I want create a model from tensorflow hub but when I try to load the model. I am getting following error.
enter image description here
`# lets create a model function
from tensorflow.keras import Sequential
IMAGE_SHAPE = (224,224)
def create_model(model_url,num_classes=10):
"""
create keras sequential
Args:
model_url(str)L a tensorflow hub feature extraction URL
num_classes (int): number of output neurons in the output layer
"""
# download a pretrained model.
feature_extraction_layer = hub.KerasLayer(model_url,trainable = False,name = 'feature_extraction_layer',
input_shape=IMAGE_SHAPE+(3,))
# creat model
model = Sequential([feature_extraction_layer,
layers.Dense(num_classes,activation='softmax',name='output_layer')
])
return model`
I tried running the same code on google colab but it didnt work. there also it gave me the same error.
Related
I am using tensorflow 2.6.2 and I downloaded the model from the Tensorflow 2 Model zoo
I am able to load the model using this
import tensorflow as tf
if __name__ == "__main__":
try:
model = tf.saved_model.load("/home/user/git/models_zoo/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model/")
But unfortunately I am not able to see all the layers of the model using the below
for v in model.trainable_variables:
print(v.name)
which should ideally print all the layers in the network, but I am getting the following error
print(model.trainable_variables)
AttributeError: '_UserObject' object has no attribute 'trainable_variables'
Can someone please tell, what I am doing wrong here.
I was able to print using this
loaded = tf.saved_model.load("/home/user/git/models_zoo/ssd_mobilenet_v2_320x320_coco17_tpu-8/saved_model/")
infer = loaded.signatures["serving_default"]
for v in infer.trainable_variables:
print(v.name)
I am trying to download the VGG19 model via TensorFlow
base_model = VGG19(input_shape = [256,256,3],
include_top = False,
weights = 'imagenet')
However the download always gets stuck before it finishes downloading. I've tried with different models too like InceptionV3 and the same happens there.
Fortunately, the prompt makes the link available where the model can be downloaded manually
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/vgg19/vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5
19546112/80134624 [======>.......................] - ETA: 11s
After downloading the model from the given link I try to import the model using
base_model = load_model('vgg19_weights_tf_dim_ordering_tf_kernels_notop.h5')
but I get this error
ValueError: No model found in config file.
How do I load in the downloaded .h5 model manually?
You're using load_model on weights, instead of a model. You need to have a defined model first, then load the weights.
weights = "path/to/weights"
model = VGG19 # the defined model
model.load_weights(weights) # the weights
Got the same problem when learning on tensorflow tutorial, too.
Transfer learning and fine-tuning: Create the base model from the pre-trained convnets
# Create the base model from the pre-trained model MobileNet V2
IMG_SIZE = (160, 160)
IMG_SHAPE = IMG_SIZE + (3,)
base_model = tf.keras.applications.MobileNetV2(input_shape=IMG_SHAPE, include_top=False, weights=None)
# load model weights manually
weights = 'mobilenet_v2_weights_tf_dim_ordering_tf_kernels_1.0_160_no_top.h5'
base_model.load_weights(weights)
I tried download the model.h5, and load manually. It works.
`
I am currently using AWS SageMaker Python SDK to train EfficientNet model (https://github.com/qubvel/efficientnet) to my data. Specifically, I use TensorFlow estimator as below. This code is in SageMaker notebook instance
import sagemaker
from sagemaker.tensorflow.estimator import TensorFlow
### sagemaker version = 1.50.17, python version = 3.6
estimator = TensorFlow("train.py", py_version = "py3", framework_version = "2.1.0",
role = sagemaker.get_execution_role(),
train_instance_type = "ml.m5.xlarge",
train_instance_count = 1,
image_name = 'xxx.dkr.ecr.xxx.amazonaws.com/xxx',
hyperparameters = {list of hyperparameters here: epochs, batch size},
subnets = [xxx],
security_group_ids = [xxx]
estimator.fit({
'class_1': 's3_path_class_1',
'class_2': 's3_path_class_2'
})
The code for train.py contains the usual training procedure, getting the image and labels from S3, transform them into the right array shape for EfficientNet input, and split into train, validation, and test set. In order to get reproducible result, I use the following reset_random_seeds function (If Keras results are not reproducible, what's the best practice for comparing models and choosing hyper parameters?) before calling EfficientNet model itself.
### code of train.py
import os
os.environ['PYTHONHASHSEED']=str(1)
import numpy as np
import tensorflow as tf
import efficientnet.tfkeras as efn
import random
### tensorflow version = 2.1.0
### tf.keras version = 2.2.4-tf
### efficientnet version = 1.1.0
def reset_random_seeds():
os.environ['PYTHONHASHSEED']=str(1)
tf.random.set_seed(1)
np.random.seed(1)
random.seed(1)
if __name__ == "__main__":
### code for getting training data
### ... (I have made sure that the training input is the same every time i re-run the code)
### end of code
reset_random_seeds()
model = efn.EfficientNetB5(include_top = False,
weights = 'imagenet',
input_shape = (80, 80, 3),
pooling = 'avg',
classes = 3)
model.compile(optimizer = 'Adam', loss = 'categorical_crossentropy')
model.fit(X_train, Y_train, batch_size = 64, epochs = 30, shuffle = True, verbose = 2)
### Prediction section here
However, each time i run the notebook instance, i always get a different result from the previous run. When I switched train_instance_type to "local" i always get the same result each time i run the notebook. Therefore, is the non-reproducible result caused by the training instance type that I have chosen? this instance (ml.m5.xlarge) has 4 vCPU, 16 Mem (GiB), and no GPU. If so, how to obtain reproducible results under this training instance?
