开源软件名称:leriomaggio/deep-learning-keras-tensorflow开源软件地址:https://github.com/leriomaggio/deep-learning-keras-tensorflow开源编程语言:Jupyter Notebook 99.7%开源软件介绍:Deep Learning with Keras and TensorflowAuthor: Valerio MaggioContacts:
git clone https://github.com/leriomaggio/deep-learning-keras-tensorflow.git Table of Contents
RequirementsThis tutorial requires the following packages:
(Optional but recommended):
The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures. Python VersionI'm currently running this tutorial with Python 3 on Anaconda !python --version
Setting the EnvironmentIn this repository, files to re-create virtual env with To re-create the virtual environments (on Linux, for example): conda env create -f deep-learning.yml For OSX, just change the filename, accordingly. Notes about Installing Theano with GPU supportNOTE: Read this section only if after pip installing Since version The goal of Here are some useful tips (hopefully) I came up with to properly install and configure
Sometimes it is suggested to install
After Theano is installed:
Installing TensorflowTo date For this reason, Tensorflow for CPU only:pip install tensorflow Tensorflow with GPU support:pip install tensorflow-gpu Note: NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Important Note:All the code provided+ in this tutorial can run even if This is exactly the power of Keras! Therefore, installing +: Apart from the 1.2 Introduction to Tensorflow tutorial, of course. Configure Keras with tensorflowBy default, Keras is configured with If you want to use
touch $HOME/.keras/keras.json
!cat ~/.keras/keras.json
Test if everything is up&running1. Check importimport numpy as np
import scipy as sp
import pandas as pd
import matplotlib.pyplot as plt
import sklearn import keras
2. Check installed Versionsimport numpy
print('numpy:', numpy.__version__)
import scipy
print('scipy:', scipy.__version__)
import matplotlib
print('matplotlib:', matplotlib.__version__)
import IPython
print('iPython:', IPython.__version__)
import sklearn
print('scikit-learn:', sklearn.__version__)
import keras
print('keras: ', keras.__version__)
# optional
import theano
print('Theano: ', theano.__version__)
import tensorflow as tf
print('Tensorflow: ', tf.__version__)
If everything worked till down here, you're ready to start! |
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