I am trying to learn how to perform feature extraction from a pre-trained model for a transfer learning task. I am currently trying to use MobileNet v2 Feature extractor from tensorhub, Although the original image shapes are a tuple of (224, 224) and my images are 384x288x3. What I tried doing was:
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")])
I am getting the error at the following line:
mobilenet_model = create_model(mobilenet_url, num_classes=2)
The error stacktrace is the following:
ValueError: Exception encountered when calling layer "feature_extractor_layer" (type KerasLayer).
in user code:
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
I'd like to know how can I use my own image shape for the feature extraction? And if it isn't possible how can I adequately input that images of those sizes for the feature extractor
You need reshape IMG_SHAPE = (384, 288) to (224,224) as your input of mobilenet_v2. One of the methods for reshaping is adding Lambda layer with tf.image.resize to your model:
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
Example Code: (You can read another example here):
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))
Output:
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