deep-learning

问题 I have created a BERT model for classifying a user generated text string as FAQ or not FAQ. I have saved my model using the export_savedmodel() function. I wish to write a function to predict the output for a new set of strings, which takes as input a list of the strings. I tried using predictor.from_saved_model() method but that method requires passing key value pairs for input id, segment id, label id and input mask. I am a beginner and I do not understand completely what to pass here.

问题 I have created a BERT model for classifying a user generated text string as FAQ or not FAQ. I have saved my model using the export_savedmodel() function. I wish to write a function to predict the output for a new set of strings, which takes as input a list of the strings. I tried using predictor.from_saved_model() method but that method requires passing key value pairs for input id, segment id, label id and input mask. I am a beginner and I do not understand completely what to pass here.

问题 Closed. This question does not meet Stack Overflow guidelines. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 8 hours ago . Improve this question I am new to Deep Learning and started with some tutorials, where I implemented VGG16 Net from Scratch. I wanted to classify integrated circuits in defect and non defect classes. I played around with it, changed the hyperparamters and got a really good result with

问题 I wanna make a model with multiple inputs. So, I try to build a model like this. # define two sets of inputs inputA = Input(shape=(32,64,1)) inputB = Input(shape=(32,1024)) # CNN x = layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu')(inputA) x = layers.Conv2D(32, (3,3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2,2))(x) x = layers.Dropout(0.2)(x) x = layers.Flatten()(x) x = layers.Dense(500, activation = 'relu')(x) x = layers.Dropout(0.5)(x) x = layers.Dense(500,

问题 I wanna make a model with multiple inputs. So, I try to build a model like this. # define two sets of inputs inputA = Input(shape=(32,64,1)) inputB = Input(shape=(32,1024)) # CNN x = layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu')(inputA) x = layers.Conv2D(32, (3,3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2,2))(x) x = layers.Dropout(0.2)(x) x = layers.Flatten()(x) x = layers.Dense(500, activation = 'relu')(x) x = layers.Dropout(0.5)(x) x = layers.Dense(500,

问题 I wanna make a model with multiple inputs. So, I try to build a model like this. # define two sets of inputs inputA = Input(shape=(32,64,1)) inputB = Input(shape=(32,1024)) # CNN x = layers.Conv2D(32, kernel_size = (3, 3), activation = 'relu')(inputA) x = layers.Conv2D(32, (3,3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2,2))(x) x = layers.Dropout(0.2)(x) x = layers.Flatten()(x) x = layers.Dense(500, activation = 'relu')(x) x = layers.Dropout(0.5)(x) x = layers.Dense(500,

问题 I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text. For example, having a pre-trained BERT model and a small corpus of medical (or any "type") text, make a language model that is able to generate medical text. The assumption is that you do not have a huge amount of "medical texts" and that is why you have to use transfer learning. Putting it as a pipeline, I would describe this as: Using a pre-trained BERT

问题 I was curious if it is possible to use transfer learning in text generation, and re-train/pre-train it on a specific kind of text. For example, having a pre-trained BERT model and a small corpus of medical (or any "type") text, make a language model that is able to generate medical text. The assumption is that you do not have a huge amount of "medical texts" and that is why you have to use transfer learning. Putting it as a pipeline, I would describe this as: Using a pre-trained BERT

问题 I have a dataset containing 1000 examples where each example has 5 features (a,b,c,d,e). I want to feed 7 examples to an LSTM so it predicts the feature (a) of the 8th day. Reading Pytorchs documentation of nn.LSTM() I came up with the following: input_size = 5 hidden_size = 10 num_layers = 1 output_size = 1 lstm = nn.LSTM(input_size, hidden_size, num_layers) fc = nn.Linear(hidden_size, output_size) out, hidden = lstm(X) # Where X's shape is ([7,1,5]) output = fc(out[-1]) output # output's

问题 I'm using Google colab TPU to train a simple Keras model. Removing the distributed strategy and running the same program on the CPU is much faster than TPU . How is that possible? import timeit import os import tensorflow as tf from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense from tensorflow.keras.optimizers import Adam # Load Iris dataset x = load_iris().data y