首先感謝點進來的各位大大們，這是小弟我第一次在此發問，排版較差，還請大家耐心看完。
問題:
目前正在學習如何使用多輸入模型，但遇到的這個問題在網路上找了許久卻找不到解決方法。
這是本該input進去的資料:
訓練資料1: origin: 5 classes, 150 images
訓練資料2: entropy: 5 classes, 150 images
驗證資料1: origin: 5 classes, 150 images
驗證資料2: entropy: 5 classes, 150 images
也就是說本來預期會得到以下這樣的結果
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
但卻只出現以下結果
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
檢查後發現只有訓練資料1: origin 和驗證資料1: origin 被抓進去，請問各位大大該如何解決此問題?
以下為程式碼
train_gen = ImageDataGenerator()
test_gen = ImageDataGenerator()
def generate_generator_multiple(generator, dir1, dir2, batch_size, img_rows, img_cols):
genX1 = generator.flow_from_directory(
dir1,
target_size = (img_rows, img_cols),
#target_size = (64, 64),
# 遵照論文所示，將batch_size 設定為8
batch_size = batch_size,
#shuffle=False,
class_mode = 'categorical',
seed=666
)
genX2 = generator.flow_from_directory(
dir2,
target_size = (img_rows, img_cols),
#target_size = (64, 64),
# 遵照論文所示，將batch_size 設定為8
batch_size = batch_size,
#shuffle=False,
class_mode = 'categorical',
seed=666
)
while True:
X1i = genX1.next()
X2i = genX2.next()
yield [X1i[0], X2i[0]], X2i[1] #Yield both images and their mutual label
inputgenerator = generate_generator_multiple(
generator=train_gen,
dir1=train_origin_dir,
dir2=train_entropy_dir,
batch_size=batch_size,
img_rows=img_rows,
img_cols=img_cols
)
testgenerator = generate_generator_multiple(
generator=test_gen,
dir1=test_origin_dir,
dir2=test_entropy_dir,
batch_size=batch_size,
img_rows=img_rows,
img_cols=img_cols
)
---- 中間為神經網路的架構----
history = model.fit(
inputgenerator,
steps_per_epoch = trainsetsize/batch_size,
validation_data = testgenerator,
validation_steps = testsetsize/batch_size,
epochs = epochs,
callbacks = callbacks_list,
verbose = 2
)
只出現
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
Epoch 1/1000
已邀請的邦友 {{ invite_list.length }}/5
flow_from_directory 同一類別只能放在一個子目錄，如下圖，因為子目錄名稱會被視為 label，可參考官網。
簡單的解法就是複製相同類別的資料至同一個子目錄。
謝謝您的解答，但這樣會產生以下情況，是否變成1個input有300張圖片?
Found 300 images belonging to 5 classes.
Found 300 images belonging to 5 classes.
以下是我原本的路徑，每個路徑的最後都是5個label的資料夾，其中又各有30張圖片，。
train_origin_dir = f"D:/img_data/{dataset_origin}/{dataset_origin}_train"
test_origin_dir = f"D:/img_data/{dataset_origin}/{dataset_origin}_test"
train_entropy_dir = f"D:/img_data/{dataset_entropy}/{dataset_entropy}_train"
test_entropy_dir = f"D:/img_data/{dataset_entropy}/{dataset_entropy}_test"
原本預期是會得到下方的結果。
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
Found 150 images belonging to 5 classes.
會有這樣的預期主要是受這篇文的啟發，但他的程式碼中gen1, gen2 的directory 是一樣的，這樣不就會發生上方Found 300 images belonging to 5 classes.的情況，可他產生的結果卻是我想要的。
1個input有300張圖片 ==> 會有問題?
原本期望的是兩個input，
input1:5個label，150張圖片
input2:5個label，150張圖片
如果照您說的那樣做是否會變成下方情況?
input1:5個label，300張圖片
input2就沒東西了
上面的訊息：
Found 300 images belonging to 5 classes.
Found 300 images belonging to 5 classes.
訓練及測試資料各有300筆，5 classes。
但我這個是multiple inputs model，有兩個inputs的話，應該要為以下結果，還是我的想法是錯的呢?
訓練資料1: origin: 5 classes, 150 images ---> input1
訓練資料2: entropy: 5 classes, 150 images ---> input2
驗證資料1: origin: 5 classes, 150 images
驗證資料2: entropy: 5 classes, 150 images
以這個例子來講，他得到結果就是2個inputs，分別有2個classes和5000張圖片，但我不明白為什麼他的directory是一樣的，想請問他是用甚麼方式把不同的inputs圖片存放在同一個directory，卻可以用.flow_from_directory讀取到那樣的結果?
因為此篇已是2年前在stackoverflow上發問的，且我的帳號等級不夠無法提問，因此在此尋求答案。
#add data_augmentation
train_aug_datagen = ImageDataGenerator(
rescale = 1./255,
rotation_range = 20,
shear_range = 0.1,
zoom_range = 0.2,
width_shift_range = 0.1,
height_shift_range = 0.1,
horizontal_flip = True
)
validation_datagen = ImageDataGenerator(rescale = 1./255)
def two_image_generator(generator,
directory,
batch_size,
shuffle = False,
img_size1 = (224,224),
img_size2 = (299,299)):
gen1 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size1,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 7)
gen2 = generator.flow_from_directory(
# This is the target directory
directory,
# All images will be resized to target height and width.
target_size=img_size2,
batch_size=batch_size,
# Since we use categorical_crossentropy loss, we need categorical labels
class_mode='categorical',
shuffle = shuffle,
seed = 7)
while True:
X1i = gen1.next()
X2i = gen2.next()
yield [X1i[0], X2i[0]], X2i[1] #Yield both images and their mutual label
train_generator = two_image_generator(train_aug_datagen,
train_dir,
batch_size = batch_size,
shuffle = True)
validation_generator = two_image_generator(validation_datagen,
validation_dir,
batch_size = batch_size,
shuffle = True)
history = model.fit_generator(train_generator,
steps_per_epoch= NUM_TRAIN //batch_size,
epochs=100,
validation_data=validation_generator,
validation_steps= NUM_TEST //batch_size,
verbose=1,
use_multiprocessing=True,
# workers=14,
callbacks=callbacks )
Epoch 1/100
Found 5000 images belonging to 2 classes.
Found 5000 images belonging to 2 classes.
Found 52700 images belonging to 2 classes.
Found 52700 images belonging to 2 classes.
340/625 [===============>..............] - ETA: 4:37 - loss: 7.7634 - acc: 0.4926
multiple inputs model ==> 應該使用 Funcational API 定義模型，可使用 concatenate layer 合併 inputs，可參閱：
https://keras.io/guides/functional_api/
照keras官方說明.fit()是可以透過generator來輸入數據的，而我已再三確認過前面的two_image_generator()和最後.fit()應該是沒有錯誤，所以是我的multiple inputs CNN有錯誤嗎?
以下是我架構的multiple inputs CNN
# the first branch operates on the first input
input_origin = Input(shape = input_shape)
## 1
x = Conv2D(64, (3, 3), strides = 1, padding='same')(input_origin)
x = BatchNormalization(axis = -1)(x)
x = Activation(activation)(x)
x = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(x)
## 2
x = Conv2D(128, (3, 3), strides = 1, padding='same')(x)
x = BatchNormalization(axis = -1)(x)
x = Activation(activation)(x)
x = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(x)
## 3
x = Conv2D(256, (3, 3), strides = 1, padding='same')(x)
x = BatchNormalization(axis = -1)(x)
x = Activation(activation)(x)
x = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(x)
## 4
x = Conv2D(512, (3, 3), strides = 1, padding='same')(x)
x = BatchNormalization(axis = -1)(x)
x = Activation(activation)(x)
x = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(x)
## 5
x = Conv2D(512, (3, 3), strides = 1, padding='same')(x)
x = BatchNormalization(axis = -1)(x)
x = Activation(activation)(x)
x = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(x)
x = Model(inputs = input_origin, outputs = x)
# the second branch opreates on the second input
input_entropy = Input(shape = input_shape)
## 1
y = Conv2D(64, (3, 3), strides = 1, padding='same')(input_entropy)
y = BatchNormalization(axis = -1)(y)
y = Activation(activation)(y)
y = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(y)
## 2
y = Conv2D(128, (3, 3), strides = 1, padding='same')(y)
y = BatchNormalization(axis = -1)(y)
y = Activation(activation)(y)
y = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(y)
## 3
y = Conv2D(256, (3, 3), strides = 1, padding='same')(y)
y = BatchNormalization(axis = -1)(y)
y = Activation(activation)(y)
y = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(y)
## 4
y = Conv2D(512, (3, 3), strides = 1, padding='same')(y)
y = BatchNormalization(axis = -1)(y)
y = Activation(activation)(y)
y = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(y)
## 5
y = Conv2D(512, (3, 3), strides = 1, padding='same')(y)
y = BatchNormalization(axis = -1)(y)
y = Activation(activation)(y)
y = MaxPooling2D(pool_size=(3, 3), strides = 2, padding='same')(y)
y = Model(inputs = input_entropy, outputs = y)
# combine the output of the two branches
combined = concatenate([x.output, y.output])
# apply a FC layer and then a regression prediction on the
# combined outputs
z = Flatten()(combined)
z = Dense(4096)(z)
z = Dense(4096)(z)
z = Dense(class_num, activation='softmax')(z)
# our model will accept the inputs of the two branches and
# then output a single value
model = Model(inputs = [input_origin, input_entropy], outputs=z)
adagrad = tf.keras.optimizers.Adagrad(learning_rate = learning_rate)
model.compile(optimizer = adagrad, loss = 'categorical_crossentropy', metrics = ['accuracy'])
執行看看，沒有錯誤訊息，準確率符合預期，就OK。
執行沒有錯誤訊息，但這樣變成一個5 classes 300 images的input，這樣只有一個input，就不是我想要的2個inputs了(不是共2個inputs 各有5 classes 150 images 的multiple inputs)。
請問我這樣的理解有誤嗎?
iThome鐵人賽
看影片追技術