Python uniform Examples

Example #1

File: test_contrib_autograd.py Project: CoderHHX/incubator-mxnet

def test_binary_func():
    x = nd.uniform(shape=(4, 5))
    y = nd.uniform(shape=(4, 5))
    f_add      = lambda x, y: x+y
    f_add_grad = lambda x, y: [nd.ones(x.shape), nd.ones(y.shape)]
    autograd_assert(x, y, func=f_add, grad_func=f_add_grad)
    f_mul      = lambda x, y: x*y
    f_mul_grad = lambda x, y: [y, x]
    autograd_assert(x, y, func=f_mul, grad_func=f_mul_grad)
    f_compose  = lambda x, y: x+x*y
    f_compose_grad = lambda x, y: [nd.ones(x.shape) + y, x]
    autograd_assert(x, y, func=f_compose, grad_func=f_compose_grad)

Example #2

File: test_autograd.py Project: tuhinsharma121/incubator-mxnet

def test_binary_func():
    x = nd.uniform(shape=(4, 5))
    y = nd.uniform(shape=(4, 5))
    f_add      = lambda x, y: x+y
    f_add_grad = lambda x, y: [nd.ones(x.shape), nd.ones(y.shape)]
    autograd_assert(x, y, func=f_add, grad_func=f_add_grad)
    f_mul      = lambda x, y: x*y
    f_mul_grad = lambda x, y: [y, x]
    autograd_assert(x, y, func=f_mul, grad_func=f_mul_grad)
    f_compose  = lambda x, y: x+x*y
    f_compose_grad = lambda x, y: [nd.ones(x.shape) + y, x]
    autograd_assert(x, y, func=f_compose, grad_func=f_compose_grad)

Example #3

File: test_autograd.py Project: xcgoner/dist-mxnet-udp

def test_operator_with_state():
    def f_fc(a, b, weight, bias):
        x = a * b
        fc = nd.FullyConnected(x, weight, bias, num_hidden=32)
        return fc

    a = nd.uniform(shape=(64, 50))
    b = nd.uniform(shape=(64, 50))
    weight = nd.uniform(shape=(32, 50))
    bias = nd.uniform(shape=(32, ))

    grad_func = grad_and_loss(f_fc)
    grad_vals, outputs = grad_func(a, b, weight, bias)

Example #4

File: test_contrib_autograd.py Project: CoderHHX/incubator-mxnet

def test_operator_with_state():
    def f_fc(a, b, weight, bias):
        x = a*b
        fc = nd.FullyConnected(
            x, weight, bias, num_hidden=32)
        return fc

    a = nd.uniform(shape=(64, 50))
    b = nd.uniform(shape=(64, 50))
    weight = nd.uniform(shape=(32, 50))
    bias = nd.uniform(shape=(32, ))

    grad_func = grad_and_loss(f_fc)
    grad_vals, outputs = grad_func(a, b, weight, bias)

Example #5

File: test_autograd.py Project: tuhinsharma121/incubator-mxnet

def test_argnum():
    def f_with_mode(a, b, mode):
        if mode:
            return a+b
        else:
            return a*b

    a = nd.uniform(shape=(3, 2))
    b = nd.uniform(shape=(3, 2))
    f_add_grad = lambda x, y, mode: [nd.ones(x.shape), nd.ones(y.shape)]
    f_mul_grad = lambda x, y, mode: [y, x]
    autograd_assert(a, b, True,
        argnum=[0, 1], func=f_with_mode, grad_func=f_add_grad)
    autograd_assert(a, b, False,
        argnum=[0, 1], func=f_with_mode, grad_func=f_mul_grad)

