keras

What is meant by sequential model in Keras

由 给你一囗甜甜゛ 提交于 2021-01-21 15:15:06
问题 I have recently started working Tensorflow for deep learning. I found this statement model = tf.keras.models.Sequential() bit different. I couldn't understand what is actually meant and is there any other models as well for deep learning? I worked a lot on MatconvNet (Matlab library for convolutional neural network). never saw any sequential definition in that. 回答1: There are two ways to build Keras models: sequential and functional. The sequential API allows you to create models layer-by

Keras fit_generator() - How does batch for time series work?

由 戏子无情 提交于 2021-01-21 12:16:12
问题 Context: I am currently working on time series prediction using Keras with Tensorflow backend and, therefore, studied the tutorial provided here. Following this tutorial, I came to the point where the generator for the fit_generator() method is described. The output this generator generates is as follows (left sample, right target): [[[10. 15.] [20. 25.]]] => [[30. 35.]] -> Batch no. 1: 2 Samples | 1 Target --------------------------------------------- [[[20. 25.] [30. 35.]]] => [[40. 45.]] -

How do I preprocess and tokenize a TensorFlow CsvDataset inside the map method?

由 允我心安 提交于 2021-01-21 10:39:09
问题 I made a TensorFlow CsvDataset , and I'm trying to tokenize the data as such: import os os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' from tensorflow import keras import tensorflow as tf from tensorflow.keras.preprocessing.text import Tokenizer import os os.chdir('/home/nicolas/Documents/Datasets') fname = 'rotten_tomatoes_reviews.csv' def preprocess(target, inputs): tok = Tokenizer(num_words=5_000, lower=True) tok.fit_on_texts(inputs) vectors = tok.texts_to_sequences(inputs) return vectors,

Keras Model for Siamese Network not Learning and always predicting the same ouput

由 做~自己de王妃 提交于 2021-01-21 05:55:12
问题 I am trying to train a Siamese neural network using Keras, with the goal of identifying if 2 images belong to same class or not. My data is shuffled and has equal number of positive examples and negative examples. My model is not learning anything and it is predicting the same output always. I am getting the same loss, validation accuracy, and validation loss every time. Training Output def convert(row): return imread(row) def contrastive_loss(y_true, y_pred): margin = 1 square_pred = K

解析 TensorFlow Eager | DevFest 2018 现场实录

由 旧城冷巷雨未停 提交于 2021-01-21 02:58:33
大家期待已久的 DevFest 2018 现场实录 终于出炉了！ 11 月 25 日，1125 位开发者之约，你在吗？ 什么？你错过了 DevFest 2018 ？ 不要担心，我们已经为大家推送本次大会的嘉宾演讲实录，不在现场也能干货满满！ 回顾前情： 谷歌资深工程师顾仁民：TensorFlow Extended 帮你快速落地项目 | DevFest 2018 实录 AI 选手别错过！谷歌云美女工程师 Shirley 教你用 AutoML 定制机器学习模型 | DevFest 2018 实录 时下最火的实时视频通信如何使用深度学习？听声网首席科学家钟声聊一聊 | DevFest 2018 实录 谷歌移动技术专家 Palances Liao 带你解析 PWA/AMP & 洞察Web趋势 | DevFest 2018 实录 谷歌机器学习专家江骏 详解 TensorFlow Hub & Tensor2Tensor | DevFest 2018 实录 Merculet 首席架构师吴翔彬 借助公链和联盟链构建融合基础设施 | DevFest 2018 实录 本文由谷歌机器学习专家、了得研究院 CEO 彭靖田为我们分享《TensorFlow Eager》。 1 关于演讲者 2 演讲实录 开场白 这里简单的做一个自我介绍，我叫彭靖田，我从 16 年的时候开始做 TensorFlow

Keras - Validation Loss and Accuracy stuck at 0

由 此生再无相见时 提交于 2021-01-20 19:20:47
问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am

Deep neural network skip connection implemented as summation vs concatenation? [closed]

由 点点圈 提交于 2021-01-20 19:19:46
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question In deep neural network, we can implement the skip connections to help: Solve problem of vanishing gradient, training faster The network learns a combination of low level and high level features Recover info loss during downsampling like max pooling. https:/

Deep neural network skip connection implemented as summation vs concatenation? [closed]

由 别等时光非礼了梦想. 提交于 2021-01-20 19:17:29
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question In deep neural network, we can implement the skip connections to help: Solve problem of vanishing gradient, training faster The network learns a combination of low level and high level features Recover info loss during downsampling like max pooling. https:/

Deep neural network skip connection implemented as summation vs concatenation? [closed]

由 ╄→尐↘猪︶ㄣ 提交于 2021-01-20 19:17:20
问题 Closed . This question needs to be more focused. It is not currently accepting answers. Want to improve this question? Update the question so it focuses on one problem only by editing this post. Closed 2 years ago . Improve this question In deep neural network, we can implement the skip connections to help: Solve problem of vanishing gradient, training faster The network learns a combination of low level and high level features Recover info loss during downsampling like max pooling. https:/

Keras - Validation Loss and Accuracy stuck at 0

由 笑着哭i 提交于 2021-01-20 19:11:47
问题 I am trying to train a simple 2 layer Fully Connected neural net for Binary Classification in Tensorflow keras. I have split my data into Training and Validation sets with a 80-20 split using sklearn's train_test_split() . When I call model.fit(X_train, y_train, validation_data=[X_val, y_val]) , it shows 0 validation loss and accuracy for all epochs , but it trains just fine. Also, when I try to evaluate it on the validation set, the output is non-zero. Can someone please explain why I am
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