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import random
from sympy import symbols, Derivative
from sympy.codegen.array_utils import (CodegenArrayTensorProduct, CodegenArrayElementwiseAdd,
CodegenArrayPermuteDims, CodegenArrayDiagonal, parse_matrix_expression)
from sympy.core.relational import Eq, Ne, Ge, Gt, Le, Lt
from sympy.external import import_module
from sympy.functions import \
Abs, ceiling, exp, floor, sign, sin, asin, sqrt, cos, \
acos, tan, atan, atan2, cosh, acosh, sinh, asinh, tanh, atanh, \
re, im, arg, erf, loggamma, log
from sympy.matrices import Matrix, MatrixBase, eye, randMatrix
from sympy.matrices.expressions import \
Determinant, HadamardProduct, Inverse, MatrixSymbol, Trace
from sympy.printing.tensorflow import tensorflow_code
from sympy.utilities.lambdify import lambdify
from sympy.testing.pytest import skip
from sympy.testing.pytest import XFAIL
tf = tensorflow = import_module("tensorflow")
if tensorflow:
# Hide Tensorflow warnings
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
M = MatrixSymbol("M", 3, 3)
N = MatrixSymbol("N", 3, 3)
P = MatrixSymbol("P", 3, 3)
Q = MatrixSymbol("Q", 3, 3)
x, y, z, t = symbols("x y z t")
if tf is not None:
llo = [[j for j in range(i, i+3)] for i in range(0, 9, 3)]
m3x3 = tf.constant(llo)
m3x3sympy = Matrix(llo)
def _compare_tensorflow_matrix(variables, expr, use_float=False):
f = lambdify(variables, expr, 'tensorflow')
if not use_float:
random_matrices = [randMatrix(v.rows, v.cols) for v in variables]
else:
random_matrices = [randMatrix(v.rows, v.cols)/100. for v in variables]
graph = tf.Graph()
r = None
with graph.as_default():
random_variables = [eval(tensorflow_code(i)) for i in random_matrices]
session = tf.compat.v1.Session(graph=graph)
r = session.run(f(*random_variables))
e = expr.subs({k: v for k, v in zip(variables, random_matrices)})
e = e.doit()
if e.is_Matrix:
if not isinstance(e, MatrixBase):
e = e.as_explicit()
e = e.tolist()
if not use_float:
assert (r == e).all()
else:
r = [i for row in r for i in row]
e = [i for row in e for i in row]
assert all(
abs(a-b) < 10**-(4-int(log(abs(a), 10))) for a, b in zip(r, e))
# Creating a custom inverse test.
# See https://github.com/sympy/sympy/issues/18469
def _compare_tensorflow_matrix_inverse(variables, expr, use_float=False):
f = lambdify(variables, expr, 'tensorflow')
if not use_float:
random_matrices = [eye(v.rows, v.cols)*4 for v in variables]
else:
random_matrices = [eye(v.rows, v.cols)*3.14 for v in variables]
graph = tf.Graph()
r = None
with graph.as_default():
random_variables = [eval(tensorflow_code(i)) for i in random_matrices]
session = tf.compat.v1.Session(graph=graph)
r = session.run(f(*random_variables))
e = expr.subs({k: v for k, v in zip(variables, random_matrices)})
e = e.doit()
if e.is_Matrix:
if not isinstance(e, MatrixBase):
e = e.as_explicit()
e = e.tolist()
if not use_float:
assert (r == e).all()
else:
r = [i for row in r for i in row]
e = [i for row in e for i in row]
assert all(
abs(a-b) < 10**-(4-int(log(abs(a), 10))) for a, b in zip(r, e))
def _compare_tensorflow_matrix_scalar(variables, expr):
f = lambdify(variables, expr, 'tensorflow')
random_matrices = [
randMatrix(v.rows, v.cols).evalf() / 100 for v in variables]
graph = tf.Graph()
r = None
with graph.as_default():
random_variables = [eval(tensorflow_code(i)) for i in random_matrices]
session = tf.compat.v1.Session(graph=graph)
