每次我尝试 predict 方法时，这就是我得到的：Input 0 of layer "conv2d_16" is incompatible with the layer

Question

我正在尝试训练我的模型来读取一些 X 射线图像，我正在使用 Jupyter Notebook，我导入了库，定义了图像属性，准备了数据集，创建了神经网络模型，定义了回调...并管理数据，训练模型，我使用 Tkinter 作为 gui 来使用方法这是我按下按钮运行方法时得到的：

WARNING:tensorflow:Model was constructed with shape (None, 128, 128, 3) for input KerasTensor(type_spec=TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name='conv2d_16_input'), name='conv2d_16_input', description="created by layer 'conv2d_16_input'"), but it was called on an input with incompatible shape (None, 128, 128).

这是我的神经网络模型：

model=Sequential()
model.add(Conv2D(32,(3,3),activation='relu',input_shape=(Image_Width,Image_Height,Image_Channels)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(64,(3,3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Conv2D(128,(3,3),activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512,activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',
  optimizer='adam',metrics=['accuracy'])

我已经将 3 个维度放入 input_shape、Image_Width=128 Image_Height=128 Image_Channels=3

模型是用形状 (None, 128, 128, 3) 构建的输入，我不明白为什么我得到 expected min_ndim=4, found ndim=3 如果我错了请纠正我我是新手，感谢您的宝贵时间。

编辑：我的 tkinter gui 代码到运行方法：

import tkinter as tk
from tkinter import filedialog
from tkinter import *
from PIL import ImageTk, Image
import numpy
from keras.models import load_model
model = load_model('C:/Users/lenovo/PneumoniaClassification/pneumoniatest9999_10epoch.h5')
#dictionary to label all traffic signs class.
classes = { 
    0:'Normal',
    1:'Pneumonia',
 
}
#initialise GUI
top=tk.Tk()
top.geometry('800x600')
top.title('Pneumonia Classification')
top.configure(background='#CDCDCD')
label=Label(top,background='#CDCDCD', font=('arial',15,'bold'))
sign_image = Label(top)
def classify(file_path):
    global label_packed
    image = Image.open(file_path)
    image = image.resize((128,128))
    image = numpy.expand_dims(image, axis=0)
    image = numpy.array(image)
    image = image/255
    pred = model.predict([image])[0]
    sign = classes[pred]
    print(sign)
    label.configure(foreground='#011638', text=sign) 
def show_classify_button(file_path):
    classify_b=Button(top,text="Classify Image",
   command=lambda: classify(file_path),
   padx=10,pady=5)
    classify_b.configure(background='#364156', foreground='white',
font=('arial',10,'bold'))
    classify_b.place(relx=0.79,rely=0.46)
def upload_image():
    try:
        file_path=filedialog.askopenfilename()
        uploaded=Image.open(file_path)
        uploaded.thumbnail(((top.winfo_width()/2.25),
    (top.winfo_height()/2.25)))
        im=ImageTk.PhotoImage(uploaded)
        sign_image.configure(image=im)
        sign_image.image=im
        label.configure(text='')
        show_classify_button(file_path)
    except:
        pass
upload=Button(top,text="Upload an image",command=upload_image,padx=10,pady=5)
upload.configure(background='#364156', foreground='white',font=('arial',10,'bold'))
upload.pack(side=BOTTOM,pady=50)
sign_image.pack(side=BOTTOM,expand=True)
label.pack(side=BOTTOM,expand=True)
heading = Label(top, text="Pneumonia Classification",pady=20, font=('arial',20,'bold'))
heading.configure(background='#CDCDCD',foreground='#364156')
heading.pack()
top.mainloop()

Answer 1

您的模型需要输入形状 (samples, width, height, channels)。因此，当您调用 model.predict 时，您应该将形状为 (1, 128, 128, 3) 的图像提供给您的模型。这对应于模型的预定义输入形状：input_shape=(Image_Width,Image_Height,Image_Channels)（不包括批次/样本维度）。我假设您想对单个图像进行预测，因此数字 1。

如果您将灰度图像输入模型，则必须将它们转换为 RGB，例如 tf.image.grayscale_to_rgb:

model.predict(tf.image.grayscale_to_rgb(image))

完整的工作示例供参考：

import tensorflow as tf

model=tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32,(3,3),activation='relu',input_shape=(128, 128, 3)))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Dropout(0.25))
model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Dropout(0.25))
model.add(tf.keras.layers.Conv2D(128,(3,3),activation='relu'))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.MaxPooling2D(pool_size=(2,2)))
model.add(tf.keras.layers.Dropout(0.25))
model.add(tf.keras.layers.Flatten())
model.add(tf.keras.layers.Dense(512,activation='relu'))
model.add(tf.keras.layers.BatchNormalization())
model.add(tf.keras.layers.Dropout(0.5))
model.add(tf.keras.layers.Dense(1,activation='sigmoid'))
model.compile(loss='binary_crossentropy',
  optimizer='adam',metrics=['accuracy'])

image = tf.random.normal((1, 128, 128, 3))
print(model.predict(image))

每次我尝试 predict 方法时，这就是我得到的：Input 0 of layer "conv2d_16" is incompatible with the layer

Each time I try the predict method this is what I get: Input 0 of layer "conv2d_16" is incompatible with the layer

python

tensorflow

tkinter

keras

tf.keras