培训期间可以使用keras实时获取输出图层吗? [英] Can output layers be taken during the training in real time with keras?

问题描述

我尝试在培训期间获取输出图层.我正在尝试对模型进行实时3D可视化并使其具有交互性.我正在将tensorflow 2.0和python 3用于google colab.

I try to get output layers during the training. I am trying to make a 3d visualization of the model in real time and to make it interactive. I am using google colab with tensorflow 2.0 and python 3.

这是我的代码:

进口

  from __future__ import absolute_import, division, print_function, unicode_literals
 try:
   # Use the %tensorflow_version magic if in colab.
     %tensorflow_version 2.x
 except Exception:
       pass

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

import tensorflow_hub as hub
import tensorflow_datasets as tfds

from tensorflow.keras import datasets, layers, models

from tensorflow.keras import backend as K
from tensorflow.keras.backend import clear_session

from tensorflow.keras.callbacks import Callback as Callback

import logging
logger = tf.get_logger()
logger.setLevel(logging.ERROR)

获取数据

splits = tfds.Split.TRAIN.subsplit([70, 30])

(training_set, validation_set), dataset_info = tfds.load('tf_flowers',with_info=True, as_supervised=True, split=splits)
 for i, example in enumerate(training_set.take(5)):
        print('Image {} shape: {} label: {}'.format(i+1, example[0].shape, example[1]))

检查班级和图片的数量

 num_classes = dataset_info.features['label'].num_classes

 num_training_examples = 0
 num_validation_examples = 0

 for example in training_set:
   num_training_examples += 1

 for example in validation_set:
   num_validation_examples += 1

 print('Total Number of Classes: {}'.format(num_classes))
 print('Total Number of Training Images: {}'.format(num_training_examples))
 print('Total Number of Validation Images: {} \n'.format(num_validation_examples))

开始创建

   IMAGE_RES = 299
   BATCH_SIZE = 32
def format_image(image, label):
   image = tf.image.resize(image, (IMAGE_RES, IMAGE_RES))/255.0
  return image, label

 (training_set, validation_set), dataset_info = tfds.load('tf_flowers', with_info=True, as_supervised=True, split=splits)
  train_batches = training_set.shuffle(num_training_examples//4).map(format_image).batch(BATCH_SIZE).prefetch(1)
    validation_batches = validation_set.map(format_image).batch(BATCH_SIZE).prefetch(1)

URL = "https://tfhub.dev/google/tf2-preview/inception_v3/feature_vector/4"
feature_extractor = hub.KerasLayer(URL,
  input_shape=(IMAGE_RES, IMAGE_RES, 3),
trainable=False)

model_inception = tf.keras.Sequential([
feature_extractor,
layers.Dense(num_classes, activation='softmax')
])

 model_inception.summary()

这是自定义回调，我在训练过程中尝试获取输出层

    import datetime
 from keras.callbacks import Callback

class MyCustomCallback(tf.keras.callbacks.Callback):

  def on_train_batch_begin(self, batch, logs=None):
     print('Training: batch {} begins at {}'.format(batch, datetime.datetime.now().time()))

  def on_train_batch_end(self, batch, logs=None):
     for i in range(len(model_inception.layers)):
      inp = self.model.input                                    # input placeholder
      outputs = [layer.output for layer in self.model.layers]     # all layer outputs
      functors = [K.function([inp, K.learning_phase()], [out]) for out in outputs]    # evaluation functions
      input_shape = [1] + list(self.model.input_shape[1:])
      test = np.random.random(input_shape)
      layer_outs = [func([test, 1.]) for func in functors] 
      print('\n Training: batch {} ends at {}'.format( layer_outs , datetime.datetime.now().time()))

  def on_test_batch_begin(self, batch, logs=None):
    print('Evaluating: batch {} begins at {}'.format(batch, datetime.datetime.now().time()))

  def on_test_batch_end(self, batch, logs=None):
   # layer_output = get_3rd_layer_output(self.validation_data)[0]  
    print('Training: batch {} ends at {} with the output layer {}'.format(batch, datetime.datetime.now().time()))

 The problem is in callback of how i can get the output/input of each layer at the end of each batch

这是我的自定义回调的模型编译和培训

 model_inception.compile(
  optimizer='adam', 
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy'])

 EPOCHS = 2

 history = model_inception.fit(train_batches,
                epochs=EPOCHS,
                steps_per_epoch=20,
                validation_data=validation_batches,callbacks=[MyCustomCallback()])

当我尝试运行它时出现当前错误

Current error when i try it run it

AttributeError                            Traceback (most recent call last)
<ipython-input-10-5909c67ba93f> in <module>()
      9                     epochs=EPOCHS,
     10                     steps_per_epoch=20,
---> 11                     validation_data=validation_batches,callbacks=[MyCustomCallback()])
     12 
     13 # #Testing

11 frames
/tensorflow-2.0.0/python3.6/tensorflow_core/python/eager/lift_to_graph.py in <listcomp>(.0)
 247   # Check that the initializer does not depend on any placeholders.
 248   sources = object_identity.ObjectIdentitySet(sources or [])
-->249   visited_ops = set([x.op for x in sources])
 250   op_outputs = collections.defaultdict(set)
 251 

AttributeError: 'int' object has no attribute 'op'

推荐答案

如果您阅读了自定义回调的来源，请

If you read the source of the custom callbacks, here

我们定义的每个自定义回调都有一个属性 model .

there is a property model for every custom callback we define.

您可以在Cutomcallbacks中定义的函数内使用模型对象.

You can make use of the model object inside the functions you defined in your Cutomcallbacks.

例如

def on_train_batch_end(self, batch, logs=None):
    #here you can get the model reference. 
    self.model.predict(dummy_data)

self.model是keras.models.Model的实例，可以使用它来调用相应的函数.

The self.model is an instance of keras.models.Model and the corrresponding function can be called using it.

可以找到更多参考信息此处

More reference can be found here and here

请按照以下评论获得答案.

Please follow the comments below in order to get the answer.

OP注释中的代码段

def on_train_batch_end(self, batch, logs=None): 
    for i in range(len(model_inception.layers)): 
        get_layer_output = K.function(inputs = self.model.layers[i].input, outputs = self.model.layers[i].output) 
        print('\n Training: output of the layer {} is {} ends at {}'.format(i, get_layer_output.outputs , datetime.datetime.now().time()))

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