To set the context, if i go to : https://huggingface.co/ProsusAI/finbert and input the following sentence on their hosted API form
Stocks rallied and the British pound gained.
I get the sentiment as 89.8% positive,6.7% neutral and the rest negative, which is as one would expect.
However if I download the tensorflow version of the model from :https://huggingface.co/ProsusAI/finbert/tree/main along with the respective Json files, and it run it locally I get the output as
array([[0.2945392 , 0.4717328 , 0.23372805]] which corresponds to a ~ 30% positive sentiment.
The code i am using locally is as follows ( modfin is the local folder where i have stored the t5_model.h5 alongwith the other files)
model = TFAutoModelForSequenceClassification.from_pretrained("C:/Users/Downloads/modfin",config="C:/Users/Downloads/modfin/config.json",num_labels=3)
tokenizer = AutoTokenizer.from_pretrained("C:/Users/Downloads/modfin",config="C:/Users/Downloads/modfin/tokenizer_config.json")
inputs = tokenizer(sentences, padding = True, truncation = True, return_tensors='tf')
outputs = model(**inputs)
nn.softmax(outputs[0]).numpy()
for the model I also get a warning as follows
All model checkpoint layers were used when initializing TFBertForSequenceClassification.
Some layers of TFBertForSequenceClassification were not initialized from the model checkpoint at C:/Users/Downloads/modfin and are newly initialized: ['classifier']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
which is strange since I would assume a pre-trained model such as finbert should already be fine-tuned. when i replace TFAutoModelForSequenceClassification with TFAutoModel i see that the transofrmer that is chosen automatically is 'TFBertModel' whose output is 10 X768 tensor which i am not able to interpret into sentiment classes. any help here would be greatly appreciated
Related
I am trying to implement image super resolution using SRGAN. In the process, I used DIV2K dataset (http://data.vision.ee.ethz.ch/cvl/DIV2K/DIV2K_train_HR.zip) as my source.
I have worked with image classification using CNN (I used keras.layers.convolutional.Conv2D). But in this case we don't have class label in my data source.
I have unzipped the file and kept in D:\Unzipped\DIV2K_train_HR. Then used following command to read the files.
img_dataset = tensorflow.keras.utils.image_dataset_from_directory("D:\\unzipped")
Then created the model as follows
model = Sequential()
model.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu",input_shape=(256,256,3)))
model.add(AveragePooling2D(pool_size=(2,2)))
model.add(Conv2D(filters=64,kernel_size=(3,3),activation="relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.compile(optimizer='sgd', loss='mse')
model.fit(img_dataset,batch_size=32, epochs=10)
But I am getting error : "Graph execution error". I am unable to find the root cause behind this error. Is this error appearing as the class label is missing (I think as per code DIV2K_train_HR is treated as one class label)? Or is this happening due to images don't have one specific size?
Note: This code does not match with SRGAN architecture. I am new to GAN and trying to move ahead step by step. I got stuck in the first step itself.
Yes, the error message is because you don't have labels in your dataset.
As a first step in GAN network you need to create a discriminator model: given some image it should recognize if it is a real or fake image. You can take images from your dataset and label them as 1 ("real images"). Then generate "fake images" by down-sampling and up-sampling images from your dataset and label them as 0. Train your discriminator model so that it can distinguish between original and processed images.
After that, you create generator model. The generator model takes a down-sampled version of the image as an input and creates an up-sampled version in original resolution. GAN model combines generator and discriminator models by passing output from generator to discriminator. The target label is 1, i.e. we want generator create up-sampled versions of images, which discriminator can't distinguish from the real ones. Now train GAN network (set 'trainable' to false for discriminator model weights).
After your generator manages to produce images, which discriminator can't distinguish from the real, you take them, label as 0 and train discriminator again. Then train generator again etc.
The process continues until discriminator can't distinguish fake images from the real ones anymore (i.e. accuracy doesn't exceed 0.5).
Please see a simple example on ("Generative Adversarial Networks"):
https://github.com/ageron/handson-ml3/blob/main/17_autoencoders_gans_and_diffusion_models.ipynb
This code is explained in ch. 17 in book "Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)" by Aurélien Géron.
I followed a simple tutorial to train a custom object detector.
I got my loss up to 0.6, however my issue is that the detected will classify other objects as what I've trained it with. For example in my case it classifies a dog as macarooni and cheese.
What am I doing wrong ?
I faced exactly the same issue, where the model "remembered" the previous objects. There is a new configuration in the config file that is was not implemented when the video was made.
