我的神经网络预测代码有什么问题？所有预测都为每个图像返回相同的类名

Question

这是我的训练代码：

def train():


#START

img_input = layers.Input(shape=(150, 150, 3))

x = layers.Conv2D(16, 3, activation='relu')(img_input)
x = layers.MaxPooling2D(2)(x)

x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPooling2D(2)(x)

x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPooling2D(2)(x)

x = layers.Flatten()(x)
x = layers.Dense(512, activation='relu')(x)


output = layers.Dense(1, activation='sigmoid')(x)

model = Model(img_input, output)

model.compile(loss='binary_crossentropy',
          optimizer=RMSprop(lr=0.001),
          metrics=['acc'])
 
#END

# All images will be rescaled by 1./255
train_datagen = ImageDataGenerator(rescale=1./255)
val_datagen = ImageDataGenerator(rescale=1./255)
bs = 20
# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(
        train_dir,  # This is the source directory for training images
        target_size=(150, 150),  # All images will be resized to 150x150
        batch_size=bs,
        # Since we use binary_crossentropy loss, we need binary labels
        class_mode='binary')

# Flow validation images in batches of 20 using val_datagen generator
validation_generator = val_datagen.flow_from_directory(
        validation_dir,
        target_size=(150, 150),
        batch_size=bs,
        class_mode='binary')
        

  history = model.fit(
  train_generator,
  steps_per_epoch=train_steps, 
  epochs=4,
  validation_data=validation_generator,
  validation_steps=val_steps,  
  verbose=1)     
  
  
  
model.save_weights("trained_weights.h5")

这是我的预测代码：

def evaluate(imgpath):
if not os.path.isfile(imgpath):
    print("No such file: {}".format(imgpath))
    sys.exit(-1)


# START
img_input = layers.Input(shape=(150, 150, 3))


x = layers.Conv2D(16, 3, activation='relu')(img_input)
x = layers.MaxPooling2D(2)(x)

x = layers.Conv2D(32, 3, activation='relu')(x)
x = layers.MaxPooling2D(2)(x)

x = layers.Conv2D(64, 3, activation='relu')(x)
x = layers.MaxPooling2D(2)(x)


x = layers.Flatten()(x)
x = layers.Dense(512, activation='relu')(x)

output = layers.Dense(1, activation='sigmoid')(x)

model = Model(img_input, output)

model.compile(loss='binary_crossentropy',
          optimizer=RMSprop(lr=0.001),
          metrics=['acc'])
 
# END

model.load_weights("trained_weights.h5")

img = image.load_img(path=imgpath,grayscale=False,target_size=(150,150),color_mode='rgb')
img_arr = image.img_to_array(img)
test_img = np.expand_dims(img_arr, axis=0)
y_prob = model.predict(test_img)


classname = y_prob.argmax(axis=-1)
print("Class: ",classname)
return classname  

我感觉错误出现在评估函数的最后 5-6 行，我正在加载图像。 问题是每当我对任何图像运行评估函数时，我的输出都是 [0]。尽管训练进行得很顺利，如下图所示。
enter image description here

我是不是在某个地方犯了一些愚蠢的错误？

Answer 1

因为你有一个神经元作为顶层，所以当你做预测时，你会得到一个预测。由于您使用 argmax 进行单个预测将始终返回 0。例如，您需要为预测设置阈值

if yprob>=.5:
    klass=1 
else:
    klass=0

正如 Snoopy 博士所指出的，您应该将图像重新缩放 1/255。