我試圖基本上復制本教程：https : //keras.io/examples/vision/image_classification_from_scratch/
但我似乎無法提高我的 val_accuracy 分數。我也有 2 種影像狗（Hunde）和貓（Katzen），但每種只有 95 個樣本。我有一個“上層”檔案夾“Hunde und Katzen”，這些示例的檔案夾就在其中。我可能需要調整一些引數，因為我的樣本量太小了，但我已經嘗試過一些代碼部分。
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import os
num_skipped = 0
for folder_name in ("Hund", "Katze"):
folder_path = os.path.join("Hund und Katze", folder_name)
for fname in os.listdir(folder_path):
fpath = os.path.join(folder_path, fname)
try:
fobj = open(fpath, "rb")
is_jfif = tf.compat.as_bytes("JFIF") in fobj.peek(10)
finally:
fobj.close()
if not is_jfif:
num_skipped = 1
# Delete corrupted image
os.remove(fpath)
print("Deleted %d images" % num_skipped)
image_size = (180, 180)
batch_size = 16
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
"Hund und Katze",
validation_split=0.5,
subset="training",
seed=9,
image_size=image_size,
batch_size=batch_size,
)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
"Hund und Katze",
validation_split=0.5,
subset="validation",
seed=9,
image_size=image_size,
batch_size=batch_size,
)
#Found 190 files belonging to 2 classes.
#Using 95 files for training.
#Found 190 files belonging to 2 classes.
#Using 95 files for validation.
data_augmentation = keras.Sequential(
[
layers.RandomFlip("horizontal"),
layers.RandomRotation(0.1),
]
)
train_ds = train_ds.prefetch(buffer_size=8)
val_ds = val_ds.prefetch(buffer_size=8)
def make_model(input_shape, num_classes):
inputs = keras.Input(shape=input_shape)
# Image augmentation block
x = data_augmentation(inputs)
# Entry block
x = layers.Rescaling(1.0 / 255)(x)
x = layers.Conv2D(16, 3, strides=2, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.Conv2D(32, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
previous_block_activation = x # Set aside residual
for size in [128, 256, 512, 728]:
x = layers.Activation("relu")(x)
x = layers.SeparableConv2D(size, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.SeparableConv2D(size, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.MaxPooling2D(3, strides=2, padding="same")(x)
# Project residual
residual = layers.Conv2D(size, 1, strides=2, padding="same")(
previous_block_activation
)
x = layers.add([x, residual]) # Add back residual
previous_block_activation = x # Set aside next residual
x = layers.SeparableConv2D(1024, 3, padding="same")(x)
x = layers.BatchNormalization()(x)
x = layers.Activation("relu")(x)
x = layers.GlobalAveragePooling2D()(x)
if num_classes == 2:
activation = "sigmoid"
units = 1
else:
activation = "softmax"
units = num_classes
x = layers.Dropout(0.5)(x)
outputs = layers.Dense(units, activation=activation)(x)
return keras.Model(inputs, outputs)
model = make_model(input_shape=image_size (3,), num_classes=2)
keras.utils.plot_model(model, show_shapes=True)
#('You must install pydot (`pip install pydot`) and install graphviz (see instructions at
#https://graphviz.gitlab.io/download/) ', 'for plot_model/model_to_dot to work.')
epochs = 10
callbacks = [
keras.callbacks.ModelCheckpoint("save_at_{epoch}.h5"),
]
model.compile(
optimizer=keras.optimizers.Adam(0.001),
loss="binary_crossentropy",
metrics=["accuracy"],
)
model.fit(
train_ds, epochs=epochs, callbacks=callbacks, validation_data=val_ds,
)
Output: Epoch 1/10
6/6 [==============================] - 8s 1s/step - loss: 0.7691 - accuracy: 0.6421 - val_loss: 0.6935 - val_accuracy: 0.4632
E:\anacondaBI\lib\site-packages\keras\engine\functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.
layer_config = serialize_layer_fn(layer)
Epoch 2/10
6/6 [==============================] - 6s 995ms/step - loss: 0.7747 - accuracy: 0.6526 - val_loss: 0.6917 - val_accuracy: 0.5368
Epoch 3/10
6/6 [==============================] - 6s 1s/step - loss: 0.6991 - accuracy: 0.7053 - val_loss: 0.6905 - val_accuracy: 0.5368
Epoch 4/10
6/6 [==============================] - 6s 1s/step - loss: 0.5411 - accuracy: 0.7368 - val_loss: 0.6935 - val_accuracy: 0.5368
Epoch 5/10
6/6 [==============================] - 6s 1s/step - loss: 0.3949 - accuracy: 0.8316 - val_loss: 0.7023 - val_accuracy: 0.5368
Epoch 6/10
6/6 [==============================] - 6s 1s/step - loss: 0.4440 - accuracy: 0.8526 - val_loss: 0.7199 - val_accuracy: 0.5368
Epoch 7/10
6/6 [==============================] - 6s 1s/step - loss: 0.3515 - accuracy: 0.8842 - val_loss: 0.7470 - val_accuracy: 0.5368
Epoch 8/10
6/6 [==============================] - 6s 1s/step - loss: 0.3249 - accuracy: 0.8526 - val_loss: 0.7955 - val_accuracy: 0.5368
Epoch 9/10
6/6 [==============================] - 6s 994ms/step - loss: 0.3953 - accuracy: 0.8421 - val_loss: 0.8570 - val_accuracy: 0.5368
Epoch 10/10
6/6 [==============================] - 6s 989ms/step - loss: 0.4363 - accuracy: 0.7789 - val_loss: 0.9189 - val_accuracy: 0.5368
<keras.callbacks.History at 0x2176ec764c0>
uj5u.com熱心網友回復：
每個類別的 95 個樣本較少以達到不錯的準確度
將您的validation_split減少到 0.05（驗證為 5%），因為您的資料點數量非常少
如果第一步不幫助你，那么你可以使用遷移學習：使用對imagenet為如良好的精度架構即MobileNets，ResNets和efficientnets
如果以上 2 個步驟沒有給你一個很好的準確性，那么嘗試增加你的資料大小并調整你的超引數。
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