`salidas_capa3` is a theano function, which is a callable object.
However, you are trying to _index_ into it using `salidas_capa3[test_model(i)]`.
What is the behaviour you would expect from that code?
On Wed, Jul 27, 2016, Beatriz G. wrote:
> Hi guays,
>
> I am having a similar problem, I am trying to compile the following theano
> function:
>
> layer3 = LogisticRegression(input=layer2.output, n_in=100, n_out=4)
>
>
> salidas_capa3 = theano.function(
> [index],
> layer3.y_pred,
> givens={
> x: test_set_x[index * batch_size: (index + 1) * batch_size]
> }
> )
>
>
>
> Which I call in the test part:
>
>
> for i in range(n_test_batches):
> test_losses = [test_model(i)]
> y_pred_test = salidas_capa3[test_model(i)]
> print y_pred_test
> test_score = numpy.mean(test_losses)
>
>
>
> And I am getting the next error:
>
>
> Traceback (most recent call last):
> File "/home/beaa/Escritorio/Theano/Separando_Lenet.py", line 416, in
> <module>
> evaluate_lenet5()
> File "/home/beaa/Escritorio/Theano/Separando_Lenet.py", line 392, in
> evaluate_lenet5
> y_pred_test = salidas_capa3[test_model(i)]
> File
> "/home/beaa/.local/lib/python2.7/site-packages/theano/compile/function_module.py",
> line 545, in __getitem__
> return self.value[item]
> File
> "/home/beaa/.local/lib/python2.7/site-packages/theano/compile/function_module.py",
> line 480, in __getitem__
> s = finder[item]
> TypeError: unhashable type: 'numpy.ndarray'
>
>
>
>
> Anyone could guide me?
>
>
> Here is my code:
>
>
> class LeNetConvPoolLayer(object):
> """Pool Layer of a convolutional network """
>
> def __init__(self, rng, input, filter_shape, image_shape, poolsize=(2,
> 2)):
> """
> Allocate a LeNetConvPoolLayer with shared variable internal
> parameters.
> :type rng: numpy.random.RandomState
> :param rng: a random number generator used to initialize weights
> :type input: theano.tensor.dtensor4
> :param input: symbolic image tensor, of shape image_shape
> :type filter_shape: tuple or list of length 4
> :param filter_shape: (number of filters, num input feature maps,
> filter height, filter width)
> :type image_shape: tuple or list of length 4
> :param image_shape: (batch size, num input feature maps,
> image height, image width)
> :type poolsize: tuple or list of length 2
> :param poolsize: the downsampling (pooling) factor (#rows, #cols)
> """
>
> assert image_shape[1] == filter_shape[1]
> #El numero de feature maps sea igual en ambas variables
> self.input = input
>
> # there are "num input feature maps * filter height * filter width"
> # inputs to each hidden unit
> fan_in = numpy.prod(filter_shape[1:])
> #el numero de neuronas en la capa anterio
> # each unit in the lower layer receives a gradient from:
> # "num output feature maps * filter height * filter width" /
> # pooling size
>
> fan_out = (filter_shape[0] * numpy.prod(filter_shape[2:]) /
> #El numero de neuronas de salida: numero de filtros*alto*ancho del
> filtro/poolsize(tam*tam)
> numpy.prod(poolsize))
> # initialize weights with random weights
>
> # initialize weights with random weights
> W_bound = numpy.sqrt(6. / (fan_in + fan_out))
> self.W = theano.shared(
> numpy.asarray(
> rng.uniform(low=-W_bound, high=W_bound, size=filter_shape),
> # Se calcula asi el W_bound por la funcion de activacion
> tangencial.