r = session.run(f(*random_variables))
e = expr.subs({k: v for k, v in zip(variables, random_matrices)})
e = e.doit()
assert abs(r-e) < 10**-6
def _compare_tensorflow_scalar(
variables, expr, rng=lambda: random.randint(0, 10)):
f = lambdify(variables, expr, 'tensorflow')
rvs = [rng() for v in variables]
graph = tf.Graph()
r = None
with graph.as_default():
tf_rvs = [eval(tensorflow_code(i)) for i in rvs]
session = tf.compat.v1.Session(graph=graph)
r = session.run(f(*tf_rvs))
e = expr.subs({k: v for k, v in zip(variables, rvs)}).evalf().doit()
assert abs(r-e) < 10**-6
def _compare_tensorflow_relational(
variables, expr, rng=lambda: random.randint(0, 10)):
f = lambdify(variables, expr, 'tensorflow')
rvs = [rng() for v in variables]
graph = tf.Graph()
r = None
with graph.as_default():
tf_rvs = [eval(tensorflow_code(i)) for i in rvs]
session = tf.compat.v1.Session(graph=graph)
r = session.run(f(*tf_rvs))
e = expr.subs({k: v for k, v in zip(variables, rvs)}).doit()
assert r == e
def test_tensorflow_printing():
assert tensorflow_code(eye(3)) == \
"tensorflow.constant([[1, 0, 0], [0, 1, 0], [0, 0, 1]])"
expr = Matrix([[x, sin(y)], [exp(z), -t]])
assert tensorflow_code(expr) == \
"tensorflow.Variable(" \
"[[x, tensorflow.math.sin(y)]," \
" [tensorflow.math.exp(z), -t]])"
# This (random) test is XFAIL because it fails occasionally
# See https://github.com/sympy/sympy/issues/18469
@XFAIL
def test_tensorflow_math():
if not tf:
skip("TensorFlow not installed")
expr = Abs(x)
assert tensorflow_code(expr) == "tensorflow.math.abs(x)"
_compare_tensorflow_scalar((x,), expr)
expr = sign(x)
assert tensorflow_code(expr) == "tensorflow.math.sign(x)"
_compare_tensorflow_scalar((x,), expr)
expr = ceiling(x)
assert tensorflow_code(expr) == "tensorflow.math.ceil(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = floor(x)
assert tensorflow_code(expr) == "tensorflow.math.floor(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = exp(x)
assert tensorflow_code(expr) == "tensorflow.math.exp(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = sqrt(x)
assert tensorflow_code(expr) == "tensorflow.math.sqrt(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = x ** 4
assert tensorflow_code(expr) == "tensorflow.math.pow(x, 4)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = cos(x)
assert tensorflow_code(expr) == "tensorflow.math.cos(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = acos(x)
assert tensorflow_code(expr) == "tensorflow.math.acos(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(0, 0.95))
expr = sin(x)
assert tensorflow_code(expr) == "tensorflow.math.sin(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = asin(x)
assert tensorflow_code(expr) == "tensorflow.math.asin(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = tan(x)
assert tensorflow_code(expr) == "tensorflow.math.tan(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = atan(x)
assert tensorflow_code(expr) == "tensorflow.math.atan(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = atan2(y, x)
assert tensorflow_code(expr) == "tensorflow.math.atan2(y, x)"
_compare_tensorflow_scalar((y, x), expr, rng=lambda: random.random())
expr = cosh(x)
assert tensorflow_code(expr) == "tensorflow.math.cosh(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.random())
expr = acosh(x)
assert tensorflow_code(expr) == "tensorflow.math.acosh(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
