def test_softmax_loss_vectorized_gradient(self):
loss_naive, grad_naive = softmax_loss_naive(self.weights, self.x,
self.y, self.reg)
loss_vect, grad_vect = softmax_loss_vectorized(self.weights, self.x,
self.y, self.reg)
np.testing.assert_allclose(loss_naive, loss_vect, 1e-04)
np.testing.assert_allclose(grad_naive, grad_vect, 1e-04)
def loss(self, X, y=None, reg=0.0):
"""
Compute the loss and gradients for a two layer fully connected neural
network.
Inputs:
- X: Input data of shape (N, D). Each X[i] is a training sample.
- y: Vector of training labels. y[i] is the label for X[i], and each y[i] is
an integer in the range 0 <= y[i] < C. This parameter is optional; if it
is not passed then we only return scores, and if it is passed then we
instead return the loss and gradients.
- reg: Regularization strength.
Returns:
If y is None, return a matrix scores of shape (N, C) where scores[i, c] is
the score for class c on input X[i].
If y is not None, instead return a tuple of:
- loss: Loss (data loss and regularization loss) for this batch of training
samples.
- grads: Dictionary mapping parameter names to gradients of those parameters
with respect to the loss function; has the same keys as self.params.
"""
# Unpack variables from the params dictionary
W1, b1 = self.params['W1'], self.params['b1']
W2, b2 = self.params['W2'], self.params['b2']
N, D = X.shape
# Compute the forward pass
h1, h2, scores = self.compute_score(X, W1, b1, W2, b2)
if y is None:
return scores
# Compute the loss
loss = softmax_loss_vectorized(
W=None, X=None, y=y, scores=scores)[0] + reg * (
np.sum(np.square(W1)) + np.sum(np.square(W2)))
# Backward pass: compute gradients
grads = {}
exp_scores = np.exp(scores)
sum_scores = np.sum(exp_scores, axis=1)
prob_scores = (exp_scores.T / sum_scores).T
prob_scores[np.arange(N), y] -= 1
dLoss_Scores = prob_scores / N
dLoss_b2 = np.sum(dLoss_Scores, axis=0)
grads['b2'] = dLoss_b2
dLoss_W2 = h2.T.dot(dLoss_Scores) + 2 * reg * W2
grads['W2'] = dLoss_W2
dLoss_h2 = dLoss_Scores.dot(W2.T)
dh2_h1 = np.zeros_like(h1)
dh2_h1[h1 <= 0] = 0
dh2_h1[h1 > 0] = 1
dLoss_h1 = dh2_h1 * dLoss_h2
dLoss_b1 = np.sum(dLoss_h1, axis=0)
grads['b1'] = dLoss_b1
dLoss_W1 = X.T.dot(dLoss_h1) + 2 * reg * W1
grads['W1'] = dLoss_W1
return loss, grads
def loss(self, X_batch, y_batch, reg):
return softmax_loss_vectorized(self.theta, X_batch, y_batch, reg)
# from gradient_check import grad_check_sparse
# f = lambda th: softmax_loss_naive(th, X_train, y_train, 0.0)[0]
# grad_numerical = grad_check_sparse(f, theta, grad, 10)
# Now that we have a naive implementation of the softmax loss function and its gradient,
# implement a vectorized version in softmax_loss_vectorized.
# The two versions should compute the same results, but the vectorized version should be
# much faster.
# tic = time.time()
# loss_naive, grad_naive = softmax_loss_naive(theta, X_train, y_train, 0.00001)
# toc = time.time()
# print 'naive loss: %e computed in %fs' % (loss_naive, toc - tic)
tic = time.time()
loss_vectorized, grad_vectorized = softmax_loss_vectorized(theta, X_train, y_train, 0.00001)
toc = time.time()
print 'vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)
