Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/agungdwiprasetyo/deep-learning
Deep Learning using Keras running on TensorFlow
https://github.com/agungdwiprasetyo/deep-learning
deep-learning
Last synced: about 1 month ago
JSON representation
Deep Learning using Keras running on TensorFlow
- Host: GitHub
- URL: https://github.com/agungdwiprasetyo/deep-learning
- Owner: agungdwiprasetyo
- Created: 2017-02-13T05:06:39.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2017-02-13T06:27:51.000Z (about 8 years ago)
- Last Synced: 2024-11-16T04:54:52.931Z (3 months ago)
- Topics: deep-learning
- Language: Jupyter Notebook
- Size: 2.83 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Deep Learning
Deep learning dalam repository ini menggunakan framework Keras yang berjalan pada TensorFlow. Deep Learning digunakan untuk menggali data yang berukuran sangat besar yang menggunakan teknik-teknik machine learning, terutama neural network.## Requirements
Menggunakan bahasa pemrograman Python, baik versi 2.7 maupun versi 3.5. Lalu sangat disarankan menggunakan sistem operasi Linux 64-bit.#### Proses instalasi TensorFlow pada python:
- Install pip
```sh
$ sudo apt-get install python-pip python-dev
```
- Install TensorFlow
```sh
$ sudo pip install tensorflow # apabila hanya menggunakan cpu untuk pemrosesannya
$ sudo pip install tensorflow-gpu # apabila support penggunaan GPU seperti CUDA pada NVIDIA
```
- Download Binary file
```sh
# Ubuntu/Linux 64-bit, CPU only, Python 2.7
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc2-cp27-none-linux_x86_64.whl# Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc2-cp27-none-linux_x86_64.whl# 64-bit, CPU only, Python 3.5
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc2-cp35-cp35m-linux_x86_64.whl# Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
# Requires CUDA toolkit 8.0 and CuDNN v5. For other versions, see "Installing from sources" below.
$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc2-cp35-cp35m-linux_x86_64.whl
```
- Install Binary file yang telah didownload sebelumnya dengan pip
```sh
# Python 2
$ sudo pip install --upgrade $TF_BINARY_URL# Python 3
$ sudo pip3 install --upgrade $TF_BINARY_URL
```
- Tes hasil instalasi
```sh
$ python
...
>>> import tensorflow as tf
>>> hello = tf.constant('Hello, TensorFlow!')
>>> sess = tf.Session()
>>> print(sess.run(hello))
Hello, TensorFlow!
>>> a = tf.constant(10)
>>> b = tf.constant(32)
>>> print(sess.run(a + b))
42
>>>
```#### Proses instalasi Keras
Install Keras menggunakan Pip
```sh
$ sudo pip install keras
```
Untuk melihat versi Keras yang terinstal
```sh
$ python -c "import keras; print keras.__version__"
```## Mulai
Struktur data yang digunakan dalam Keras yaitu berupa model. Model yang sering digunakan yaitu Sequential.
```python
#!/usr/bin/env python
from keras.models import Sequential
model = Sequential()
```
- Run program pada ``` keras/glass.py ```
- Hasil (termasuk akurasi dengan epochs sebanyak 150)
