質問編集履歴
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エラー内容の更新
test
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Python UnboundLocalError: local variable 'batch_x' referenced before assignment
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https://qiita.com/koshian2/items/909360f50e3dd5922f32
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しかし、コードを実行してみると、
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しかし、コードを実行してみると、以下のようなエラーが発生してしまいますが、どう修正したらいいかわからず困っております。
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お手数をお掛けするのですが、
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お手数をお掛けするのですが、修正方法についてお分かりの方がいらっしゃいましたら教えていただけないでしょうか。
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どうぞよろしくお願いいたします。
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###
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while True:
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40 batch_x_2, batch_y_2 = next(batches)
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---> 41 m1, m2 = batch_x.shape[0], batch_x_2.shape[0]
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42 if m1 < m2:
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43 batch_x_2 = batch_x_2[:m1]
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UnboundLocalError: local variable 'batch_x' referenced before assignment
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### 該当のソースコード
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```Python
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# model構築の準備
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import keras
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from keras.models import Model
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from keras.layers import Dense, GlobalAveragePooling2D,Input,Dropout,Activation
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from keras.preprocessing.image import ImageDataGenerator
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from keras import applications
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from keras.preprocessing.image import ImageDataGenerator
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from keras.optimizers import Adam
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from keras.callbacks import CSVLogger,EarlyStopping
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import numpy as np
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from keras import backend as K
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from keras.engine.topology import Layer
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import numpy as np
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import tensorflow as tf
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from keras.preprocessing.image import
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from keras.preprocessing.image import load_img, img_to_array, array_to_img
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import os
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import matplotlib.pyplot as plt
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%matplotlib inline
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#認識されない
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train_generator=train_datagen.flow_from_directory(
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train_dir,
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validation_datagen=ImageDataGenerator(rescale=1.0/255.)
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validation_generator=validation_datagen.flow_from_directory(
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batch_size = 12
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base_model=keras.applications.inception_resnet_v2.InceptionResNetV2(input_shape=(299,299,3),
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weights='imagenet',
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include_top=False)
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x = base_model.output
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x = GlobalAveragePooling2D()(x)
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x = Dense(1024, activation='relu')(x)
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predictions = Dense(4, activation='softmax')(x)
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model = Model(inputs=base_model.input,outputs=predictions)
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model.summary()
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model.compile(optimizer=Adam(lr=0.001),
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loss='categorical_crossentropy',
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metrics=['accuracy'])
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callbacks_list = [
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callbacks.ModelCheckpoint(
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filepath="model.ep{epoch:02d}.h5",#delsavepath,
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save_best_only=True),
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#バリデーションlossが改善しなくなったら学習率を変更する
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callbacks.ReduceLROnPlateau(
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monitor="val_loss",
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factor=0.8,
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patience=5,
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verbose=1)]#,
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#callbacks.EarlyStopping(monitor='val_loss', patience=7, verbose=1)]
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model.fit_generator(train_generator, steps_per_epoch=7208, epochs=10, validation_steps=1805, validation_data=validation_generator, callbacks=callbacks_list)
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```
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### 試したこと
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train_generator=flow_from_directory(
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train_dir,
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target_size=(299,299),
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batch_size=12,#25,
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class_mode='categorical',
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shuffle=True)
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に修正してみたところ、「'flow_from_directory' is not defined」というエラーが発生しました。
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