# We use the Frobenius norm to compare the two versions
# of the gradient.
# grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
# print 'Loss difference: %f' % np.abs(loss_naive - loss_vectorized)
# print 'Gradient difference: %f' % grad_difference
learning_rates = [1e-7]
regularization_strengths = [1e8]
# best_softmax, results, best_val = pick_hyperparams(X_train,y_train, X_val, y_val, learning_rates, regularization_strengths)
from util import plt, np, load_data, grad_check_sparse, time_elapse
from softmax import softmax_loss_vectorized
from linear_classifier import Softmax
cifar_dir = '../cifar-10-batches-py'
X_train, y_train, X_val, y_val, X_test, y_test, X_dev, y_dev = load_data(
cifar_dir, num_test=500)
# ininialize W
W = np.random.randn(3073, 10) * 0.0001
# test loss
loss, grad = softmax_loss_vectorized(W, X_dev, y_dev, 0.0)
#print('loss: %f' % loss)
#print('sanity check: %f' % (-np.log(0.1)))
# test gradient without regularization
#def f(w): return softmax_loss_vectorized(W, X_dev, y_dev, 0.0)[0]
#grad_numerical = grad_check_sparse(f, W, grad, 10)
# test gradient with regularization
#def f(w): return softmax_loss_vectorized(W, X_dev, y_dev, 1e2)[0]
#grad_numerical = grad_check_sparse(f, W, grad, 10)
softmax = Softmax()
loss_history = softmax.train(X_train,
y_train,
learning_rate=1e-7,
reg=5e4,
num_iters=1500,
verbose=True)
def test_softmax_loss_vectorized_loss(self):
loss, _ = softmax_loss_vectorized(self.weights, self.x, self.y,
self.reg)
np.testing.assert_allclose(loss, self.expected, 1e-04)
X_val = np.vstack((np.ones(X_val.shape[0]), X_val.T)).T
theta = np.random.randn(3073, 10) * 0.0001
loss, grad = softmax_loss_naive(theta, X_train, y_train, 0.0)
# Loss should be something close to - log(0.1)
print 'loss:', loss, ' should be close to ', -np.log(0.1)
tic = time.time()
loss_naive, grad_naive = softmax_loss_naive(theta, X_train, y_train, 0.00001)
toc = time.time()
print 'naive loss: %e computed in %fs' % (loss_naive, toc - tic)
tic = time.time()
loss_vectorized, grad_vectorized = softmax_loss_vectorized(
theta, X_train, y_train, 0.00001)
toc = time.time()
print 'vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic)
# We use the Frobenius norm to compare the two versions
# of the gradient.
grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
print 'Loss difference: %f' % np.abs(loss_naive - loss_vectorized)
print 'Gradient difference: %f' % grad_difference
results = {}
best_val = -1
best_softmax = None
#learning_rates = [1e-7, 5e-7, 1e-6, 5e-6]
#regularization_strengths = [ 5e4, 1e5, 5e5, 1e8]
loss, grad = softmax.softmax_loss_naive(W, X_dev, Y_dev, 5e1)
f = lambda w: softmax.softmax_loss_naive(w, X_dev, Y_dev, 5e1)[0]
Tools.grad_check_sparse(f, W, grad, 10)
# Now that we have a naive implementation of the softmax loss function and its gradient,
# implement a vectorized version in softmax_loss_vectorized.
# The two versions should compute the same results, but the vectorized version should be
# much faster.
tic = time.time()
loss_naive, grad_naive = softmax.softmax_loss_naive(W, X_dev, Y_dev, 0.000005)
toc = time.time()
print('naive loss: %e computed in %fs' % (loss_naive, toc - tic))
f
tic = time.time()
loss_vectorized, grad_vectorized = softmax.softmax_loss_vectorized(
W, X_dev, Y_dev, 0.000005)
toc = time.time()
print('vectorized loss: %e computed in %fs' % (loss_vectorized, toc - tic))
# As we did for the SVM, we use the Frobenius norm to compare the two versions
# of the gradient.
grad_difference = np.linalg.norm(grad_naive - grad_vectorized, ord='fro')
print('Loss difference: %f' % np.abs(loss_naive - loss_vectorized))
print('Gradient difference: %f' % grad_difference)
# Use the validation set to tune hyperparameters (regularization strength and
# learning rate). You should experiment with different ranges for the learning
# rates and regularization strengths; if you are careful you should be able to
# get a classification accuracy of over 0.35 on the validation set.
results = {}
best_val = -1
def loss(self, X, y, reg):
return softmax_loss_vectorized(X, y, reg)
def loss(self, X, y=None, reg=0.0):
"""
Compute the loss and gradients for a two layer fully connected neural
network.
