轉自http://blog.csdn.net/u011239443/article/details/73721903

這次我們要做一件比較有趣的事——講影象風格轉化。

如何將一張杭州西湖圖片：

將其風格轉化為和梵高的《星夜》一樣具有鮮明藝術的風格呢？

先給出完整的程式碼：https://github.com/xiaoyesoso/TensorFlowinAction/blob/master/InActionB2/chapter4/tranImage.py

接下來我們來講解它。

影象資料

if __name__ == '__main__':
    style = Image.open('star.jpg'
 
)
    style = np.array(style).astype(np.float32) - 128.0
    content = Image.open('me.jpg')
    content = np.array(content).astype(np.float32) - 128.0
stylize(style,content,0.5,500)
  

   
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上述style讀取的是《星夜》，將其轉為浮點陣列，並且減去128.0，這樣就以0為中心，可以加快收斂。content讀取的是《西湖》，操作相同。

要注意的是，style，content

    print(content.shape)
    print(style.shape)
  

   
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可見：

(224, 224, 3)
(224, 224, 3)
  

   
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風格轉化

def stylize(style_image,content_image,learning_rate=0.1
 
,epochs=500):
    # 結果圖片
    target = tf.Variable(tf.random_normal(content_image.shape),dtype=tf.float32)
    style_input = tf.constant(style_image,dtype=tf.float32)
    content_input = tf.constant(content_image, dtype=tf.float32)
    cost = loss_function(style_input,content_input,target)
    train_op = tf.train.AdamOptimizer(learning_rate).minimize(cost)
    with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess:
        tf.initialize_all_variables().run()
        for i in range(epochs):
            _,loss,target_image = sess.run([train_op,cost,target])
            print("iter:%d,loss:%.9f" % (i, loss))
            if (i+1) % 100 == 0:
                image = np.clip(target_image + 128,0,255).astype(np.uint8)
                Image.fromarray(image).save("./neural_me_%d.jpg" % (i + 1))
  

   
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這裡比較有趣的是，以前我們通常調優的是引數矩陣，然後配合測試資料預測出結果。而這邊，我們沒有測試資料，而所要調優的target矩陣，就是我們要得結果。

VGGNet19模型

VGGNet模型：

要注意的是，在這裡我們並沒有想試圖訓練VGGNet19模型，我們只是想獲得圖片在VGGNet19模型中的某些隱藏層上的特徵矩陣。所以我們這裡使用已經訓練好的VGGNet19模型的引數，下載地址：http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat

然後只需要載入它據可以了：

_vgg_params = None

def vgg_params():
    global _vgg_params
    if _vgg_params is None:
        _vgg_params = sio.loadmat('imagenet-vgg-verydeep-19.mat')
    return _vgg_params
  

   
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def vgg19(input_image):
    layers = (
        'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
        'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
        'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4','pool3',
        'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
        'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5'
    )

    weights = vgg_params()['layers'][0]
    net = input_image
    network = {}
    for i,name in enumerate(layers):
        layer_type = name[:4]
        # 若是卷積層
        if layer_type == 'conv':
            kernels,bias = weights[i][0][0][0][0]
            # 由於 imagenet-vgg-verydeep-19.mat 中的引數矩陣和我們定義的長寬位置顛倒了，所以需要交換
            kernels = np.transpose(kernels,(1,0,2,3))
            conv = tf.nn.conv2d(net,tf.constant(kernels),strides=(1,1,1,1),padding='SAME',name=name)
            net = tf.nn.bias_add(conv,bias.reshape(-1))
            net = tf.nn.relu(net)
        # 若是池化層
        elif layer_type == 'pool':
            net = tf.nn.max_pool(net,ksize=(1,2,2,1),strides=(1,2,2,1),padding='SAME')
        # 將隱藏層加入到集合中
        # 若為`啟用函式`直接加入集合
        network[name] = net

    return network
  

   
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我們將weights打印出來看看：

print(weights.shape)
  

   
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得到：

(43,)
  

   
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可知imagenet-vgg-verydeep-19.mat還可以支援比VGGNet19更多層的VGGNet模型。

