ValueError: Shapes (None, 1) and (None, 2) are incompatible
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
conv-neural-network
CNN – Image Resizing VS Padding (keeping aspect ratio or not?)
According to Jeremy Howard, padding a big piece of the image (64×160 pixels) will have the following effect: The CNN will have to learn that the black part of the image is not relevant and does not help distinguishing between the classes (in a classification setting), as there is no correlation between the pixels in … Read more
How does one debug NaN values in TensorFlow?
There are a couple of reasons WHY you can get a NaN-result, often it is because of too high a learning rate but plenty other reasons are possible like for example corrupt data in your input-queue or a log of 0 calculation. Anyhow, debugging with a print as you describe cannot be done by a … Read more
Keras ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=5
The problem is input_shape. It should actually contain 3 dimensions only. And internally keras will add the batch dimension making it 4. Since you probably used input_shape with 4 dimensions (batch included), keras is adding the 5th. You should use input_shape=(32,32,1).
2-D convolution as a matrix-matrix multiplication [closed]
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
Convert Keras model to C++ [closed]
To answer my own question and have a solution – I wrote a plain c++ solution called keras2cpp (its code available on github). In this solution you store network architecture (in json) and weights (in hdf5). Then you can dump a network to a plain text file with provided script. You can use obtained text … Read more
Dimension of shape in conv1D
td; lr you need to reshape you data to have a spatial dimension for Conv1d to make sense: X = np.expand_dims(X, axis=2) # reshape (569, 30) to (569, 30, 1) # now input can be set as model.add(Conv1D(2,2,activation=’relu’,input_shape=(30, 1)) Essentially reshaping a dataset that looks like this: features .8, .1, .3 .2, .4, .6 .7, … Read more
What does the parameter retain_graph mean in the Variable’s backward() method?
@cleros is pretty on the point about the use of retain_graph=True. In essence, it will retain any necessary information to calculate a certain variable, so that we can do backward pass on it. An illustrative example Suppose that we have a computation graph shown above. The variable d and e is the output, and a … Read more
How to calculate the number of parameters for convolutional neural network?
Let’s first look at how the number of learnable parameters is calculated for each individual type of layer you have, and then calculate the number of parameters in your example. Input layer: All the input layer does is read the input image, so there are no parameters you could learn here. Convolutional layers: Consider a … Read more
Instance Normalisation vs Batch normalisation
Definition Let’s begin with the strict definition of both: Batch normalization Instance normalization As you can notice, they are doing the same thing, except for the number of input tensors that are normalized jointly. Batch version normalizes all images across the batch and spatial locations (in the CNN case, in the ordinary case it’s different); … Read more