tensorflow - 在 macOS el capitan 的 Tensorflow 0.12.0 版本中运行 CNN 的问题

Question

我正在尝试运行这个 CNN 分类来区分猫和狗。当我尝试在 tensorflow 0.12.0 平台上运行它时，我反复遇到类型错误问题。我试图用 sparse_categorical_crossentropy 的损失函数来解决这个问题，但我仍然无法正确处理 CNN。这里可能是什么问题？

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/andrewsilvanus/Desktop/Classifier/training_set',target_size = (64, 64),batch_size = 32,class_mode = 'binary')
test_set=test_datagen.flow_from_directory('/Users/andrewsilvanus/Desktop/Classifier/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)
# Part 3 - Making new predictions
import numpy as np
from keras.preprocessing import image
test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.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'

TypeError: sigmoid_cross_entropy_with_logits() 得到了一个意外的关键字参数“标签”

InvalidArgumentError（参见上面的回溯）：收到的标签值 1 超出了 [0, 1) 的有效范围。标签值：1 0 1 1 1 0 0 1 1 1 1 1 1 1 1 0 1 1 0 1 1 1 0 1 1 0 1 1 1 1 1 1

[[Node:loss_5/dense_14_loss/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits = SparseSoftmaxCrossEntropyWithLogits[T=DT_FLOAT, Tlabels=DT_INT64, _device="/job:localhost/replica:0/task:0/cpu:0"](loss_5/dense_14_loss/Reshape_1, loss_5/dense_14_loss/Cast)]]

标签： tensorflowdeep-learning