[keras] keras测试

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加载模型、特征提取

根据InceptionV3提取图片特征

from keras.preprocessing import image
from keras.applications.inception_v3 import InceptionV3, preprocess_input
from keras.models import Model, load_model
from keras.layers import Input
import numpy as np

class Extractor():
    def __init__(self, weights=None):
        """Either load pretrained from imagenet, or load our saved
        weights from our own training."""

        self.weights = weights  # so we can check elsewhere which model

        if weights is None:
            # Get model with pretrained weights.
            base_model = InceptionV3(
                weights='imagenet',
                include_top=True
            )

            # We'll extract features at the final pool layer.
            self.model = Model(
                inputs=base_model.input,
                outputs=base_model.get_layer('avg_pool').output
            )

        else:
            # Load the model first.
            self.model = load_model(weights)

            # Then remove the top so we get features not predictions.
            # From: https://github.com/fchollet/keras/issues/2371
            self.model.layers.pop()
            self.model.layers.pop()  # two pops to get to pool layer
            self.model.outputs = [self.model.layers[-1].output]
            self.model.output_layers = [self.model.layers[-1]]
            self.model.layers[-1].outbound_nodes = []

    def extract(self, image_path):
        img = image.load_img(image_path, target_size=(299, 299))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)
        x = preprocess_input(x)
        # Get the prediction.
        features = self.model.predict(x)
        if self.weights is None:
            # For imagenet/default network:
            features = features[0]
        else:
            # For loaded network:
            features = features[0]

        return features

evaluate/test_on_batch

计算精度，不仅需要输入X，还需要输出y，给出的是最终的metric，top1精度或者top5之类的

evaluate(self, x, y, batch_size=32, verbose=1, sample_weight=None)
本函数返回一个测试误差的标量值（如果模型没有其他评价指标），或一个标量的list（如果模型还有其他的评价指标）。model.metrics_names将给出list中各个值的含义。
metric在函数compile的时候输入

test_on_batch(self, x, y, sample_weight=None)

evaluate_generator(self, generator, steps, max_q_size=10, workers=1, pickle_safe=False)
生成器应返回与test_on_batch的输入数据相同类型的数据。
generator：生成输入batch数据的生成器
val_samples：生成器应该返回的总样本数
steps：生成器要返回数据的轮数
max_q_size：生成器队列的最大容量
nb_worker：使用基于进程的多线程处理时的进程数
pickle_safe：若设置为True，则使用基于进程的线程。注意因为它的实现依赖于多进程处理，不可传递不可pickle的参数到生成器中，因为它们不能轻易的传递到子进程中。

mertics

predict/predict_on_batch/predict_generator

只输入X，用法情形与evaluate相同