the issue about fit_generator

[tinazliu]

大大您好，謝謝你提供這麼棒的source code，讓我受益良多 :)
以下我有一些關於fit generator的問題，還望大大能夠解惑~

問題1:

hist = model.fit_generator(generator=data_generator_centerloss(X=[x_train, y_train_a_class_value], Y=[y_train_a_class,y_train_a, random_y_train_a], batch_size=batch_size),
                                   steps_per_epoch=train_num // batch_size,
                                   validation_data=([x_test,y_test_a_class_value], [y_test_a_class,y_test_a, random_y_test_a]),
                                   epochs=nb_epochs, verbose=1,
                                   callbacks=callbacks)

以上是大大提供fit_generator的範本，我好奇的是為何Y=[y_train_a_class,y_train_a, random_y_train_a]有3個輸出 ? y_train_a代表什麼意思?
會有這個好奇點是因為我看在TTY.mnist.py是用.fit實踐的 --> model_centerloss.fit([x_train,y_train_value], [y_train, random_y_train], batch_size=batch_size, epochs=epochs, verbose=1, validation_data=([x_test,y_test_value], [y_test,random_y_test]), callbacks=[histories]) ，照我的理解，其為雙輸入雙輸出的格式。故我覺得.fit_generator也要為雙輸入雙輸出~

問題2

我依照雙輸入和雙輸出的想法建構自己的fit_generator，出現一個很奇怪的問題

以下是我輸入和輸出的 .shape，感覺大小是正確的

所以有點摸不著頭緒，是不是我的generator和l2_loss的格是不相符，不過我看l2_loss的型態都是?，感覺怪怪的QQ

補充

以下是我實作generator的方式:，我的generator會return這些東西

然後我的fit_generator是這樣實踐的

不好意思打擾您，真的很謝謝你提供那麼棒的程式，讓我在實踐center loss時有一個很棒的參考對象~謝謝

,Tina

[shamangary]

啊，三個輸出是因為我之前在嘗試age estimation時有(classification loss, regression loss, center loss)的組合才有這樣的。另外我這個project是按照
https://kexue.fm/archives/4493
做出來的完整範例，要感謝蘇大。

根據你的error你的l2_loss是(?,?,?,?)還蠻奇怪的。四個維度都變成batchsize的感覺，先檢查一下吧。
至於fit_generator我記得要定義輸入的X和Y是多少

類似這樣

hist = model.fit_generator(generator=data_generator_centerloss(X=[x_train, y_train_a_class_value], Y=[y_train_a_class, random_y_train_a], batch_size=batch_size),
                                   steps_per_epoch=train_num // batch_size,
                                   validation_data=([x_test,y_test_a_class_value], [y_test_a_class, random_y_test_a]),
                                   epochs=nb_epochs, verbose=1,
                                   callbacks=callbacks)

data generator例子：

def data_generator_centerloss(X,Y,batch_size):
    X1 = X[0]
    X2 = X[1]
    Y1 = Y[0]
    Y2 = Y[1]

    while True:
        idxs = np.random.permutation(len(X1))
        X1 = X1[idxs] #images
        X2 = X2[idxs] #labels for center loss
        Y1 = Y1[idxs]
        Y2 = Y2[idxs]

        p1,p2,q1,q2 = [],[],[],[]
        for i in range(len(X1)):
            p1.append(X1[i])
            p2.append(X2[i])
            q1.append(Y1[i])
            q2.append(Y2[i])

            if len(p1) == batch_size:
                yield [np.array(p1),np.array(p2)],[np.array(q1),np.array(q2)]
                p1,p2,q1,q2 = [],[],[],[]
        if p1:
            yield [np.array(p1),np.array(p2)],[np.array(q1),np.array(q2)]
            p1,p2,q1,q2 = [],[],[],[]

[tinazliu]

大大您好:
謝謝您的回覆，我在實作時也有拜讀到蘇大的文章，真的很感謝您們兩位!! :)

• 針對問題2: 是我架構的問題，我餵進去的ip1是四維的，我那時沒有注意到，所以l2_loss才會出現這個問題~大大實在太猛了!
• 針對問題1: 我有看到 issue1 大大提供的範例程式，我的修改也像大大所說的~可惜遇到一個奇怪的問題
  

這邊我還在查找，如果大大有遇過類似的問題，還望您給我一點方向!謝謝大大 ~ 如果沒有遇過也沒關係，哈哈一直打擾您真不好意思!
🎈[更新]🎈

• 針對第二個問題，是我實作generator時的錯誤，以下是我實作的generator。我是用return返回fit_gen的輸入，才會造成上述問題~
  

最後謝謝大大在百忙中回覆我的問題!

