keras | 易学教程

Keras predict loop memory leak using tf.data.Dataset but not with a numpy array

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问题 I encounter a memory leak and decreasing performance when looping over a Keras model predict function when using a tf.data.Dataset to feed the model, but not when feeding it with a numpy array. Does anyone understand what is causing this and/or how to resolve the issue? Minimal reproducible code snippet (copy/paste runnable): import tensorflow as tf import numpy as np import time SIZE = 5000 inp = tf.keras.layers.Input(shape=(SIZE,), dtype='float32') x = tf.keras.layers.Dense(units=SIZE)(inp)

Why are my results still not reproducible?

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问题 I want to get reproducible results for a CNN. I use Keras and Google Colab with GPU. In addition to recommendations to insert certain code snippets, which should allow a reproducibility, I also added seeds to the layers. ###### This is the first code snipped to run ##### !pip install -U -q PyDrive from pydrive.auth import GoogleAuth from pydrive.drive import GoogleDrive from google.colab import auth from oauth2client.client import GoogleCredentials # Authenticate and create the PyDrive client

Heroku: deploying Deep Learning model

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问题 I have developed a rest API using Flask to expose a Python Keras Deep Learning model (CNN for text classification). I have a very simple script that loads the model into memory and outputs class probabilities for a given text input. The API works perfectly locally. However, when I git push heroku master , I get Compiled slug size: 588.2M is too large (max is 500M) . The model is 83MB in size, which is quite small for a Deep Learning model. Notable dependencies include Keras and its tensorflow

Single Thread Impacts Model Accuracy and Loss with TensorFlow Keras Backend

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问题 Question Why does setting the number of threads in the TensorFlow backend for Keras, intra_op_parallelism_threads and inter_op_parallelism_threads, to 1 negatively impact the accuracy and loss of a model? Background I am using Keras (2.1.6) to train an MNIST CNN with TensorFlow (1.7.0) as the backend. I was running some training in an AWS EC2 instance and noticed my accuracy decreased greatly after switching from a t2.medium instance to a t2.small. This happened without changing the code at

Find most similar images by using neural networks

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问题 I am working with Python, scikit-learn and keras. I have 3000 thousands images of front-faced watches like the following ones: Watch_1, Watch_2, Watch_3. I like to write program which receives as an input a photo of a real watch which maybe taken under less ideal conditions than the photos above (different background colour, darker lightning etc) and find the most similar watches among the 3000 ones to it. By similarity I mean that if I give as an input a photo of a round, brown watch with

Add DropOut after loading the weights in Keras

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问题 I am doing king of transfer learning. What I have done is First train the model with the big datasets and save the weights. Then I train the model with my dataset by freezing the layers. But I see there was some overfitting. So I try to change the dropout of the model and load the weights since the numbers are changing while drop out are changing. I find difficulties to change the dropout. Directly my question is, Is it possible to change the model's dropout while loading the weights? my

How to do zero padding in keras conv layer?

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问题 I am trying to implement model from scientific article, which says they are using zero padding. Is it possible to configure this padding in keras Conv2D? Only possible values for padding I see are padding: one of "valid" or "same" (case-insensitive). Is it possible to pad with zeros or other constant values? 回答1: "same" means zero padding. It is currently not possible to pad with other constants in an efficient way. 回答2: When you use padding='valid' , there's no padding. When you use padding=

How to do zero padding in keras conv layer?

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How to do zero padding in keras conv layer?

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小白学PyTorch | 15 TF2实现一个简单的服装分类任务

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