Example #6

File: test_contrib_autograd.py Project: CoderHHX/incubator-mxnet

def test_argnum():
    def f_with_mode(a, b, mode):
        if mode:
            return a+b
        else:
            return a*b

    a = nd.uniform(shape=(3, 2))
    b = nd.uniform(shape=(3, 2))
    f_add_grad = lambda x, y, mode: [nd.ones(x.shape), nd.ones(y.shape)]
    f_mul_grad = lambda x, y, mode: [y, x]
    autograd_assert(a, b, True,
        argnum=[0, 1], func=f_with_mode, grad_func=f_add_grad)
    autograd_assert(a, b, False,
        argnum=[0, 1], func=f_with_mode, grad_func=f_mul_grad)

Example #7

def test_binary_func():
    def check_binary_func(x, y):
        f_add      = lambda x, y: x+y
        f_add_grad = lambda x, y: [nd.ones(x.shape), nd.ones(y.shape)]
        autograd_assert(x, y, func=f_add, grad_func=f_add_grad)
        f_mul      = lambda x, y: x*y
        f_mul_grad = lambda x, y: [y, x]
        autograd_assert(x, y, func=f_mul, grad_func=f_mul_grad)
        f_compose  = lambda x, y: x+x*y
        f_compose_grad = lambda x, y: [nd.ones(x.shape) + y, x]
        autograd_assert(x, y, func=f_compose, grad_func=f_compose_grad)
    uniform_x = nd.uniform(shape=(4, 5))
    uniform_y = nd.uniform(shape=(4, 5))
    stypes = ['row_sparse', 'csr', 'default']
    for stype_x in stypes:
        for stype_y in stypes:
            x = uniform_x.tostype(stype_x)
            y = uniform_y.tostype(stype_y)
            check_binary_func(x, y)

Example #8

File: test_autograd.py Project: tuhinsharma121/incubator-mxnet

def test_unary_func():
    x = nd.uniform(shape=(4, 5))
    f_exp         = lambda x: nd.exp(x)
    f_exp_grad    = lambda x: [nd.exp(x)]
    autograd_assert(x, func=f_exp, grad_func=f_exp_grad)
    f_half        = lambda x: x/2
    f_half_grad   = lambda x: [nd.ones(x.shape) * 0.5]
    autograd_assert(x, func=f_half, grad_func=f_half_grad)
    f_square      = lambda x: x**2
    f_square_grad = lambda x: [2*x]
    autograd_assert(x, func=f_square, grad_func=f_square_grad)

Example #9

File: test_contrib_autograd.py Project: CoderHHX/incubator-mxnet

def test_unary_func():
    x = nd.uniform(shape=(4, 5))
    f_exp         = lambda x: nd.exp(x)
    f_exp_grad    = lambda x: [nd.exp(x)]
    autograd_assert(x, func=f_exp, grad_func=f_exp_grad)
    f_half        = lambda x: x/2
    f_half_grad   = lambda x: [nd.ones(x.shape) * 0.5]
    autograd_assert(x, func=f_half, grad_func=f_half_grad)
    f_square      = lambda x: x**2
    f_square_grad = lambda x: [2*x]
    autograd_assert(x, func=f_square, grad_func=f_square_grad)

Example #10

def test_binary_func():
    def check_binary_func(x, y):
        f_add      = lambda x, y: x+y
        f_add_grad = lambda x, y: [nd.ones(x.shape), nd.ones(y.shape)]
        autograd_assert(x, y, func=f_add, grad_func=f_add_grad)
        f_mul      = lambda x, y: x*y
        f_mul_grad = lambda x, y: [y, x]
        autograd_assert(x, y, func=f_mul, grad_func=f_mul_grad)
        f_compose  = lambda x, y: x+x*y
        f_compose_grad = lambda x, y: [nd.ones(x.shape) + y, x]
        autograd_assert(x, y, func=f_compose, grad_func=f_compose_grad)
    uniform_x = nd.uniform(shape=(4, 5))
    uniform_y = nd.uniform(shape=(4, 5))
    stypes = ['default', 'row_sparse', 'csr']
    with EnvManager('MXNET_STORAGE_FALLBACK_LOG_VERBOSE', '0'):
        for stype_x in stypes:
            for stype_y in stypes:
                x = uniform_x.tostype(stype_x)
                y = uniform_y.tostype(stype_y)
                check_binary_func(x, y)

