I used tf.layers.dense to build a fully connected autoencoder. and I want to save it and restore only the encoder to get the embedding output.
How to use tf.train.saver to restore only the encoder? Because I want to set different batch size of the restored model, to input only one data into it.
I saw many tutorials but there is no tutorials about this.
Is there any standard solution about this
Thank you very much
If you don't care about memory space the easiest way is by saving the whole graph (encoder and decoder) and when using it for prediction, you can pass the last layer of the encoder as the fetch argument. Tensorflow will only calculate to this point and you don't have any computational difference compared to only saving the encoder.
Otherwise you can create two graphs (one for the encoder, one for the decoder) an train them at the same time and train them together. But this is I bit more complex.
Related
So this may be a silly question but how exactly do the preprocessing layers in keras work, especially in the context of as a part of the model itself. This being compared to preprocessing being applied outside the model then inputting the results for training.
I'm trying to understand running data augmentation in keras models. Lets say I have 1000 images for training. Out of model I can apply augmentation 10x and get 10000 resultant images for training.
But I don't understand what's happening when you use a preprocess layer for augmentation. Does this (or these if you use many) layers take each image and apply the transformations before training? Does this mean the total number of images used for training (and validation I assume) to be the number of epochs*the original number of images?
Is one option better than the other? Does that depend on the number of images one originally has before augmentation?
The benefit of preprocessing layers is that the model is truly end-to-end, i.e. raw data comes in and a prediction comes out. It makes your model portable since the preprocessing procedure is included in the SavedModel.
However, it will run everything on the GPU. Usually it makes sense to load the data using CPU worker(s) in the background while the GPU optimizes the model.
Alternatively, you could use a preprocessing layer outside of the model and inside a Dataset. The benefit of that is that you can easily create an inference-only model including the layers, which then gives you the portability at inference time but still the speedup during training.
For more information, see the Keras guide.
We now have a trained network for classification task. The top of the network is like
so the layer relu_fc1 is something like extracted features, then softmax to class prediction.
Now we want to extract these features directly. In normal case, we can do it by
y = sess.graph.get_tensor_by_name('relu_fc1:0') sess.run(y,...)
That's great, but we still want to make it faster, so we use TensorRT to convert the saved model. However, after the conversion, we can't get the right tensor in the relu_fc1 because TensorRT mixed the operation up and produced something like TRTENgineOp_1.
I want to know is there a way to get the intermediate layer's output after TensorRT? I guess maybe it's easier for us can delete the last layers in the network then do the conversion, but can't find practical materials for removing the layers in tensorflow.
I want to know is there a way to get the intermediate layer's output after TensorRT? I guess maybe it's easier for us can delete the last layers in the network then do the conversion, but can't find practical materials for removing the layers in tensorflow.
For this question, when you do the tf-to-onnx conversion, you can specify which layer as the final output for the onnx model. Then, you can do the onnx-to-tensorrt conversion.
For more details, see tensorflow-onnx. The --outputs parameter is what you want.
I wish to repeat a series of image classification experiments by reusing a CNN with the same CNN with identical hyperparameters especially initializations. So, if I save a model after I have instantiated it and before I train it, does that also save the initializations so I then reload it later and train with a different data set and labels, does it start this new model with the same hyperparameters and initializations as the first model I trained with the first data set/classification labels? I am currently using fastai which is, of course, a library/set of API's, built on Pythorch but I think that everyone would be helped with a more general explanation that covers all CNN's using any library.
I expect an answer that says, "after this point in the workflow creating a CNN, the model is initialized and if you save it at this point, you can reload it later and use the same hyperparameters and initializations in your next model."
you can save the learner as soon it is created.
Example:
learn = cnn_learner(data,models.resnet34,metrics=error_rate)
learn.save('init')
later on:
learn.load('init)
I am trying to replicate a neural network for depth estimation. The original authors have taken a pre-trained network and added between the fully connected layer and the convolutional layer a 'Superpixel Pooling Layer'. In this layer, the convolutional feature maps are upsampled and the features per superpixel are averaged.
My problem is that in order to successfully achieve this, I need to calculate the superpixels per image. How can I access the data being used by keras/tensorflow during batch processing to perform SLIC oversegmentation?
I considered splitting the tasks and working by pieces i.e. feed the images into the convolutional network. Process the outputs separately and then feed them into a fully connected layer. However, this makes further training of the network impossible.
At the time it seems to be impossible to actually access the data within the symbolic tensor. It also seems unlikely that such functionality will be added in the future since in the Tensorflow page it says:
A Tensor object is a symbolic handle to the result of an operation, but
does not actually hold the values of the operation's output.
Keras allows for the creation of personalized layers. However, these are limited by the available backend operations. As such, it is simply not possible to access the batch data.
I would like to train a caffe network with the python interface.
The main reason behind this is I use multi dimentional input of a few Tbs of data and I dont want to convert all this to LMDB and train it.
I have found a this one answer on stack overflow.
But his loads this complete data at once and has initialized weights.
I would like to load data to a numpy and then pass it to the caffe.
And save the weights of the caffemodel to a .caffemodel file once every 1000 iterations.
the print_network() get_accuracy() & load_data() are very useful. And gives me a good inside.
Beside using PythonLayer , one thing you can do is use MemoryData layer and feed in each batch of data at a time by using solver.net.set_input_arrays(your_data) after however many iteration is needed to go through one batch of data.
Remember, you can always restore the training state by using .solverstate file from your snapshots.