CODEWITHSUNDEEP
tensorflowkerasgoogle-colaboratory
Laurent R
Laurent R

Reputation: 832

Low accurcacy - Transfer learning + bottle-neck keras-tensorflow (resnet50)

I'm trying to do transfer learning / bottle neck with keras/tensorflow on a google Colaboratory notebook. My problem is that the accuracy doesn't go over 6% (Kaggle's dog breed challenge, 120 classes, data generated with datagen.flow_from_directory)

Below is my code, is there something I'm missing?

tr_model=ResNet50(include_top=False,
                  weights='imagenet',
                 input_shape = (224, 224, 3),)

datagen = ImageDataGenerator(rescale=1. / 255)

#### Training ####
train_generator = datagen.flow_from_directory(train_data_dir,
                                                    target_size=(image_size,image_size),
                                                    class_mode=None,
                                                    batch_size=batch_size,
                                                    shuffle=False)
bottleneck_features_train = tr_model.predict_generator(train_generator)
train_labels = to_categorical(train_generator.classes , num_classes=num_classes)

#### Validation ####
validation_generator = datagen.flow_from_directory(validation_data_dir, 
                                                    target_size=(image_size,image_size),
                                                    class_mode=None,
                                                    batch_size=batch_size,
                                                    shuffle=False)
bottleneck_features_validation = tr_model.predict_generator(validation_generator)
validation_labels = to_categorical(validation_generator.classes, num_classes=num_classes)

#### Model creation ####
model = Sequential()
model.add(Flatten(input_shape=bottleneck_features_train.shape[1:]))
model.add(Dense(num_class, activation='softmax'))

model.compile(optimizer='rmsprop',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

history = model.fit(bottleneck_features_train, train_labels,
                    epochs=30,
                    batch_size=batch_size,
                    validation_data=(bottleneck_features_validation, validation_labels))

I get a val_acc = 0.0592

When I use ResNet50 with the last layer, I get a score of 82%.

Can anyone spot what's wrong with my code.

Upvotes: 1

Views: 254

Answers (1)

Laurent R
Laurent R

Reputation: 832

Suppress the rescale and add the preprocessing helped a lot.

Those modifications help immensely:

from keras.applications.resnet50 import preprocess_input
datagen = ImageDataGenerator(preprocessing_function=preprocess_input)

I now have an accuracy of 80%

Upvotes: 1

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