def test_backprop(self):
x = np.random.randn(self.N, self.img_height, self.img_width)
w = np.random.randn(self.D, self.H)
b = np.random.randn(self.H)
grad_output = np.random.randn(self.N, self.H)
# Numerical gradient w.r.t inputs
num_grad_x = eval_numerical_gradient_array(f=lambda x: self.layer.forward_prop(x, w, b),
x=x,
df=grad_output)
# Numerical gradient w.r.t weights
num_grad_w = eval_numerical_gradient_array(f=lambda w: self.layer.forward_prop(x, w, b),
x=w,
df=grad_output)
# Numerical gradient w.r.t. biases
num_grad_b = eval_numerical_gradient_array(f=lambda b: self.layer.forward_prop(x, w, b),
x=b,
df=grad_output)
# Compute gradients using backprop algorithm
grad_x, grad_w, grad_b = self.layer.backprop(grad_output)
np.testing.assert_array_almost_equal(num_grad_x, grad_x, decimal=7)
np.testing.assert_array_almost_equal(num_grad_w, grad_w, decimal=7)
np.testing.assert_array_almost_equal(num_grad_b, grad_b, decimal=7)
def test_backward(self):
np.random.seed(271)
N, T, H, M = 2, 3, 4, 5
# Create the layer
layer = TemporalAffineLayer(H, M)
W = np.random.randn(H, M)
b = np.random.randn(M)
# Create some arbitrary inputs
x = np.random.randn(N, T, H)
out = layer.forward(x, W=W, b=b)
grad_out = np.random.randn(*out.shape)
grad_x, grad_W, grad_b = layer.backward(grad_out)
fx = lambda x: layer.forward(x, W=W, b=b)
fw = lambda W: layer.forward(x, W=W, b=b)
fb = lambda b: layer.forward(x, W=W, b=b)
grad_x_num = eval_numerical_gradient_array(fx, x, grad_out)
grad_W_num = eval_numerical_gradient_array(fw, W, grad_out)
grad_b_num = eval_numerical_gradient_array(fb, b, grad_out)
self.assertAlmostEqual(rel_error(grad_x_num, grad_x), 1e-9, places=2)
self.assertAlmostEqual(rel_error(grad_W_num, grad_W), 1e-9, places=2)
self.assertAlmostEqual(rel_error(grad_b_num, grad_b), 1e-9, places=2)
def loss(self, X, lag=None, phi=None, sigma=None, intercept=None):
if lag is None:
lag = self._lag
if phi is None:
phi = self.params['phi']
if sigma is None:
sigma = self.params['sigma']
if intercept is None:
intercept = self.params['intercept']
loglikelihood = self.get_loglikelihood(X,
lag=lag,
phi=phi,
sigma=sigma,
intercept=intercept)
"""grad_phi is a column vector"""
grads = {}
grads['phi'] = eval_numerical_gradient_array(
lambda phi: self.get_loglikelihood(X, lag, phi, sigma, intercept),
phi, 1)
grads['intercept'] = eval_numerical_gradient_array(
lambda intercept: self.get_loglikelihood(X, lag, phi, sigma,
intercept), intercept, 1)
grads['sigma'] = eval_numerical_gradient_array(
lambda sigma: self.get_loglikelihood(X, lag, phi, sigma, intercept
), sigma, 1)
return loglikelihood, grads
def test_linear_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
C = np.random.choice(range(1, 10))
x = np.random.randn(N, D)
dout = np.random.randn(N, C)
layer_params = {
'input_size': D,
'output_size': C,
'reg': 0.0,
'weight_scale': 0.001
}
layer = LinearLayer(layer_params)
layer.initialize()
layer.set_train_mode()
out = layer.forward(x)
dx = layer.backward(dout)
dw = layer.grads['w']
dx_num = eval_numerical_gradient_array(
lambda xx: layer.forward(xx), x, dout)
dw_num = eval_numerical_gradient_array(lambda w: layer.forward(x),
layer.params['w'], dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
self.assertLess(rel_error(dw, dw_num), rel_error_max)
def test_linear_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
C = np.random.choice(range(1, 10))
x = np.random.randn(N, D)
dout = np.random.randn(N, C)
