@Fred and Pascal, I think Beatriz's question was answered in another thread.
On Wednesday, August 31, 2016 at 12:58:11 PM UTC-7, Pascal Lamblin wrote:
>
> `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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