The mnist object is returned from the read_data_sets() function defined in the tf.contrib.learn module. The mnist.train.next_batch(batch_size) method is implemented here, and it returns a tuple of two arrays, where the first represents a batch of batch_size MNIST images, and the second represents a batch of batch-size labels corresponding to those images. The images are returned … Read more
I am assuming you have a 3-layer NN with W1, b1 for is associated with the linear transformation from input layer to hidden layer and W2, b2 is associated with linear transformation from hidden layer to output layer. Z1 and Z2 are the input vector to the hidden layer and output layer. a1 and a2 represents the output of the hidden layer and output layer. a2 is your predicted output. delta3 and delta2 are the … Read more
There are a couple of ways. If timing the results with the following code: We get: So the multiplication seems to be the fastest.
Regression with neural networks is hard to get working because the output is unbounded, so you are especially prone to the exploding gradients problem (the likely cause of the nans). Historically, one key solution to exploding gradients was to reduce the learning rate, but with the advent of per-parameter adaptive learning rate algorithms like Adam, you no … Read more
You could try something like pyrender. For documentation purposes here’s one of their example: or Open3D. Again, one of the library supplied examples:
If my goal is to fine-tune the network for the entire dataset It is not clear what you mean by “fine-tune”, or even what exactly is your purpose for performing cross-validation (CV); in general, CV serves one of the following purposes: Model selection (choose the values of hyperparameters) Model assessment Since you don’t define any … Read more
Single layer To initialize the weights of a single layer, use a function from torch.nn.init. For instance: Alternatively, you can modify the parameters by writing to conv1.weight.data (which is a torch.Tensor). Example: The same applies for biases: nn.Sequential or custom nn.Module Pass an initialization function to torch.nn.Module.apply. It will initialize the weights in the entire … Read more
Just to answer this question in a little more detail, and as Pavel said, Batch Normalization is just another layer, so you can use it as such to create your desired network architecture. The general use case is to use BN between the linear and non-linear layers in your network, because it normalizes the input … Read more
In the following TensorFlow function, we must feed the activation of artificial neurons in the final layer. That I understand. But I don’t understand why it is called logits? Isn’t that a mathematical function?
here i wanna run this code for try neural network with python : at the beginning it must install keras, and success. but when try to run the code at the first the error is : ImportError : No Moduled Name “tensorflow” then i install using pip : pip install tensorflow after installation i try … Read more