layer = LinearModule(D, C)
out = layer.forward(x)
dx = layer.backward(dout)
dw = layer.grads['weight']
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
dw_num = eval_numerical_gradient_array(lambda w: layer.forward(x), layer.params['weight'], dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
self.assertLess(rel_error(dw, dw_num), rel_error_max)
def test_backprop(self):
x = np.random.randn(self.N, self.D)
grad_output = np.random.randn(*x.shape)
# Numerical gradient w.r.t inputs
num_grad_x = eval_numerical_gradient_array(
f=lambda x: self.layer.forward_prop(x), x=x, df=grad_output)
# Compute gradients using backprop algorithm
grad_x = self.layer.backprop(grad_output)
np.testing.assert_array_almost_equal(num_grad_x, grad_x, decimal=7)
def test_linear_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20)) #batch size
D = np.random.choice(range(1, 100)) #num in_features
C = np.random.choice(range(1, 10)) #num classes = out_features
x = np.random.randn(N, D) #mini-batch
dout = np.random.randn(N, C) #cross-entropy loss?
layer = LinearModule(D, C)
out = layer.forward(x)
dx = layer.backward(dout)
dw = layer.grads['weight']
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
dw_num = eval_numerical_gradient_array(lambda w: layer.forward(x), layer.params['weight'], dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
self.assertLess(rel_error(dw, dw_num), rel_error_max)
def test_backward(self):
np.random.seed(271)
N, D, T, H = 2, 3, 10, 6
# Create the layer
layer = LSTMRecurrentLayer(D, H)
Wx = np.random.randn(D, 4 * H)
Wh = np.random.randn(H, 4 * H)
b = np.random.randn(4 * H)
# Create some arbitrary inputs
x = np.random.randn(N, T, D)
h0 = np.random.randn(N, H)
hidden_state_over_time = layer.forward(x, h0, Wx=Wx, Wh=Wh, b=b)
grad_h_over_time = np.random.randn(*hidden_state_over_time.shape)
grad_x, grad_h0, grad_Wx, grad_Wh, grad_b = layer.backward(
grad_h_over_time)
fx = lambda x: layer.forward(x, h0, Wx=Wx, Wh=Wh, b=b)
fh0 = lambda h0: layer.forward(x, h0, Wx=Wx, Wh=Wh, b=b)
fWx = lambda Wx: layer.forward(x, h0, Wx=Wx, Wh=Wh, b=b)
fWh = lambda Wh: layer.forward(x, h0, Wx=Wx, Wh=Wh, b=b)
fb = lambda b: layer.forward(x, h0, Wx=Wx, Wh=Wh, b=b)
grad_x_num = eval_numerical_gradient_array(fx, x, grad_h_over_time)
grad_h0_num = eval_numerical_gradient_array(fh0, h0, grad_h_over_time)
grad_Wx_num = eval_numerical_gradient_array(fWx, Wx, grad_h_over_time)
grad_Wh_num = eval_numerical_gradient_array(fWh, Wh, grad_h_over_time)
grad_b_num = eval_numerical_gradient_array(fb, b, grad_h_over_time)
self.assertAlmostEqual(rel_error(grad_x_num, grad_x), 1e-9, places=2)
self.assertAlmostEqual(rel_error(grad_h0_num, grad_h0), 1e-9, places=2)
self.assertAlmostEqual(rel_error(grad_Wx_num, grad_Wx), 1e-9, places=2)
self.assertAlmostEqual(rel_error(grad_Wh_num, grad_Wh), 1e-9, places=2)
self.assertAlmostEqual(rel_error(grad_b_num, grad_b), 1e-9, places=2)
def test_elu_backward(self):
np.random.seed(42)
rel_error_max = 1e-6
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
x = np.random.randn(N, D)
dout = np.random.randn(*x.shape)
layer = ELUModule()
out = layer.forward(x)
dx = layer.backward(dout)
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
def affine_relu_check():
np.random.seed(231)
x = np.random.randn(2, 3, 4)
w = np.random.randn(12, 10)
b = np.random.randn(10)
dout = np.random.randn(2, 10)
out, cache = affine_relu_forward(x, w, b)
