def test_softmax_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
temp = be.zeros((3, 1))
outputs = np.exp(inputs-1) / np.sum(np.exp(inputs-1))
sftmx.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_softmax_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
temp = be.zeros((3, 1))
outputs = np.exp(inputs - 1) / np.sum(np.exp(inputs - 1))
sftmx.apply_function(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_softmax_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
sftmx.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_softmax_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
be = GPU(rng_seed=0)
temp = be.zeros((3, 1))
sftmx.apply_function(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_softmax_derivative_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_softmax_derivative_cputensor():
sftmx = Softmax()
inputs = np.array([0, 1, -2]).reshape((3, 1))
be = CPU(rng_seed=0)
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, CPUTensor(inputs), temp)
assert_tensor_near_equal(CPUTensor(outputs), temp)
def test_softmax_derivative_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
be = GPU(rng_seed=0)
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
def test_softmax_derivative_cc2tensor():
sftmx = Softmax()
from neon.backends.cc2 import GPU, GPUTensor
inputs = np.array([0, 1, -2]).reshape((3, 1))
outputs = np.exp(inputs) / np.sum(np.exp(inputs))
errmat = np.ones(inputs.shape)
a = np.einsum('ij,ji->i', errmat.T, outputs)
outputs = outputs * (errmat - a[np.newaxis, :])
be = GPU(rng_seed=0)
temp = be.zeros(inputs.shape)
sftmx.apply_derivative(be, GPUTensor(inputs), temp)
assert_tensor_near_equal(GPUTensor(outputs), temp)
