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]])")
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_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.add((-1)*B, A))[0, 0]"
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(variables, expr):
f = lambdify(variables, expr, 'tensorflow')
random_matrices = [Matrix([[random.randint(0, 10) for k in
range(i.shape[1])] for j in range(i.shape[0])]) for i in variables]
random_variables = [eval(tensorflow_code(i)) for i in
random_matrices]
r = session.run(f(*random_variables))
e = expr.subs({k: v for k, v in zip(variables, random_matrices)}).doit()
if e.is_Matrix:
e = e.tolist()
assert (r == e).all()
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)
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 test_tensorflow_matrix():
if not tf:
skip("TensorFlow not installed")
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.sin(y)], [tensorflow.exp(z), -t]])"
expr = M
assert tensorflow_code(expr) == "M"
_compare_tensorflow_matrix((M, ), expr)
expr = M + N
assert tensorflow_code(expr) == "M + N"
_compare_tensorflow_matrix((M, N), expr)
expr = M * N
assert tensorflow_code(expr) == "tensorflow.matmul(M, N)"
_compare_tensorflow_matrix((M, N), expr)
expr = M * N * P * Q
assert tensorflow_code(
expr
) == "tensorflow.matmul(tensorflow.matmul(tensorflow.matmul(M, N), P), Q)"
_compare_tensorflow_matrix((M, N, P, Q), expr)
expr = M**3
assert tensorflow_code(
expr) == "tensorflow.matmul(tensorflow.matmul(M, M), M)"
_compare_tensorflow_matrix((M, ), expr)
expr = M.T
assert tensorflow_code(expr) == "tensorflow.matrix_transpose(M)"
_compare_tensorflow_matrix((M, ), expr)
expr = Trace(M)
assert tensorflow_code(expr) == "tensorflow.trace(M)"
_compare_tensorflow_matrix((M, ), expr)
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 test_tensorflow_matrix():
if not tf:
skip("TensorFlow not installed")
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.sin(y)], [tensorflow.exp(z), -t]])"
expr = M
assert tensorflow_code(expr) == "M"
_compare_tensorflow_matrix((M,), expr)
expr = M + N
assert tensorflow_code(expr) == "tensorflow.add(M, N)"
_compare_tensorflow_matrix((M, N), expr)
expr = M*N
assert tensorflow_code(expr) == "tensorflow.matmul(M, N)"
_compare_tensorflow_matrix((M, N), expr)
expr = M*N*P*Q
assert tensorflow_code(expr) == "tensorflow.matmul(tensorflow.matmul(tensorflow.matmul(M, N), P), Q)"
_compare_tensorflow_matrix((M, N, P, Q), expr)
expr = M**3
assert tensorflow_code(expr) == "tensorflow.matmul(tensorflow.matmul(M, M), M)"
_compare_tensorflow_matrix((M,), expr)
expr = M.T
assert tensorflow_code(expr) == "tensorflow.matrix_transpose(M)"
_compare_tensorflow_matrix((M,), expr)
expr = Trace(M)
assert tensorflow_code(expr) == "tensorflow.trace(M)"
_compare_tensorflow_matrix((M,), expr)
def test_codegen_extra():
if not tf:
skip("TensorFlow not installed")
session = tf.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.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.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.add(tensorflow.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.add(tensorflow.add(tensorflow.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.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.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_tensorflow_Derivative():
expr = Derivative(sin(x), x)
assert tensorflow_code(expr) == \
"tensorflow.gradients(tensorflow.math.sin(x), x)[0]"
def test_tensorflow_Derivative():
f = Function("f")
expr = Derivative(sin(x), x)
assert tensorflow_code(expr) == "tensorflow.gradients(tensorflow.sin(x), x)[0]"
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 = ArrayTensorProduct(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 = ArrayAdd(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 = ArrayAdd(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 = ArrayAdd(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 = PermuteDims(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 = PermuteDims(ArrayTensorProduct(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 = ArrayDiagonal(ArrayTensorProduct(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_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_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_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_Derivative():
f = Function("f")
expr = Derivative(sin(x), x)
assert tensorflow_code(
expr) == "tensorflow.gradients(tensorflow.sin(x), x)[0]"