This is a complete guide to what is going on: https://github.com/pymc-devs/pymc3/issues/2176
On Wednesday, May 24, 2017 at 2:32:19 PM UTC+2, nouiz wrote: > > What did do you had have? Without this at can't help you. > > Le mar. 23 mai 2017 18:14, <[email protected] <javascript:>> a écrit : > >> I have edited the code now to include input var and target var in the >> givens for the function, but it still doesn't work. Yes I want to define >> the model with input var and target var, which I now do with given, since >> shared variables cannot be used directly as input >> *From: *Frédéric Bastien >> *Sent: *Wednesday, May 24, 2017 12:00 AM >> *To: *[email protected] <javascript:> >> *Reply To: *[email protected] <javascript:> >> *Subject: *Re: [theano-users] Using shared variable as inputs to theano >> function (values not being updated) >> >> Hi, >> >> You don't use input_var or target_var in your Theano function. So Theano >> ignore there value. Did you wanted to define the model with input_var and >> target_var instead of X and Y? If so, that could work by calling >> set_value(). >> >> Frédéric >> >> On Thu, May 11, 2017 at 5:55 PM Tara <[email protected] <javascript:>> >> wrote: >> >>> I am trying to combine pymc3 with Theano for a simple recurrent neural >>> network.However, when I complete training and change the input of the >>> shared variables to the test set, the values are not updated in the graph >>> even though the shared variables are updated. >>> Any ideas will be appreciated. >>> Here is the code : >>> >>> # CREATE PYMC3 + THEANO IMPLEMENTATION OF A SIMPLE RECURRENT NETWORK >>> import timeit >>> start = timeit.default_timer() >>> import theano >>> import theano.tensor as T >>> import numpy as np >>> import pymc3 as pm >>> from scipy.stats import mode >>> theano.config.compute_test_value = 'ignore' >>> >>> input_dim = 2 >>> output_dim = 2 >>> ### PARAMETERS OF THE MODEL ### >>> hidden_dim = 64 >>> learning_rate = 0.1 >>> nb_epochs = 10 >>> >>> np.random.seed(0) >>> >>> # Initialization /placeholder values >>> X = T.dtensor3('X') >>> Y = T.dtensor3('Y') >>> >>> # begin by generating dataset so we have an array of lists >>> # .... >>> >>> NUM_EXAMPLES = 1500 >>> test_input = X_data[NUM_EXAMPLES:] >>> test_output = y_data[NUM_EXAMPLES:] >>> >>> train_input = X_data[:NUM_EXAMPLES] >>> train_output = y_data[:NUM_EXAMPLES] >>> >>> input_var = theano.shared(np.asarray(train_input).astype(np.float64), >>> borrow = True) >>> target_var = theano.shared(np.asarray(train_output).astype(np.float64), >>> borrow = True) >>> >>> # Reference >>> # From paper :IMPROVING PERFORMANCE OF RECURRENT NEURAL NETWORK WITH >>> RELU NONLINEARITY >>> def norm_positive_definite(r): >>> A = np.dot(r, r.transpose())/hidden_dim >>> values, vectors = np.linalg.eig(A) >>> e = np.amax(values) >>> >>> >> -- >> >> --- >> You received this message because you are subscribed to a topic in the >> Google Groups "theano-users" group. >> To unsubscribe from this topic, visit >> https://groups.google.com/d/topic/theano-users/_sxgPvgMeYo/unsubscribe. >> To unsubscribe from this group and all its topics, send an email to >> [email protected] <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> >> -- >> >> --- >> You received this message because you are subscribed to the Google Groups >> "theano-users" group. >> To unsubscribe from this group and stop receiving emails from it, send an >> email to [email protected] <javascript:>. >> For more options, visit https://groups.google.com/d/optout. >> > -- --- You received this message because you are subscribed to the Google Groups "theano-users" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. For more options, visit https://groups.google.com/d/optout.