Example #11

def test_binary_func():
    def check_binary_func(x, y):
        f_add      = lambda x, y: x+y
        f_add_grad = lambda x, y: [nd.ones(x.shape), nd.ones(y.shape)]
        autograd_assert(x, y, func=f_add, grad_func=f_add_grad)
        f_mul      = lambda x, y: x*y
        f_mul_grad = lambda x, y: [y, x]
        autograd_assert(x, y, func=f_mul, grad_func=f_mul_grad)
        f_compose  = lambda x, y: x+x*y
        f_compose_grad = lambda x, y: [nd.ones(x.shape) + y, x]
        autograd_assert(x, y, func=f_compose, grad_func=f_compose_grad)
    uniform_x = nd.uniform(shape=(4, 5))
    uniform_y = nd.uniform(shape=(4, 5))
    stypes = ['default', 'row_sparse', 'csr']
    with EnvManager('MXNET_STORAGE_FALLBACK_LOG_VERBOSE', '0'):
        for stype_x in stypes:
            for stype_y in stypes:
                x = uniform_x.tostype(stype_x)
                y = uniform_y.tostype(stype_y)
                check_binary_func(x, y)

Example #12

def test_unary_func():
    def check_unary_func(x):
        f_exp         = lambda x: nd.exp(x)
        f_exp_grad    = lambda x: [nd.exp(x)]
        autograd_assert(x, func=f_exp, grad_func=f_exp_grad)
        f_half        = lambda x: x/2
        f_half_grad   = lambda x: [nd.ones(x.shape) * 0.5]
        autograd_assert(x, func=f_half, grad_func=f_half_grad)
        f_square      = lambda x: x**2
        f_square_grad = lambda x: [2*x]
        autograd_assert(x, func=f_square, grad_func=f_square_grad)
    uniform = nd.uniform(shape=(4, 5))
    stypes = ['row_sparse', 'csr', 'default']
    for stype in stypes:
        check_unary_func(uniform.tostype(stype))

Example #13

def test_unary_func():
    def check_unary_func(x):
        f_exp         = lambda x: nd.exp(x)
        f_exp_grad    = lambda x: [nd.exp(x)]
        autograd_assert(x, func=f_exp, grad_func=f_exp_grad)
        f_half        = lambda x: x/2
        f_half_grad   = lambda x: [nd.ones(x.shape) * 0.5]
        autograd_assert(x, func=f_half, grad_func=f_half_grad)
        f_square      = lambda x: x**2
        f_square_grad = lambda x: [2*x]
        autograd_assert(x, func=f_square, grad_func=f_square_grad)
    uniform = nd.uniform(shape=(4, 5))
    stypes = ['default', 'row_sparse', 'csr']
    with EnvManager('MXNET_STORAGE_FALLBACK_LOG_VERBOSE', '0'):
        for stype in stypes:
            check_unary_func(uniform.tostype(stype))

Example #14

def test_unary_func():
    def check_unary_func(x):
        f_exp         = lambda x: nd.exp(x)
        f_exp_grad    = lambda x: [nd.exp(x)]
        autograd_assert(x, func=f_exp, grad_func=f_exp_grad)
        f_half        = lambda x: x/2
        f_half_grad   = lambda x: [nd.ones(x.shape) * 0.5]
        autograd_assert(x, func=f_half, grad_func=f_half_grad)
        f_square      = lambda x: x**2
        f_square_grad = lambda x: [2*x]
        autograd_assert(x, func=f_square, grad_func=f_square_grad)
    uniform = nd.uniform(shape=(4, 5))
    stypes = ['default', 'row_sparse', 'csr']
    with EnvManager('MXNET_STORAGE_FALLBACK_LOG_VERBOSE', '0'):
        for stype in stypes:
            check_unary_func(uniform.tostype(stype))