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 point of stating that a mathematical model is Turing Complete is to reveal the capability of the model to perform any calculation, given a sufficient amount of resources (i.e. infinite), not to show whether a specific implementation of a model does have those resources. Non-Turing complete models would not be able to handle a … Read more
Usually, it’s glorot_uniform by default. Different layer types might have different default kernel_initializer. When in doubt, just look in the source code. For example, for Dense layer: class Dense(Layer): … def __init__(self, units, activation=None, use_bias=True, kernel_initializer=”glorot_uniform”, bias_initializer=”zeros”, kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs):
Difference between CNN and RNN are as follows: CNN: CNN takes a fixed size inputs and generates fixed-size outputs. CNN is a type of feed-forward artificial neural network – are variations of multilayer perceptrons which are designed to use minimal amounts of preprocessing. CNNs use connectivity pattern between its neurons and is inspired by the … Read more
Loop over the shape of every variable in tf.trainable_variables(). total_parameters = 0 for variable in tf.trainable_variables(): # shape is an array of tf.Dimension shape = variable.get_shape() print(shape) print(len(shape)) variable_parameters = 1 for dim in shape: print(dim) variable_parameters *= dim.value print(variable_parameters) total_parameters += variable_parameters print(total_parameters) Update: I wrote an article to clarify the dynamic/static shapes in … 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
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
First of all, give up any notions that artificial neural networks have anything to do with the brain but for a passing similarity to networks of biological neurons. Learning biology won’t help you effectively apply neural networks; learning linear algebra, calculus, and probability theory will. You should at the very least make yourself familiar with … Read more
Having two different functions is a convenience, as they produce the same result. The difference is simple: For sparse_softmax_cross_entropy_with_logits, labels must have the shape [batch_size] and the dtype int32 or int64. Each label is an int in range [0, num_classes-1]. For softmax_cross_entropy_with_logits, labels must have the shape [batch_size, num_classes] and dtype float32 or float64. Labels … Read more
It depends. ADAM updates any parameter with an individual learning rate. This means that every parameter in the network has a specific learning rate associated. But the single learning rate for each parameter is computed using lambda (the initial learning rate) as an upper limit. This means that every single learning rate can vary from … Read more