Adding test for dot product
This commit is contained in:
Willi Richert
Родитель
00d96ae808
Коммит
fc2f3aa42d
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@ -12,7 +12,7 @@ the forward and the backward pass
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from __future__ import division
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import numpy as np
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import pytest
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from .ops_test_utils import unittest_helper, AA, I, precision, PRECISION_TO_TYPE
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from .ops_test_utils import unittest_helper, AA, I, precision, PRECISION_TO_TYPE, batch_dense_to_sparse, left_matrix_type, right_matrix_type
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from ...graph import *
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from .. import *
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@ -47,7 +47,7 @@ def test_op_plus(left_operand, right_operand, device_id, precision):
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# we need two surrounding brackets
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# the first for sequences (length=1, since we have dynamic_axis='')
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# the second for batch of one sample
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expected = [[AA(left_operand) + AA(right_operand)]]
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expected = [[AA(left_operand, dtype=PRECISION_TO_TYPE[precision]) + AA(right_operand, dtype=PRECISION_TO_TYPE[precision])]]
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a = I([left_operand])
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b = I([right_operand])
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@ -86,7 +86,7 @@ def test_op_minus(left_operand, right_operand, device_id, precision):
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# we need two surrounding brackets
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# the first for sequences (length=1, since we have dynamic_axis='')
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# the second for batch of one sample
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expected = [[AA(left_operand) - AA(right_operand)]]
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expected = [[AA(left_operand, dtype=PRECISION_TO_TYPE[precision]) - AA(right_operand, dtype=PRECISION_TO_TYPE[precision])]]
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a = I([left_operand])
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b = I([right_operand])
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@ -124,7 +124,7 @@ def test_op_element_times(left_operand, right_operand, device_id, precision):
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# we need two surrounding brackets
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# the first for sequences (length=1, since we have dynamic_axis='')
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# the second for batch of one sample
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expected = [[AA(left_operand) * AA(right_operand)]]
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expected = [[AA(left_operand, dtype=PRECISION_TO_TYPE[precision]) * AA(right_operand, dtype=PRECISION_TO_TYPE[precision])]]
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a = I([left_operand])
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b = I([right_operand])
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@ -163,7 +163,7 @@ def test_op_element_divide(left_operand, right_operand, device_id, precision):
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# we need two surrounding brackets
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# the first for sequences (length=1, since we have dynamic_axis='')
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# the second for batch of one sample
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expected = [[AA(left_operand) / AA(right_operand)]]
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expected = [[AA(left_operand, dtype=PRECISION_TO_TYPE[precision]) / AA(right_operand, dtype=PRECISION_TO_TYPE[precision])]]
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a = I([left_operand])
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b = I([right_operand])
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@ -184,14 +184,14 @@ def test_op_element_divide(left_operand, right_operand, device_id, precision):
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# For left: d/da (a/b) = 1/b
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# For right: d/db (a/b) = a * d/db (1/b) = a * -1/b^2 = -a/b^2
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expected_left = [[[np.ones_like(x) / x for x in right_operand]]]
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expected_right = [[-AA(left_operand) / AA(right_operand)**2]]
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expected_right = [[-AA(left_operand, dtype=PRECISION_TO_TYPE[precision]) / AA(right_operand, dtype=PRECISION_TO_TYPE[precision])**2]]
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unittest_helper(left_as_input, None, expected_left, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=True, input_node=a)
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unittest_helper(right_as_input, None, expected_right, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=True, input_node=b)
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TENSORS = [
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IDENTITY_TENSORS = [
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([30.]),
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([[30.]]),
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([[1.5, 2.1]]),
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@ -201,7 +201,7 @@ TENSORS = [
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# -- identity function tests --
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@pytest.mark.parametrize("tensor", TENSORS)
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@pytest.mark.parametrize("tensor", IDENTITY_TENSORS)
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def test_op_identity(tensor, device_id, precision):
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def numpy_op(x):
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@ -232,3 +232,82 @@ def test_op_identity(tensor, device_id, precision):
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unittest_helper(op_node, None, expected, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=True,
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input_node=input_node)
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TIMES_PAIRS = [
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([[30.]], [[10.]]),
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([[1.5, 2.1]], [[10.], [20.]]),
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([[100., 200.], [300., 400.]], [[10.], [20.]]),
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]
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@pytest.mark.parametrize("left_operand, right_operand", TIMES_PAIRS)
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def test_op_times(left_operand, right_operand, device_id, precision,
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left_matrix_type, right_matrix_type):
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if left_matrix_type == 'sparse':
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pytest.skip('first operator of times() has to be dense')
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dt = PRECISION_TO_TYPE[precision]
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# Forward pass test
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#==================
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# we compute the expected output for the forward pass
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# we need two surrounding brackets
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# the first for sequences (length=1, since we have dynamic_axis='')
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# the second for batch of one sample
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expected = [[np.dot(AA(left_operand, dtype=dt), AA(right_operand, dtype=dt))]]
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a = I([left_operand])
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if right_matrix_type == 'sparse':
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b = SI(*batch_dense_to_sparse([right_operand]))
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else:
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b = I([right_operand])
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from cntk.ops import times, constant
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left_as_input = times(a, constant(right_operand))
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right_as_input = times(constant(left_operand), b)
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unittest_helper(left_as_input, None, expected, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=False)
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unittest_helper(right_as_input, None, expected, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=False)
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unittest_helper(times(a, b), None, expected, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=False)
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# Backward pass test
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#==================
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def op_grad(A, B):
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'''
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Compute derivative of A with respect to B. For simplicity, assume A
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and B to be matrices.
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Let A be 2x2 and B be 2x1, then we have
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[a11 a12] [b11] = [ a11 b11 + a12 b21 ]
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[a21 a22] [b21] [ a21 b11 + a22 b21 ]
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The derivative for A with respect to B is
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[b11 b21]
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[b11 b21]
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The derivative for B with respect to A:
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[a11 + a12]
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[a21 + a22]
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'''
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assert len(A.shape) == len(B.shape) == 2
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D = np.zeros_like(A)
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D[:,:] = B.sum(axis=1)
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return D
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if 'sparse' not in [left_matrix_type, right_matrix_type]:
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# FIXME: disabling until the Pass node supports sparse
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expected_left = [[op_grad(AA(left_operand, dtype=dt), AA(right_operand, dtype=dt))]]
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expected_right = [[op_grad(AA(right_operand, dtype=dt).T, AA(left_operand, dtype=dt).T).T]]
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unittest_helper(left_as_input, None, expected_left, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=True, input_node=a)
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# BUG: Fails because of Pass node?
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unittest_helper(right_as_input, None, expected_right, device_id=device_id,
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precision=precision, clean_up=True, backward_pass=True, input_node=b)
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