Inputs:
- X: Input data of shape (N, D). Each X[i] is a training sample.
- y: Vector of training labels. y[i] is the label for X[i], and each y[i] is
an integer in the range 0 <= y[i] < C. This parameter is optional; if it
is not passed then we only return scores, and if it is passed then we
instead return the loss and gradients.
- reg: Regularization strength.
Returns:
If y is None, return a matrix scores of shape (N, C) where scores[i, c] is
the score for class c on input X[i].
If y is not None, instead return a tuple of:
- loss: Loss (data loss and regularization loss) for this batch of training
samples.
- grads: Dictionary mapping parameter names to gradients of those parameters
with respect to the loss function; has the same keys as self.params.
"""
# Unpack variables from the params dictionary
W1, b1 = self.params['W1'], self.params['b1']
W2, b2 = self.params['W2'], self.params['b2']
N, D = X.shape
#print "REG:",reg
X1 = np.insert(X, 0, 1, axis=1)
W11 = np.insert(W1, 0, b1, axis=0)
W21 = np.insert(W2, 0, b2, axis=0)
scores = None
z1 = X1.dot(W11)
Layer1ub = np.maximum(0, z1)
Layer1 = np.insert(np.maximum(0, z1), 0, 1, axis=1)
# Compute the forward pass
scores = Layer1.dot(W21)
#############################################################################
# TODO: Perform the forward pass, computing the class scores for the input. #
# Store the result in the scores variable, which should be an array of #
# shape (N, C). #
#############################################################################
pass
#############################################################################
# END OF YOUR CODE #
#############################################################################
# If the targets are not given then jump out, we're done
if y is None:
return scores
loss = 0.0
# Compute the loss
L = np.exp(scores)
#print sc
for i in range(X.shape[0]):
loss -= scores[i][y[i]]
loss += math.log(sum(L[i]))
loss /= X.shape[0]
loss += +0.5 * reg * np.sum(W1 * W1) + 0.5 * reg * np.sum(W2 * W2)
#############################################################################
# TODO: Finish the forward pass, and compute the loss. This should include #
# both the data loss and L2 regularization for W1 and W2. Store the result #
# in the variable loss, which should be a scalar. Use the Softmax #
# classifier loss. So that your results match ours, multiply the #
# regularization loss by 0.5 #
#############################################################################
pass
#############################################################################
# END OF YOUR CODE #
#############################################################################
# Backward pass: compute gradients
l1, w2gradient = softmax_loss_vectorized(W21, Layer1, y, reg)
derror = error_class(Layer1ub, y, W2,
b2) #obtain error in outermost layer
dhidden = np.dot(derror, W2.T) #backprop error to hidden layer
dhidden[Layer1ub <= 0] = 0 #apply ReLu
w1grads = np.dot(X.T, dhidden) #compute grad
#print "grad:",w1grads.shape,W1.shape
grads = {}
grads['W2'] = w2gradient[1:] + reg * sum(
W1 * W1) #np.dot(Layer1,gradient)
grads['W1'] = w1grads + reg * W1 #w1Lgradient[1:]+reg*W1
grads['b2'] = w2gradient[0]
grads['b1'] = np.sum(dhidden, axis=0) #w1Lgradient[0]
#print grads['W1']
#############################################################################
# TODO: Compute the backward pass, computing the derivatives of the weights #
# and biases. Store the results in the grads dictionary. For example, #
# grads['W1'] should store the gradient on W1, and be a matrix of same size #
#############################################################################
pass
