neural-network

问题 i'm working in a time series prediction using keras and tensorflow. I need to retrain the model with future data. My question is, is this possible in keras and how we can do that? 回答1: yes. Save your model as .h5 When you want to train your model, load it again and do a model.fit as normal. Make sure you do not compile your model after loading it as this will reset your weights. See this link for more info 回答2: I am updating the answer for any new user as it was long time back. if you are

问题 So, I'm new to deep learning and I've started with cats and dogs dataset for a CNN Model using Keras. In my code, I'm unable to get probabilities as output for both classifier.predict or classifier.predict_proba . I'm just getting the output as [[0,1]] or [[1,0]] . I've tried with several images. But I'm looking for something like, [[0.4,0.6]] , [[0.89,0.11]] I've tried changing loss function from binary_crossentropy to categorical_crossentropy . I've tried changing the activation function of

问题 In the first example of Learning PyTorch with Examples, the author demonstrates how to create a neural network with numpy. Their code is pasted below for convenience: # from: https://pytorch.org/tutorials/beginner/pytorch_with_examples.html # -*- coding: utf-8 -*- import numpy as np # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. N, D_in, H, D_out = 64, 1000, 100, 10 # Create random input and output data x = np.random.randn(N, D_in) y = np

问题 I want to feed my data into a LSTM network, but can't find any similar question or tutorial. My dataset is something like: person 1: t1 f1 f2 f3 t2 f1 f2 f3 ... tn f1 f2 f3 . . . person K: t1 f1 f2 f3 t2 f1 f2 f3 ... tn f1 f2 f3 So i have k person and for each person i have a matrix like input. The first column of each row is incremental time stamp (like a time-line, so t1 < t2 ) and other columns are features of person in that time. In mathematical aspect: i have a (number of example,number

问题 I want to feed my data into a LSTM network, but can't find any similar question or tutorial. My dataset is something like: person 1: t1 f1 f2 f3 t2 f1 f2 f3 ... tn f1 f2 f3 . . . person K: t1 f1 f2 f3 t2 f1 f2 f3 ... tn f1 f2 f3 So i have k person and for each person i have a matrix like input. The first column of each row is incremental time stamp (like a time-line, so t1 < t2 ) and other columns are features of person in that time. In mathematical aspect: i have a (number of example,number

问题 To make the results reproducible I've red more than 20 articles and added to my script maximum of the functions ... but failed. In the official source I red there are 2 kinds of seeds - global and operational. May be, the key to solving my problem is setting the operational seed, but I don't understand where to apply it. Would you, please, help me to achieve reproducible results with tensorflow (version > 2.0)? Thank you very much. from keras.models import Sequential from keras.layers import

问题 To make the results reproducible I've red more than 20 articles and added to my script maximum of the functions ... but failed. In the official source I red there are 2 kinds of seeds - global and operational. May be, the key to solving my problem is setting the operational seed, but I don't understand where to apply it. Would you, please, help me to achieve reproducible results with tensorflow (version > 2.0)? Thank you very much. from keras.models import Sequential from keras.layers import

问题 I used Chris Mccormick tutorial on BERT using pytorch-pretained-bert to get a sentence embedding as follows: tokenized_text = tokenizer.tokenize(marked_text) indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text) segments_ids = [1] * len(tokenized_text) tokens_tensor = torch.tensor([indexed_tokens]) segments_tensors = torch.tensor([segments_ids]) model = BertModel.from_pretrained('bert-base-uncased') model.eval() with torch.no_grad(): encoded_layers, _ = model(tokens_tensor,

问题 I am currently seeing the API of theano, theano.tensor.nnet.conv2d(input, filters, input_shape=None, filter_shape=None, border_mode='valid', subsample=(1, 1), filter_flip=True, image_shape=None, **kwargs) where the filter_shape is a tuple of (num_filter, num_channel, height, width) , I am confusing about this because isn't that the number of filter decided by the stride while sliding the filter window on the image? How can I specify on filter number just like this? It would be reasonable to

问题 I need to create 30 * 30 px small images from splitting a large image. I need these parts separately saved in separate files after being split up like so: 回答1: Here you go: import cv2 img = cv2.imread('image.png') for r in range(0,img.shape[0],30): for c in range(0,img.shape[1],30): cv2.imwrite(f"img{r}_{c}.png",img[r:r+30, c:c+30,:]) 来源： https://stackoverflow.com/questions/53755910/how-can-i-split-a-large-image-into-small-pieces-in-python