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test_nn_activations.py
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# flake8: noqa
import time
import numpy as np
from numpy.testing import assert_almost_equal
from scipy.special import expit
import torch
import torch.nn.functional as F
from numpy_ml.utils.testing import random_stochastic_matrix, random_tensor
def torch_gradient_generator(fn, **kwargs):
def get_grad(z):
z1 = torch.autograd.Variable(torch.from_numpy(z), requires_grad=True)
z2 = fn(z1, **kwargs).sum()
z2.backward()
grad = z1.grad.numpy()
return grad
return get_grad
#######################################################################
# Debug Formatter #
#######################################################################
def err_fmt(params, golds, ix, warn_str=""):
mine, label = params[ix]
err_msg = "-" * 25 + " DEBUG " + "-" * 25 + "\n"
prev_mine, prev_label = params[max(ix - 1, 0)]
err_msg += "Mine (prev) [{}]:\n{}\n\nTheirs (prev) [{}]:\n{}".format(
prev_label, prev_mine, prev_label, golds[prev_label]
)
err_msg += "\n\nMine [{}]:\n{}\n\nTheirs [{}]:\n{}".format(
label, mine, label, golds[label]
)
err_msg += warn_str
err_msg += "\n" + "-" * 23 + " END DEBUG " + "-" * 23
return err_msg
#######################################################################
# Test Suite #
#######################################################################
#
#
# def test_activations(N=50):
# print("Testing Sigmoid activation")
# time.sleep(1)
# test_sigmoid_activation(N)
# test_sigmoid_grad(N)
#
# # print("Testing Softmax activation")
# # time.sleep(1)
# # test_softmax_activation(N)
# # test_softmax_grad(N)
#
# print("Testing Tanh activation")
# time.sleep(1)
# test_tanh_grad(N)
#
# print("Testing ReLU activation")
# time.sleep(1)
# test_relu_activation(N)
# test_relu_grad(N)
#
# print("Testing ELU activation")
# time.sleep(1)
# test_elu_activation(N)
# test_elu_grad(N)
#
# print("Testing SELU activation")
# time.sleep(1)
# test_selu_activation(N)
# test_selu_grad(N)
#
# print("Testing LeakyRelu activation")
# time.sleep(1)
# test_leakyrelu_activation(N)
# test_leakyrelu_grad(N)
#
# print("Testing SoftPlus activation")
# time.sleep(1)
# test_softplus_activation(N)
# test_softplus_grad(N)
#
#######################################################################
# Activations #
#######################################################################
def test_sigmoid_activation(N=50):
from numpy_ml.neural_nets.activations import Sigmoid
N = np.inf if N is None else N
mine = Sigmoid()
gold = expit
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_tensor((1, n_dims))
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
def test_softplus_activation(N=50):
from numpy_ml.neural_nets.activations import SoftPlus
N = np.inf if N is None else N
mine = SoftPlus()
gold = lambda z: F.softplus(torch.FloatTensor(z)).numpy()
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
def test_elu_activation(N=50):
from numpy_ml.neural_nets.activations import ELU
N = np.inf if N is None else N
i = 0
while i < N:
n_dims = np.random.randint(1, 10)
z = random_tensor((1, n_dims))
alpha = np.random.uniform(0, 10)
mine = ELU(alpha)
gold = lambda z, a: F.elu(torch.from_numpy(z), alpha).numpy()
assert_almost_equal(mine.fn(z), gold(z, alpha))
print("PASSED")
i += 1
def test_relu_activation(N=50):
from numpy_ml.neural_nets.activations import ReLU
N = np.inf if N is None else N
mine = ReLU()
gold = lambda z: F.relu(torch.FloatTensor(z)).numpy()
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
