ValueError：密集层的输入 0 与该层不兼容（新手问题）

Question

我想用MNIST设置做点什么，但它不起作用，如果有人可以帮助我（ https://www.youtube.com/watch?v=ZI4I7Q0ZRBS＆amp;lc;lc; lc; lc = ugxa2zg7_jxi7jj3pj3pj3pjl4aaaaaaaabag.9ogalubwzvo9_ztcihrygbbbbbbbb ）

import cv2 as cv
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

# load dataset of handwritten digits
mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()

# normalise training data and cut down between 0 and 255 (greyscale)
x_train = tf.keras.utils.normalize(x_train, axis=1)
x_test = tf.keras.utils.normalize(x_test, axis=1)

# y_values already declared because they are from 0-9
model = tf.keras.models.Sequential()

# flatten layer
model.add(tf.keras.layers.Flatten(input_shape=(28,28)))

# all neurons are connected with the layers, units make neurons you wanna have in layer
# 2 dense hidden layers
model.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))
model.add(tf.keras.layers.Dense(units=128, activation=tf.nn.relu))

# output layer
model.add(tf.keras.layers.Dense(units=10, activation=tf.nn.softmax))

model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"])

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

loss, accuracy = model.evaluate(x_test, y_test)
print(accuracy)
print(loss)

model.save("digits.model")


for x in range(1,4):
    img = cv.imread(f"{x}.png")[:,:,0]
    # invert to make it black and white digits
    img = np.invert(np.array([img]))
    prediction = model.predict(img)
    print(f"The result ist probably: {np.argmax(prediction)}")
    plt.imshow(img[0], cmap=plt.cm.binary)
    plt.show()

我得到这个错误：

ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 784 but received input with shape [None, 829440]

如何解决此问题？

谢谢。

Answer 1

您传递给模型的训练数据可能不在（28,28）形状中。如果它们处于（28,28）的形状，则其输入形状为（28*28）= 784。因此，请尝试调整输入图像并将其调整到（28,28）。
尽管我试图在Google Colab＆amp;中运行您的代码。训练零件对我来说没有任何错误。