dx, dw, db = affine_relu_backward(dout, cache)
dx_num = eval_numerical_gradient_array(
lambda x: affine_relu_forward(x, w, b)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: affine_relu_forward(x, w, b)[0], w, dout)
db_num = eval_numerical_gradient_array(
lambda b: affine_relu_forward(x, w, b)[0], b, dout)
# Relative error should be around e-10 or less
print('Testing affine_relu_forward and affine_relu_backward:')
print('dx error: ', rel_error(dx_num, dx))
print('dw error: ', rel_error(dw_num, dw))
print('db error: ', rel_error(db_num, db))
pass
def test_linear_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
C = np.random.choice(range(1, 10))
x = np.random.randn(N, D)
dout = np.random.randn(N, C)
layer_params = {'input_size': D, 'output_size': C, 'reg': 0.0, 'weight_scale': 0.001}
layer = LinearLayer(layer_params)
layer.initialize()
layer.set_train_mode()
out = layer.forward(x)
dx = layer.backward(dout)
dw = layer.grads['w']
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
dw_num = eval_numerical_gradient_array(lambda w: layer.forward(x), layer.params['w'], dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
self.assertLess(rel_error(dw, dw_num), rel_error_max)
def test_relu_backward(self):
np.random.seed(42)
rel_error_max = 1e-6
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
x = np.random.randn(N, D)
dout = np.random.randn(*x.shape)
layer = ReLULayer()
layer.initialize()
layer.set_train_mode()
out = layer.forward(x)
dx = layer.backward(dout)
dx_num = eval_numerical_gradient_array(lambda xx: layer.forward(xx), x, dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
def test_softmax_backward(self):
np.random.seed(42)
rel_error_max = 1e-5
for test_num in range(10):
N = np.random.choice(range(1, 20))
D = np.random.choice(range(1, 100))
x = np.random.randn(N, D)
dout = np.random.randn(*x.shape)
layer = SoftMaxLayer()
layer.initialize()
layer.set_train_mode()
out = layer.forward(x)
dx = layer.backward(dout)
dx_num = eval_numerical_gradient_array(
lambda xx: layer.forward(xx), x, dout)
self.assertLess(rel_error(dx, dx_num), rel_error_max)
def test_backward(self):
np.random.seed(271)
N, T, V, D = 50, 3, 5, 6
# Create the layer
layer = WordEmbeddingLayer(V, D)
layer.W = np.random.randn(V, D)
# Create some arbitrary inputs
x = np.random.randint(V, size=(N, T))
out = layer.forward(x)
grad_out = np.random.randn(*out.shape)
grad_W = layer.backward(grad_out)
f = lambda W: layer.forward(x, W=W)
grad_W_num = eval_numerical_gradient_array(f, layer.W, grad_out)
self.assertAlmostEqual(rel_error(grad_W_num, grad_W), 1e-9, places=2)
################################################################################### # Affine layer: backward. # ################################################################################### # In the file layers.py implement the affine_backward function. # # Once you are done you can test your implementation using numeric gradient. # ################################################################################### # Test the affine_backward function x = np.random.randn(10, 2, 3) theta = np.random.randn(6, 5) theta_0 = np.random.randn(5) dout = np.random.randn(10, 5) if layers.affine_forward(x,theta,theta_0)[0] is not None: dx_num = eval_numerical_gradient_array(lambda x: layers.affine_forward(x, theta, theta_0)[0], x, dout) dtheta_num = eval_numerical_gradient_array(lambda theta: layers.affine_forward(x, theta, theta_0)[0], theta, dout) dtheta_0_num = eval_numerical_gradient_array(lambda b: layers.affine_forward(x, theta, theta_0)[0], theta_0, dout) _, cache = layers.affine_forward(x, theta, theta_0) dx, dtheta, dtheta_0 = layers.affine_backward(dout, cache) # The error should be around 1e-10 print 'Testing affine_backward function:' print 'dx error (should be around 1e-10): ', rel_error(dx_num, dx) print 'dtheta error (should be around 1e-10): ', rel_error(dtheta_num, dtheta) print 'dtheta_0 error (should be around 1e-10): ', rel_error(dtheta_0_num, dtheta_0) # Problem 3.1.3 ###################################################################################
#------------------------------------------------------------------------------
#%%
print('\n--------- affine_sigmoid_affine_backward test --------- ')
np.random.seed(231)
x = np.random.randn(10, 10)
w1 = np.random.randn(10, 5)
b1 = np.random.randn(5)
w2 = np.random.randn(5, 7)
b2 = np.random.randn(7)
dout = np.random.randn(10, 7)
dout1 = np.random.randn(10, 5)
out, cache = affine_sigmoid_affine_forward(x, w1, b1, w2, b2)
dx, dw1, db1, dw2, db2 = affine_sigmoid_affine_backward(dout, cache)
dx_num = eval_numerical_gradient_array(
lambda x: affine_sigmoid_affine_forward(x, w1, b1, w2, b2)[0], x, dout)
dw1_num = eval_numerical_gradient_array(
lambda w1: affine_sigmoid_affine_forward(x, w1, b1, w2, b2)[0], w1, dout)
db1_num = eval_numerical_gradient_array(
lambda b1: affine_sigmoid_affine_forward(x, w1, b1, w2, b2)[0], b1, dout)
dw2_num = eval_numerical_gradient_array(
lambda w2: affine_sigmoid_affine_forward(x, w1, b1, w2, b2)[0], w2, dout)
db2_num = eval_numerical_gradient_array(
lambda b2: affine_sigmoid_affine_forward(x, w1, b1, w2, b2)[0], b2, dout)
#out, cache = affine_sigmoid_forward(x, w1, b1)
#dx, dw, db = affine_sigmoid_backward(dout1, cache)
#dx_num = eval_numerical_gradient_array(lambda x:
# affine_sigmoid_forward(x, w1, b1)[0], x, dout1)
print('\ndx_num: ', dx_num)
print('\ndx: ', dx)
h = np.random.randn(N, H) Wx = np.random.randn(D, H) Wh = np.random.randn(H, H) b = np.random.randn(H) out, cache = rnn_step_forward(x, h, Wx, Wh, b) dnext_h = np.random.randn(*out.shape) fx = lambda x: rnn_step_forward(x, h, Wx, Wh, b)[0] fh = lambda prev_h: rnn_step_forward(x, h, Wx, Wh, b)[0] fWx = lambda Wx: rnn_step_forward(x, h, Wx, Wh, b)[0] fWh = lambda Wh: rnn_step_forward(x, h, Wx, Wh, b)[0] fb = lambda b: rnn_step_forward(x, h, Wx, Wh, b)[0] dx_num = eval_numerical_gradient_array(fx, x, dnext_h) dprev_h_num = eval_numerical_gradient_array(fh, h, dnext_h) dWx_num = eval_numerical_gradient_array(fWx, Wx, dnext_h) dWh_num = eval_numerical_gradient_array(fWh, Wh, dnext_h) db_num = eval_numerical_gradient_array(fb, b, dnext_h) dx, dprev_h, dWx, dWh, db = rnn_step_backward(dnext_h, cache) print 'dx error: ', rel_error(dx_num, dx) print 'dprev_h error: ', rel_error(dprev_h_num, dprev_h) print 'dWx error: ', rel_error(dWx_num, dWx) print 'dWh error: ', rel_error(dWh_num, dWh) print 'db error: ', rel_error(db_num, db) #Vanilla RNN: forward N, T, D, H = 2, 3, 4, 5
print('Mean of test-time output: ', out_test.mean())
print('Fraction of train-time output set to zero: ', (out == 0).mean())
print('Fraction of test-time output set to zero: ', (out_test == 0).mean())
print()
# # Dropout backward pass
# In the file `cs231n/layers.py`, implement the backward pass for dropout. After doing so, run the following cell to numerically gradient-check your implementation.
