Keras 简明教程
Real Time Prediction using ResNet Model
ResNet 是一个预训练模型，它使用 ImageNet 进行了训练。在 ImageNet 上预训练了 ResNet 模型权重。它的语法如下所示：
keras.applications.resnet.ResNet50 (
include_top = True,
weights = 'imagenet',
input_tensor = None,
input_shape = None,
pooling = None,
classes = 1000
)在此，
-
include_top 指网络顶部的全连接层。
-
weights 指在 ImageNet 上的预训练。
-
input_tensor 指用于作为模型图像输入的可选 Keras 张量。
-
input_shape 指可选形状元组。该模型的默认输入大小为 224x224。
-
classes 指对图像进行分类的可选类别数。
让我们通过写一个简单的示例来了解该模型：
Step 1: import the modules
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 resnet50Step 2: Select an input
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，我们加载了一张图像 (banana.jpg) 并显示了它。
Step 3: Convert images into NumPy array
让我们将我们的输入 Banana 转换成 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)
>>>Step 4: Model prediction
让我们将我们的输入输入模型以获取预测
>>> 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)