#############################################################################
# END OF YOUR CODE #
#############################################################################
return loss, grads
x_val = np.reshape(x_val, (x_val.shape[0], -1))
x_dev = np.reshape(x_dev, (x_dev.shape[0], -1))
#下面进行归一化处理，对每个特征减去平均值来中心化
mean_image = np.mean(x_train, axis=0)
x_train -= mean_image
x_val -= mean_image
x_test -= mean_image
x_dev -= mean_image
#权重矩阵W其实是W和b，因此我们需要x增加一个维度
x_train = np.hstack([x_train, np.ones((x_train.shape[0], 1))])
x_val = np.hstack([x_val, np.ones((x_val.shape[0], 1))])
x_test = np.hstack([x_test, np.ones((x_test.shape[0], 1))])
x_dev = np.hstack([x_dev, np.ones((x_dev.shape[0], 1))])
#损失函数和梯度计算
w = np.random.randn(3073, 10) * 0.0001 #返回(3073,10)尺寸的符合正态分布的随机数组
loss, grad = softmax.softmax_loss_vectorized(
w, x_dev, y_dev, 0.00001) #也可使用公式计算梯度 svm_loss_vectorized
print('loss is : %f' % loss)
#梯度检验，用公式计算梯度速度很快,但是实现过程中容易出错，为了解决这个问题，需要进行梯度检验
#把分析梯度法的结果和数值梯度法的结果做比较
def grad_check_sparse(f, x, analytic_grad, num_checks=10, h=1e-5):
for i in range(num_checks):
ix = tuple([random.randrange(m) for m in x.shape])
oldval = x[ix]
x[ix] = oldval + h
fxph = f(x)
x[ix] = oldval - h
fxmh = f(x)
x[ix] = oldval
grad_numerical = (fxph - fxmh) / (2 * h)
def loss(self,X_batch,Y_batch,regularization):
return softmax.softmax_loss_vectorized(self.W,X_batch,Y_batch,regularization)
return X_train, y_train, X_val, y_val, X_test, y_test X_train, y_train, X_val, y_val, X_test, y_test = get_CIFAR10_data() ##print 'Train data shape: ', X_train.shape ##print 'Train labels shape: ', y_train.shape ##print 'Validation data shape: ', X_val.shape ##print 'Validation labels shape: ', y_val.shape ##print 'Test data shape: ', X_test.shape ##print 'Test labels shape: ', y_test.shape W = np.random.randn(10,3073)*0.0001 ##loss, grad = softmax_loss_naive(W, X_train, y_train, 0.0) ##print 'loss: %f' % loss loss, grad = softmax_loss_vectorized(W, X_train, y_train, 0.0) print 'loss: %f' % loss from gradient_check import grad_check_sparse f = lambda w: softmax_loss_naive(w, X_train, y_train, 0.0)[0] grad_numerical = grad_check_sparse(f, W, grad, 10) ##import numpy as np ##a = np.arange(15).reshape(3, 5)*0.1 ##probs = a / np.sum(a, axis=0) ##y = np.random.choice(3, 5) # Use the validation set to tune hyperparameters (regularization strength and # learning rate). You should experiment with different ranges for the learning # rates and regularization strengths; if you are careful you should be able to # get a classification accuracy of over 0.35 on the validation set.
X=np.concatenate((X,np.ones([X.shape[0],1])),axis=1) X_test=np.concatenate((X_test,np.ones([X_test.shape[0],1])),axis=1) X_train, X_val, y_train, y_val = cross_validation.train_test_split(X,y, test_size=0.4, random_state=0) # First implement the naive softmax loss function with nested loops. # Open the file softmax.py and implement the # softmax_loss_naive function. # Generate a random softmax theta matrix and use it to compute the loss. theta = np.random.randn(3073,10) * 0.0001 loss, grad = softmax_loss_vectorized(theta, X_train, y_train, 0.0) # Loss should be something close to - log(0.1) print 'loss:', loss, ' should be close to ', - np.log(0.1) # Use numeric gradient checking as a debugging tool. # The numeric gradient should be close to the analytic gradient. (within 1e-7) # Now that we have a naive implementation of the softmax loss function and its gradient, # implement a vectorized version in softmax_loss_vectorized. # The two versions should compute the same results, but the vectorized version should be # much faster.
def loss1(self, X, y=None):
"""
Compute loss and gradient for a minibatch of data.