expr = sinh(x)
assert tensorflow_code(expr) == "tensorflow.math.sinh(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
expr = asinh(x)
assert tensorflow_code(expr) == "tensorflow.math.asinh(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
expr = tanh(x)
assert tensorflow_code(expr) == "tensorflow.math.tanh(x)"
_compare_tensorflow_scalar((x,), expr, rng=lambda: random.uniform(1, 2))
expr = atanh(x)
assert tensorflow_code(expr) == "tensorflow.math.atanh(x)"
_compare_tensorflow_scalar(
(x,), expr, rng=lambda: random.uniform(-.5, .5))
expr = erf(x)
assert tensorflow_code(expr) == "tensorflow.math.erf(x)"
_compare_tensorflow_scalar(
(x,), expr, rng=lambda: random.random())
expr = loggamma(x)
assert tensorflow_code(expr) == "tensorflow.math.lgamma(x)"
_compare_tensorflow_scalar(
(x,), expr, rng=lambda: random.random())
def test_tensorflow_complexes():
assert tensorflow_code(re(x)) == "tensorflow.math.real(x)"
assert tensorflow_code(im(x)) == "tensorflow.math.imag(x)"
assert tensorflow_code(arg(x)) == "tensorflow.math.angle(x)"
def test_tensorflow_relational():
if not tf:
skip("TensorFlow not installed")
expr = Eq(x, y)
assert tensorflow_code(expr) == "tensorflow.math.equal(x, y)"
_compare_tensorflow_relational((x, y), expr)
expr = Ne(x, y)
assert tensorflow_code(expr) == "tensorflow.math.not_equal(x, y)"
_compare_tensorflow_relational((x, y), expr)
expr = Ge(x, y)
assert tensorflow_code(expr) == "tensorflow.math.greater_equal(x, y)"
_compare_tensorflow_relational((x, y), expr)
expr = Gt(x, y)
assert tensorflow_code(expr) == "tensorflow.math.greater(x, y)"
_compare_tensorflow_relational((x, y), expr)
expr = Le(x, y)
assert tensorflow_code(expr) == "tensorflow.math.less_equal(x, y)"
_compare_tensorflow_relational((x, y), expr)
expr = Lt(x, y)
assert tensorflow_code(expr) == "tensorflow.math.less(x, y)"
_compare_tensorflow_relational((x, y), expr)
# This (random) test is XFAIL because it fails occasionally
# See https://github.com/sympy/sympy/issues/18469
@XFAIL
def test_tensorflow_matrices():
if not tf:
skip("TensorFlow not installed")
expr = M
assert tensorflow_code(expr) == "M"
_compare_tensorflow_matrix((M,), expr)
expr = M + N
assert tensorflow_code(expr) == "tensorflow.math.add(M, N)"
_compare_tensorflow_matrix((M, N), expr)
expr = M * N
assert tensorflow_code(expr) == "tensorflow.linalg.matmul(M, N)"
_compare_tensorflow_matrix((M, N), expr)
expr = HadamardProduct(M, N)
assert tensorflow_code(expr) == "tensorflow.math.multiply(M, N)"
_compare_tensorflow_matrix((M, N), expr)
expr = M*N*P*Q
assert tensorflow_code(expr) == \
"tensorflow.linalg.matmul(" \
"tensorflow.linalg.matmul(" \
"tensorflow.linalg.matmul(M, N), P), Q)"
_compare_tensorflow_matrix((M, N, P, Q), expr)
expr = M**3
assert tensorflow_code(expr) == \
"tensorflow.linalg.matmul(tensorflow.linalg.matmul(M, M), M)"
_compare_tensorflow_matrix((M,), expr)
expr = Trace(M)
assert tensorflow_code(expr) == "tensorflow.linalg.trace(M)"
_compare_tensorflow_matrix((M,), expr)
expr = Determinant(M)
assert tensorflow_code(expr) == "tensorflow.linalg.det(M)"
_compare_tensorflow_matrix_scalar((M,), expr)
expr = Inverse(M)
assert tensorflow_code(expr) == "tensorflow.linalg.inv(M)"
_compare_tensorflow_matrix_inverse((M,), expr, use_float=True)
expr = M.T
assert tensorflow_code(expr, tensorflow_version='1.14') == \
