Yes, it is possible and you should also use a doubly block circulant matrix (which is a special case of Toeplitz matrix). I will give you an example with a small size of kernel and the input, but it is possible to construct Toeplitz matrix for any kernel. So you have a 2d input x … Read more
The tensor must be passed to the layer when you are calling it, and not as an argument. Therefore it must be like this: x = Flatten()(x) # first the layer is constructed and then it is called on x To make it more clear, it is equivalent to this: flatten_layer = Flatten() # instantiate … Read more
There are two main differences between a “train” prototxt and a “deploy” one: 1. Inputs: While for training data is fixed to a pre-processed training dataset (lmdb/HDF5 etc.), deploying the net require it to process other inputs in a more “random” fashion. Therefore, the first change is to remove the input layers (layers that push … Read more
You are correct in observing that the scaling you are doing for the log(prob) is exactly what “InfogainLoss” layer is doing (You can read more about it here and here). As for the derivative (back-prop): the loss computed by this layer is L = – sum_j infogain_mat[label * dim + j] * log( prob(j) ) … Read more
One thing I don’t get is if you’ve your own classifier (2) why then use imagenet_utils.decode_predictions? I’m not sure if my following answer will satisfy you or not. But here are some pointer. DataSet import tensorflow as tf import numpy as np (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data() # train set / data x_train = … Read more
Assuming you are trying to do image classification. These should be the steps for finetuning a model: 1. Classification layer The original classification layer “loss3/classifier” outputs predictions for 1000 classes (it’s mum_output is set to 1000). You’ll need to replace it with a new layer with appropriate num_output. Replacing the classification layer: Change layer’s name … Read more
Change Categorical Cross Entropy to Binary Cross Entropy since your output label is binary. Also Change Softmax to Sigmoid since Sigmoid is the proper activation function for binary data
The easiest way is to create a new model in Keras, without calling the backend. You’ll need the functional model API for this: from keras.models import Model XX = model.input YY = model.layers[0].output new_model = Model(XX, YY) Xaug = X_train[:9] Xresult = new_model.predict(Xaug)
If you are interested in how to input your own data in TensorFlow, you can look at this tutorial. I’ve also written a guide with best practices for CS230 at Stanford here. New answer (with tf.data) and with labels With the introduction of tf.data in r1.4, we can create a batch of images without placeholders … Read more
you can use this formula [(W−K+2P)/S]+1. W is the input volume – in your case 128 K is the Kernel size – in your case 5 P is the padding – in your case 0 i believe S is the stride – which you have not provided. So, we input into the formula: Output_Shape = … Read more