[lily0101]

@shamangary hi, I wanna know if I use the function flow_flow_directory to get the data generator, How I define the Embedding's input. that means, I don't get the labels(the target_input). Can you help me to solve it? I am not sure about the embedding's input. Can I give it a one-hot vector?

[wangjue-wzq]

@tinazliu @shamangary @lily0101
I have some problem when I use centerloss in image classification with keras.
1、in custom_vgg_model.fit(y = {'fc2':y_train,'predictions':y_train}),'fc2':y_train have error that

ValueError: Error when checking target: expected fc2 to have shape (None, 4096) but got array with shape (6300, 45)

y_train is the labels. If I do like this custom_vgg_model.fit(y = {'fc2':dummy1,'predictions':y_train}),the model will train successful. The dummy1 have same shape with 'fc2' output(feature).
dummy1 = np.zeros((y_train.shape[0],4096))
But can't improve the accuracy of the model.So it is wrong coding.
2、It is wrong to use ImageDataGenerator.flow(x = X_train, y = {'fc2':dummy1,'predictions':y_train}, batch_size=batch_Sizes) .So I can't expand my data.

image_input = Input(shape=(224, 224, 3))
model = VGG16(input_tensor=image_input, include_top=True,weights='imagenet')
model.summary()
last_layer = model.get_layer('fc2').output
feature = last_layer
out = Dense(num_classes,activation = 'softmax',name='predictions')(last_layer)
custom_vgg_model = Model(inputs = image_input, outputs = [out,feature])
custom_vgg_model.summary()
for layer in custom_vgg_model.layers[:-3]:
    layer.trainable = False
custom_vgg_model.layers[3].trainable    
sgd = optimizers.SGD(lr=learn_Rate,decay=decay_Rate,momentum=0.9,nesterov=True)
center_loss = lossclass.get_center_loss(alpha=0.5, num_classes=45,feature_dim = 4096)
custom_vgg_model.compile(loss={'predictions': "categorical_crossentropy", 'fc2': center_loss},
                         loss_weights={'fc2': 1, 'predictions': 1},optimizer= sgd,
                                      metrics={'predictions': 'accuracy'})
t=time.time()
dummy1 = np.zeros((y_train.shape[0],4096))
dummy2 = np.zeros((y_test.shape[0],4096))
if not data_Augmentation:
    hist = custom_vgg_model.fit(x = X_train,y = {'fc2':y_train,'predictions':y_train},batch_size=batch_Sizes,
                                epochs=epoch_Times, verbose=1,validation_data=(X_test, {'fc2':y_test,'predictions':y_test}))
else:
    datagen = ImageDataGenerator(
            featurewise_center=False,
            samplewise_center=False,
            featurewise_std_normalization=False,
            samplewise_std_normalization=False,
            zca_whitening=False,
            rotation_range=20,
            width_shift_range=0.2,
            height_shift_range=0.2,
            horizontal_flip=True,
            vertical_flip=True,
            rescale=None,
            preprocessing_function=None,
            data_format=None)
    print('x_train.shape[0]:{:d}'.format(X_train.shape[0]))
    hist = custom_vgg_model.fit_generator(datagen.flow(x = X_train, y = {'fc2':dummy1,'predictions':y_train}, batch_size=batch_Sizes),
                                          steps_per_epoch=X_train.shape[0]/batch_Sizes,epochs=epoch_Times,
                                                                       verbose=1, validation_data=(X_test, {'fc2':y_test,'predictions':y_test}))
# lossclass.py
def _center_loss_func(labels,features, alpha, num_classes, centers, feature_dim):
    assert feature_dim == features.get_shape()[1]    
    labels = K.argmax(labels, axis=1)
    labels = tf.to_int32(labels)
    centers_batch = K.gather(centers, labels)
    diff = (1 - alpha) * (centers_batch - features)
    centers = tf.scatter_sub(centers, labels, diff)
    centers_batch = K.gather(centers, labels)
    loss = K.mean(K.square(features - centers_batch))
    return loss

def get_center_loss(alpha, num_classes, feature_dim):
    """Center loss based on the paper "A Discriminative 
       Feature Learning Approach for Deep Face Recognition"
       (http://ydwen.github.io/papers/WenECCV16.pdf)
    """    
    # Each output layer use one independed center: scope/centers
    centers = K.zeros([num_classes, feature_dim], dtype='float32')
    @functools.wraps(_center_loss_func)
    def center_loss(y_true, y_pred):
        return _center_loss_func(y_true, y_pred, alpha, num_classes, centers, feature_dim)
    return center_loss