Inside the ssd_mobilenet_v1_pet.config file you have to specify the path to the checkpoint where the training will start, so it will have all the weights from the previous training, this config is fine_tune_checkpoint, below that there is from_detection_checkpoint so it will use the specified checkpoint, after that there is load_all_detection_checkpoint_vars which is set to true by default, but must be false if you want the model to "forget" the objects that it was trained on.
The problem is that load_all_detection_checkpoint_vars will load and fix all the weights, including the ones in the final layers not just the lower layer ones, so it will remember the classification and detection from past objects and misclassify with the new ones, since your *.pbtxt has different classifications. If you set it to false it will load the data and learn new weights for the final layers based only on your training set.
I have a Keras model that I would like to convert to a Tensorflow protobuf (e.g. saved_model.pb).
This model comes from transfer learning on the vgg-19 network in which and the head was cut-off and trained with fully-connected+softmax layers while the rest of the vgg-19 network was frozen
I can load the model in Keras, and then use keras.backend.get_session() to run the model in tensorflow, generating the correct predictions:
frame = preprocess(cv2.imread("path/to/img.jpg")
keras_model = keras.models.load_model("path/to/keras/model.h5")
keras_prediction = keras_model.predict(frame)
print(keras_prediction)
with keras.backend.get_session() as sess:
tvars = tf.trainable_variables()
output = sess.graph.get_tensor_by_name('Softmax:0')
input_tensor = sess.graph.get_tensor_by_name('input_1:0')
tf_prediction = sess.run(output, {input_tensor: frame})
print(tf_prediction) # this matches keras_prediction exactly
If I don't include the line tvars = tf.trainable_variables(), then the tf_prediction variable is completely wrong and doesn't match the output from keras_prediction at all. In fact all the values in the output (single array with 4 probability values) are exactly the same (~0.25, all adding to 1). This made me suspect that weights for the head are just initialized to 0 if tf.trainable_variables() is not called first, which was confirmed after inspecting the model variables. In any case, calling tf.trainable_variables() causes the tensorflow prediction to be correct.
The problem is that when I try to save this model, the variables from tf.trainable_variables() don't actually get saved to the .pb file:
with keras.backend.get_session() as sess:
tvars = tf.trainable_variables()
constant_graph = graph_util.convert_variables_to_constants(sess, sess.graph.as_graph_def(), ['Softmax'])
graph_io.write_graph(constant_graph, './', 'saved_model.pb', as_text=False)
What I am asking is, how can I save a Keras model as a Tensorflow protobuf with the tf.training_variables() intact?
Thanks so much!
So your approach of freezing the variables in the graph (converting to constants), should work, but isn't necessary and is trickier than the other approaches. (more on this below). If your want graph freezing for some reason (e.g. exporting to a mobile device), I'd need more details to help debug, as I'm not sure what implicit stuff Keras is doing behind the scenes with your graph. However, if you want to just save and load a graph later, I can explain how to do that, (though no guarantees that whatever Keras is doing won't screw it up..., happy to help debug that).
So there are actually two formats at play here. One is the GraphDef, which is used for Checkpointing, as it does not contain metadata about inputs and outputs. The other is a MetaGraphDef which contains metadata and a graph def, the metadata being useful for prediction and running a ModelServer (from tensorflow/serving).
In either case you need to do more than just call graph_io.write_graph because the variables are usually stored outside the graphdef.
There are wrapper libraries for both these use cases. tf.train.Saver is primarily used for saving and restoring checkpoints.
However, since you want prediction, I would suggest using a tf.saved_model.builder.SavedModelBuilder to build a SavedModel binary. I've provided some boiler plate for this below:
from tensorflow.python.saved_model.signature_constants import DEFAULT_SERVING_SIGNATURE_DEF_KEY as DEFAULT_SIG_DEF
builder = tf.saved_model.builder.SavedModelBuilder('./mymodel')
with keras.backend.get_session() as sess:
output = sess.graph.get_tensor_by_name('Softmax:0')
input_tensor = sess.graph.get_tensor_by_name('input_1:0')
sig_def = tf.saved_model.signature_def_utils.predict_signature_def(
{'input': input_tensor},
{'output': output}
)
builder.add_meta_graph_and_variables(
sess, tf.saved_model.tag_constants.SERVING,
signature_def_map={
DEFAULT_SIG_DEF: sig_def
}
)
builder.save()
After running this code you should have a mymodel/saved_model.pb file as well as a directory mymodel/variables/ with protobufs corresponding to the variable values.
