keras:cnn+；使用时间分布层运行时错误的lstm模型
我使用cnn和lstm模型，使用时间分布层进行图像分类。虽然我已经编译了模型，但它仍然显示keras:cnn+；使用时间分布层运行时错误的lstm模型,keras,deep-learning,python-3.5,lstm,convolutional-neural-network,Keras,Deep Learning,Python 3.5,Lstm,Convolutional Neural Network,我使用cnn和lstm模型，使用时间分布层进行图像分类。虽然我已经编译了模型，但它仍然显示 RuntimeError: You must compile your model before using it. 我在多个网站上搜索，但找不到解决问题的方法。 这是我的密码： from keras.models import Sequential from keras.layers import Convolution2D from keras.layers import MaxPooling2D
RuntimeError: You must compile your model before using it.
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)
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.
找到了1088张属于8个类别的图像。
找到了352张属于8类的图片。
---------------------------------------------------------------------------
运行时错误回溯（上次最近调用）
在（）
81 nb_历元=1，
82验证数据=测试集，
--->83 nb_val_样本=352）
包装中的~/.local/lib/python3.5/site-packages/keras/legacy/interfaces.py（*args，**kwargs）
89警告。警告（'更新您的`+对象\u名称+
90'`对Keras 2 API的调用：'+签名，stacklevel=2）
--->91返回函数（*args，**kwargs）
92包装器._原始函数=func
93返回包装器
~/.local/lib/python3.5/site-packages/keras/engine/training.py-in-fit\u生成器（self、生成器、每个历元的步骤、历元、冗余、回调、验证数据、验证步骤、类权重、最大队列大小、工作者、使用多处理、无序、初始历元）
1424使用多处理=使用多处理，
1425洗牌=洗牌，
->1426初始_历元=初始_历元）
1427
1428@interfaces.legacy\u生成器\u方法\u支持
~/.local/lib/python3.5/site-packages/keras/engine/training\u generator.py in-fit\u generator（模型、生成器、每个历元的步骤、历元、冗余、回调、验证数据、验证步骤、类权重、最大队列大小、工作人员、使用多处理、无序、初始历元）
35
36 do\U验证=bool（验证数据）
--->37型号._make_train_function（）
38如果进行验证：
39型号(制造)(测试)(功能)
~/.local/lib/python3.5/site-packages/keras/engine/training.py in\u make\u train\u函数（self）
482 def生成列车功能（自）：
483如果不是HASTATR（自身，“列车功能”）：
-->484 raise RUNTIMERROR（'在使用模型之前必须编译模型'）
485自我检查可训练重量一致性（）
486如果self.train_功能为无：
RuntimeError：您必须在使用模型之前编译它。
感谢为了使用TimeDistributed作为输入层，您必须在TimeDistributed构造函数中指定输入形状，而不是卷积（或任何您想要分发的层）。请记住，您必须在此构造函数中提供时间步数（帧）。在您的情况下，它看起来是这样的：
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)))