> # Los pesos dependen del tamanyo del filtro(neuronas)
>
> dtype=theano.config.floatX # Para que sea valido en gpu
> ),
> borrow=True
> )
>
> # the bias is a 1D tensor -- one bias per output feature map
> b_values = numpy.zeros((filter_shape[0],), dtype=theano.config.floatX)
> self.b = theano.shared(value=b_values, borrow=True)
>
> # convolve input feature maps with filters
> conv_out = conv.conv2d(
> input=input,
> filters=self.W,
> filter_shape=filter_shape,
> image_shape=image_shape
> )
>
> # downsample each feature map individually, using maxpooling
> pooled_out = downsample.max_pool_2d(
> input=conv_out,
> ds=poolsize,
> ignore_border=True
> )
>
> print pooled_out
>
> # add the bias term. Since the bias is a vector (1D array), we first
> # reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
> # thus be broadcasted across mini-batches and feature map
> # width & height
> self.output = theano.tensor.nnet.relu((pooled_out +
> self.b.dimshuffle('x', 0, 'x', 'x')) , alpha=0)
>
>
> # store parameters of this layer
> self.params = [self.W, self.b]
>
> # keep track of model input
> self.input = input
>
> self.conv_out=conv_out
> self.pooled_out=pooled_out
>
> self.salidas_capa = [self.conv_out, self.pooled_out, self.output]
>
>
>
> def evaluate_lenet5(learning_rate=0.001, n_epochs=2, nkerns=[48, 96],
> batch_size=20):
> """ Demonstrates lenet on MNIST dataset
>
> :type learning_rate: float
> :param learning_rate: learning rate used (factor for the stochastic
> gradient)
>
> :type n_epochs: int
> :param n_epochs: maximal number of epochs to run the optimizer
>
> :type dataset: string
> :param dataset: path to the dataset used for training /testing (MNIST
> here)
>
> :type nkerns: list of ints
> :param nkerns: number of kernels on each layer
> """
>
> rng = numpy.random.RandomState(2509)
>
> train_set_x, test_set_x, train_set_y, test_set_y, valid_set_x,
> valid_set_y = Load_casia_Data2()
>
> train_set_x = theano.shared(numpy.array(train_set_x, dtype='float64',))
> test_set_x = theano.shared(numpy.array(test_set_x, dtype='float64'))
> train_set_y = theano.shared(numpy.array(train_set_y, dtype='int32'))
> test_set_y = theano.shared(numpy.array(test_set_y, dtype='int32'))
> valid_set_x = theano.shared(numpy.array(valid_set_x, dtype='float64'))
> valid_set_y = theano.shared(numpy.array(valid_set_y, dtype='int32'))
>
> print("n_batches:")
>
> # compute number of minibatches for training, validation and testing
> n_train_batches = train_set_x.get_value(borrow=True).shape[0]
> n_valid_batches = valid_set_x.get_value(borrow=True).shape[0]
> n_test_batches = test_set_x.get_value(borrow=True).shape[0]
> print("n_train_batches: %d" % n_train_batches)
> print("n_valid_batches: %d" % n_valid_batches)
> print("n_test_batches: %d" % n_test_batches)
>
> n_train_batches /= batch_size
> n_valid_batches /= batch_size
> n_test_batches /= batch_size
>
> # allocate symbolic variables for the data
> index = T.lscalar() # index to a [mini]batch
>
> # start-snippet-1
> x = T.matrix('x') # the data is presented as rasterized images
> y = T.ivector('y') # the labels are presented as 1D vector of
> # [int] labels
>
> ######################
> # BUILD ACTUAL MODEL #
> ######################
> print '... building the model'
>
> # Reshape matrix of rasterized images of shape (batch_size, 28 * 28)
> # to a 4D tensor, compatible with our LeNetConvPoolLayer
> # (28, 28) is the size of MNIST images.
> layer0_input = x.reshape((batch_size, 3, 104, 52))
>
> # Construct the first convolutional pooling layer:
> # filtering reduces the image size to (28-5+1 , 28-5+1) = (24, 24)
> # maxpooling reduces this further to (24/2, 24/2) = (12, 12)
> # 4D output tensor is thus of shape(batch_size, nkerns[0], 12, 12)
> layer0 = LeNetConvPoolLayer(
> rng,
> input=layer0_input,
> image_shape=(batch_size, 3, 104, 52),
> filter_shape=(nkerns[0], 3, 5, 5),
> poolsize=(2, 2)
> )
>
> salidas_capa0 = theano.function(
> [index],
> layer0.salidas_capa,
> givens={
> x: test_set_x[index * batch_size: (index + 1) * batch_size],
> }
> )
>
>
> # Construct the second convolutional pooling layer
> # filtering reduces the image size to (12-5+1, 12-5+1) = (8, 8)
> # maxpooling reduces this further to (8/2, 8/2) = (4, 4)
> # 4D output tensor is thus of shape (batch_size, nkerns[1], 4, 4)
> layer1 = LeNetConvPoolLayer(
> rng,
> input=layer0.output,
> image_shape=(batch_size, nkerns[0], 50, 24),
> filter_shape=(nkerns[1], nkerns[0], 5, 5),
> poolsize=(2, 2)
> )
>
> salidas_capa1 = theano.function(
> [index],
> layer1.salidas_capa,
> givens={
> x: test_set_x[index * batch_size: (index + 1) * batch_size],
> }
> )
>
> # the HiddenLayer being fully-connected, it operates on 2D matrices of
> # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
> # This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
> # or (500, 50 * 4 * 4) = (500, 800) with the default values.
> layer2_input = layer1.output.flatten(2)
>
> # construct a fully-connected sigmoidal layer
> layer2 = HiddenLayer(
> rng,
> input=layer2_input,
> n_in=nkerns[1] * 23 * 10,
> n_out=100,
> activation=T.nnet.relu
> )
>
> # classify the values of the fully-connected sigmoidal layer
> svm = SVC()
>
>
> layer3 = LogisticRegression(input=layer2.output, n_in=100, n_out=4)
>
>
> salidas_capa3 = theano.function(
> [index],
> layer3.y_pred,
> givens={
> x: test_set_x[index * batch_size: (index + 1) * batch_size]
> }
> )
>
> # the cost we minimize during training is the NLL of the model
> cost = layer3.negative_log_likelihood(y)
>
> # create a function to compute the mistakes that are made by the model
> test_model = theano.function(
> [index],
> layer3.errors(y),
> givens={
> x: test_set_x[index * batch_size: (index + 1) * batch_size],
> #Los given son algo para la gpu
> y: test_set_y[index * batch_size: (index + 1) * batch_size]
> }
> )
>
>
> validate_model = theano.function(
> [index],
> layer3.errors(y),
> givens={
> x: valid_set_x[index * batch_size: (index + 1) * batch_size],
> y: valid_set_y[index * batch_size: (index + 1) * batch_size]
> }
> )
>
> # create a list of all model parameters to be fit by gradient descent
> params = layer3.params + layer2.params + layer1.params + layer0.params
>
> # create a list of gradients for all model parameters
> grads = T.grad(cost, params)
>
> # train_model is a function that updates the model parameters by
> # SGD Since this model has many parameters, it would be tedious to
> # manually create an update rule for each model parameter. We thus
> # create the updates list by automatically looping over all
> # (params[i], grads[i]) pairs.
> updates = [
> (param_i, param_i - learning_rate * grad_i)
> for param_i, grad_i in zip(params, grads)
> ]
>
> train_model = theano.function(
> [index],
> cost,
> updates=updates,
> givens={
> x: train_set_x[index * batch_size: (index + 1) * batch_size],
> y: train_set_y[index * batch_size: (index + 1) * batch_size]
> }
> )
> # end-snippet-1
>
>
> ###############
> # TRAIN MODEL #
> ###############
> print '... training'
> # early-stopping parameters
> patience = 10000 # look as this many examples regardless
> patience_increase = 2 # wait this much longer when a new best is found
> improvement_threshold = 0.995 # a relative improvement of this much is
> # considered significant
> validation_frequency = min(n_train_batches, patience / 2)
> # go through this many minibatche before checking the network
> # on the validation set; in this case we check every epoch
>
> best_validation_loss = numpy.inf
> best_iter = 0
> test_score = 0.
> start_time = timeit.default_timer()
>
> lista_coste = []
>
> epoch = 0
> done_looping = False
>
> # save_file =
> open('/home/beaa/PycharmProjects/TFM/LearningTheano/salidas0.pkl', 'wb')
> # save_file1 =
> open('/home/beaa/PycharmProjects/TFM/LearningTheano/wb0.pkl', 'wb')
> # save_file2 =
> open('/home/beaa/PycharmProjects/TFM/LearningTheano/salidas1.pkl', 'wb')
> # save_file3 =
> open('/home/beaa/PycharmProjects/TFM/LearningTheano/wb1.pkl', 'wb')
>
> print 'n_train_batches'
> print n_train_batches
> while (epoch < n_epochs) and (not done_looping):
> epoch = epoch + 1
> for minibatch_index in range(n_train_batches):
>
> iter = (epoch - 1) * n_train_batches + minibatch_index
>
> if iter % 100 == 0:
> print('training @ iter = ', iter)
> cost_ij = train_model(minibatch_index)
>
> if (iter + 1) % validation_frequency == 0:
>
> # compute zero-one loss on validation set
>
> validation_losses = [validate_model(i) for i
> in range(n_valid_batches)]
> this_validation_loss = numpy.mean(validation_losses)
> print('epoch %i, minibatch %i/%i, validation error %f %%' %
> (epoch, minibatch_index + 1, n_train_batches,
> this_validation_loss * 100.))
