def test_backward(self):
layer = Sigmoid()
x = np.random.rand(2)
y = layer.forward(x)
deriv_grad = layer.backward(np.ones(1))
numerical_grad_matrix = numerical_gradient.calc(layer.forward, x)
# the numerical grad in this case is a matrix made of zeros with
# dJ/dx_i only in the diagonal
num_grad = np.diagonal(numerical_grad_matrix)
numerical_gradient.assert_are_similar(deriv_grad, num_grad)
layer1.forward(trainingData[batch])
activation1.forward(layer1.outputs)
layer2.forward(activation1.outputs)
activation2.forward(layer2.outputs)
cost.forward(activation2.outputs, labels[batch], 10)
for sample in range(activation2.outputs.shape[1]):
if np.argmax(activation2.outputs[:, sample]) == np.argmax(
labels[batch, sample]):
correct += 1
cost.backward(activation2.outputs, labels[batch], 10)
activation2.backward(layer2.outputs, layer2.weights.shape[0],
BATCH_SIZE)
layer2.backward(activation1.outputs)
activation1.backward(layer1.outputs)
layer1.backward(trainingData[batch])
delta1 = np.zeros((cost.prime.shape[0], cost.prime.shape[1]))
for i in range(cost.prime.shape[0]):
delta1[i] = np.matmul(cost.prime[i], activation2.prime[i])
delta1_wrt_L2 = np.matmul(delta1, layer2.input_prime)
delta2 = np.zeros(
(activation1.prime.shape[0], activation1.prime.shape[2]))
for i in range(activation1.prime.shape[2]):
delta2[:, i] = np.matmul(delta1_wrt_L2[i], activation1.prime[:, :,
i])
C_wrt_W2 = np.zeros(
(delta1.shape[0], delta1.shape[1], layer2.weights_prime.shape[1]))
from layers import Sigmoid
except ImportError:
print('Library Module Can Not Found')
# Test1(Vector)
layer = Sigmoid()
x = np.array([0.1, -0.2, 0.3, -0.4, 0.5])
print(x)
y = layer.forward(x)
print(y)
print(layer.out)
dout = np.array([-0.1, -0.2, -0.3, 0.4, -0.5])
dout = layer.backward(dout)
print(dout)
print('=========================================')
# Test2(Matrix)
x = np.array([
[0.1, -0.5, 1.0],
[0.2, -0.6, 2.0],
[0.3, -0.7, 3.0],
[0.4, -0.8, 4.0]
])
y = layer.forward(x)
print(y)
print(layer.out)