"tensorflow.linalg.matrix_transpose(M)"
assert tensorflow_code(expr, tensorflow_version='1.13') == \
"tensorflow.matrix_transpose(M)"
_compare_tensorflow_matrix((M,), expr)
def test_codegen_einsum():
if not tf:
skip("TensorFlow not installed")
graph = tf.Graph()
with graph.as_default():
session = tf.compat.v1.Session(graph=graph)
M = MatrixSymbol("M", 2, 2)
N = MatrixSymbol("N", 2, 2)
cg = parse_matrix_expression(M*N)
f = lambdify((M, N), cg, 'tensorflow')
ma = tf.constant([[1, 2], [3, 4]])
mb = tf.constant([[1,-2], [-1, 3]])
y = session.run(f(ma, mb))
c = session.run(tf.matmul(ma, mb))
assert (y == c).all()
def test_codegen_extra():
if not tf:
skip("TensorFlow not installed")
graph = tf.Graph()
with graph.as_default():
session = tf.compat.v1.Session()
M = MatrixSymbol("M", 2, 2)
N = MatrixSymbol("N", 2, 2)
P = MatrixSymbol("P", 2, 2)
Q = MatrixSymbol("Q", 2, 2)
ma = tf.constant([[1, 2], [3, 4]])
mb = tf.constant([[1,-2], [-1, 3]])
mc = tf.constant([[2, 0], [1, 2]])
md = tf.constant([[1,-1], [4, 7]])
cg = CodegenArrayTensorProduct(M, N)
assert tensorflow_code(cg) == \
'tensorflow.linalg.einsum("ab,cd", M, N)'
f = lambdify((M, N), cg, 'tensorflow')
y = session.run(f(ma, mb))
c = session.run(tf.einsum("ij,kl", ma, mb))
assert (y == c).all()
cg = CodegenArrayElementwiseAdd(M, N)
assert tensorflow_code(cg) == 'tensorflow.math.add(M, N)'
f = lambdify((M, N), cg, 'tensorflow')
y = session.run(f(ma, mb))
c = session.run(ma + mb)
assert (y == c).all()
cg = CodegenArrayElementwiseAdd(M, N, P)
assert tensorflow_code(cg) == \
'tensorflow.math.add(tensorflow.math.add(M, N), P)'
f = lambdify((M, N, P), cg, 'tensorflow')
y = session.run(f(ma, mb, mc))
c = session.run(ma + mb + mc)
assert (y == c).all()
cg = CodegenArrayElementwiseAdd(M, N, P, Q)
assert tensorflow_code(cg) == \
'tensorflow.math.add(' \
'tensorflow.math.add(tensorflow.math.add(M, N), P), Q)'
f = lambdify((M, N, P, Q), cg, 'tensorflow')
y = session.run(f(ma, mb, mc, md))
c = session.run(ma + mb + mc + md)
assert (y == c).all()
cg = CodegenArrayPermuteDims(M, [1, 0])
assert tensorflow_code(cg) == 'tensorflow.transpose(M, [1, 0])'
f = lambdify((M,), cg, 'tensorflow')
y = session.run(f(ma))
c = session.run(tf.transpose(ma))
assert (y == c).all()
cg = CodegenArrayPermuteDims(CodegenArrayTensorProduct(M, N), [1, 2, 3, 0])
assert tensorflow_code(cg) == \
'tensorflow.transpose(' \
'tensorflow.linalg.einsum("ab,cd", M, N), [1, 2, 3, 0])'
f = lambdify((M, N), cg, 'tensorflow')
y = session.run(f(ma, mb))
c = session.run(tf.transpose(tf.einsum("ab,cd", ma, mb), [1, 2, 3, 0]))
assert (y == c).all()
cg = CodegenArrayDiagonal(CodegenArrayTensorProduct(M, N), (1, 2))
assert tensorflow_code(cg) == \
'tensorflow.linalg.einsum("ab,bc->acb", M, N)'
f = lambdify((M, N), cg, 'tensorflow')
y = session.run(f(ma, mb))
c = session.run(tf.einsum("ab,bc->acb", ma, mb))
assert (y == c).all()
def test_MatrixElement_printing():
A = MatrixSymbol("A", 1, 3)
B = MatrixSymbol("B", 1, 3)
C = MatrixSymbol("C", 1, 3)
assert tensorflow_code(A[0, 0]) == "A[0, 0]"
assert tensorflow_code(3 * A[0, 0]) == "3*A[0, 0]"
F = C[0, 0].subs(C, A - B)
assert tensorflow_code(F) == "(tensorflow.math.add((-1)*B, A))[0, 0]"
def test_tensorflow_Derivative():
expr = Derivative(sin(x), x)
assert tensorflow_code(expr) == \
"tensorflow.gradients(tensorflow.math.sin(x), x)[0]"
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