使用ImageDataGenerator(Keras)时，fit_generator输入尺寸错误 [英] fit_generator input dimensions error when using ImageDataGenerator (Keras)

问题描述

我正在将Keras 2.04与tensorflow后端一起使用.我正在尝试在MNIST图像上使用ImageDataGenerator训练一个简单的模型.但是，我不断从fit_generator收到以下错误:

I am using Keras 2.04 with tensorflow backend. I am trying to train a simple model with ImageDataGenerator on MNIST images. However, I keep getting the following error from fit_generator:

ValueError:检查输入时出错:预期input_1具有2 尺寸，但数组的形状为(8，28，28，1).

ValueError: Error when checking input: expected input_1 to have 2 dimensions, but got array with shape (8, 28, 28, 1).

这是代码:

#loading data & reshaping
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.reshape(x_train.shape[0], 28, 28,1)

#building the model
input_img = Input(shape=(784,))
encoded = Dense(30, activation='relu')(input_img)
decoded = Dense(784, activation='sigmoid')(encoded)
autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adam', loss='mse')

#creating ImageDataGenerator
datagen = ImageDataGenerator(featurewise_center=True, featurewise_std_normalization=True)
datagen.fit(x_train)

autoencoder.fit_generator(
        #x_train, x_train because the target is to reconstruct the input
        datagen.flow(x_train, x_train, batch_size=8),
        steps_per_epoch=int(len(x_train)/8),
        epochs=64,
       )

据我了解，ImageDataGenerator应该在每次迭代时都生成一批训练示例，就像它实际所做的那样(在这种情况下，batch_size = 8)，但是从错误中看来，它似乎期望一个训练示例.

As far as I understand ImageDataGenerator should generate a batch of training examples each iteration, as it actually does (in this case, batch_size=8) but from the error it seems as if it expects a single training example.

谢谢！

推荐答案

已解决-应该应该是:

autoencoder = Sequential()
autoencoder.add(Reshape((784,), input_shape=(28,28,1)))
autoencoder.add(Dense(30, activation='relu'))
autoencoder.add(Dense(784, activation='relu'))
.
.
.

autoencoder.fit_generator(
        datagen.flow(x_train, x_train.reshape(len(x_train),784,), batch_size=8),
        steps_per_epoch=int(len(x_train)/8),
        epochs=64,
       )

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