I am building a CNN model with Keras and tensorflow backend. I've trained the model for 6 hours. Now, I want to predict my custom external image using my model. How to do that in Keras? Thank you
To simplify, you could say there is usually 4 main steps when working with models (not just in Keras) :
building the model and compiling it : model.compile()
training the model with your training data : model.fit()
evaluating the model with your test data : model.evaluate()
making predictions with your target data : model.predict()
Depending on which output you need, you will have to use model.predict_proba() or model.predict_classes().
See https://keras.io/models/sequential/#sequential-model-methods for complete reference and arguments.
Keras repository has also plenty of examples : https://github.com/fchollet/keras/tree/master/examples
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I have 600 000 images and I want to classify them using keras. I am just trying the pretrained model on greyscale images. And I am trying to use the model architecture of pre-trained models like resnet50, inceptionv3, etc. But accuracy and validation accuracy of the model has not changed which is stuck at 67%. I tried changing the network, applying more epochs and also changing the pretrained model, but I always get the same result like 67% accuracy and validation accuracy. I don't understand why I am getting the same result. Please recommended some ideas on how can I solve this problem. This is my code. In this steps_per_epochs = no. of images/batch size and batch size is 128.No of images in the training dataset is 479369 and in the validation dataset is 136962.This is the output of the code.
I think you are using a pre-trained model. so, that is why it gets showed the same accuracy. my suggestion is to change the pre-trained model and tryna your custom model and then see the changes.
I'm trying to create a model that includes some layers from a Keras model followed by a sklearn pipeline that will provide the predicted class for the sample inputted and then transformed by the Keras model.
If either of these were stand alone models, I know that I would simply run .fit on them using the training data. However, I'm not sure how to combine them into one model.
*** I used a pretrained BertModel to fine-tune model on squadv1 then fine-tune result model to final model.
This how I can load my trined model on squadv1
output_dir="mybert_squadv1/checkpoint-60000/"
model = AutoModelForQuestionAnswering.from_pretrained(output_dir)
tokenizer = AutoTokenizer.from_pretrained(output_dir, do_lower_case=do_lower_case)
after I loaded my model on a dataset, I want to fine-tune this model on another dataset. Let say squadv3
Can I do that or not ? if so, how i can do it?
you just get the tokenizer.
After that,you should add your own Neural Networks after that and loss function.
you can see how to train a normal network.
I am working on a project involving neural machine translation (translating English to French).
I have worked through some examples online, and have now finished the model. Once a model is trained using Keras, how do I then get a prediction of a translation without training the entire model again, because with the large dataset I am using, each epoch takes some time and of course, I can't train the model every time I want a translation.
So what is the correct way of then generating predictions on new inputs without training the whole model again?
Thanks
You need to save your model the model and its weights when the fit ends using :
keras.model.save(model_name)
At any time, you can load your trained model using
model = keras.load(model_name)
then perform predictions as
y_pred = model.predict(x_test)
Hope this will be helpful
You can use the .predict() function which you can pass new inputs into it and it give you a prediction. The docs for this function are here: keras
I have trained a cnn model using tf.estimator and tf.data.TFRecordDataset, which define a model in model_fn funcition and input in input_fn function. Also using an one-shot iterator to get one batch examples at a time.
Now I have trained model files(ckpt, meta, index) in a directory. What I want to do is predicting a image's label based on the trained model without training and evaluation again. The image can be numpy array but not possible a TFRecords file(which used when traing).
I can't find an effictive solution after trying all day. I only can get the value of weights and biases and don't know how to make my predicting image and model compatible.
FYI, my training code is here.
The similar question is Prediction from model saved with tf.estimator.Estimator in Tensorflow
, but no accepted answer and my model input is using the dataset api.
So reaaally need help. Thanks.
I have answered a similar question here.
To make predictions using a custom input, you need to use the built-in predict method of Estimators:
estimator = tf.estimator.Estimator(model_fn, ...)
predict_input_fn = ... # define this using tf.data
predict_results = estimator.predict(predict_input_fn)
for idx, prediction in enumerate(predict_results):
print(idx)
for key in prediction:
print("...{}: {}".format(key, prediction[key]))