np.random.seed(231)
x = np.random.randn(10, 10) + 10
dout = np.random.randn(*x.shape)
dropout_param = {'mode': 'train', 'p': 0.8, 'seed': 123}
out, cache = dropout_forward(x, dropout_param)
dx = dropout_backward(dout, cache)
dx_num = eval_numerical_gradient_array(
lambda xx: dropout_forward(xx, dropout_param)[0], x, dout)
print('dx relative error: ', rel_error(dx, dx_num))
# # Fully-connected nets with Dropout
# In the file `cs231n/classifiers/fc_net.py`, modify your implementation to use dropout. Specificially, if the constructor the the net receives a nonzero value for the `dropout` parameter, then the net should add dropout immediately after every ReLU nonlinearity. After doing so, run the following to numerically gradient-check your implementation.
np.random.seed(231)
N, D, H1, H2, C = 2, 15, 20, 30, 10
X = np.random.randn(N, D)
y = np.random.randint(C, size=(N, ))
for dropout in [0, 0.25, 0.5]:
print('Running check with dropout = ', dropout)
model = FullyConnectedNet([H1, H2],
input_dim=D,
from gradient_check import eval_numerical_gradient_array import numpy as np from layers import * N = 2 D = 3 M = 4 x = np.random.normal(size=(N, D)) w = np.random.normal(size=(D, M)) b = np.random.normal(size=(M, )) # dout = np.random.normal(size=(N, M)) dout = np.random.normal(size=(N, D)) # out, cache = affine_forward(x, w, b) # f=lambda x: affine_forward(x, w, b)[0] # grad = affine_backward(dout, cache)[0] # ngrad = eval_numerical_gradient_array(f, x, dout) # # print(grad - ngrad) out, cache = relu_forward(x) f = lambda x: relu_forward(x)[0] grad = relu_backward(dout, cache) ngrad = eval_numerical_gradient_array(f, x, dout) print(grad - ngrad)
imshow_noax(out[1, 1])
plt.show()
# # Convolution: Naive backward pass
# Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.
