Add this modification to your script then InputImage is viewed as a Tensor:
resized_input_tensor_name = 'InputImage:0'I am trying to adapt the example retrain script ( https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py ) to use the Inception V4 model. , 1 week ago Yritän mukauttaa esimerkkikurssikomentosarjaa (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/image_retraining/retrain.py) käyttämään ... , 1 week ago inception-retrain. Standalone version of Tensorflow Inception Retrain. Google Codelabs can be found here. This example downloads a pre-trained version of the inception model and re-trains the last layers to recognize custom categories of images. In particular we will use photos of flowers. Steps 1. (optional) Download flower files , 1 week ago Aug 10, 2017 · Using inception v4 in retrain example. 761. Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2. 3. Determining input nodes when freezing Tensorflow graphs using tf.data.Datasets. 0. I want to retrain inception-resnet-v2, but GPU kernel is not avaliable.
elif architecture == 'inception_v4': data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
#this won 't make any difference bottleneck_tensor_name = '
InceptionV4 / Logits / Logits / MatMul ' bottleneck_tensor_size = 1001 input_width = 299 input_height = 299 input_depth = 3 resized_input_tensor_name = '
InputImage ' model_file_name = '
inception_v4.pb ' input_mean = 128 input_std = 128 elif architecture == 'inception_v4': data_url = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
#this won 't make any difference bottleneck_tensor_name = '
InceptionV4 / Logits / Logits / MatMul ' bottleneck_tensor_size = 1001 input_width = 299 input_height = 299 input_depth = 3 resized_input_tensor_name = '
InputImage ' model_file_name = '
inception_v4.pb ' input_mean = 128 input_std = 128 python retrain.py--architecture = inception_v4--bottleneck_dir = test2 / bottlenecks--model_dir = inception_v4--summaries_dir = test2 / summaries / basic--output_graph = test2 / graph_flowers.pb--output_labels = test2 / labels_flowers.txt--image_dir = datasets / flowers / flower_photos--how_many_training_steps 100
resized_input_tensor_name = 'InputImage:0'Replace the model name with the variant you want to use, e.g. inception_v4. You can find the IDs in the model summaries at the top of this page.,Inception-v4 is a convolutional neural network architecture that builds on previous iterations of the Inception family by simplifying the architecture and using more inception modules than Inception-v3.,Your model lacks metadata. Adding metadata gives context on how your model was trained.,You can follow the timm recipe scripts for training a new model afresh.
{
"Parameters": 62000000 "FLOPs": 524000000 "Training Time": "24 hours",
"Training Resources": "8 NVIDIA V100 GPUs",
"Training Data": ["ImageNet, Instagram"],
"Training Techniques": ["AdamW, CutMix"]
}import timm
m = timm.create_model('inception_v4', pretrained = True)
m.eval()@misc {
szegedy2016inceptionv4,
title = {
Inception - v4,
Inception - ResNet and the Impact of Residual Connections on Learning
},
author = {
Christian Szegedy and Sergey Ioffe and Vincent Vanhoucke and Alex Alemi
},
year = {
2016
},
eprint = {
1602.07261
},
archivePrefix = {
arXiv
},
primaryClass = {
cs.CV
}
}Last updated 2022-06-30 UTC.
def build_dataset(subset):
return tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split = .20,
subset = subset,
label_mode = "categorical",
# Seed needs to provided when using validation_split and shuffle = True.# A fixed seed is used so that the validation set is stable across runs.seed = 123,
image_size = IMAGE_SIZE,
batch_size = 1)
train_ds = build_dataset("training")
class_names = tuple(train_ds.class_names)
train_size = train_ds.cardinality().numpy()
train_ds = train_ds.unbatch().batch(BATCH_SIZE)
train_ds = train_ds.repeat()
normalization_layer = tf.keras.layers.Rescaling(1. / 255)
preprocessing_model = tf.keras.Sequential([normalization_layer])
do_data_augmentation = False
if do_data_augmentation:
preprocessing_model.add(
tf.keras.layers.RandomRotation(40))
preprocessing_model.add(
tf.keras.layers.RandomTranslation(0, 0.2))
preprocessing_model.add(
tf.keras.layers.RandomTranslation(0.2, 0))
# Like the old tf.keras.preprocessing.image.ImageDataGenerator(),
# image sizes are fixed when reading, and then a random zoom is applied.
# If all training inputs are larger than image_size, one could also use
# RandomCrop with a batch size of 1 and rebatch later.
preprocessing_model.add(
tf.keras.layers.RandomZoom(0.2, 0.2))
preprocessing_model.add(
tf.keras.layers.RandomFlip(mode = "horizontal"))
train_ds = train_ds.map(lambda images, labels:
(preprocessing_model(images), labels))
val_ds = build_dataset("validation")
valid_size = val_ds.cardinality().numpy()
val_ds = val_ds.unbatch().batch(BATCH_SIZE)
val_ds = val_ds.map(lambda images, labels:
(normalization_layer(images), labels))optimize_lite_model = False
num_calibration_examples = 60
representative_dataset = None
if optimize_lite_model and num_calibration_examples:
# Use a bounded number of training examples without labels
for calibration.
# TFLiteConverter expects a list of input tensors, each with batch size 1.
representative_dataset = lambda: itertools.islice(
([image[None, ...]]
for batch, _ in train_ds
for image in batch),
num_calibration_examples)
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_path)
if optimize_lite_model:
converter.optimizations = [tf.lite.Optimize.DEFAULT]
if representative_dataset: # This is optional, see above.
converter.representative_dataset = representative_dataset
lite_model_content = converter.convert()
with open(f "/tmp/lite_flowers_model_{model_name}.tflite", "wb") as f:
f.write(lite_model_content)
print("Wrote %sTFLite model of %d bytes." %
("optimized "
if optimize_lite_model
else "", len(lite_model_content)))