![](/img/trans.png)
[英]How to use keras image_dataset_from_directory with custom structures?
[英]Keras: one-hot for labels in `image_dataset_from_directory`
我正在尝试使用 efficientNet 进行二值图像分类。 以下是我的代码。
import matplotlib.pyplot as plt
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras.models import Sequential
from tensorflow.keras import layers
from tensorflow.data import AUTOTUNE
DATA_DIR = "img/"
IMG_SIZE = 224
NUM_CLASSES = 2
EPOCH = 50
def train_val_split(DATA_DIR, IMG_SIZE):
val_data = image_dataset_from_directory(
DATA_DIR,
labels="inferred",
label_mode="binary",
color_mode="rgb",
batch_size=32,
image_size=(IMG_SIZE, IMG_SIZE),
validation_split=0.2,
subset="training",
seed=1
)
train_data = image_dataset_from_directory(
DATA_DIR,
labels="inferred",
label_mode="binary",
color_mode="rgb",
batch_size=32,
image_size=(IMG_SIZE, IMG_SIZE),
validation_split=0.2,
subset="validation",
seed=1
)
train_data = train_data.cache().prefetch(buffer_size=AUTOTUNE)
val_data = val_data.cache().prefetch(buffer_size=AUTOTUNE)
return train_data, val_data
def model_arch(NUM_CLASSES, IMG_SIZE):
"""efficientnet transfer learning"""
inputs = layers.Input(shape=(IMG_SIZE, IMG_SIZE, 3))
img_augmentation = Sequential(
[
layers.RandomRotation(factor=0.15),
layers.RandomTranslation(height_factor=0.1, width_factor=0.1),
layers.RandomFlip(),
layers.RandomContrast(factor=0.1),
],
name="img_augmentation",
)
x = img_augmentation(inputs)
# model = EfficientNetB0(include_top=False, input_tensor=x, weights="imagenet")
model = EfficientNetB0(include_top=False, input_tensor=x, weights='model/efficientnetb0_notop.h5')
# Freeze the pretrained weights
model.trainable = False
# Rebuild top
x = layers.GlobalAveragePooling2D(name="avg_pool")(model.output)
x = layers.BatchNormalization()(x)
top_dropout_rate = 0.2
x = layers.Dropout(top_dropout_rate, name="top_dropout")(x)
outputs = layers.Dense(NUM_CLASSES, activation="softmax", name="pred")(x)
# Compile
model = tf.keras.Model(inputs, outputs, name="EfficientNet")
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-2)
model.compile(
optimizer=optimizer,
loss="binary_crossentropy",
metrics=["accuracy"]
)
return model
if __name__ == "__main__":
train_data, val_data = train_val_split(DATA_DIR, IMG_SIZE)
model = model_arch(NUM_CLASSES, IMG_SIZE)
hist = model.fit(train_data,
epochs=EPOCH,
validation_data=val_data,
verbose=1)
但是,我遇到了以下错误。
ValueError: logits and labels must have the same shape ((None, 2) vs (None, 1))
我发现这是因为image_dataset_from_directory
中加载的标签不是单热编码的。
print(train_data)
<PrefetchDataset shapes: ((None, 224, 224, 3), (None, 1)), types: (tf.float32, tf.float32)>
我如何调整train_data
和val_data
的代码,以便它可以毫无问题地适应 model?
谢谢。
设法找出答案!
import tensorflow as tf
# one-hot encoding
train_data = train_data.map(lambda x, y: (x, tf.one_hot(y, depth=NUM_CLASSES)))
val_data = val_data.map(lambda x, y: (x, tf.one_hot(y, depth=NUM_CLASSES)))
您需要将label_mode
更改为更好的选项。
在你的情况下,我认为你想要label_mode='categorical',
。
作为@Dr。 史努比说的资料在这里: https://www.tensorflow.org/api_docs/python/tf/keras/utils/image_dataset_from_directory
声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.