def test_selu_activation(N=50):
from numpy_ml.neural_nets.activations import SELU
N = np.inf if N is None else N
mine = SELU()
gold = lambda z: F.selu(torch.FloatTensor(z)).numpy()
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
def test_leakyrelu_activation(N=50):
from numpy_ml.neural_nets.activations import LeakyReLU
N = np.inf if N is None else N
i = 0
while i < N:
n_dims = np.random.randint(1, 100)
z = random_stochastic_matrix(1, n_dims)
alpha = np.random.uniform(0, 10)
mine = LeakyReLU(alpha=alpha)
gold = lambda z: F.leaky_relu(torch.FloatTensor(z), alpha).numpy()
assert_almost_equal(mine.fn(z), gold(z))
print("PASSED")
i += 1
#######################################################################
# Activation Gradients #
#######################################################################
def test_sigmoid_grad(N=50):
from numpy_ml.neural_nets.activations import Sigmoid
N = np.inf if N is None else N
mine = Sigmoid()
gold = torch_gradient_generator(torch.sigmoid)
i = 0
while i < N:
n_ex = np.random.randint(1, 100)
n_dims = np.random.randint(1, 100)
z = random_tensor((n_ex, n_dims))
assert_almost_equal(mine.grad(z), gold(z))
print("PASSED")
i += 1
def test_elu_grad(N=50):
from numpy_ml.neural_nets.activations import ELU
N = np.inf if N is None else N
i = 0
while i < N:
n_ex = np.random.randint(1, 10)
n_dims = np.random.randint(1, 10)
alpha = np.random.uniform(0, 10)
z = random_tensor((n_ex, n_dims))
mine = ELU(alpha)
gold = torch_gradient_generator(F.elu, alpha=alpha)
assert_almost_equal(mine.grad(z), gold(z), decimal=6)
print("PASSED")
i += 1
def test_tanh_grad(N=50):
from numpy_ml.neural_nets.activations import Tanh
N = np.inf if N is None else N
mine = Tanh()
gold = torch_gradient_generator(torch.tanh)
i = 0
while i < N:
n_ex = np.random.randint(1, 100)
n_dims = np.random.randint(1, 100)
z = random_tensor((n_ex, n_dims))
assert_almost_equal(mine.grad(z), gold(z))
print("PASSED")
i += 1
def test_relu_grad(N=50):
from numpy_ml.neural_nets.activations import ReLU
N = np.inf if N is None else N
mine = ReLU()
gold = torch_gradient_generator(F.relu)
i = 0
while i < N:
n_ex = np.random.randint(1, 100)
n_dims = np.random.randint(1, 100)
z = random_tensor((n_ex, n_dims))
assert_almost_equal(mine.grad(z), gold(z))
print("PASSED")
i += 1
def test_selu_grad(N=50):
from numpy_ml.neural_nets.activations import SELU
N = np.inf if N is None else N
mine = SELU()
gold = torch_gradient_generator(F.selu)
i = 0
while i < N:
n_ex = np.random.randint(1, 100)
n_dims = np.random.randint(1, 100)
z = random_tensor((n_ex, n_dims))
assert_almost_equal(mine.grad(z), gold(z), decimal=6)
print("PASSED")
i += 1
def test_leakyrelu_grad(N=50):
from numpy_ml.neural_nets.activations import LeakyReLU
N = np.inf if N is None else N
i = 0
while i < N:
n_ex = np.random.randint(1, 10)
n_dims = np.random.randint(1, 10)
alpha = np.random.uniform(0, 10)
z = random_tensor((n_ex, n_dims))
mine = LeakyReLU(alpha)
gold = torch_gradient_generator(F.leaky_relu, negative_slope=alpha)
assert_almost_equal(mine.grad(z), gold(z), decimal=6)
print("PASSED")
i += 1
def test_softplus_grad(N=50):
from numpy_ml.neural_nets.activations import SoftPlus
N = np.inf if N is None else N
mine = SoftPlus()
gold = torch_gradient_generator(F.softplus)
i = 0
while i < N:
n_ex = np.random.randint(1, 100)
n_dims = np.random.randint(1, 100)
z = random_tensor((n_ex, n_dims), standardize=True)
assert_almost_equal(mine.grad(z), gold(z))
print("PASSED")
i += 1
if __name__ == "__main__":
test_activations(N=50)