Я использую модели EfficientNetB7 и EfficientNetB0 для обучения своего набора данных и столкнулся с серьезной аномалией. EfficientNetB7 дал точность 96,4% с 40 эпохами, lr_callback, 4 nb_classes, весами imagenet.
GCS_DS_PATH = KaggleDatasets().get_gcs_path('plant-pathology-2020-fgvc7')
path='../input/plant-pathology-2020-fgvc7/'
train = pd.read_csv(path + 'train.csv')
test = pd.read_csv(path + 'test.csv')
sub = pd.read_csv(path + 'sample_submission.csv')
train_paths = train.image_id.apply(lambda x : GCS_DS_PATH + '/images/' + x + '.jpg').values
test_paths = test.image_id.apply(lambda x : GCS_DS_PATH + '/images/' + x + '.jpg').values
train_labels = train.loc[:,'healthy':].values.astype(int)
train_labels_healthy = train.loc[:,'healthy'].values.astype(int)
train_labels_multiple_diseases = train.loc[:,'multiple_diseases'].values.astype(int)
train_labels_rust = train.loc[:,'rust'].values.astype(int)
train_labels_scab = train.loc[:,'scab'].values.astype(int)
train_dataset = (
tf.data.Dataset
.from_tensor_slices((train_paths, train_labels))
.map(decode_image, num_parallel_calls=AUTO)
.map(data_augment, num_parallel_calls=AUTO)
.repeat()
.shuffle(512)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
train_dataset1 = (
tf.data.Dataset
.from_tensor_slices((train_paths, train_labels_healthy_one_hot))
.map(decode_image, num_parallel_calls=AUTO)
.map(data_augment, num_parallel_calls=AUTO)
.repeat()
.shuffle(512)
.batch(BATCH_SIZE)
.prefetch(AUTO)
)
nb_classes=4
def get_model():
base_model = efn.EfficientNetB7(weights='imagenet', include_top=False, pooling='avg', input_shape=(img_size, img_size, 3))
x = base_model.output
predictions = Dense(nb_classes, activation="softmax")(x)
return Model(inputs=base_model.input, outputs=predictions)
with strategy.scope():
model = get_model()
model.compile(optimizer='adam', loss='categorical_crossentropy',metrics=['accuracy'])
model.summary()
model.fit(
train_dataset,
steps_per_epoch=train_labels.shape[0] // BATCH_SIZE,
epochs=10
)
Output: Train for 28 steps Epoch 1/10 28/28 [==============================] - 253s 9s/step - loss: 0.2862 - accuracy: 0.8951 Epoch 2/10 28/28 [==============================] - 15s 535ms/step - loss: 0.1453 - accuracy: 0.9520 Epoch 3/10 28/28 [==============================] - 34s 1s/step - loss: 0.1450 - accuracy: 0.9554 Epoch 4/10 28/28 [==============================] - 35s 1s/step - loss: 0.1271 - accuracy: 0.9587 Epoch 5/10 28/28 [==============================] - 35s 1s/step - loss: 0.0935 - accuracy: 0.9621 Epoch 6/10 28/28 [==============================] - 35s 1s/step - loss: 0.0951 - accuracy: 0.9621 Epoch 7/10 28/28 [==============================] - 35s 1s/step - loss: 0.0615 - accuracy: 0.9721 Epoch 8/10 28/28 [==============================] - 35s 1s/step - loss: 0.0674 - accuracy: 0.9833 Epoch 9/10 28/28 [==============================] - 35s 1s/step - loss: 0.0654 - accuracy: 0.9743 Epoch 10/10 28/28 [==============================] - 35s 1s/step - loss: 0.0435 - accuracy: 0.9821
Итак, я попытался повысить точность, используя 4 модели EfficientNetB0 для независимого прогнозирования 4 классов, но точность застряла на уровне 50%. Я попытался изменить скорость обучения, чтобы увидеть, не застрял ли он в локальном минимуме, но точность такая же.
nb_classes=1
def get_model():
base_model = efn.EfficientNetB0(weights='imagenet', include_top=False, pooling='avg', input_shape=(img_size, img_size, 3))
x = base_model.output
predictions = Dense(nb_classes, activation="softmax")(x)
return Model(inputs=base_model.input, outputs=predictions)
adam = Adam(learning_rate=0.05) #Tried 0.0001,0.001,0.01,0.05
with strategy.scope():
model1 = get_model()
#print('1')
# model2 = get_model()
# print('2')
# model3 = get_model()
# print('3')
# model4 = get_model()
# print('4')
model1.compile(optimizer=adam, loss='binary_crossentropy',metrics=['accuracy'])
#model2.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])
#model3.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])
#model4.compile(optimizer='adam', loss='binary_crossentropy',metrics=['accuracy'])
model1.summary()
#model2.summary()
#model3.summary()
#model4.summary()
model1.fit(
train_dataset1,
steps_per_epoch=train_labels_rust.shape[0] // BATCH_SIZE,
epochs=10
)
Output: Train for 28 steps Epoch 1/10 28/28 [==============================] - 77s 3s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 2/10 28/28 [==============================] - 32s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 3/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 4/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 5/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 6/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 7/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 8/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 9/10 28/28 [==============================] - 33s 1s/step - loss: 7.6666 - accuracy: 0.5000 Epoch 10/10 28/28 [==============================] - 34s 1s/step - loss: 7.6666 - accuracy: 0.5000
Я также пробовал другие нейронные сети, такие как ResNet50, но точность оставалась на уровне 50%. Может ли кто-нибудь сказать мне, где я совершаю ошибку.
