Question

我正在尝试使用macOS Anaconda运行基本的CNN。所有Keras ati都是最新的（我至少这样认为，但我确定是这样）

除了需要运行此行时，我能够运行所有内容，

classifier.fit_generator('training_set',
                     steps_per_epoch = 8000,
                     epochs = 25,
                     validation_data = test_set

当我尝试运行时出现错误，

TypeError：“ str”对象不是迭代器

这是我的代码，

# Importing the Keras libraries and packages
from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

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

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_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,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/Users/Dan/Desktop/CNN/dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')

test_set = test_datagen.flow_from_directory('/Users/Dan/Desktop/CNN/dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')

classifier.fit_generator('training_set',
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

# Saving Weights
weights = classifier.save_weights

"""
Single Prediction
"""
import numpy as np
from keras.preprocessing import image


test_image = image.load_img(('dataset/predictions/cat_or_dog_2.jpg'), target_size=(64, 64))
test_image = image.img_to_array(test_image)
test_image = np.expand_dims(test_image, axis = 0)
result = classifier.predict(test_image)
training_set.class_indices
if result[0][0] == 1:
    prediction = 'Dog'
else:
    prediction = 'Cat'

这是代码本身正在运行的错误，

from keras.models import Sequential
from keras.layers import Conv2D
from keras.layers import MaxPooling2D
from keras.layers import Flatten
from keras.layers import Dense

# Initialising the CNN
classifier = Sequential()

# Step 1 - Convolution
classifier.add(Conv2D(32, (3, 3), input_shape = (64, 64, 3), activation = 'relu'))

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

# Adding a second convolutional layer
classifier.add(Conv2D(32, (3, 3), activation = 'relu'))
classifier.add(MaxPooling2D(pool_size = (2, 2)))

# Step 3 - Flattening
classifier.add(Flatten())

# Step 4 - Full connection
classifier.add(Dense(units = 128, activation = 'relu'))
classifier.add(Dense(units = 1, activation = 'sigmoid'))

# Compiling the CNN
classifier.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])
Using TensorFlow backend.
2019-11-25 19:39:19.093497: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2019-11-25 19:39:19.095093: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 4. Tune using inter_op_parallelism_threads for best performance.

from keras.preprocessing.image import ImageDataGenerator

train_datagen = ImageDataGenerator(rescale = 1./255,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

test_datagen = ImageDataGenerator(rescale = 1./255)

training_set = train_datagen.flow_from_directory('/Users/Dan/Desktop/CNN/dataset/training_set',
                                                 target_size = (64, 64),
                                                 batch_size = 32,
                                                 class_mode = 'binary')
Found 8000 images belonging to 2 classes.

test_set = test_datagen.flow_from_directory('/Users/Dan/Desktop/CNN/dataset/test_set',
                                            target_size = (64, 64),
                                            batch_size = 32,
                                            class_mode = 'binary')
Found 2000 images belonging to 2 classes.

classifier.fit_generator('training_set',
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)
Epoch 1/25
Traceback (most recent call last):

  File "<ipython-input-7-e4696e5027ff>", line 5, in <module>
    validation_steps = 2000)

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training.py", line 1732, in fit_generator
    initial_epoch=initial_epoch)

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/keras/engine/training_generator.py", line 185, in fit_generator
    generator_output = next(output_generator)

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/keras/utils/data_utils.py", line 742, in get
    six.reraise(*sys.exc_info())

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/six.py", line 696, in reraise
    raise value

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/keras/utils/data_utils.py", line 711, in get
    inputs = future.get(timeout=30)

  File "/Users/Dan/opt/anaconda3/lib/python3.7/multiprocessing/pool.py", line 657, in get
    raise self._value

  File "/Users/Dan/opt/anaconda3/lib/python3.7/multiprocessing/pool.py", line 121, in worker
    result = (True, func(*args, **kwds))

  File "/Users/Dan/opt/anaconda3/lib/python3.7/site-packages/keras/utils/data_utils.py", line 650, in next_sample
    return six.next(_SHARED_SEQUENCES[uid])

TypeError: 'str' object is not an iterator

有什么我想念的吗？或错误的行，因为我确定一切正确。

Answer 1

您要传递字符串作为第一个参数，您要传递training_set变量。

classifier.fit_generator(training_set,
                         steps_per_epoch = 8000,
                         epochs = 25,
                         validation_data = test_set,
                         validation_steps = 2000)

Answer 2

不熟悉该软件包，但查看文档后会发现training_set应该是生成器：

generator：生成器或Sequence的实例（keras.utils.Sequence）对象，以避免出现重复数据使用多重处理。发电机的输出必须是元组（输入，目标）元组（输入，目标，sample_weights）。该元组（生成器的单个输出）进行单个批处理。因此，此元组中的所有数组必须具有相同的长度（等于到此批次的大小）。不同批次可能有不同大小。例如，最后一个时期通常较小如果数据集的大小不能被其他人整除批量大小。预计生成器将遍历其数据无限期地在steps_per_epoch批处理完成后，纪元结束被模型看到。

但是您使用的值为'training_set'的字符串，我猜您的意思是training_set（不带引号）。 https://keras.io/models/sequential/

TypeError：“ str”对象不是迭代器

2 个答案: