Seems like this is due to pandas version higher than 0.21.0 and numpy version 1.13.3 that is pulled by TensorFlow not playing well together. Here is how I fixed it in my end:
I’ll give an example to make it clearer: x: input image of shape [2, 3], 1 channel valid_pad: max pool with 2×2 kernel, stride 2 and VALID padding. same_pad: max pool with 2×2 kernel, stride 2 and SAME padding (this is the classic way to go) The output shapes are: valid_pad: here, no padding so the output … Read more
You have problem with i:(i+window_size), 0 in dataset[i:(i+window_size), 0]. In your code dataset means train_data[‘seq’] which is single column – single-dimensional Series – but you use i:(i+window_size), 0 like in two-dimensional DataFrame. You can use only single integer like dataset[0] or slice dataset[i:(i+window_size)]
Apparently the new instructions on TensorFlow website run without issues I just tried the instructions posted on How to Retrain Inception’s Final Layer for New Categories It worked without any problems
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
Use torch.unsqueeze(input, dim, out=None) >>> a = a.unsqueeze(0) >>> a 1 2 3 4 5 [torch.FloatTensor of size 1×5]
Simply: categorical_crossentropy (cce) produces a one-hot array containing the probable match for each category, sparse_categorical_crossentropy (scce) produces a category index of the most likely matching category. Consider a classification problem with 5 categories (or classes). In the case of cce, the one-hot target may be [0, 1, 0, 0, 0] and the model may predict [.2, .5, .1, .1, .1] (probably right) In the … Read more
I’m playing with the reuters-example dataset and it runs fine (my model is trained). I read about how to save a model, so I could load it later to use again. But how do I use this saved model to predict a new text? Do I use models.predict()? Do I have to prepare this text in … Read more
Generally Your pkl file is, in fact, a serialized pickle file, which means it has been dumped using Python’s pickle module. To un-pickle the data you can: For the MNIST data set Note gzip is only needed if the file is compressed: Where each set can be further divided (i.e. for the training set): Those would be the inputs (digits) and outputs (labels) … Read more
stack Concatenates sequence of tensors along a new dimension. cat Concatenates the given sequence of seq tensors in the given dimension. So if A and B are of shape (3, 4), torch.cat([A, B], dim=0) will be of shape (6, 4) and torch.stack([A, B], dim=0) will be of shape (2, 3, 4).