def numerical_gradient(self, x, t): loss_W = lambda W: self.loss(x, t) grads = {} grads['W1'] = numerical_gradient(loss_W, self.params['W1']) grads['b1'] = numerical_gradient(loss_W, self.params['b1']) grads['W2'] = numerical_gradient(loss_W, self.params['W2']) grads['b2'] = numerical_gradient(loss_W, self.params['b2']) return grads
def numerical_gradient(self, x, t): loss_W = lambda W: self.loss(x, t) grads = {} grads['W1'] = numerical_gradient(loss_W, self.params['W1']) grads['b1'] = numerical_gradient(loss_W, self.params['b1']) grads['W2'] = numerical_gradient(loss_W, self.params['W2']) grads['b2'] = numerical_gradient(loss_W, self.params['b2']) return grads
def test_backward2(self): nm_x_grad = numerical_gradient(self.layer, self.x, self.x) self.layer.forward(self.x) y = np.ones((1, 3, 2, 2)) x_grad = self.layer.backward(y) self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward3(self): self.x = fake_data((2, 3, 5, 5)) nm_x_grad = numerical_gradient(self.layer, self.x, self.x) self.layer.forward(self.x) y = np.ones((2, 3, 2, 2)) x_grad = self.layer.backward(y) self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward4(self): h = 5 layer = ConvLayer(2, 5, h) x = fake_data((2, 2, 8, 8)) layer.W = fake_data((5, 2, h, h)) layer.b = fake_data(layer.b.shape) y = layer.forward(x) x_grad = layer.backward(np.ones_like(y)) nm_x_grad = numerical_gradient(layer, x, x) nm_w_grad = numerical_gradient(layer, x, layer.W) nm_b_grad = numerical_gradient(layer, x, layer.b) self.assertTrue(np.allclose(nm_x_grad, x_grad)) self.assertTrue(np.allclose(nm_w_grad, layer.W_grad)) self.assertTrue(np.allclose(nm_b_grad, layer.b_grad))
def test_backward3_5(self): layer = ConvLayer(5, 3, 3) x = fake_data((2, 5, 3, 3)) layer.W = fake_data(layer.W.shape) layer.b = fake_data(layer.b.shape) y = layer.forward(x) x_grad = layer.backward(np.ones_like(y)) # do numerical gradients nm_x_grad = numerical_gradient(layer, x, x) nm_w_grad = numerical_gradient(layer, x, layer.W) nm_b_grad = numerical_gradient(layer, x, layer.b) self.assertTrue(np.allclose(nm_x_grad, x_grad)) self.assertTrue(np.allclose(nm_w_grad, layer.W_grad)) self.assertTrue(np.allclose(nm_b_grad, layer.b_grad))
def test_backward5(self): h = 5 layer = ConvLayer(2, 5, h) x = fake_data((2, 2, 8, 8)) layer.W = fake_data((5, 2, h, h)) layer.b = fake_data(layer.b.shape) y = layer.forward(x) y_grad = fake_data(y.shape) x_grad = layer.backward(y_grad) nm_x_grad = numerical_gradient(layer, x, x, y_grad) nm_w_grad = numerical_gradient(layer, x, layer.W, y_grad) nm_b_grad = numerical_gradient(layer, x, layer.b, y_grad) self.assertTrue(np.allclose(nm_x_grad, x_grad)) self.assertTrue(np.allclose(nm_w_grad, layer.W_grad)) self.assertTrue(np.allclose(nm_b_grad, layer.b_grad))
def test_backward4(self): x = np.array([[[[0, 0, 0, 0], [0, 0, 1, 1], [2, 0, 0, 3], [2, 0, 3, 0]]]]).astype('float64') nm_x_grad = numerical_gradient(self.layer, x, x) out = self.layer.forward(x) y = np.ones((1, 1, 2, 2)) x_grad = self.layer.backward(y) self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward1(self): layer = FlattenLayer() x = fake_data((1, 2, 3, 3)) y = layer.forward(x) x_grad = layer.backward(np.ones_like(y)) # do numerical gradients nm_x_grad = numerical_gradient(layer, x, x) self.assertTrue(np.allclose(nm_x_grad, x_grad))
def test_backward2(self): layer = ConvLayer(2, 1, 3) x = fake_data((1, 2, 4, 4)) layer.W = fake_data((1, 2, 3, 3)) layer.b = fake_data(layer.b.shape) y = layer.forward(x) x_grad = layer.backward(np.ones_like(y)) # do numerical gradients nm_x_grad = numerical_gradient(layer, x, x) nm_w_grad = numerical_gradient(layer, x, layer.W) nm_b_grad = numerical_gradient(layer, x, layer.b) self.assertTrue(np.allclose(nm_x_grad, x_grad)) print "Pass x_grad" self.assertTrue(np.allclose(nm_w_grad, layer.W_grad)) print "Pass w_grad" self.assertTrue(np.allclose(nm_b_grad, layer.b_grad)) print "Pass b_grad"
def test_backward1(self): layer = ConvLayer(1, 1, 3) x = fake_data((1, 1, 8, 8)) layer.W = fake_data((1, 1, 3, 3)) layer.b = fake_data(layer.b.shape) y = layer.forward(x) x_grad = layer.backward(np.ones_like(y)) # do numerical gradients nm_x_grad = numerical_gradient(layer, x, x) nm_w_grad = numerical_gradient(layer, x, layer.W) nm_b_grad = numerical_gradient(layer, x, layer.b) # note that this does not check the gradients of the padded elements self.assertTrue(np.allclose(nm_x_grad, x_grad)) self.assertTrue(np.allclose(nm_w_grad, layer.W_grad)) self.assertTrue(np.allclose(nm_b_grad, layer.b_grad))
b = np.zeros((1, 1, 1, 1)) params = {'stride': 2, 'pad': 2} res = conv_forward(im, filter, b, params) res = res.reshape(res.shape[0], res.shape[1]) plt.subplot(1, 2, 2) plt.imshow(res.astype('uint8'), cmap=cm.Greys_r) plt.show() # test conv_backward x = np.random.randn(5, 5, 3, 4) w = np.random.randn(3, 3, 3, 2) b = np.random.randn(1, 1, 1, 2) dout = np.random.randn(5, 5, 2, 4) conv_param = {'stride': 1, 'pad': 1} dx_num = numerical_gradient(lambda x: conv_forward(x, w, b, conv_param), x, dout) dw_num = numerical_gradient(lambda w: conv_forward(x, w, b, conv_param), w, dout) db_num = numerical_gradient(lambda b: conv_forward(x, w, b, conv_param), b, dout) out = conv_forward(x, w, b, conv_param) dx, dw, db = conv_backward(x, w, b, conv_param, dout) # Your errors should be around 1e-9' print 'Testing conv_backward function' print 'dx error: ', rel_error(dx, dx_num) print 'dw error: ', rel_error(dw, dw_num) print 'db error: ', rel_error(db, db_num) x_shape = (2, 3, 4, 4) x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape) pool_param = {'HF': 2, 'WF': 2, 'stride': 2}