Question

因此，我得到了一个循序渐进的模型，主要是跟随this视频一起观看。我还用JS完成了一个绘图应用程序，并使用JQuery将画布的内容发送到烧瓶服务器。所有这些似乎都可以正常工作，但是当我从画布上给模型提供图像并调用预测时，它总是会给我相同的准确响应。每个索引的值始终相同。

我想念一些简单的东西吗？我是在服务器中称预测为错误，还是与模型本身有关？它可以在笔记本中获得正确的预测。

感谢您的时间！

烧瓶服务器：

model = load_model('MyModel.h5')
imageWidth = 28
imageHeight = 28
dim = (imageWidth, imageHeight)

#   Create the flask web application
app = fl.Flask(__name__)

@app.route('/uploadimage', methods = ['GET', 'POST'])
def uploadimage():
    #   Get the image from the request
    theImage = fl.request.values.get("theImage", "")

    #Decode the string to an image
    decodedimg = base64.b64decode(theImage[22:])

    #   Save the image
    with open ("theImage.png", "wb") as f:
        f.write(decodedimg)

    #   Open the image from the request as originalImage
    originalImage = Image.open("theImage.png")

    #   Resize it
    resizedImage = ImageOps.fit(originalImage, dim, Image.ANTIALIAS)

    #   Confirm the dimensions of the resized image
    w1, h1 = resizedImage.size
    print(w1, h1)

    #   Save it locally
    resizedImage.save("resizedImage.png", quality=100, optimize=True)

    #   Convert to grayscale and then convert that to an array
    grayscaleImage = ImageOps.grayscale(resizedImage)
    grayscaleArray = np.array(grayscaleImage)
    grayscaleArray.shape    #   Gives (20, 20)

    grayscaleArray = grayscaleArray.reshape(1, 28, 28)

    setPrediction = model.predict(grayscaleArray)
    print(setPrediction)    #   Always gives back same values
    getPrediction = np.array(setPrediction[0])

    predictedNumber = str(np.argmax(getPrediction))
    print(predictedNumber)  #   Always '5'

    return predictedNumber

型号：

model = kr.models.Sequential() # Create a new sequential neural network
model.add(kr.layers.Flatten()) # Input layer
model.add(kr.layers.Dense(128, activation="relu")) # 128 neurons and the 'basic' activation function.
model.add(kr.layers.Dense(128, activation="relu"))
model.add(kr.layers.Dense(10, activation="softmax"))

model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) # Played around with 'sgd' and 'rmsporp' optimizer also.

model.fit(X_train, y_train, epochs=3)

val_loss, val_acc = model.evaluate(X_test, y_test)
print(val_loss, val_acc)

顺序模型总是预测5

0 个答案: