質問編集履歴
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### 前提
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３入力１出力のCNNを構築しています。
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（例）
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今回はmodel.fitの段階でエラーが生じたため質問させていただきます。
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■■な機能を実装中に以下のエラーメッセージが発生しました。
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### 実現したいこと
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構築したCNNで正常に学習させたい
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- [ ] ▲▲機能を動作するようにする
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### 発生している問題・エラーメッセージ
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Traceback (most recent call last):
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File "C:\dl\data\3input.py", line 128, in <module>
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hist = model.fit([train_generator1,train_generator2,train_generator3],
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File "C:\Users\sherl\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
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raise e.with_traceback(filtered_tb) from None
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File "C:\Users\sherl\AppData\Local\Programs\Python\Python310\lib\site-packages\keras\engine\data_adapter.py", line 1083, in select_data_adapter
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raise ValueError(
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ValueError: Failed to find data adapter that can handle input: (<class 'list'> containing values of types {"<class 'keras.preprocessing.image.DirectoryIterator'>"}), <class 'NoneType'>
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### 該当のソースコード
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from keras.layers import Input, Dense, concatenate
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from keras.models import Model
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from keras.optimizers import SGD
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from keras.preprocessing.image import ImageDataGenerator
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from PIL import Image
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import keras
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import glob
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import numpy as np
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import os
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num_classes = 3
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batch_size = 16
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epochs = 100
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data_dir = './comp'
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filename = '3inputs'
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inputs1 = Input(shape=(256,256,3))
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inputs2 = Input(shape=(256,256,3))
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inputs3 = Input(shape=(256,256,3))
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X = []
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Y = []
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#for Dimple
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for f in glob.glob('/comp/D/*.jpg'):
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fname = os.path.split(f)[1]
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file_path = '/comp/D/'+ fname
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im = np.assary(Image.open(file_path))
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X.append(im)
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label = [0]
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Y.append(label)
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#for QC
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for f in glob.glob('/comp/QC/*.jpg'):
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fname = os.path.split(f)[1]
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file_path = '/comp/QC/'+ fname
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im = np.assary(Image.open(file_path))
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X.append(im)
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label = [1]
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Y.append(label)
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#for IG
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for f in glob.glob('/comp/IG/*.jpg'):
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fname = os.path.split(f)[1]
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file_path = '/comp/IG/'+ fname
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im = np.assary(Image.open(file_path))
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X.append(im)
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label = [2]
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Y.append(label)
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X = np.array(X)
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Y = np.array(Y)
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X_train = X.astype('float32') / 256
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Y_train = keras.utils.to_categorical(Y, num_classes)
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x = Dense(64, activation = "relu")(inputs1)
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x = Dense(64, activation = "relu")(x)
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y = Dense(64, activation = "relu")(inputs2)
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y = Dense(64, activation = "relu")(y)
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z = Dense(64, activation = "relu")(inputs3)
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z = Dense(64, activation = "relu")(z)
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combined = concatenate([x, y, z])
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prediction = Dense(10, activation = "softmax")(combined)
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model = Model(inputs = [inputs1, inputs2, inputs3], outputs = prediction)
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model.compile(optimizer = SGD(lr = 0.0001, momentum = 0.9),
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loss = 'categorical_crossentropy',
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metrics = ['accuracy'],
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)
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model.summary()
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#//------Generator1-------//
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train_datagen1 = ImageDataGenerator(
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rescale= 1./255,
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shear_range = 0.2,
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zoom_range = 0.2,
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horizontal_flip= True
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)
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validation_datagen1 = ImageDataGenerator(rescale = 1./255)
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train_generator1 = train_datagen1.flow_from_directory(
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data_dir,
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target_size = (256,256),
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batch_size = batch_size,
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class_mode = 'categorical',
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shuffle = True
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)
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validation_generator1 = validation_datagen1.flow_from_directory(
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data_dir,
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target_size = (256,256),
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batch_size = batch_size,
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class_mode = 'categorical',
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shuffle = True
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)
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#//------Generator2-------//
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train_datagen2 = ImageDataGenerator(
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rescale= 1./255,
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shear_range = 0.2,
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zoom_range = 0.2,
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horizontal_flip= True
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)
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validation_datagen2 = ImageDataGenerator(rescale = 1./255)
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train_generator2 = train_datagen2.flow_from_directory(
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data_dir,
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target_size = (256,256),
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batch_size = batch_size,
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class_mode = 'categorical',
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shuffle = True
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)
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validation_generator2 = validation_datagen2.flow_from_directory(
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data_dir,
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target_size = (256,256),
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batch_size = batch_size,
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class_mode = 'categorical',
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shuffle = True
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)
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#//------Generator3-------//
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train_datagen3 = ImageDataGenerator(
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rescale= 1./255,
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shear_range = 0.2,
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zoom_range = 0.2,
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horizontal_flip= True
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)
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validation_datagen3 = ImageDataGenerator(rescale = 1./255)
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train_generator3 = train_datagen3.flow_from_directory(
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data_dir,
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target_size = (256,256),
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batch_size = batch_size,
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class_mode = 'categorical',
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shuffle = True
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)
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validation_generator3 = validation_datagen3.flow_from_directory(
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data_dir,
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target_size = (256,256),
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batch_size = batch_size,
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class_mode = 'categorical',
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shuffle = True
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)
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#//-------Generator END-------//
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hist = model.fit([train_generator1,train_generator2,train_generator3],
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epochs = epochs,
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verbose= 1,
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validation_data = [validation_generator1, validation_generator2, validation_generator3]
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)
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### 試したこと
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エラー文を見る限り、データ前処理の段階で不具合があるようなので、色々書き換えてみましたが、現状は迷走してしまっています、、、。
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### 補足情報（FW/ツールのバージョンなど）
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python
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