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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