Python 使用 Tensorflow 的 Keras 图像识别 model.predict returns [[0.]]

Question

我一直在学习教程here to process images of cats, and see if a specific picture contains a cat. The data set I use is here. Is there something missing in the way I read in an image for testing? In my results from model.predict(filePath), I always get the value '[[0.]]' when reading an image containing a cat. The train and validation sets seem to work correctly. I am only having issues reading in an image. (Source code is copied from here)

from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Activation, Dropout, Flatten, Dense
from keras import backend as K
import numpy as np
from keras.preprocessing import image

# dimensions of our images.
img_width, img_height = 150, 150

train_data_dir = 'data/train'
validation_data_dir = 'data/validation'
nb_train_samples = 2000
nb_validation_samples = 800
epochs = 50
batch_size = 16

if K.image_data_format() == 'channels_first':
    input_shape = (3, img_width, img_height)
else:
    input_shape = (img_width, img_height, 3)

model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=input_shape))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(32, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Conv2D(64, (3, 3)))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))

model.add(Flatten())
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'])

# 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)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

validation_generator = test_datagen.flow_from_directory(
    validation_data_dir,
    target_size=(img_width, img_height),
    batch_size=batch_size,
    class_mode='binary')

model.fit_generator(
    train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size)

model.save_weights('first_try.h5')


def _LoadImage(filePath):
    test_image = image.load_img(filePath, target_size = (150,150))
    test_image = image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis=0)
    return test_image


test_this = _LoadImage('test.jpg')
result = model.predict(test_this)
print(result)

Answer 1

看起来“0”是猫 ("The training archive contains 25,000 images of dogs and cats. Train your algorithm on these files and predict the labels for test1.zip (1 = dog, 0 = cat).") 的标签，因此您的模型预测似乎是正确的。请记住，该模型正在预测（猫和狗）标签，而不是您自己可能与标签对应的 class 字符串。尝试喂狗的图像，你应该在 return.

中得到“1”

Python 使用 Tensorflow 的 Keras 图像识别 model.predict returns [[0.]]

Python Keras Image Recognition using Tensorflow model.predict returns [[0.]]

python

image-processing

image-recognition

keras

tensorflow