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100 lines
3.5 KiB
100 lines
3.5 KiB
import theano
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import theano.tensor as T
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k = T.iscalar("k")
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A = T.vector("A")
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# Symbolic description of the result
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result, updates = theano.scan(fn=lambda prior_result, A: prior_result * A,
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outputs_info=T.ones_like(A),
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non_sequences=A,
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n_steps=k)
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# We only care about A**k, but scan has provided us with A**1 through A**k.
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# Discard the values that we don't care about. Scan is smart enough to
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# notice this and not waste memory saving them.
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final_result = result[-1]
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# compiled function that returns A**k
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power = theano.function(inputs=[A,k], outputs=final_result, updates=updates)
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print(power(range(10),2))
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print(power(range(10),4))
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print('P2:')
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import numpy
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coefficients = theano.tensor.vector("coefficients")
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x = T.scalar("x")
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max_coefficients_supported = 10000
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# Generate the components of the polynomial
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components, updates = theano.scan(fn=lambda coefficient, power, prior_result, free_variable: prior_result + (coefficient * (free_variable ** power)),
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outputs_info=T.zeros(1),
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sequences=[coefficients, theano.tensor.arange(max_coefficients_supported)],
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non_sequences=x)
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# Sum them up
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polynomial = components.sum()
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pol = components[-1]
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# Compile a function
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calculate_polynomial = theano.function(inputs=[coefficients, x], outputs=components)
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# Test
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test_coefficients = numpy.asarray([1, 0, 2], dtype=numpy.float32)
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test_value = 3
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print(calculate_polynomial(test_coefficients, test_value))
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print(1.0 * (3 ** 0) + 0.0 * (3 ** 1) + 2.0 * (3 ** 2))
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print('P3:')
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import numpy as np
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import theano
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import theano.tensor as T
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up_to = T.iscalar("up_to")
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# define a named function, rather than using lambda
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def accumulate_by_adding(arange_val, prior_result):
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return prior_result + arange_val
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seq = T.arange(up_to)
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# An unauthorized implicit downcast from the dtype of 'seq', to that of
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# 'T.as_tensor_variable(0)' which is of dtype 'int8' by default would occur
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# if this instruction were to be used instead of the next one:
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# outputs_info = T.as_tensor_variable(0)
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outputs_info = T.as_tensor_variable(np.asarray(0, seq.dtype))
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scan_result, scan_updates = theano.scan(fn=accumulate_by_adding,
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outputs_info=outputs_info,
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sequences=seq)
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triangular_sequence = theano.function(inputs=[up_to], outputs=scan_result)
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# test
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some_num = 15
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print(triangular_sequence(some_num))
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print([n * (n + 1) // 2 for n in range(some_num)])
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print('P4:')
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location = T.imatrix("location")
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values = T.vector("values")
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output_model = T.matrix("output_model")
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def set_value_at_position(a_location, a_value, output_model):
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zeros = T.zeros_like(output_model)
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zeros_subtensor = zeros[a_location[0], a_location[1]]
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return T.set_subtensor(zeros_subtensor, a_value)
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result, updates = theano.scan(fn=set_value_at_position,
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outputs_info=None,
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sequences=[location, values],
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non_sequences=output_model)
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assign_values_at_positions = theano.function(inputs=[location, values, output_model], outputs=result)
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# test
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test_locations = numpy.asarray([[1, 1], [2, 3]], dtype=numpy.int32)
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test_values = numpy.asarray([42, 50], dtype=numpy.float32)
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test_output_model = numpy.zeros((5, 5), dtype=numpy.float32)
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print(assign_values_at_positions(test_locations, test_values, test_output_model)) |