Python GoodsDataset.get_images_for_label 예제들

예제 #1

        mode='max'),
    keras.callbacks.TensorBoard(
        log_dir='./tensorboard-incv4',
        write_images=True,
    )
]

goods_dataset = GoodsDataset("dataset-181018.list", "dataset-181018.labels",
                             settings.IMAGE_SIZE, settings.train_batch,
                             settings.valid_batch, settings.multiply,
                             settings.valid_percentage)
train_dataset = goods_dataset.get_train_dataset()
valid_dataset = goods_dataset.get_valid_dataset()

results = model.evaluate(
    goods_dataset.get_images_for_label(94).batch(16).repeat(), steps=6)
print(results)

model.fit(
    train_dataset.prefetch(2).repeat(),
    callbacks=callbacks,
    epochs=30,
    steps_per_epoch=1157,
    validation_data=valid_dataset.repeat(),
    validation_steps=77,
)
"""
1) num_last_trainable_layers = 60 
optimizer=Adagrad(lr=0.001) 
(новая аугментация с вращ и транс. - 734s 634ms/step)
Epoch 1/50 - loss: 1.9875 - acc: 0.4700 - top_6: 0.8023 - val_loss: 2.9080 - val_acc: 0.4233 - val_top_6: 0.7297

예제 #2

        monitor='val_top_6',
        mode='max'
    ),
    keras.callbacks.TensorBoard(
        log_dir='./tensorboard-incv4',
        write_images=True,
    )
]

goods_dataset = GoodsDataset("dataset-181018.list", "dataset-181018.labels", 
  settings.IMAGE_SIZE, settings.train_batch, settings.valid_batch, settings.multiply, 
  settings.valid_percentage)
train_dataset = goods_dataset.get_train_dataset()
valid_dataset = goods_dataset.get_valid_dataset()

results = model.evaluate(goods_dataset.get_images_for_label(94).batch(16).repeat(), steps=6)
print(results)

model.fit(train_dataset.prefetch(16).repeat(), # was prefetch(2)
          callbacks=callbacks,
          epochs=200,
          steps_per_epoch=1157,
          validation_data=valid_dataset.repeat(),
          validation_steps=77,
          )


"""
1) num_last_trainable_layers = 60 
optimizer=Adagrad(lr=0.001) 
(новая аугментация с вращ и транс. - 734s 634ms/step)