in model_iteration
    batch_outs = batch_function(*batch_data)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/keras/engine/training.py", line 1017, in train_on_batch
    outputs = self.train_function(ins)  # pylint: disable=not-callable
  File "/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/keras/backend.py", line 3476, in __call__
    run_metadata=self.run_metadata)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow_core/python/client/session.py", line 1472, in __call__
    run_metadata_ptr)

Traceback:

1. model %>% fit_generator(train_generator, steps_per_epoch = 100, 
 .     epochs = 30, validation_data = validation_generator, validation_steps = 50)
2. withVisible(eval(quote(`_fseq`(`_lhs`)), env, env))
3. eval(quote(`_fseq`(`_lhs`)), env, env)
4. eval(quote(`_fseq`(`_lhs`)), env, env)
5. `_fseq`(`_lhs`)
6. freduce(value, `_function_list`)
7. withVisible(function_list[[k]](value))
8. function_list[[k]](value)
9. fit_generator(., train_generator, steps_per_epoch = 100, epochs = 30, 
 .     validation_data = validation_generator, validation_steps = 50)
10. call_generator_function(object$fit_generator, list(generator = generator, 
  .     steps_per_epoch = as.integer(steps_per_epoch), epochs = as.integer(epochs), 
  .     verbose = as.integer(verbose), callbacks = normalize_callbacks_with_metrics(view_metrics, 
  .         callbacks), validation_data = validation_data, validation_steps = as_nullable_integer(validation_steps), 
  .     class_weight = as_class_weight(class_weight), max_queue_size = as.integer(max_queue_size), 
  .     workers = as.integer(workers), initial_epoch = as.integer(initial_epoch)))
11. do.call(func, args)
12. (structure(function (...) 
  . {
  .     dots <- py_resolve_dots(list(...))
  .     result <- py_call_impl(callable, dots$args, dots$keywords)
  .     if (convert) {
  .         result <- py_to_r(result)
  .         if (is.null(result)) 
  .             invisible(result)
  .         else result
  .     }
  .     else {
  .         result
  .     }
  . }, class = c("python.builtin.instancemethod", "python.builtin.object"
  . ), py_object = <environment>))(generator = <environment>, steps_per_epoch = 100L, 
  .     epochs = 30L, verbose = 1L, callbacks = list(<environment>), 
  .     validation_data = <environment>, validation_steps = 50L, 
  .     class_weight = NULL, max_queue_size = 10L, workers = 1L, 
  .     initial_epoch = 0L, use_multiprocessing = FALSE)
13. py_call_impl(callable, dots$args, dots$keywords)```

标签： tensorflowkerasrstudioconv-neural-network