Inputs:
- X: Array of input data of shape (N, d_1, ..., d_k)
- y: Array of labels, of shape (N,). y[i] gives the label for X[i].
Returns:
If y is None, then run a test-time forward pass of the model and return:
- scores: Array of shape (N, C) giving classification scores, where
scores[i, c] is the classification score for X[i] and class c.
If y is not None, then run a training-time forward and backward pass and
return a tuple of:
- loss: Scalar value giving the loss
- grads: Dictionary with the same keys as self.params, mapping parameter
names to gradients of the loss with respect to those parameters.
"""
scores = None
W1, b1 = self.params['W1'], self.params['b1']
W2, b2 = self.params['W2'], self.params['b2']
N = X.shape[0]
feat = 1
for num_wei in X.shape[1:]:
feat *= num_wei
#out = None
Xfeat = X.reshape([X.shape[0], feat])
#print "X1: shape",X1.shape
X1 = np.insert(Xfeat, 0, 1, axis=1)
#print "X1: shape",X1.shape
W11 = np.insert(W1, 0, b1, axis=0)
W21 = np.insert(W2, 0, b2, axis=0)
scores = None
z1 = X1.dot(W11)
Layer1ub = np.maximum(0, z1)
scoresub = np.exp(Layer1ub.dot(W2) + b2)
Layer1 = np.insert(np.maximum(0, z1), 0, 1, axis=1)
#print "ALyer1",Layer1.shape
scores = Layer1.dot(W21)
############################################################################
# TODO: Implement the forward pass for the two-layer net, computing the #
# class scores for X and storing them in the scores variable. #
############################################################################
pass
############################################################################
# END OF YOUR CODE #
############################################################################
# If y is None then we are in test mode so just return scores
if y is None:
return scores
loss, grads = 0, {}
loss = 0.0
# Compute the loss
L = np.exp(scores)
#print sc
for i in range(X.shape[0]):
loss -= scores[i][y[i]]
loss += math.log(sum(L[i]))
loss /= X.shape[0]
loss += +0.5 * self.reg * np.sum(W1 * W1) + 0.5 * self.reg * np.sum(
W2 * W2)
############################################################################
# TODO: Implement the backward pass for the two-layer net. Store the loss #
# in the loss variable and gradients in the grads dictionary. Compute data #
# loss using softmax, and make sure that grads[k] holds the gradients for #
# self.params[k]. Don't forget to add L2 regularization! #
# #
# NOTE: To ensure that your implementation matches ours and you pass the #
# automated tests, make sure that your L2 regularization includes a factor #
# of 0.5 to simplify the expression for the gradient. #
############################################################################
pass
############################################################################
# END OF YOUR CODE #
############################################################################
l1, w2gradient = softmax_loss_vectorized(W21, Layer1, y, self.reg)
derror = error_class(Layer1ub, y, W2,
b2) #obtain error in outermost layer
dhidden = np.dot(derror, W2.T) #backprop error to hidden layer
dhidden[Layer1ub <= 0] = 0 #apply ReLu
w1grads = np.dot(Xfeat.T, dhidden) #compute grad
#print "grad:",w1grads.shape,W1.shape
grads = {}
#print "W1:",w1grads.shape,W1.shape
grads['W2'] = w2gradient[1:] #.reshape(#np.dot(Layer1,gradient)
grads['W1'] = w1grads #+self.reg*W1#w1Lgradient[1:]+reg*W1
grads['b2'] = w2gradient[0] #np.sum(scoresub, axis=0)#
grads['b1'] = np.sum(dhidden, axis=0) #w1Lgradient[0]
#print "grads over"
return loss, grads
