Question

我是深度学习的新手。我使用Keras和以下简单代码来训练我的inception_resnet_v2模型进行火灾探测（我将10K图像分为两类：火灾图像和非火灾图像）。

现在，当尝试加载模型并传递火灾和非火灾图像时，它会针对所有图像显示此结果：

[[0. 1.]]
[[0. 1.]]
[[0. 1.]]
[[0. 1.]]
[[0. 1.]]

当我执行argmax时，它的意思是fire。我做错什么了吗？我应该以其他方式得到结果吗？

这是我的预测代码：

from __future__ import print_function
from keras.models import load_model, model_from_json
import cv2, os, glob
import numpy as np
from keras.preprocessing import image

if __name__ == '__main__':
    model = load_model('Resnet_26_0.79_model_weights.h5')

    os.chdir("test")
    for file in glob.glob("*.jpg"):
        img_path = file
        img = image.load_img(img_path, target_size=(300, 300))
        x = image.img_to_array(img)
        x = np.expand_dims(x, axis=0)

        dictionary = {0: 'non-fire', 1: 'fire'}

        results = model.predict(x)
        print(results)
        predicted_class= np.argmax(results)
        acc = 100*results[0][predicted_class]
        print("Network prediction is: file: "+ file+", "+dictionary[predicted_class]+", %{:0.2f}".format(acc))

这是培训：

from keras.applications.inception_resnet_v2 import InceptionResNetV2, preprocess_input
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import Dense, Activation, Flatten, Dropout
from keras.models import Sequential, Model
from keras.optimizers import SGD, Adam
from keras.callbacks import ModelCheckpoint
from keras.metrics import binary_accuracy
import os
import json
#==========================
HEIGHT = 300
WIDTH = 300
TRAIN_DIR = "data"
BATCH_SIZE = 8 #8
steps_per_epoch = 1000 #1000
NUM_EPOCHS = 50 #50
lr= 0.00001
#==========================
FC_LAYERS = [1024, 1024]
dropout = 0.5

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 layer
    predictions = Dense(num_classes, activation='sigmoid')(x) 
    finetune_model = Model(inputs=base_model.input, outputs=predictions)
    return finetune_model

train_datagen =  ImageDataGenerator(preprocessing_function=preprocess_input, rotation_range=90, horizontal_flip=True, vertical_flip=True
                                    ,validation_split=0.2)
train_generator = train_datagen.flow_from_directory(TRAIN_DIR, target_size=(HEIGHT, WIDTH), batch_size=BATCH_SIZE
                                                    ,subset="training")
#split validation manually
validation_generator = train_datagen.flow_from_directory(TRAIN_DIR, target_size=(HEIGHT, WIDTH), batch_size=BATCH_SIZE,subset="validation")

base_model = InceptionResNetV2(weights='imagenet', include_top=False, input_shape=(HEIGHT, WIDTH, 3))

root=TRAIN_DIR
class_list = [ item for item in os.listdir(root) if os.path.isdir(os.path.join(root, item)) ]
print ("class_list: "+str(class_list))

finetune_model = build_finetune_model(base_model, dropout=dropout, fc_layers=FC_LAYERS, num_classes=len(class_list))

adam = Adam(lr)
# change to categorical_crossentropy for multiple classes
finetune_model.compile(adam, loss='binary_crossentropy', metrics=['accuracy'])

filepath="./checkpoints/" + "Resnet_{epoch:02d}_{acc:.2f}" +"_model_weights.h5"
checkpoint = ModelCheckpoint(filepath, monitor=["val_accuracy"], verbose=1, mode='max', save_weights_only=False)
callbacks_list = [checkpoint]

history = finetune_model.fit_generator(train_generator, epochs=NUM_EPOCHS, workers=BATCH_SIZE, 
                                    validation_data=validation_generator, validation_steps = validation_generator.samples, 
                                       steps_per_epoch=steps_per_epoch, 
                                       shuffle=True, callbacks=callbacks_list)

我在Keras中训练简单的模型的预测问题

0 个答案: