def test_neuralNetwork_adam():
from sklearn.neural_network._stochastic_optimizers import AdamOptimizer
np.random.seed(2019)
X = np.random.normal(size=(1, 500))
target = 3.9285985 * X
nn = NeuralNetwork(inputs=1,
neurons=3,
outputs=1,
activations='sigmoid',
silent=True)
nn.addLayer()
nn.addLayer()
nn.addOutputLayer(activations='identity')
learning_rate = 0.001
yhat = nn.forward_pass(X)
nn.backpropagation(yhat.T, target.T)
nn.learning_rate = learning_rate
nn.initializeAdam()
nn.adam()
skl_adam = AdamOptimizer(params=nn.param, learning_rate_init=learning_rate)
upd = skl_adam._get_updates(nn.grad)
for update_nn, update_skl in zip(nn.change, upd):
assert update_nn == pytest.approx(update_skl)