Is it possible that your inconsistent result is getting from the
tf.random.set_seed()
Came across a post here: Tensorflow: Different results with the same random seed
When inspecting the Keras model yolov3-tiny.h5 using netron
I see that the input node is called input_1 and has type float32[?,?,?,3]. I would expect float32[?,416,416,3]
How can I force it to be float32[?,416,416,3]?
This is needed for downstream processing. The Keras model has to be converted to a frozen_model.pb in Tensorflow and then be further processed for deployement.
The deployement tools cannot handle an input with unknow w,h size.
Here is how I generated the Keras model. I downloaded the yolov3-tiny.cfg (https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg) and yolov3-tiny.weigths (https://pjreddie.com/media/files/yolov3-tiny.weights)
And then converted the model to a keras model using the following command :
python convert.py -p yolov3-tiny.cfg yolov3-tiny.weights model_data/yolov3-tiny.h5
(this code is obtained by cloning https://github.com/qqwweee/keras-yolo3)
Making a prediction using the saved Keras model works fine :
python yolo_video.py --image --model model_data/yolov3-tiny.h5
However when inspecting the Keras model yolov3-tiny.h5 using netron
I see that the input node is called input_1 and has type float32[?,?,?,3]
I would expect float32[?,416,416,3]
How can I force it to be float32[?,416,416,3]?
You could do that while converting the keras model file to tflite by passing input_shapes as a dictionary with input node name as the key and shape as the value.
Check out the TFLiteConverter class here
def _main_(args):
kmodel = args.kmodel
lmodel = args.lmodel
converter = tf.lite.TFLiteConverter.from_keras_model_file(kmodel, input_shapes={"input_1":[1,416,416,3]})
converter.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_model = converter.convert()
open("lmodel", "wb").write(tflite_model)
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description='Quantize a trained yolo model')
argparser.add_argument('-k', '--kmodel', help='path to model weights file input')
argparser.add_argument('-l', '--lmodel', help='path to tensor flow lite output')
args = argparser.parse_args()
_main_(args)
I have trained a Keras model based on this repo.
After the training I save the model as checkpoint files like this:
sess=tf.keras.backend.get_session()
saver = tf.train.Saver()
saver.save(sess, current_run_path + '/checkpoint_files/model_{}.ckpt'.format(date))
Then I restore the graph from the checkpoint files and freeze it using the standard tf freeze_graph script. When I want to restore the frozen graph I get the following error:
Input 0 of node Conv_BN_1/cond/ReadVariableOp/Switch was passed float from Conv_BN_1/gamma:0 incompatible with expected resource
How can I fix this issue?
Edit: My problem is related to this question. Unfortunately, I can't use the workaround.
Edit 2:
I have opened an issue on github and created a gist to reproduce the error.
https://github.com/keras-team/keras/issues/11032
Just resolved the same issue. I connected this few answers: 1, 2, 3 and realized that issue originated from batchnorm layer working state: training or learning. So, in order to resolve that issue you just need to place one line before loading your model:
keras.backend.set_learning_phase(0)
Complete example, to export model
import tensorflow as tf
from tensorflow.python.framework import graph_io
from tensorflow.keras.applications.inception_v3 import InceptionV3
def freeze_graph(graph, session, output):
with graph.as_default():
graphdef_inf = tf.graph_util.remove_training_nodes(graph.as_graph_def())
graphdef_frozen = tf.graph_util.convert_variables_to_constants(session, graphdef_inf, output)
graph_io.write_graph(graphdef_frozen, ".", "frozen_model.pb", as_text=False)
tf.keras.backend.set_learning_phase(0) # this line most important
base_model = InceptionV3()
session = tf.keras.backend.get_session()
INPUT_NODE = base_model.inputs[0].op.name
OUTPUT_NODE = base_model.outputs[0].op.name
freeze_graph(session.graph, session, [out.op.name for out in base_model.outputs])
to load *.pb model:
from PIL import Image
import numpy as np
import tensorflow as tf
# https://i.imgur.com/tvOB18o.jpg
im = Image.open("/home/chichivica/Pictures/eagle.jpg").resize((299, 299), Image.BICUBIC)
im = np.array(im) / 255.0
im = im[None, ...]
graph_def = tf.GraphDef()
with tf.gfile.GFile("frozen_model.pb", "rb") as f:
graph_def.ParseFromString(f.read())
graph = tf.Graph()
with graph.as_default():
net_inp, net_out = tf.import_graph_def(
graph_def, return_elements=["input_1", "predictions/Softmax"]
)
with tf.Session(graph=graph) as sess:
out = sess.run(net_out.outputs[0], feed_dict={net_inp.outputs[0]: im})
print(np.argmax(out))
This is bug with Tensorflow 1.1x and as another answer stated, it is because of the internal batch norm learning vs inference state. In TF 1.14.0 you actually get a cryptic error when trying to freeze a batch norm layer.
Using set_learning_phase(0) will put the batch norm layer (and probably others like dropout) into inference mode and thus the batch norm layer will not work during training, leading to reduced accuracy.
My solution is this:
Create the model using a function (do not use K.set_learning_phase(0)):
def create_model():
inputs = Input(...)
...
return model
model = create_model()
Train model
Save weights:
model.save_weights("weights.h5")
Clear session (important so layer names are the same) and set learning phase to 0:
K.clear_session()
K.set_learning_phase(0)
Recreate model and load weights:
model = create_model()
model.load_weights("weights.h5")
Freeze as before
Thanks for pointing the main issue! I found that keras.backend.set_learning_phase(0) to be not working sometimes, at least in my case.
Another approach might be: for l in keras_model.layers: l.trainable = False