def convpool(X, W, b, poolsize=(2, 2)):
conv_out = conv2d(input=X, filters=W)
# downsample each feature map individually, using maxpooling
pooled_out = downsample.max_pool_2d(input=conv_out,
ds=poolsize,
ignore_border=True)
# add the bias term. Since the bias is a vector (1D array), we first
# reshape it to a tensor of shape (1, n_filters, 1, 1). Each bias will
# thus be broadcasted across mini-batches and feature map
# width & height
# return T.tanh(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))
return relu(pooled_out + b.dimshuffle('x', 0, 'x', 'x'))
def forward(self, X):
if self.use_tanh:
Z = np.tanh(X.dot(self.W1) + self.b1)
else:
Z = relu(X.dot(self.W1) + self.b1)
return sigmoid(Z.dot(self.W2) + self.b2), Z
def main():
# load the data, transform as needed
train = loadmat('../large_files/train_32x32.mat')
test = loadmat('../large_files/test_32x32.mat')
# Need to scale! don't leave as 0..255
# Y is a N x 1 matrix with values 1..10 (MATLAB indexes by 1)
# So flatten it and make it 0..9
# Also need indicator matrix for cost calculation
Xtrain = rearrange(train['X'])
Ytrain = train['y'].flatten() - 1
del train
Xtrain, Ytrain = shuffle(Xtrain, Ytrain)
Ytrain_ind = y2indicator(Ytrain)
Xtest = rearrange(test['X'])
Ytest = test['y'].flatten() - 1
del test
Ytest_ind = y2indicator(Ytest)
max_iter = 8
print_period = 10
lr = np.float32(0.00001)
reg = np.float32(0.01)
mu = np.float32(0.99)
N = Xtrain.shape[0]
batch_sz = 500
n_batches = N / batch_sz
M = 500
K = 10
poolsz = (2, 2)
# after conv will be of dimension 32 - 5 + 1 = 28
# after downsample 28 / 2 = 14
W1_shape = (20, 3, 5, 5) # (num_feature_maps, num_color_channels, filter_width, filter_height)
W1_init = init_filter(W1_shape, poolsz)
b1_init = np.zeros(W1_shape[0], dtype=np.float32) # one bias per output feature map
# after conv will be of dimension 14 - 5 + 1 = 10
# after downsample 10 / 2 = 5
W2_shape = (50, 20, 5, 5) # (num_feature_maps, old_num_feature_maps, filter_width, filter_height)
W2_init = init_filter(W2_shape, poolsz)
b2_init = np.zeros(W2_shape[0], dtype=np.float32)
# vanilla ANN weights
W3_init = np.random.randn(W2_shape[0]*5*5, M) / np.sqrt(W2_shape[0]*5*5 + M)
b3_init = np.zeros(M, dtype=np.float32)
W4_init = np.random.randn(M, K) / np.sqrt(M + K)
b4_init = np.zeros(K, dtype=np.float32)
# define theano variables and expressions
X = T.tensor4('X', dtype='float32')
Y = T.matrix('T')
W1 = theano.shared(W1_init, 'W1')
b1 = theano.shared(b1_init, 'b1')
W2 = theano.shared(W2_init, 'W2')
b2 = theano.shared(b2_init, 'b2')
W3 = theano.shared(W3_init.astype(np.float32), 'W3')
b3 = theano.shared(b3_init, 'b3')
W4 = theano.shared(W4_init.astype(np.float32), 'W4')
b4 = theano.shared(b4_init, 'b4')
# momentum changes
dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32), 'dW1')
db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32), 'db1')
dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32), 'dW2')
db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32), 'db2')
dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32), 'dW3')
db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32), 'db3')
dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32), 'dW4')
db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32), 'db4')
# forward pass
Z1 = convpool(X, W1, b1)
Z2 = convpool(Z1, W2, b2)
Z3 = relu(Z2.flatten(ndim=2).dot(W3) + b3)
pY = T.nnet.softmax( Z3.dot(W4) + b4)
# define the cost function and prediction
params = (W1, b1, W2, b2, W3, b3, W4, b4)
reg_cost = reg*np.sum((param*param).sum() for param in params)
cost = -(Y * T.log(pY)).sum() + reg_cost
prediction = T.argmax(pY, axis=1)
# step 3: training expressions and functions
update_W1 = W1 + mu*dW1 - lr*T.grad(cost, W1)
update_b1 = b1 + mu*db1 - lr*T.grad(cost, b1)
update_W2 = W2 + mu*dW2 - lr*T.grad(cost, W2)
update_b2 = b2 + mu*db2 - lr*T.grad(cost, b2)
update_W3 = W3 + mu*dW3 - lr*T.grad(cost, W3)
update_b3 = b3 + mu*db3 - lr*T.grad(cost, b3)
update_W4 = W4 + mu*dW4 - lr*T.grad(cost, W4)
update_b4 = b4 + mu*db4 - lr*T.grad(cost, b4)
# update weight changes
update_dW1 = mu*dW1 - lr*T.grad(cost, W1)
update_db1 = mu*db1 - lr*T.grad(cost, b1)
update_dW2 = mu*dW2 - lr*T.grad(cost, W2)
update_db2 = mu*db2 - lr*T.grad(cost, b2)
update_dW3 = mu*dW3 - lr*T.grad(cost, W3)
update_db3 = mu*db3 - lr*T.grad(cost, b3)
update_dW4 = mu*dW4 - lr*T.grad(cost, W4)
update_db4 = mu*db4 - lr*T.grad(cost, b4)
train = theano.function(
inputs=[X, Y],
updates=[
(W1, update_W1),
(b1, update_b1),
(W2, update_W2),
(b2, update_b2),
(W3, update_W3),
(b3, update_b3),
(W4, update_W4),
(b4, update_b4),
(dW1, update_dW1),
(db1, update_db1),
(dW2, update_dW2),
(db2, update_db2),
(dW3, update_dW3),
(db3, update_db3),
(dW4, update_dW4),
(db4, update_db4),
],
)
# create another function for this because we want it over the whole dataset
get_prediction = theano.function(
inputs=[X, Y],
outputs=[cost, prediction],
)
t0 = datetime.now()
LL = []
for i in xrange(max_iter):
for j in xrange(n_batches):
Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]
Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]
train(Xbatch, Ybatch)
if j % print_period == 0:
cost_val, prediction_val = get_prediction(Xtest, Ytest_ind)
err = error_rate(prediction_val, Ytest)
print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, cost_val, err))
LL.append(cost_val)
print("Elapsed time:", (datetime.now() - t0))
plt.plot(LL)
plt.show()
# visualize W1 (20, 3, 5, 5)
W1_val = W1.get_value()
grid = np.zeros((8*5, 8*5))
m = 0
n = 0
for i in range(20):
for j in range(3):
filt = W1_val[i,j]
grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt
m += 1
if m >= 8:
m = 0
n += 1
plt.imshow(grid, cmap='gray')
plt.title("W1")
plt.show()
# visualize W2 (50, 20, 5, 5)
W2_val = W2.get_value()
grid = np.zeros((32*5, 32*5))
m = 0
n = 0
for i in range(50):
for j in range(20):
filt = W2_val[i,j]
grid[m*5:(m+1)*5,n*5:(n+1)*5] = filt
m += 1
if m >= 32:
m = 0
n += 1
plt.imshow(grid, cmap='gray')
plt.title("W2")
plt.show()
def forward(self, X): return relu(X.dot(self.W) + self.b)