theano

I know the default backend for Keras has switched from Theano to TensorFlow, but with the dev version of Theano I can train on the GPU with OpenCL (I have an AMD card). However, when I import Keras, it only uses the TensorFlow backend even after I changed the values in the Keras configuration file : ~ $ cat $HOME/.keras/keras.json {"epsilon": 1e-07, "floatx": "float32", "backend": "theano"} ~ $ python -c 'import keras' Using TensorFlow backend. ~ $ KERAS_BACKEND=theano python -c 'import keras' Using Theano backend. Mapped name None to device opencl0:2: AMD Radeon R9 M370X Compute Engine In

I'm trying to train a CNN for object detection on images with the CIFAR10 dataset for a seminar at my university but I get the following Error: AssertionError: AbstractConv2d Theano optimization failed: there is no implementation available supporting the requested options. Did you exclude both "conv_dnn" and "conv_gemm" from the optimizer? If on GPU, is cuDNN available and does the GPU support it? If on CPU, do you have a BLAS library installed Theano can link against? I am running Anaconda 2.7 within a Jupyter notebook (CNN training on CPU) from a Windows 10 machine. As I already have updated

I have this code: import numpy as np from keras.models import Sequential from keras.layers.core import Dense, Dropout, Activation from keras.optimizers import SGD from sklearn import datasets import theano iris = datasets.load_iris() X = iris.data[:,0:3] # we only take the first two features. Y = iris.target X = X.astype(theano.config.floatX) Y = Y.astype(theano.config.floatX) model = Sequential() model.add(Dense(150, 1, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add(Dense(150, 1, init='uniform')) model.add(Activation('tanh')) model.add(Dropout(0.5)) model.add

For example, the implementation of Keras' Adagrad has been: class Adagrad(Optimizer): """Adagrad optimizer. It is recommended to leave the parameters of this optimizer at their default values. # Arguments lr: float >= 0. Learning rate. epsilon: float >= 0. decay: float >= 0. Learning rate decay over each update. # References - [Adaptive Subgradient Methods for Online Learning and Stochastic Optimization](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) """ def __init__(self, lr=0.01, epsilon=1e-8, decay=0., **kwargs): super(Adagrad, self).__init__(**kwargs) self.lr = K.variable(lr)

I am try to write a REST api in django that uses a Keras model to return a prediction. However the load_model() function takes some time to load the model and I don't want my users to have to wait so long (each time the model is initialized). What would be the correct way to initialize the model so that is is loaded once and the predictions are done using that same model? On a side note one method that I thought cold be possible was to initialize the model in settings.py as below : settings.py json_file=open("model.json","r") loaded_json=json_file.read() json_file.close() model=model_from_json