我正在嘗試學習如何從預訓練模型中執行特征提取以完成遷移學習任務。我目前正在嘗試使用來自 tensorhub 的 MobileNet v2 特征提取器，雖然原始影像形狀是 (224, 224) 的元組并且我的影像是 384x288x3。我嘗試做的是：
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras import layers
IMG_SHAPE = (384, 288)
BATCH_SIZE = 32
train_dir = '/content/drive/MyDrive/dataset_split/Training'
test_dir = '/content/drive/MyDrive/dataset_split/Test'
train_datagen = ImageDataGenerator(rescale=1/255.)
test_datagen = ImageDataGenerator(rescale=1/255.)
training_dataset = train_datagen.flow_from_directory(train_dir, target_size=IMG_SHAPE,
batch_size=BATCH_SIZE, class_mode='categorical')
print("Testing Images: ")
test_data = test_datagen.flow_from_directory(test_dir, target_size=IMG_SHAPE,
batch_size=BATCH_SIZE, class_mode='categorical')
mobilenet_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
def create_model(model_url, num_classes=2):
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False, name="feature_extractor_layer", input_shape=IMG_SHAPE)
model = tf.keras.Sequential([feature_extractor_layer, layers.Dense(num_classes, activation="softmax", name="output_layer")])
return model
mobilenet_model = create_model(mobilenet_url, num_classes=2)
mobilenet_model.compile(loss='categorical_crossentropy',
optimizer=tf.keras.optimizers.Adam(),
metrics=['accuracy'])
history = mobilenet_model.fit(training_dataset, epochs=5, steps_per_epoch=len(training_dataset), validation_data=test_data,
validation_steps=len(test_data),
callbacks=[create_tensorboard_callback(dir_name="tensorflow_hub",
experiment_name="MobileNet_v2")])
我在以下行收到錯誤：
mobilenet_model = create_model(mobilenet_url, num_classes=2)
錯誤堆疊跟蹤如下：
ValueError：呼叫層“feature_extractor_layer”（KerasLayer 型別）時遇到例外。
在用戶代碼中：
File "/usr/local/lib/python3.7/dist-packages/tensorflow_hub/keras_layer.py", line 237, in call *
result = smart_cond.smart_cond(training,
ValueError: Could not find matching concrete function to call loaded from the SavedModel. Got:
Positional arguments (4 total):
* Tensor("inputs:0", shape=(None, 224, 224), dtype=float32)
* False
* False
* 0.99
Keyword arguments: {}
Expected these arguments to match one of the following 4 option(s):
Option 1:
Positional arguments (4 total):
* TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
* True
* False
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Option 2:
Positional arguments (4 total):
* TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
* True
* True
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Option 3:
Positional arguments (4 total):
* TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
* False
* True
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Option 4:
Positional arguments (4 total):
* TensorSpec(shape=(None, 224, 224, 3), dtype=tf.float32, name='inputs')
* False
* False
* TensorSpec(shape=(), dtype=tf.float32, name='batch_norm_momentum')
Keyword arguments: {}
Call arguments received:
? inputs=tf.Tensor(shape=(None, 224, 224), dtype=float32)
? training=None
我想知道如何使用自己的影像形狀進行特征提取？如果不可能，我怎樣才能為特征提取器充分輸入這些尺寸的影像
uj5u.com熱心網友回復：
您需要重塑IMG_SHAPE = (384, 288)為(224,224)mobilenet_v2 的輸入。重塑的方法之一是將Lambda layerwith添加tf.image.resize到您的模型中：
def create_model(model_url, num_classes=2):
inp = tf.keras.layers.Input((384, 288,3))
resize_img = tf.keras.layers.Lambda(lambda image: tf.image.resize(image, (224,224)))
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False,
name="feature_extractor_layer",
input_shape=(224,224,3))
model = tf.keras.Sequential([
inp,
resize_img,
feature_extractor_layer,
tf.keras.layers.Dense(num_classes,
activation="softmax",
name="output_layer")
])
return model
示例代碼：（您可以在此處閱讀另一個示例）：
import numpy
from PIL import Image
import tensorflow as tf
import tensorflow_hub as hub
from tensorflow.keras.preprocessing.image import ImageDataGenerator
for loc, rep in zip(['training', 'test'], [20,10]):
for idx, c in enumerate([f'c/{loc}/1/', f'c/{loc}/2/']*rep):
array = numpy.random.rand(384,288,3) * 255
img = Image.fromarray(array.astype('uint8')).convert('RGB')
img.save('{}img_{}.png'.format(c, idx))
IMG_SHAPE = (384, 288)
BATCH_SIZE = 32
train_dir = 'c/training'
test_dir = 'c/test'
train_datagen = ImageDataGenerator(rescale=1/255.)
test_datagen = ImageDataGenerator(rescale=1/255.)
training_dataset = train_datagen.flow_from_directory(train_dir, target_size=IMG_SHAPE,
batch_size=BATCH_SIZE, class_mode='categorical')
test_dataset = test_datagen.flow_from_directory(test_dir, target_size=IMG_SHAPE,
batch_size=BATCH_SIZE, class_mode='categorical')
mobilenet_url = "https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4"
def create_model(model_url, num_classes=2):
inp = tf.keras.layers.Input((384, 288,3))
resize_img = tf.keras.layers.Lambda(lambda image: tf.image.resize(image, (224,224)))
feature_extractor_layer = hub.KerasLayer(model_url, trainable=False,
name="feature_extractor_layer",
input_shape=(224,224,3))
model = tf.keras.Sequential([
inp,
resize_img,
feature_extractor_layer,
tf.keras.layers.Dense(num_classes,
activation="softmax",
name="output_layer")
])
return model
mobilenet_model = create_model(mobilenet_url, num_classes=3)
mobilenet_model.compile(loss='categorical_crossentropy',optimizer=tf.keras.optimizers.Adam(),metrics=['accuracy'])
history = mobilenet_model.fit(training_dataset, epochs=5, steps_per_epoch=len(training_dataset),
validation_data=test_dataset,validation_steps=len(test_dataset))
輸出：
Found 40 images belonging to 3 classes.
Found 20 images belonging to 3 classes.
Epoch 1/5
2/2 [==============================] - 18s 7s/step - loss: 0.9844 - accuracy: 0.5000 - val_loss: 0.8181 - val_accuracy: 0.5500
Epoch 2/5
2/2 [==============================] - 5s 4s/step - loss: 0.7603 - accuracy: 0.5250 - val_loss: 0.7505 - val_accuracy: 0.4500
Epoch 3/5
2/2 [==============================] - 4s 2s/step - loss: 0.7311 - accuracy: 0.4750 - val_loss: 0.7383 - val_accuracy: 0.4500
Epoch 4/5
2/2 [==============================] - 2s 1s/step - loss: 0.7099 - accuracy: 0.5250 - val_loss: 0.7220 - val_accuracy: 0.4500
Epoch 5/5
2/2 [==============================] - 2s 1s/step - loss: 0.6894 - accuracy: 0.5000 - val_loss: 0.7162 - val_accuracy: 0.5000
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