我正在做一个项目，该项目训练可以对浮游生物图像进行分类的模型。但是，当我执行代码并运行它时，它表示我的图像位于无法识别的文件夹中。首先，我认为文件名有些问题。我将所有jpg更改为png。但是什么都没有改变。该文件夹上的每个图像都可以正常工作。
我没有使用PIL，因此我仍在尝试找出问题所在，但未进行任何更改。文件夹存在，并且图像正常工作。
这是我的代码：
import sys
import os
from keras.preprocessing.image import ImageDataGenerator
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dropout, Flatten, Dense, Activation
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras import callbacks
train_data_path = '/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/data/train'
validation_data_path = '/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/data/validation'
"""
Parameters
"""
img_width, img_height = 128, 128
batch_size = 16
samples_per_epoch = 1000
validation_steps = 300
nb_filters1 = 32
nb_filters2 = 64
conv1_size = 3
conv2_size = 2
pool_size = 2
classes_num = 3 ## change this number with number of plankton folder you have
lr = 0.0004
epochs = 20
model = Sequential()
model.add(Convolution2D(nb_filters1, conv1_size, conv1_size, border_mode ="same", input_shape=(img_width, img_height, 3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size)))
model.add(Convolution2D(nb_filters2, conv2_size, conv2_size, border_mode ="same"))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(pool_size, pool_size), dim_ordering='th'))
model.add(Flatten())
model.add(Dense(256))
model.add(Activation("relu"))
model.add(Dropout(0.5))
model.add(Dense(classes_num, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=optimizers.RMSprop(lr=lr),
metrics=['accuracy'])
print(model.summary())
train_datagen = ImageDataGenerator(rescale=1. / 255)
test_datagen = ImageDataGenerator(rescale=1. / 255)
train_generator = train_datagen.flow_from_directory(
train_data_path,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical')
validation_generator = test_datagen.flow_from_directory(
validation_data_path,
target_size=(img_height, img_width),
batch_size=batch_size,
class_mode='categorical')
"""
Tensorboard log
"""
log_dir = '/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/data/validation'
tb_cb = callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
cbks = [tb_cb]
model.fit_generator(
train_generator,
steps_per_epoch=samples_per_epoch,
epochs=epochs,
validation_data=validation_generator,
callbacks=cbks,
validation_steps=validation_steps)
target_dir = '/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/models'
if not os.path.exists(target_dir):
os.mkdir(target_dir)
model.save('/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/models/model.h5')
model.save_weights('/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/models/weights.h5')
我的错误跟踪
Using TensorFlow backend.
/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/train.py:77: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(32, (3, 3), input_shape=(128, 128,..., padding="same")`
model.add(Convolution2D(nb_filters1, conv1_size, conv1_size, border_mode ="same", input_shape=(img_width, img_height, 3)))
/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/train.py:81: UserWarning: Update your `Conv2D` call to the Keras 2 API: `Conv2D(64, (2, 2), padding="same")`
model.add(Convolution2D(nb_filters2, conv2_size, conv2_size, border_mode ="same"))
/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/train.py:83: UserWarning: Update your `MaxPooling2D` call to the Keras 2 API: `MaxPooling2D(pool_size=(2, 2), data_format="channels_first")`
model.add(MaxPooling2D(pool_size=(pool_size, pool_size), dim_ordering='th'))
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 128, 128, 32) 896
_________________________________________________________________
activation_1 (Activation) (None, 128, 128, 32) 0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 64, 64, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 64, 64, 64) 8256
_________________________________________________________________
activation_2 (Activation) (None, 64, 64, 64) 0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 64, 32, 32) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 65536) 0
_________________________________________________________________
dense_1 (Dense) (None, 256) 16777472
_________________________________________________________________
activation_3 (Activation) (None, 256) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 256) 0
_________________________________________________________________
dense_2 (Dense) (None, 3) 771
=================================================================
Total params: 16,787,395
Trainable params: 16,787,395
Non-trainable params: 0
_________________________________________________________________
None
Found 6309 images belonging to 9 classes.
Found 891 images belonging to 9 classes.
Epoch 1/20
Traceback (most recent call last):
File "/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/train.py", line 124, in <module>
validation_steps=validation_steps)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras/engine/training.py", line 1418, in fit_generator
initial_epoch=initial_epoch)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras/engine/training_generator.py", line 181, in fit_generator
generator_output = next(output_generator)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras/utils/data_utils.py", line 709, in get
six.reraise(*sys.exc_info())
File "/home/sexybeam/.local/lib/python3.6/site-packages/six.py", line 693, in reraise
raise value
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras/utils/data_utils.py", line 685, in get
inputs = self.queue.get(block=True).get()
File "/usr/lib/python3.6/multiprocessing/pool.py", line 670, in get
raise self._value
File "/usr/lib/python3.6/multiprocessing/pool.py", line 119, in worker
result = (True, func(*args, **kwds))
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras/utils/data_utils.py", line 626, in next_sample
return six.next(_SHARED_SEQUENCES[uid])
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py", line 100, in __next__
return self.next(*args, **kwargs)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py", line 112, in next
return self._get_batches_of_transformed_samples(index_array)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras_preprocessing/image/iterator.py", line 226, in _get_batches_of_transformed_samples
interpolation=self.interpolation)
File "/home/sexybeam/.local/lib/python3.6/site-packages/keras_preprocessing/image/utils.py", line 104, in load_img
img = pil_image.open(path)
File "/home/sexybeam/.local/lib/python3.6/site-packages/PIL/Image.py", line 2687, in open
% (filename if filename else fp))
OSError: cannot identify image file '/media/sexybeam/Suzuya/Study/Group-Project/Python/train et test/data/train/amoeba5/IFCB1_2009_0_5167.jpg'
答案 0 :(得分：0)
您的代码和图像没有问题。我有同样的问题。该问题是由于Keras ImageDataGenerator 引起的。如果您使用tensorflow cpu版本，它将起作用。但是tensorflow-gpu无法正常工作。如果您想节省时间，就不用理会ImageDataGenerator并自己读取图像了。我不记得是什么原因了。
scikit-image是用于处理图像的好软件包。您可以读取如下文件：
from skimage.io import imread
from skimage.transform import resize
from skimage import img_as_float
#place holder for images
train_Images = numpy.zeros((sample_counts, height, width, channels))
for index,file in enumerate(ListOfFileNames):
img = io.imread(file)
#In case you want to resize images use the line blow
img = resize(img, (height,width,channels), mode='reflect', anti_aliasing=True)
train_Images[index] = img_as_float(img)