Question

我将cnn与lstm模型配合使用，并使用时间分布层进行图像分类。尽管我已经编译了模型，但仍然显示

RuntimeError: You must compile your model before using it.

我在多个站点上进行了搜索，但是找不到解决我问题的方法。这是我的代码：

from keras.models import Sequential
from keras.layers import Convolution2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import TimeDistributed
from keras.layers import LSTM

import warnings
warnings.filterwarnings('ignore')

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(TimeDistributed(Convolution2D(32, (3, 3), padding = 'same', input_shape = (128, 128, 3), 
                                             activation = 'relu')))

# Step 2 - 
classifier.add(TimeDistributed(MaxPooling2D(pool_size = (2, 2))))

# Adding a second convolutional layer
classifier.add(TimeDistributed(Convolution2D(64, (3, 3), padding = 'same', activation = 'relu')))
classifier.add(TimeDistributed(MaxPooling2D(pool_size = (2, 2))))

# Adding a third conolutional layer
classifier.add(TimeDistributed(Convolution2D(64, (3, 3), padding = 'same', activation = 'relu')))
classifier.add(TimeDistributed(MaxPooling2D(pool_size = (2, 2))))

# Step 3 - Flattening
classifier.add(TimeDistributed(Flatten()))
classifier.add(Dropout(rate = 0.5))

# Step 4 - Full connection
classifier.add(LSTM(256, return_sequences=False, dropout=0.5))
classifier.add(Dense(output_dim = 8, activation = 'softmax'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'categorical_crossentropy', metrics = ['accuracy'])

# Part 2 - Fitting the CNN to the images

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   height_shift_range =  0.1,
                                   width_shift_range = 0.1,
                                   channel_shift_range = 10)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('dataset/mel/train/',
                                                 target_size = (128, 128),
                                                 batch_size = 32,
                                                 class_mode = 'categorical')

test_set = test_datagen.flow_from_directory('dataset/mel/test/',
                                            target_size = (128, 128),
                                            batch_size = 32,
                                            class_mode = 'categorical')

classifier.fit_generator(training_set,
                         samples_per_epoch = 1088,
                         nb_epoch = 1,
                         validation_data = test_set,
                         nb_val_samples = 352)

这是完整的输出消息：

Found 1088 images belonging to 8 classes.
Found 352 images belonging to 8 classes.
---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-8-6a3839aea8f8> in <module>()
     81                          nb_epoch = 1,
     82                          validation_data = test_set,
---> 83                          nb_val_samples = 352)

~/.local/lib/python3.5/site-packages/keras/legacy/interfaces.py in wrapper(*args, **kwargs)
     89                 warnings.warn('Update your `' + object_name +
     90                               '` call to the Keras 2 API: ' + signature, stacklevel=2)
---> 91             return func(*args, **kwargs)
     92         wrapper._original_function = func
     93         return wrapper

~/.local/lib/python3.5/site-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   1424             use_multiprocessing=use_multiprocessing,
   1425             shuffle=shuffle,
-> 1426             initial_epoch=initial_epoch)
   1427 
   1428     @interfaces.legacy_generator_methods_support

~/.local/lib/python3.5/site-packages/keras/engine/training_generator.py in fit_generator(model, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
     35 
     36     do_validation = bool(validation_data)
---> 37     model._make_train_function()
     38     if do_validation:
     39         model._make_test_function()

~/.local/lib/python3.5/site-packages/keras/engine/training.py in _make_train_function(self)
    482     def _make_train_function(self):
    483         if not hasattr(self, 'train_function'):
--> 484             raise RuntimeError('You must compile your model before using it.')
    485         self._check_trainable_weights_consistency()
    486         if self.train_function is None:

RuntimeError: You must compile your model before using it.

可能是什么错误。谢谢

Answer 1

为了使用TimeDistributed作为输入层，必须在TimeDistributed构造函数中指定input_shape，而不是卷积层（或要分发的任何层）中。请记住，您必须在此构造函数中提供多个时间步（帧）。在您的情况下，看起来像这样：

num_frames = 10 # e.g.
# Step 1 - Convolution
classifier.add(TimeDistributed(Convolution2D(32, (3, 3), padding = 'same', activation = 
'relu'), input_shape = (num_frames,128, 128, 3)))

keras：使用时间分布层运行时错误的cnn + lstm模型

1 个答案: