deep-learning

问题 I am working on a seq2seq keras/tensorflow 2.0 model. Every time the user inputs something, my model prints the response perfectly fine. However on the last line of each response I get this: You: WARNING:tensorflow:Your input ran out of data; interrupting training. Make sure that your dataset or generator can generate at least steps_per_epoch * epochs batches (in this case, 2 batches). You may need to use the repeat() function when building your dataset. The "You:" is my last output, before

问题 I am using this code, and saw model.eval() in some cases. I understand it is supposed to allow me to "evaluate my model", but I don't understand when I should and shouldn't use it, or how to turn if off. I would like to run the above code to train the network, and also be able to run validation every epoch. I wasn't able to do it still. 回答1: model.eval() is a kind of switch for some specific layers/parts of the model that behave differently during training and inference (evaluating) time. For

问题 This question already has answers here : TensorFlow Only running on 1/32 of the Training data provided (1 answer) Keras not training on entire dataset (2 answers) Training MNIST data set in google colab issue: [duplicate] (2 answers) actual training samples and samples visible in epochs while running are different [duplicate] (1 answer) Model fitting doesn't use all of the provided data [duplicate] (1 answer) Closed 7 months ago . I am trying to create a simple CNN to classify images in MNIST

问题 My code works well with VGG19 loaded from a .mat file and is used in the following function like this (I use tensorflow 1.14.0) : #initial method : load VGG19 via file and function conv2D_relu VGG19 = scipy.io.loadmat('imagenet-vgg-verydeep-19.mat') VGG19_layers = VGG19['layers'][0] def conv2d_relu_old(prev_layer, n_layer, layer_name,VGG19_layers): # get weights for this layer: weights = VGG19_layers[n_layer][0][0][2][0][0] W = tf.constant(weights) bias = VGG19_layers[n_layer][0][0][2][0][1]

问题 I am new to PyTorch and recently, I have been trying to work with Transformers. I am using pretrained tokenizers provided by HuggingFace. I am successful in downloading and running them. But if I try to save them and load again, then some error occurs. If I use AutoTokenizer.from_pretrained to download a tokenizer, then it works. [1]: tokenizer = AutoTokenizer.from_pretrained('distilroberta-base') text = "Hello there" enc = tokenizer.encode_plus(text) enc.keys() Out[1]: dict_keys(['input_ids'

问题 I am new to PyTorch and recently, I have been trying to work with Transformers. I am using pretrained tokenizers provided by HuggingFace. I am successful in downloading and running them. But if I try to save them and load again, then some error occurs. If I use AutoTokenizer.from_pretrained to download a tokenizer, then it works. [1]: tokenizer = AutoTokenizer.from_pretrained('distilroberta-base') text = "Hello there" enc = tokenizer.encode_plus(text) enc.keys() Out[1]: dict_keys(['input_ids'

问题 I am confused on how to predict future results with a time series multivariate LSTM model. I am trying to build a model for a stock market prediction and I have the following data features Date DailyHighPrice DailyLowPrice Volume ClosePrice If I train my model on 5 years of data up until today and I want to predict tomorrows ClosePrice, essentially I will need to predict all the data features for tomorrow. This is where I am confused.... Because if all the data features are dependent on one

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 days ago . Improve this question I have a structure like this: file01,file02... file_output(all dictionaries) structure of files where each file is a dataframe with features as columns and a single file in output with 4 numbers that rapresent the output or y of my network. How can I feed multiple folders like

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 days ago . Improve this question I have a structure like this: file01,file02... file_output(all dictionaries) structure of files where each file is a dataframe with features as columns and a single file in output with 4 numbers that rapresent the output or y of my network. How can I feed multiple folders like

问题 Closed . This question needs details or clarity. It is not currently accepting answers. Want to improve this question? Add details and clarify the problem by editing this post. Closed 5 days ago . Improve this question I have a structure like this: file01,file02... file_output(all dictionaries) structure of files where each file is a dataframe with features as columns and a single file in output with 4 numbers that rapresent the output or y of my network. How can I feed multiple folders like