Then to load the model again, simply use tf.saved_model.loader:
# Does Keras give you the ability to start with a fresh graph?
# If not you'll need to do this in a separate program to avoid
# conflicts with the old default graph
with tf.Session(graph=tf.Graph()):
meta_graph_def = tf.saved_model.loader.load(
sess,
tf.saved_model.tag_constants.SERVING,
'./mymodel'
)
# From this point variables and graph structure are restored
sig_def = meta_graph_def.signature_def[DEFAULT_SIG_DEF]
print(sess.run(sig_def.outputs['output'], feed_dict={sig_def.inputs['input']: frame}))
Obviously there's a more efficient prediction available with this code through tensorflow/serving, or Cloud ML Engine, but this should work.
It's possible that Keras is doing something under the hood which will interfere with this process as well, and if so we'd like to hear about it (and I'd like to make sure that Keras users are able to freeze graphs as well, so if you want to send me a gist with your full code or something maybe I can find someone who knows Keras well to help me debug.)
EDIT: You can find an end to end example of this here: https://github.com/GoogleCloudPlatform/cloudml-samples/blob/master/census/keras/trainer/model.py#L85
I manage to retrain my specific classification model using the generic inception model following this tutorial. I would like now to deploy it on the google cloud machine learning following this steps.
I already managed to export it as MetaGraph but I can't manage to get the proper inputs and outputs.
Using it locally, my entry point to the graph is DecodeJpeg/contents:0 which is fed with a jpeg image in binary format. The output are my predictions.
The code I use locally (which is working) is:
softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
predictions = sess.run(softmax_tensor,{'DecodeJpeg/contents:0': image_data})
Should the input tensor be DecodeJpeg? What would be the changes I need to make if I would like to have a base64 image as input ?
I defined the output as:
outputs = {'prediction':softmax_tensor.name}
Any help is highly appreciated.
In your example, the input tensor is 'DecodeJpeg/contents:0', so you would have something like:
inputs = {'image': 'DecodeJpeg/contents:0')
outputs = {'prediction': 'final_result:0')
(Be sure to follow all of the instructions for preparing a model).
The model directory you intend to export should have files such as:
gs://my_bucket/path/to/model/export.meta
gs://my_bucket/path/to/model/checkpoint*
When you deploy your model, be sure to set gs://my_bucket/path/to/model as the deployment_uri.
To send an image to the service, as you suggest, you will need to base64 encode the image bytes. The body of your request should look like the following (note the 'tag', 'b64', indicating the data is base-64 encoded):
{'instances': [{'b64': base64.b64encode(image)}]}
We've now released a tutorial on how to retrain the Inception model, including instructions for how to deploy the model on the CloudML service.
https://cloud.google.com/blog/big-data/2016/12/how-to-train-and-classify-images-using-google-cloud-machine-learning-and-cloud-dataflow
Is it possible to only load specific layers (convolutional layers) out of one checkpoint file?
I've trained some CNNs fully-supervised and saved my progress (I'm doing object localization). To do auto-labelling I thought of building a weakly-supervised CNNs out of my current model...but since the weakly-supervised version has different fully-connected layers, I would like to select only the convolutional filters of my TensorFlow checkpoint file.
Of course I could manually save the weights of the corresponding layers, but due to the fact that they're already included in TensorFlow's checkpoint file I would like to extract them there, in order to have one single storing file.
TensorFlow 2.1 has many different public facilities for loading checkpoints (model.save, Checkpoint, saved_model, etc), but to the best of my knowledge, none of them has filtering API. So, let me suggest a snippet for hard cases which uses tooling from the TF2.1 internal development tests.
checkpoint_filename = '/path/to/our/weird/checkpoint.ckpt'
model = tf.keras.Model( ... ) # TF2.0 Model to initialize with the above checkpoint
variables_to_load = [ ... ] # List of model weight names to update.
from tensorflow.python.training.checkpoint_utils import load_checkpoint, list_variables
reader = load_checkpoint(checkpoint_filename)
for w in model.weights:
name=w.name.split(':')[0] # See (b/29227106)
if name in variables_to_load:
print(f"Updating {name}")
w.assign(reader.get_tensor(
# (Optional) Handle variable renaming
{'/var_name1/in/model':'/var_name1/in/checkpoint',
'/var_name2/in/model':'/var_name2/in/checkpoint',
# ... and so on
}.get(name,name)))
Note: model.weights and list_variables may help to inspect variables in Model and in the checkpoint
Note also, that this method will not restore model's optimizer state.