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
This question was raised at the Keras github repository in Issue #4446: Quick Question: can a model be fit for multiple times? It was closed by François Chollet with the following statement: Yes, successive calls to fit will incrementally train the model. So, yes, you can call fit multiple times.
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
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
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
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).
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
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
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
@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