theano

theano.base_compiledir refers to the directory where the compiled files are stored. Is there a way where I could permanently set theano.base_compiledir to a different location, perhaps by modifying the content of some internal theano files ? http://deeplearning.net/software/theano/library/config.html does explain ways for configuring theano in some aspect but I still couldn't address my question. I am using Ubuntu. Thanks & Cheers! As the documentation explains, you can set this, or any other Theano config flag, permanently by altering either the THEANO_FLAGS environment variable (e.g. in your

Using Keras + Theano I successfully made a recurrent bidirectional-LSTM neural network that is capable of training on and classifying DNA sequences of arbitrary lengths, using the following model (for fully working code see: http://pastebin.com/jBLv8B72 ): sequence = Input(shape=(None, ONE_HOT_DIMENSION), dtype='float32') dropout = Dropout(0.2)(sequence) # bidirectional LSTM forward_lstm = LSTM( output_dim=50, init='uniform', inner_init='uniform', forget_bias_init='one', return_sequences=True, activation='tanh', inner_activation='sigmoid', )(dropout) backward_lstm = LSTM( output_dim=50, init=

UPDATE : I have to re-write this question as after some investigation I realise that this is a different problem. Context: running keras in a gridsearch setting using the kerasclassifier wrapper with scikit learn. Sys: Ubuntu 16.04, libraries: anaconda distribution 5.1, keras 2.0.9, scikitlearn 0.19.1, tensorflow 1.3.0 or theano 0.9.0, using CPUs only. Code: I simply used the code here for testing: https://machinelearningmastery.com/use-keras-deep-learning-models-scikit-learn-python/ , the second example 'Grid Search Deep Learning Model Parameters'. Pay attention to line 35, which reads: grid

I want to build a neural network where the two first layers are feedforward and the last one is recurrent. here is my code : model = Sequential() model.add(Dense(150, input_dim=23,init='normal',activation='relu')) model.add(Dense(80,activation='relu',init='normal')) model.add(SimpleRNN(2,init='normal')) adam =OP.Adam(lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08) model.compile(loss="mean_squared_error", optimizer="rmsprop") and I get this error : Exception: Input 0 is incompatible with layer simplernn_11: expected ndim=3, found ndim=2. model.compile(loss='mse', optimizer=adam) It is

I'm quite new in python and of course I'm also new with Theano. I'm trying to use it under windows along with anaconda python. I have installed all the compulsory requirements (except CUDA since on this laptop I don't have a NVIDIA GPU). I installed the same GCC and set the path as suggested in the walkthrough page. Still I get the following error: Problem occurred during compilation with the command line below: C:\TDM-GCC-64\bin\g++.exe -shared -g -march=broadwell -mmmx -mno-3dnow -msse -msse2 -msse3 -mssse3 -mno-sse4a -mcx16 -msahf -mmovbe -maes -mno-sha -mpclmul -mpopcnt -mabm -mno-lwp

How to implement Weighted Binary CrossEntropy on theano? My Convolutional neural network only predict 0 ~~ 1 (sigmoid). I want to penalize my predictions in this way : Basically, i want to penalize MORE when the model predicts 0 but the truth was 1. Question : How can I create this Weighted Binary CrossEntropy function using theano and lasagne ? I tried this below prediction = lasagne.layers.get_output(model) import theano.tensor as T def weighted_crossentropy(predictions, targets): # Copy the tensor tgt = targets.copy("tgt") # Make it a vector # tgt = tgt.flatten() # tgt = tgt.reshape(3000) #

I am running the example kaggle_otto_nn.py of Keras with backend of theano . When I set cnmem=1 , the following error comes out: cliu@cliu-ubuntu:keras-examples$ THEANO_FLAGS=mode=FAST_RUN,device=gpu,floatX=float32,lib.cnmem=1 python kaggle_otto_nn.py Using Theano backend. ERROR (theano.sandbox.cuda): ERROR: Not using GPU. Initialisation of device gpu failed: initCnmem: cnmemInit call failed! Reason=CNMEM_STATUS_OUT_OF_MEMORY. numdev=1 /usr/local/lib/python2.7/dist-packages/Theano-0.8.0rc1-py2.7.egg/theano/tensor/signal/downsample.py:6: UserWarning: downsample module has been moved to the