#
# When you are done, run the following to check your backward pass with a numeric gradient check.
np.random.seed(231)
x = np.random.randn(4, 3, 5, 5)
w = np.random.randn(2, 3, 3, 3)
b = np.random.randn(2, )
dout = np.random.randn(4, 2, 5, 5)
conv_param = {'stride': 1, 'pad': 1}
dx_num = eval_numerical_gradient_array(
lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)
db_num = eval_numerical_gradient_array(
lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)
out, cache = conv_forward_naive(x, w, b, conv_param)
dx, dw, db = conv_backward_naive(dout, cache)
# Your errors should be around 1e-8'
print('Testing conv_backward_naive 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))
# # Max pooling: Naive forward
###################################################################################
# Implement the backward pass for the convolution operation in the #
# function conv_backward_naive in the file layers.py. Again, you #
# don't need to worry too much about computational efficiency. When you #
# are done, run the following to check your backward pass with a numeric #
# gradient check. #
###################################################################################
x = np.random.randn(4, 3, 5, 5)
theta = np.random.randn(2, 3, 3, 3)
theta0 = np.random.randn(2,)
dout = np.random.randn(4, 2, 5, 5)
conv_param = {'stride': 1, 'pad': 1}
if layers.conv_forward_naive(x,theta,theta0, conv_param)[0] is not None:
dx_num = eval_numerical_gradient_array(lambda x: layers.conv_forward_naive(x, theta, theta0, conv_param)[0], x, dout)
dtheta_num = eval_numerical_gradient_array(lambda theta: layers.conv_forward_naive(x, theta, theta0, conv_param)[0], theta, dout)
dtheta0_num = eval_numerical_gradient_array(lambda theta0: layers.conv_forward_naive(x, theta, theta0, conv_param)[0], theta0, dout)
out, cache = layers.conv_forward_naive(x, theta, theta0, conv_param)
dx, dtheta, dtheta0 = layers.conv_backward_naive(dout, cache)
# Your errors should be around 1e-9
print 'Testing conv_backward_naive function'
print 'dx error: ', rel_error(dx, dx_num)
print 'dtheta error: ', rel_error(dtheta, dtheta_num)
print 'dtheta0 error: ', rel_error(dtheta0, dtheta0_num)
# Problem 3.2.3
###################################################################################
# Max pooling: Naive forward #
gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])
out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)
print('After spatial batch normalization (nontrivial gamma, beta):')
print(' Shape: ', out.shape)
print(' Means: ', out.mean(axis=(0, 2, 3)))
print(' Stds: ', out.std(axis=(0, 2, 3)))
'''
##############backward##############
np.random.seed(231)
N, C, H, W = 2, 3, 4, 5
x = 5 * np.random.randn(N, C, H, W) + 12
gamma = np.random.randn(C)
beta = np.random.randn(C)
dout = np.random.randn(N, C, H, W)
bn_param = {'mode': 'train'}
fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]
fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]
fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]
dx_num = eval_numerical_gradient_array(fx, x, dout)
da_num = eval_numerical_gradient_array(fg, gamma, dout)
db_num = eval_numerical_gradient_array(fb, beta, dout)
_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)
dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)
print('dx error: ', rel_error(dx_num, dx))
print('dgamma error: ', rel_error(da_num, dgamma))
print('dbeta error: ', rel_error(db_num, dbeta))
[[-0.14526316, -0.13052632],
[-0.08631579, -0.07157895]],
[[-0.02736842, -0.01263158],
[ 0.03157895, 0.04631579]]],
[[[ 0.09052632, 0.10526316],
[ 0.14947368, 0.16421053]],
[[ 0.20842105, 0.22315789],
[ 0.26736842, 0.28210526]],
[[ 0.32631579, 0.34105263],
[ 0.38526316, 0.4 ]]]])
# Compare your output with ours. Difference should be around 1e-8.
print('Testing max_pool_forward_naive function:')
print('difference: ', rel_error(out, correct_out))
'''
#######################################backward###########################################
np.random.seed(231)
x = np.random.randn(3, 2, 8, 8)
dout = np.random.randn(3, 2, 4, 4)
pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}
dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)
out, cache = max_pool_forward_naive(x, pool_param)
dx = max_pool_backward_naive(dout, cache)
# Your error should be around 1e-12
print('Testing max_pool_backward_naive function:')
print('dx error: ', rel_error(dx, dx_num))
'''
def gradient_check(X, model, y): loss, grads = chess_convnet(X, model, y) dx_num = eval_numerical_gradient_array(lambda x: chess_convnet(x, model)[1]['W1'], x, grads) return rel_error(dx_num, grads['W1'])
import numpy as np
from gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from layers import *
# Test the affine_backward function
x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)
dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x,
dout)
out, _ = affine_forward(x, w, b)
correct_out = np.array([[ 1.49834967, 1.70660132, 1.91485297],
[ 3.25553199, 3.5141327, 3.77273342]])