>
> # if we got the best validation score until now
> if this_validation_loss < best_validation_loss:
>
> # improve patience if loss improvement is good enough
> if this_validation_loss < best_validation_loss * \
> improvement_threshold:
> patience = max(patience, iter * patience_increase)
>
> # save best validation score and iteration number
> best_validation_loss = this_validation_loss
> best_iter = iter
>
> w0_test = layer0.W.get_value()
> b0_test = layer0.b.get_value()
>
> w1_test = layer1.W.get_value()
> b1_test = layer1.b.get_value()
>
> w2_test = layer2.W.get_value()
> b2_test = layer2.b.get_value()
>
>
>
>
> if patience <= iter:
> done_looping = True
> break
>
>
> ###############################
> ### TESTING MODEL ###
> ###############################
> #Aqui se tiene que cargar la red
>
> layer0.W.set_value(w0_test)
> layer0.b.set_value(b0_test)
>
> layer1.W.set_value(w1_test)
> layer1.b.set_value(b1_test)
>
> layer2.W.set_value(w2_test)
> layer2.b.set_value(b2_test)
>
> # test it on the test set
> for i in range(n_test_batches):
> test_losses = [test_model(i)]
> y_pred_test = salidas_capa3[test_model(i)]
> print y_pred_test
> test_score = numpy.mean(test_losses)
>
> print((' test error of best model %f %%') % (test_score * 100.))
>
>
> end_time = timeit.default_timer()
> print('Optimization complete.')
> print('Best validation score of %f %% obtained at iteration %i, '
> 'with test performance %f %%' %
> (best_validation_loss * 100., best_iter + 1, test_score * 100.))
>
> print("start time: %d" % start_time)
> print("end_time: %d" % end_time)
>
> # print(('The code for file ' +
> # os.path.split(__file__)[1] +
> # ' ran for %.2fm' % ((end_time - start_time) / 60.)),
> file=sys.stderr)
>
>
>
>
> if __name__ == '__main__':
> evaluate_lenet5()
>
>
> def experiment(state, channel):
> evaluate_lenet5(state.learning_rate, dataset=state.dataset)
>
>
>
>
>
>
> Thanks in advance.
>
>
> Regards.
>
>
>
>
>
>
>
> El martes, 28 de mayo de 2013, 23:56:29 (UTC+2), Pascal Lamblin escribió:
> >
> > On Wed, May 29, 2013, Анатолий wrote:
> > > That's how i define test values:
> > >
> > > ishape = (288, 384)
> > > batch_size = numpy.int16(batch_size)
> > > index = T.lscalar().tag.test_value = numpy.int16(0)
> > > x = T.matrix('x').tag.test_value = numpy.random.rand(batch_size,
> > > numpy.prod(ishape)).astype(dtype=theano.config.floatX)
> > > y = T.ivector('y').tag.test_value = numpy.random.randint(0,2,
> > > size=batch_size).astype(dtype=numpy.int16)
> >
> > In fact, as Olivier Delalleau pointed it, here is your problem. Here,
> > you assign the test value to the name 'x', rather than assigning the
> > symbolic variable. You should do the following instead:
> >
> > # In two different statements:
> > # first create 'index' as a symbolic variable
> > index = T.lscalar()
> > # then assign a test value to it
> > index.tag.test_value = numpy.int16(0)
> >
> > # same thing for the other variables:
> > x = T.matrix('x')
> > x.tag.test_value = numpy.random.rand(batch_size,
> > numpy.prod(ishape)).astype(dtype=theano.config.floatX)
> >
> > y = T.ivector('y')
> > y.tag.test_value = numpy.random.randint(0, 2,
> > size=batch_size).astype(dtype=numpy.int16)
> >
> > Hope this helps,
> > --
> > Pascal
> >
>
> --
>
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--
Pascal
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