若是卷積層，如conv1_1：

print(weights[0][0][0][0][0])
  

   
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得到的是引數矩陣和偏置：

[ array([[[[ 0.39416704, -0.08419707, -0.03631314, ..., -0.10720515,
          -0.03804016,  0.04690642],
         [ 0.46418372,  0.03355668,  0.10245045, ..., -0.06945956,
          -0.04020201,  0.04048637],
         [ 0.34119523,  0.09563112,  0.0177449 , ..., -0.11436455,
          -0.05099866, -0.00299793]],

        [[ 0.37740308, -0.07876257, -0.04775979, ..., -0.11827433,
          -0.19008617, -0.01889699],
         [ 0.41810837,  0.05260524,  0.09755926, ..., -0.09385028,
          -0.20492788, -0.0573062 ],
         [ 0.33999205,  0.13363543,  0.02129423, ..., -0.13025227,
          -0.16508926, -0.06969624]],

        [[-0.04594866, -0.11583115, -0.14462094, ..., -0.12290562,
          -0.35782176, -0.27979308],
         [-0.04806903, -0.00658076, -0.02234544, ..., -0.0878844 ,
          -0.3915486 , -0.34632796],
         [-0.04484424,  0.06471398, -0.07631404, ..., -0.12629718,
          -0.29905206, -0.28253639]]],


       [[[ 0.2671299 , -0.07969447,  0.05988706, ..., -0.09225675,
           0.31764674,  0.42209673],
         [ 0.30511212,  0.05677647,  0.21688674, ..., -0.06828708,
           0.3440761 ,  0.44033417],
         [ 0.23215917,  0.13365699,  0.12134422, ..., -0.1063385 ,
           0.28406844,  0.35949969]],

        [[ 0.09986369, -0.06240906,  0.07442063, ..., -0.02214639,
           0.25912452,  0.42349899],
         [ 0.10385381,  0.08851637,  0.2392226 , ..., -0.01210995,
           0.27064082,  0.40848857],
         [ 0.08978214,  0.18505956,  0.15264879, ..., -0.04266965,
           0.25779948,  0.35873157]],

        [[-0.34100872, -0.13399366, -0.11510294, ..., -0.11911335,
          -0.23109646, -0.19202407],
         [-0.37314063, -0.00698938,  0.02153259, ..., -0.09827439,
          -0.2535741 , -0.25541356],
         [-0.30331427,  0.08002605, -0.03926321, ..., -0.12958746,
          -0.19778992, -0.21510386]]],


       [[[-0.07573577, -0.07806503, -0.03540679, ..., -0.1208065 ,
           0.20088433,  0.09790061],
         [-0.07646758,  0.03879711,  0.09974211, ..., -0.08732687,
           0.2247974 ,  0.10158388],
         [-0.07260918,  0.10084777,  0.01313597, ..., -0.12594968,
           0.14647409,  0.05009392]],

        [[-0.28034249, -0.07094654, -0.0387974 , ..., -0.08843154,
           0.18996507,  0.07766484],
         [-0.31070709,  0.06031388,  0.10412455, ..., -0.06832542,
           0.20279962,  0.05222717],
         [-0.246675  ,  0.1414054 ,  0.02605635, ..., -0.10128672,
           0.16340195,  0.02832468]],