# Compare your output with ours. The error should be around e-9 or less.
print('Testing affine_forward function:')
print(rel_error(out, correct_out)<1e-8)
np.random.seed(231)
x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)
dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)
dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)
db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)
_, cache = affine_forward(x, w, b)
dx, dw, db = affine_backward(dout, cache)
# The error should be around e-10 or less
print('Testing affine_backward function:')
print('dx error: ', rel_error(dx_num, dx)<=1e-9)
print('dw error: ', rel_error(dw_num, dw)<=1e-9)
print('db error: ', rel_error(db_num, db)<=1e-9)
# Test the relu_forward function
[ 3.25553199, 3.5141327, 3.77273342]])
# Compare your output with ours. The error should be around 1e-9.
print('Testing affine_forward function:')
print('difference: ', rel_maxError(out, correct_out))
#------------------------------------------------------------------------------
#%%
# Test the affine_backward function
print('\n--------- affine_backward test --------- ')
np.random.seed(231)
x = np.random.randn(10, 2, 3)
w = np.random.randn(6, 5)
b = np.random.randn(5)
dout = np.random.randn(10, 5)
dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)
dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)
db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)
_, cache = affine_forward(x, w, b)
dx, dw, db = affine_backward(dout, cache)
# The error should be around 1e-10
print('Testing affine_backward function:')
print('dx error: ', rel_maxError(dx_num, dx))
print('dw error: ', rel_maxError(dw_num, dw))
print('db error: ', rel_maxError(db_num, db))
#------------------------------------------------------------------------------
#%%
print('\n--------- affine_backward test --------- ')
np.random.seed(231)
Wh = np.random.randn(H, 4 * H)
b = np.random.randn(4 * H)
out, cache = lstm_forward(x, h0, Wx, Wh, b)
dout = np.random.randn(*out.shape)
dx, dh0, dWx, dWh, db = lstm_backward(dout, cache)
fx = lambda x: lstm_forward(x, h0, Wx, Wh, b)[0]
fh0 = lambda h0: lstm_forward(x, h0, Wx, Wh, b)[0]
fWx = lambda Wx: lstm_forward(x, h0, Wx, Wh, b)[0]
fWh = lambda Wh: lstm_forward(x, h0, Wx, Wh, b)[0]
fb = lambda b: lstm_forward(x, h0, Wx, Wh, b)[0]
dx_num = eval_numerical_gradient_array(fx, x, dout)
dh0_num = eval_numerical_gradient_array(fh0, h0, dout)
dWx_num = eval_numerical_gradient_array(fWx, Wx, dout)
dWh_num = eval_numerical_gradient_array(fWh, Wh, dout)
db_num = eval_numerical_gradient_array(fb, b, dout)
print 'dx error: ', rel_error(dx_num, dx)
print 'dh0 error: ', rel_error(dx_num, dx)
print 'dWx error: ', rel_error(dx_num, dx)
print 'dWh error: ', rel_error(dx_num, dx)
print 'db error: ', rel_error(dx_num, dx)
#LSTM captioning model: test loss
N, D, W, H = 10, 20, 30, 40
word_to_idx = {'<NULL>': 0, 'cat': 2, 'dog': 3}
V = len(word_to_idx)
from layers import conv_backward_naive, conv_forward_naive, max_pool_backward_naive, max_pool_forward_naive
import numpy as np
def rel_error(x, y):
""" returns relative error """
return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))
x = np.random.randn(4, 3, 5, 5)
w = np.random.randn(2, 3, 3, 3)
b = np.random.randn(2, )
dout = np.random.randn(4, 2, 5, 5)
conv_param = {'stride': 1, 'pad': 1}
dx_num = eval_numerical_gradient_array(
lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)
dw_num = eval_numerical_gradient_array(
lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)
db_num = eval_numerical_gradient_array(
lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)
out, cache = conv_forward_naive(x, w, b, conv_param)
dx, dw, db = conv_backward_naive(dout, cache)
# Your errors should be around 1e-9'
print('Testing conv_backward_naive 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))
pass
