檢查輸入時出錯：預期dense_1_input 具有形狀(784,) 但得到形狀為(10,) 的數組

Question

我正在通過一本書學習 Keras。
我執行了書中的代碼，但出現錯誤。

from keras.utils import np_utils
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Activation
import numpy as np
from numpy import argmax

(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784).astype('float32') / 255.0
x_test = x_test.reshape(10000, 784).astype('float32') / 255.0

x_train = np_utils.to_categorical(y_train)
x_test = np_utils.to_categorical(y_test)

x_val = x_train[:42000]
x_train = x_train[42000:]
y_val = y_train[:42000]
y_train = y_train[42000:]

model = Sequential()
model.add(Dense(units=64, input_dim=28*28, activation='relu'))
model.add(Dense(units=10, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy'])

model.fit(x_train, y_train, epochs=5, batch_size=32, validation_data=(x_val, y_val))

loss_and_metrics = model.evaluate(x_test, y_test, batch_size=32)
print('')
print('loss_and_metrics : ' + str(loss_and_metrics))

from keras.models import load_model
model.save('mnist_mlp_model.h5')

錯誤信息：

ValueError: Error when checking input: expected dense_1_input to have shape (784,) but got array with shape (10,)

我發現了其他相關問題。 它是由尺寸問題引起的，但我認為我的不是這個原因。

我該怎么辦？

Answer 1

MNIST 只有 10 個類別，因為它們是從 0 到 9 的數字，但是在您的代碼中，您有一行：

model.add(Dense(units=64, input_dim=28*28, activation='relu'))

其中說， input_dim=768 - 你可能想要擺脫：

x_train = np_utils.to_categorical(y_train)
x_test = np_utils.to_categorical(y_test)

因為：

>>> x_test = np_utils.to_categorical(y_test)
>>> x_test.shape
(10000, 10)