我正在尝试使用Keras和Tensorflow实现卷积神经网络.
我有以下代码：
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
model = Sequential()
model.add(Conv2D(32, (2, 2), input_shape=(3, 150, 150), padding='SAME'))
model.add(Activation('relu'))
# model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))
model.add(Conv2D(32, (2, 2), padding='SAME'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))
model.add(Conv2D(64, (2, 2), padding='SAME'))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2), data_format="channels_first"))
print("after declaring models")
model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors
model.add(Dense(64))
model.add(Activation('relu'))
model.add(Dropout(0.5))
model.add(Dense(1))
model.add(Activation('sigmoid'))
model.compile(loss='binary_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
print("After creating the model\n")
batch_size = 16
# this is the augmentation configuration we will use for training
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
# this is the augmentation configuration we will use for testing:
# only rescaling
test_datagen = ImageDataGenerator(rescale=1./255)
# this is a generator that will read pictures found in
# subfolers of 'data/train', and indefinitely generate
# batches of augmented image data
train_generator = train_datagen.flow_from_directory(
'../input/train', # this is the target directory
target_size=(150, 150), # all images will be resized to 150x150
batch_size=batch_size,
class_mode='binary') # since we use binary_crossentropy loss, we need binary labels
model.fit_generator(
train_generator,
steps_per_epoch=2000 // batch_size,
epochs=50)
问题是在最后一行我得到：
Epoch 1/50
17.3s
6
Exception in thread Thread-20:
Traceback (most recent call last):
File "/opt/conda/lib/python3.6/threading.py", line 916, in _bootstrap_inner
self.run()
File "/opt/conda/lib/python3.6/threading.py", line 864, in run
self._target(*self._args, **self._kwargs)
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/utils/data_utils.py", line 590, in data_generator_task
generator_output = next(self._generator)
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/preprocessing/image.py", line 737, in __next__
return self.next(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/preprocessing/image.py", line 1026, in next
index_array, current_index, current_batch_size = next(self.index_generator)
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/preprocessing/image.py", line 720, in _flow_index
current_index = (self.batch_index * batch_size) % n
ZeroDivisionError: integer division or modulo by zero
17.3s
7
Traceback (most recent call last):
File "../src/script.py", line 115, in <module>
epochs=50)
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/legacy/interfaces.py", line 87, in wrapper
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/models.py", line 1117, in fit_generator
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/legacy/interfaces.py", line 87, in wrapper
File "/opt/conda/lib/python3.6/site-packages/Keras-2.0.5-py3.6.egg/keras/engine/training.py", line 1809, in fit_generator
StopIteration
如何划分为0？
我没看到任何变量如何都是0.
解决方法:
除以零来自n等于零的事实. n是要循环的数据集中的样本总数,因此这意味着您的图像生成器无法提供任何数据.最有可能的原因是训练数据的排列方式. Keras希望将图像排列在每个图像类包含一个子目录的目录中,例如
input/
train/
class_0/
class_0_0.jpg
class_0_1.jpg
...
class_1/
class_1_0.jpg
class_1_1.jpg
...
...
请注意,即使对于非分类任务,这也适用.当class_mode为None时,flow_from_directory仍然期望包含带有图像的子目录的目录.
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