Keras CNN Autoencoder输入形状错误

Question

I have build a CNN autoencoder using keras and it worked fine for the MNIST test data set. I am now trying it with a different data set collected from another source. There are pure images and I have to read them in using cv2 which works fine. I then convert these images into a numpy array which again I think works fine. But when I try to do the .fit method it gives me this error.

Error when checking target: expected conv2d_39 to have shape (100, 100, 1) but got array with shape (100, 100, 3)

I tried converting the images to grey scale but they then get the shape (100,100) and not (100,100,1) which is what the model wants. What am I doing wrong here?

Here is the code that I am using:

def read_in_images(path):
    images = []
    for files in os.listdir(path):
        img = cv2.imread(os.path.join(path, files))
        if img is not None:
            images.append(img)
    return images

train_images = read_in_images(train_path)
test_images = read_in_images(test_path)
x_train = np.array(train_images)
x_test = np.array(test_images) # (36, 100, 100, 3)

input_img = Input(shape=(100,100,3))
x = Conv2D(32, (3, 3), activation='relu', padding='same')(input_img)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
x = MaxPooling2D((2, 2), padding='same')(x)
x = Conv2D(16, (3, 3), activation='relu', padding='same')(x)
encoded = MaxPooling2D((2, 2), padding='same')(x)


x = Conv2D(16, (3, 3), activation='relu', padding='same')(encoded)
x = UpSampling2D((2, 2))(x)
x = Conv2D(168, (3, 3), activation='relu', padding='same')(x)
x = UpSampling2D((2, 2))(x)
x = Conv2D(32, (3, 3), activation='relu')(x)
x = UpSampling2D((2, 2))(x)
decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)


autoencoder = Model(input_img, decoded)
autoencoder.compile(optimizer='adadelta', loss='binary_crossentropy')


autoencoder.fit(x_train, x_train,
            epochs=25,
            batch_size=128,
            shuffle=True,
            validation_data=(x_test, x_test),
            callbacks=[TensorBoard(log_dir='/tmp/autoencoder')])

The model works fine with the MNIST data set but not with my own data set. Any help will be appreciated.

Answer 1

Your input and output shapes are different. That triggers the error (I think).

decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x)

should be

decoded = Conv2D(num_channels, (3, 3), activation='sigmoid', padding='same')(x)

Answer 2

I ran some tests, and with data loaded in grayscale like that :

img = cv2.imread(os.path.join(path, files), 0)

then expand the dim of the final loaded array like :

x_train = np.expand_dims(x_train, -1)

and finaly normalize you data with a simple :

x_train = x_train / 255.

(the input of your model must be : input_img = Input(shape=(100, 100, 1) )

The loss becomes normal again and the model run well !

UPDATE after comment

In order to keep all the rgb channel throught the network, you need an output corresponding to your input shape.
Here if you want image with shape (100, 100, 3), you need an output of (100, 100, 3) from your decoder.

The decoded = Conv2D(1, (3, 3), activation='sigmoid', padding='same')(x) will shrink the output to have a shape (100, 100, 1)

So you simply need to change the number of filters, here we want 3 colors channels so the conv must be like that :

decoded = Conv2D(3, (3, 3), activation='sigmoid', padding='same')(x)