Hi,
I am using theano to do a simple Logistic Regression on the Iris Dataset.
I am running into an issue. I am pretty sure I am doing something wrong.
Not sure what
Error Message?
<TensorType(int64, scalar)>
Traceback (most recent call last):
File "iris_logistic_sgd.py", line 520, in <module>
lgst.sgd_optimization_mnist()
File "iris_logistic_sgd.py", line 358, in sgd_optimization_mnist
y: test_set_y[index * batch_size: (index + 1) * batch_size]
File "/usr/local/lib/python2.7/dist-packages/theano/compile/function.py",
line 320, in function
output_keys=output_keys)
File "/usr/local/lib/python2.7/dist-packages/theano/compile/pfunc.py",
line 479, in pfunc
output_keys=output_keys)
File
"/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py",
line 1776, in orig_function
output_keys=output_keys).create(
File
"/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py",
line 1415, in __init__
self._check_unused_inputs(inputs, outputs, on_unused_input)
File
"/usr/local/lib/python2.7/dist-packages/theano/compile/function_module.py",
line 1553, in _check_unused_inputs
i.variable, err_msg))
theano.compile.function_module.UnusedInputError: theano.function was asked
to create a function computing outputs given certain inputs, but the
provided input variable at index 0 is not part of the computational graph
needed to compute the outputs: <TensorType(int64, scalar)>.
To make this error into a warning, you can pass the parameter
on_unused_input='warn' to theano.function. To disable it completely, use
on_unused_input='ignore'.
-------Code---------------
datasets = self.load_data(dataset)
train_set_x, train_set_y = datasets[0]
valid_set_x, valid_set_y = datasets[1]
test_set_x, test_set_y = datasets[2]
# compute number of minibatches for training, validation and testing
n_train_batches = train_set_x.get_value(borrow=True).shape[0] //
batch_size
n_valid_batches = valid_set_x.get_value(borrow=True).shape[0] //
batch_size
n_test_batches = test_set_x.get_value(borrow=True).shape[0] //
batch_size
######################
# BUILD ACTUAL MODEL #
######################
# allocate symbolic variables for the data
index = T.lscalar() # index to a [mini]batch
print (index)
# generate symbolic variables for input (x and y represent a
# minibatch)
x = T.matrix('x') # data, presented as rasterized images
y = T.ivector('y') # labels, presented as 1D vector of [int] labels
# construct the logistic regression class
# Each MNIST image has size 28*28
classifier = LogisticRegression(input=x, n_in=28 * 28, n_out=10)
# the cost we minimize during training is the negative log
likelihood of
# the model in symbolic format
cost = classifier.negative_log_likelihood(y)
# compiling a Theano function that computes the mistakes that are
made by
# the model on a minibatch
#print ('------->>', index.eval())
#print ('------>', test_set_y[index * batch_size: (index + 1) *
batch_size].eval() )
#return
#index = 0
test_model = theano.function(
inputs=[index],
outputs=classifier.errors(y),
givens={
x: test_set_x[index * batch_size: (index + 1) * batch_size],
y: test_set_y[index * batch_size: (index + 1) * batch_size]
}
)
Please advice
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