#cut and paste the following and save it as mnist1.py in c:\\temp1 #Can use the following procedure to run the mnist code: # testing program to make sure your system is working # I then ran the same Jupyter notebook using a "kernel" created for that env.
#Conda install opencv python==3.2.0.6 install
`conda install tensorflow` without the "-gpu" part. I created a new "env" naming it "tf-CPU" and installed the CPU only version of TensorFlow i.e. Note: I used the same procedure for doing the CPU version. That's why you use GPU's for this stuff!** **It took 80 seconds utilizing the NVIDIA GTX 980 on my old test system! For reference it took 1345 seconds using all cores at 100% on the Intel i7-4770 CPU in that machine. That MNIST digits training example was a model with 1.2 million training parameters and a dataset with 60,000 images. #older method: In anacondas (right click administrator ): conda install -c menpo dlib # to install the latest dlib face trackġ0c) pip install imutils #used for dlib programs Clink the icon to start the installation you need.ġ0a) conda>pip install opencv-contrib-python # for opencv (with cv2.aruco etc), not pip install opencv-python because they conflict each otherġ0b) # In anacondas (right click administrator ): > conda install -c conda-forge dlib #if you are using python 3.7 You can see you can install spyder (conda>pip install spyder) and jupyter (conda>pip install jupyter). If you have completed the above steps 1-8 in, run win10-start / anaconda navigator (Anaconda 3) , on win32ĥ-final), (after 5a or 5b) after installation of tensorflow-python, test if it is thereĦ) Optional install jupter notdebook (see another link)ħ)An Example Convolution Neural Network training using Keras with TensorFlow.Ĭ:\Users\khwong2\Anaconda3\pkgs\tensorflow-1.15.0.ĩ) Spyder is a good editor for python/tensorflow (optional but recommendation) #If necessary, check the Tensor-flow version by : >conda install tensorflow=1.15 # this is good for cpu only, then python version becomes lower (Not discussed here, see the document :)PythonEnvironmentSetupwithAnacondaPythonĥb) if you don't have gpu, use cpu only, create the kernel (tf-cpu) (control-D to exit)ĥa) If you have gpu, create the kernel (tf-gpu) Type "help", "copyright", "credits" or "license" for more information. In Administrator: Anaconda Powershell Prompt: (base)> you see
#Conda install opencv python==3.2.0.6 windows
anaconda 2020.9 for windows 64-bit and python 3.8 version (or the latest)Ģ) in win10/start/ type anacond powershell prompt(right click for administrator mode)ģ) In Administrator: Anaconda Powershell Prompt: (base)>Ĥ) In Administrator: Anaconda Powershell Prompt: run the followings Need to change to if you use Tensorflow 2.x instead of tensorflow1įrom import Sequential #add tensorflowįrom .recurrent import LSTM #add tensorflowįrom import Dense #add tensorflowįrom import Adam #add tensorflow instead of tensorflow.pythonĮ.g. #for music genre lstm_genre_classifier_keras.py from ()įrom import LSTM changes needed for new tensor-flow-with-keras-įrom import Sequentialįrom .recurrent import LSTMįrom import Adamįrom import Sequential ** if you use tensorflow2 on old project s, e.g. >import tensorflow as tf print(tf._version_) #tensorflow version If the tensorflow tutorials cannot run, try to downgrade to an older tf version >pip install tensorflow=2.0 Tensor_windows (installation through Anaconda3)įix:from _utils import get_file -> from .data_utils import get_fileįix: in variational_autoencoder_deconv : use: vae.save_weights('vae_cnn_mnist.tf') #instead of vae.save_weights('vae_cnn_mnist.h5') >pip list #to see all your installed python versions If you installed tensorflow version 2 but use v1 programs, pleas do the following, in yoru program:
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