106 lines
2.7 KiB
Python
106 lines
2.7 KiB
Python
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import numpy as np
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import theano.tensor as T
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from theano import function
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# ALGEBRA
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x = T.dmatrix('x')
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y = T.dmatrix('y')
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z = x + y
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f = function([x, y], z)
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# print(f(2, 3))
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# print(numpy.allclose(f(16.3, 12.1), 28.4))
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print(f([[1, 2], [3, 4]], [[10, 20], [30, 40]]))
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# exercise
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import theano
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a = T.vector() # declare variable
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b = T.vector() # declare variable
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out = a ** 2 + b ** 2 + 2 * a * b # build symbolic expression
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f = function([a, b], out) # compile function
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print(f([1, 2], [4, 5]))
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###################################################
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# OTHER EXAMPLES
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# logistic function
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x = T.dmatrix('x')
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logistic_eq = 1 / (1 + T.exp(-x))
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logistic = function([x], logistic_eq)
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print(logistic([[0, 1], [-1, -2]]))
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# multiple things calculation
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a, b = T.dmatrices('a', 'b')
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diff = a - b
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abs_diff = abs(diff)
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diff_squared = diff**2
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f = function([a, b], [diff, abs_diff, diff_squared])
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print(f([[1, 1], [1, 1]], [[0, 1], [2, 3]]))
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# default value
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c = T.matrix('c')
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c = a + b
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f = function([a, theano.In(b, value=[[1, 1], [1, 1]])], c)
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print(f([[1, 1], [1, 1]]))
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# accumulator
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state = theano.shared([[0, 0], [0, 0]])
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print("accumulator")
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print(state.get_value())
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state = theano.shared(np.matrix('0 0; 0 0', dtype=np.int32))
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print(type(np.matrix('0 0; 0 0', dtype=np.int64)))
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print(type(np.matrix('0 1; 2 3', dtype=np.int64)))
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inc = T.imatrix('inc')
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expression = state+inc
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print(type(expression))
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accumulator = function([inc], state, updates=[(state, state+inc)])
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accumulator(np.matrix('1 2; 3 4', dtype=np.int32))
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print(state.get_value())
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accumulator(np.matrix('1 1; 1 1', dtype=np.int32))
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print(state.get_value())
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# function copy
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print("function copy")
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new_state = theano.shared(np.matrix('0 0; 0 0', dtype=np.int32))
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new_accumulator = accumulator.copy(swap={state: new_state})
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new_accumulator(np.matrix('1 2; 3 4', dtype=np.int32))
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print(new_state.get_value())
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print(state.get_value())
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# random numbers
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# POSSIBLE THAT THIS DOES NOT WORK ON GPU
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print("random numbers")
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srng = T.shared_randomstreams.RandomStreams(seed=234)
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rv_u = srng.uniform((2, 2))
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rv_n = srng.normal((2, 2))
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f = function([], rv_u)
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g = function([], rv_n, no_default_updates=True) # Not updating rv_n.rng
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nearly_zeros = function([], rv_u + rv_u - 2 * rv_u)
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print(f())
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print(f())
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print(g())
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print(g())
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print("sharing streams between functions")
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state_after_v0 = rv_u.rng.get_value().get_state()
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# nearly_zeros() # this affects rv_u's generator
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v1 = f()
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rng = rv_u.rng.get_value(borrow=True)
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rng.set_state(state_after_v0)
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rv_u.rng.set_value(rng, borrow=True)
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v2 = f() # v2 != v1
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v3 = f() # v3 == v1
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print(v1)
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print(v2)
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print(v3)
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