python - 将数组传递给顺序神经网络模型

Question

所以我跟随了 this有关创建顺序神经网络并为其提供 MNIST 数据集以进行预测的视频。

我还有一个 flask 网络服务器，我试图通过它传递从 Canvas 绘图应用程序获得的图像，将其大小调整为 20x20，因为这是 MNIST 图像的尺寸，将其转换为灰度然后使用 numpy 进入一个数组，最后将它提供给我的模型并让它做出预测。然后我会将其传递回网页。

但是我得到了错误:

Error when checking input: expected sequential_1_input to have 3 dimensions, but got array with shape (20, 20)

如何使数组成为 3 维数组？

模型:

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"))

#   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)

    print(grayscaleArray.reshape(20, 20, 1))

    setPrediction = model.predict(grayscaleArray)
    getPrediction = np.array(setPrediction[0])

    predictedNumber = str(np.argmax(getPrediction))
    print(predictedNumber)

Answer 1

问题是 reshape 没有到位。 你应该这样做:

grayscaleArray = grayscaleArray.reshape(20, 20, 1)