模块 'keras.preprocessing.image' 没有属性'ndim' - 自定义 resnet50 迁移学习

Question

我试图在水果数据集上训练keras resnet50模型，并能够做到这一点。该模型会生成，但是当我使用它在单个图像上执行预测时，我会遇到此错误。

module 'keras.preprocessing.image' has no attribute 'ndim'

这是培训代码：

from keras.applications.resnet50 import ResNet50, preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ModelCheckpoint
import OpenSSL
import os
HEIGHT = 100
WIDTH = 100
TRAIN_DIR = "dataset"
BATCH_SIZE = 8

base_model = ResNet50(weights='imagenet', 
                      include_top=False, 
input_shape=(HEIGHT, WIDTH, 3))

train_datagen =  ImageDataGenerator(
      preprocessing_function=preprocess_input,
      rotation_range=90,
      horizontal_flip=True,
      vertical_flip=True
    )
train_generator = train_datagen.flow_from_directory(TRAIN_DIR, 
                                                    target_size=(HEIGHT, WIDTH), 
batch_size=BATCH_SIZE)
from keras.layers import Dense, Activation, Flatten, Dropout
from keras.models import Sequential, Model
def build_finetune_model(base_model, dropout, fc_layers, num_classes):
    for layer in base_model.layers:
        layer.trainable = False
    x = base_model.output
    x = Flatten()(x)
    for fc in fc_layers:
        # New FC layer, random init
        x = Dense(fc, activation='relu')(x) 
        x = Dropout(dropout)(x)
    # New softmax layer
    predictions = Dense(num_classes, activation='softmax')(x) 
    finetune_model = Model(inputs=base_model.input, outputs=predictions)
    return finetune_model

class_list = os.listdir(os.path.join(os.getcwd(),"dataset"))
FC_LAYERS = [1024, 1024]
dropout = 0.5
finetune_model = build_finetune_model(base_model, 
                                  dropout=dropout, 
                                  fc_layers=FC_LAYERS, 
num_classes=len(class_list))
from keras.optimizers import SGD, Adam
NUM_EPOCHS = 6
BATCH_SIZE = 8
num_train_images = 1000
adam = Adam(lr=0.00001)
finetune_model.compile(adam, loss='categorical_crossentropy', metrics=['accuracy'])
filepath="./checkpoints/" + "ResNet50" + "_model_weights.h5"
checkpoint = ModelCheckpoint(filepath, monitor=["acc"], verbose=1, mode='max')
callbacks_list = [checkpoint]
history = finetune_model.fit_generator(train_generator, epochs=NUM_EPOCHS, workers=8, 
                                   steps_per_epoch=num_train_images // BATCH_SIZE, 
                                   shuffle=True, callbacks=callbacks_list)

finetune_model.save("mymodel.hdf5")

如所述，该模型正在创建。我用以下来测试单图预测

import numpy as np
import os
from keras.models import load_model
import os
import cv2
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input
model = load_model("mymodel.hdf5")
fruits = os.listdir(os.path.join(os.getcwd(),'dataset'))

def predictFromImagePath(img_path):
    img = image.load_img(img_path, target_size=(100,100))
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = preprocess_input(x)
    print(x.shape)
    return predictFromImage(image)

def predictFromImage(image):
    index = model.predict(image)
    print(index)
    return fruits[index]

if __name__ == "__main__":
    img_path = 'apple1.jpg'   
    predictFromImagePath(img_path)

Answer 1

在predictFromImagePath函数中，返回时，您将image模块传递到predictFromImage函数。

return predictFromImage(image)

基于行上方的代码，我假设您要在变量x上调用predictFromImage函数。因此，将上述函数调用更改为以下。

return predictFromImage(x)