使用 ResNet 模型进行实时预测

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ResNet是一个预训练的模型。它是使用ImageNet进行训练的。在ImageNet上预训练的 ResNet 模型权重。它具有以下语法 -

keras.applications.resnet.ResNet50 (
   include_top = True, 
   weights = 'imagenet', 
   input_tensor = None, 
   input_shape = None, 
   pooling = None, 
   classes = 1000
)

这里，

• include_top指的是网络顶部的全连接层。

• 权重指的是 ImageNet 上的预训练。

• input_tensor指用作模型图像输入的可选 Keras 张量。

• input_shape指可选的形状元组。该模型的默认输入尺寸为 224x224。

• 类是指对图像进行分类的可选数量的类。

让我们通过编写一个简单的例子来理解该模型 -

第 1 步：导入模块

让我们加载必要的模块，如下所示 -

>>> import PIL 
>>> from keras.preprocessing.image import load_img 
>>> from keras.preprocessing.image import img_to_array 
>>> from keras.applications.imagenet_utils import decode_predictions 
>>> import matplotlib.pyplot as plt 
>>> import numpy as np 
>>> from keras.applications.resnet50 import ResNet50 
>>> from keras.applications import resnet50

第 2 步：选择输入

让我们选择一个输入图像Lotus ，如下所示 -

>>> filename = 'banana.jpg' 
>>> ## load an image in PIL format 
>>> original = load_img(filename, target_size = (224, 224)) 
>>> print('PIL image size',original.size)
PIL image size (224, 224) 
>>> plt.imshow(original) 
<matplotlib.image.AxesImage object at 0x1304756d8> 
>>> plt.show()

在这里，我们加载了一个图像(banana.jpg)并显示它。

第 3 步：将图像转换为 NumPy 数组

让我们将输入Banana转换为 NumPy 数组，以便将其传递到模型中以进行预测。

>>> #convert the PIL image to a numpy array 
>>> numpy_image = img_to_array(original) 

>>> plt.imshow(np.uint8(numpy_image)) 
<matplotlib.image.AxesImage object at 0x130475ac8> 

>>> print('numpy array size',numpy_image.shape) 
numpy array size (224, 224, 3) 

>>> # Convert the image / images into batch format 
>>> image_batch = np.expand_dims(numpy_image, axis = 0) 

>>> print('image batch size', image_batch.shape) 
image batch size (1, 224, 224, 3)
>>> 

第四步：模型预测

让我们将输入输入到模型中以获得预测

>>> prepare the image for the resnet50 model >>> 
>>> processed_image = resnet50.preprocess_input(image_batch.copy()) 

>>> # create resnet model 
>>>resnet_model = resnet50.ResNet50(weights = 'imagenet') 
>>> Downloavding data from https://github.com/fchollet/deep-learning-models/releas
es/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5 
102858752/102853048 [==============================] - 33s 0us/step 

>>> # get the predicted probabilities for each class 
>>> predictions = resnet_model.predict(processed_image) 

>>> # convert the probabilities to class labels 
>>> label = decode_predictions(predictions) 
Downloading data from https://storage.googleapis.com/download.tensorflow.org/
data/imagenet_class_index.json 
40960/35363 [==================================] - 0s 0us/step 

>>> print(label)

输出

[
   [
      ('n07753592', 'banana', 0.99229723), 
      ('n03532672', 'hook', 0.0014551596), 
      ('n03970156', 'plunger', 0.0010738898), 
      ('n07753113', 'fig', 0.0009359837) , 
      ('n03109150', 'corkscrew', 0.00028538404)
   ]
]

在这里，模型正确地将图像预测为香蕉。