        [[-0.41602272, -0.11491341, -0.14672887, ..., -0.13079506,
          -0.1379628 , -0.26588449],
         [-0.46453714, -0.00576723, -0.02660675, ..., -0.10017379,
          -0.15603794, -0.32566148],
         [-0.33683276,  0.06601517, -0.08144748, ..., -0.13460518,
          -0.1342358 , -0.27096185]]]], dtype=float32)
 array([[ 0.73017758,  0.06493629,  0.03428847,  0.8260386 ,  0.2578029 ,
         0.54867655, -0.01243854,  0.34789944,  0.55108708,  0.06297145,
         0.60699058,  0.26703122,  0.649414  ,  0.17073655,  0.47723091,
         0.38250586,  0.46373144,  0.21496128,  0.46911287,  0.23825859,
         0.47519219,  0.70606434,  0.27007523,  0.68552732,  0.03216552,
         0.60252881,  0.35034859,  0.446798  ,  0.77326518,  0.58191687,
         0.39083108,  1.75193536,  0.66117406,  0.30213955,  0.53059655,
         0.67737472,  0.33273223,  0.49127793,  0.26548928,  0.18805602,
         0.07412001,  1.10810876,  0.28224325,  0.86755145,  0.19422948,
         0.810332  ,  0.36062282,  0.50720042,  0.42472315,  0.49632648,
         0.15117475,  0.79454446,  0.33494323,  0.47283995,  0.41552398,
         0.08496041,  0.37947032,  0.60067391,  0.47174454,  0.81309211,
         0.45521152,  1.08920074,  0.47757268,  0.4072122 ]], dtype=float32)]
  

   
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若是啟用函式，如relu1_1：

print(weights[1][0][0][0][0])
  

   
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輸出：

relu
  

   
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若是池化層，如pool1：

print(weights[4][0][0][0][0])
  

   
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輸出：

pool1
  

   
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損失函式

STYLE_WEIGHT = 1
CONTENT_WEIGHT = 1
STYLE_LAYERS = ['relu1_2','relu2_2','relu3_2']
CONTENT_LAYERS = ['relu1_2']

def loss_function(style_image,content_image,target_image):
    style_features = vgg19([style_image])
    content_features = vgg19([content_image])
    target_features = vgg19([target_image])
    loss = 0.0
    for layer in CONTENT_LAYERS:
        loss += CONTENT_WEIGHT * content_loss(target_features[layer],content_features[layer])

    for layer in STYLE_LAYERS:
        loss += STYLE_WEIGHT * style_loss(target_features[layer],style_features[layer])

    return loss
  

   
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可以看到權重STYLE_WEIGHT和CONTENT_WEIGHT可以控制優化更
取趨於風格還是趨於內容。而STYLE_LAYERS的層數越多，就能挖掘出《星夜》越多樣的風格特徵，而CONTENT_LAYERS中的越深的掩藏層得到的特徵越抽象。

建議STYLE_LAYERS中的層數儘可能的多，這樣更加能挖掘出《星夜》風格特徵。當然層數多了或者深了，所需要的迭代次數需要很大才能得到比較好的效果。由於機子不給力，我只迭代了500輪，只選了三個隱藏層'relu1_2','relu2_2','relu3_2'。而CONTENT_LAYERS中的隱藏層的越淺就表達了目標圖中原內容就越具像。

• 內容損失函式很簡單，就是特徵值誤差：

def content_loss(target_features,content_features):
    _,height,width,channel = map(lambda i:i.value,content_features.get_shape())
    content_size = height * width * channel
    return tf.nn.l2_loss(target_features - content_features) / content_size
  

   
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• 風格損失函式。我們現將三維特徵矩陣(-1,channel)重塑為二維矩陣，即一行代表一個特徵值，三列分別是RGB。使用其格拉姆矩陣（ATA” role=”presentation” style=”position: relative;”>ATAATA）誤差作為返回結果。

def style_loss(target_features,style_features):
    _,height,width,channel = map(lambda i:i.value,target_features.get_shape())
    size = height * width * channel
    target_features = tf.reshape(target_features,(-1,channel))
    target_gram = tf.matmul(tf.transpose(target_features),target_features) / size

    style_features = tf.reshape(style_features,(-1,channel))
    style_gram = tf.matmul(tf.transpose(style_features),style_features) / size

    return tf.nn.l2_loss(target_gram - style_gram) / size
  

   
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結果圖

好啦，接下來看看我們的作品吧。

迭代100輪：

迭代200輪：

迭代300輪：

迭代400輪：

迭代500輪：

可見結果圖的內容的原來越清晰的，但是過於清晰反而不具有藝術感，可以適當加深加多CONTENT_LAYERS中的隱藏層。注意圖片的右上部分，也可以發現越發具有類似於《星夜》的旋轉的風格紋路細節。

贈送一隻胖子～

原：

100：

200：

300：

400：

500：

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