Question

我刚刚开始使用Keras和深度学习，所以我的问题的答案对某些人来说可能是显而易见的，但对我来说并非如此。

我根据Floydhub上的文章（我在那里训练它）制作了一个模型来为一些黑白照片着色。当我用相似的图片（例如人脸）训练时，它的效果很好但是我只要我使用更大的数据集作为具有不同图片的输入，损失保持稳定并且不会变得更好。

我尝试过不同的学习率和优化程序，但却无法取得好成绩。

我可以改变什么来获得更好的结果？

这是代码（感谢Emil Wallner关于Floydhub的文章）

# Get images
X = []
for filename in os.listdir('/data/images/Train/'):
    X.append(img_to_array(load_img('/data/images/Train/'+filename)))
X = np.array(X, dtype=float)
Xtrain = 1.0/255*X

#Load weights
inception = InceptionResNetV2(weights=None, include_top=True)
inception.load_weights('/data/inception_resnet_v2_weights_tf_dim_ordering_tf_kernels.h5')
inception.graph = tf.get_default_graph()

embed_input = Input(shape=(1000,))

#Encoder
encoder_input = Input(shape=(256, 256, 1,))
encoder_output = Conv2D(64, (3,3), activation='relu', padding='same', strides=2)(encoder_input)
encoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(128, (3,3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3,3), activation='relu', padding='same', strides=2)(encoder_output)
encoder_output = Conv2D(512, (3,3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(512, (3,3), activation='relu', padding='same')(encoder_output)
encoder_output = Conv2D(256, (3,3), activation='relu', padding='same')(encoder_output)

#Fusion
fusion_output = RepeatVector(32 * 32)(embed_input) 
fusion_output = Reshape(([32, 32, 1000]))(fusion_output)
fusion_output = concatenate([encoder_output, fusion_output], axis=3) 
fusion_output = Conv2D(256, (1, 1), activation='relu', padding='same')(fusion_output) 

#Decoder
decoder_output = Conv2D(128, (3,3), activation='relu', padding='same')(fusion_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(64, (3,3), activation='relu', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)
decoder_output = Conv2D(32, (3,3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(16, (3,3), activation='relu', padding='same')(decoder_output)
decoder_output = Conv2D(2, (3, 3), activation='tanh', padding='same')(decoder_output)
decoder_output = UpSampling2D((2, 2))(decoder_output)

model = Model(inputs=[encoder_input, embed_input], outputs=decoder_output)

#Create embedding
def create_inception_embedding(grayscaled_rgb):
    grayscaled_rgb_resized = []
    for i in grayscaled_rgb:
        i = resize(i, (299, 299, 3), mode='constant')
        grayscaled_rgb_resized.append(i)
    grayscaled_rgb_resized = np.array(grayscaled_rgb_resized)
    grayscaled_rgb_resized = preprocess_input(grayscaled_rgb_resized)
    with inception.graph.as_default():
        embed = inception.predict(grayscaled_rgb_resized)
    return embed

# Image transformer
datagen = ImageDataGenerator(
        shear_range=0.4,
        zoom_range=0.4,
        rotation_range=40,
        horizontal_flip=True)

#Generate training data
batch_size = 20

def image_a_b_gen(batch_size):
    for batch in datagen.flow(Xtrain, batch_size=batch_size):
        grayscaled_rgb = gray2rgb(rgb2gray(batch))
        embed = create_inception_embedding(grayscaled_rgb)
        lab_batch = rgb2lab(batch)
        X_batch = lab_batch[:,:,:,0]
        X_batch = X_batch.reshape(X_batch.shape+(1,))
        Y_batch = lab_batch[:,:,:,1:] / 128
        yield ([X_batch, create_inception_embedding(grayscaled_rgb)], Y_batch)

#Train model      
tensorboard = TensorBoard(log_dir="/output")
model.compile(optimizer='adam', loss='mse')
model.fit_generator(image_a_b_gen(batch_size), callbacks=[tensorboard], epochs=1000, steps_per_epoch=20)

#Make a prediction on the unseen images
color_me = []
for filename in os.listdir('../Test/'):
    color_me.append(img_to_array(load_img('../Test/'+filename)))
color_me = np.array(color_me, dtype=float)
color_me = 1.0/255*color_me
color_me = gray2rgb(rgb2gray(color_me))
color_me_embed = create_inception_embedding(color_me)
color_me = rgb2lab(color_me)[:,:,:,0]
color_me = color_me.reshape(color_me.shape+(1,))


# Test model
output = model.predict([color_me, color_me_embed])
output = output * 128

# Output colorizations
for i in range(len(output)):
    cur = np.zeros((256, 256, 3))
    cur[:,:,0] = color_me[i][:,:,0]
    cur[:,:,1:] = output[i]
    imsave("result/img_"+str(i)+".png", lab2rgb(cur))

Keras模型不适用于更大的数据集

0 个答案: