base_model = tf.keras.applications.MobileNet(weights = "imagenet", include_top = False, input_shape = input_shape) base_model.trainable = False inputs = keras.Input(shape = input_shape) x = base_model(inputs, training = False) x = tf.keras.layers.GlobalAveragePooling2D()(x) x = tf.keras.layers.Dropout(0.2)(x) x = tf.keras.layers.Dense(len(categories), activation="softmax")(x) model = keras.Model(inputs = inputs, outputs = x, name="LeafDisease_MobileNet") weight_path = os.path.join(base_dir, 'checkpoints', 'my_checkpoint') model.load_weights(weight_path) img = plt.imread(img_path) img = img / 255. img = cv2.resize(img, (224, 224)) img = img.reshape(-1, 224, 224, 3) img.astype('float32') result = model.predict(img) cate_result = categories[np.argmax(result, axis=1)[0]] return cate_result
时间: 2023-11-09 09:06:41 浏览: 248
vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5
这段代码看起来是使用预训练的 MobileNet 模型对植物叶片疾病进行分类。首先加载了 MobileNet 模型的权重,并将其冻结以防止在训练过程中对其进行更新。然后定义了一个包含分类输出的新模型,并将该模型的权重加载为之前训练好的权重。最后,读取待分类的图像并将其调整为适合 MobileNet 模型输入的大小,然后将其传递给新模型进行预测,返回预测结果的类别。
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