def main():
# step 1: load the data, transform as needed
train, test = get_data()
# 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)
# step 2: 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 range(max_iter):
for j in range(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 main():
train, test = get_data()
# 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)
# Gradient descent parameters
max_iter = 6
print_period = 10
N = Xtrain.shape[0]
batch_sz = 500
n_batches = N // batch_sz
# Limit samples since input will always have to be same size
# Could also do N = N / batch_sz * batch_sz
Xtrain = Xtrain[:73000, ]
Ytrain = Ytrain[:73000]
Xtest = Xtest[:26000, ]
Ytest = Ytest[:26000]
Ytest_ind = Ytest_ind[:26000, ]
# Initial weights
M = 500
K = 10
poolsz = (2, 2)
# W1_shape = (filter_width, filter_height,
# num_color_channels, num_feature_maps)
W1_shape = (5, 5, 3, 20)
W1_init = init_filter(W1_shape, poolsz)
# One bias per output feature map
b1_init = np.zeros(W1_shape[-1], dtype=np.float32)
# W2_shape = (filter_width, filter_height,
# old_num_feature_maps, num_feature_maps)
W2_shape = (5, 5, 20, 50)
W2_init = init_filter(W2_shape, poolsz)
b2_init = np.zeros(W2_shape[-1], dtype=np.float32)
# Vanilla ANN weights
W3_init = np.random.randn(W2_shape[-1] * 8 * 8,
M) / np.sqrt(W2_shape[-1] * 8 * 8 + 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 variables and expressions
# Using None as the first shape element takes up too much RAM
X = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X')
T = tf.placeholder(tf.float32, shape=(batch_sz, K), name='T')
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
W4 = tf.Variable(W4_init.astype(np.float32))
b4 = tf.Variable(b4_init.astype(np.float32))
Z1 = convpool(X, W1, b1)
Z2 = convpool(Z1, W2, b2)
Z2_shape = Z2.get_shape().as_list()
Z2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])])
Z3 = tf.nn.relu(tf.matmul(Z2r, W3) + b3)
Yish = tf.matmul(Z3, W4) + b4
cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(logits=Yish, labels=T))
train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99,
momentum=0.9).minimize(cost)
# Use this to calculate the error rate
predict_op = tf.argmax(Yish, 1)
t0 = datetime.now()
LL = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_sz:(j * batch_sz + batch_sz), ]
Ybatch = Ytrain_ind[j * batch_sz:(j * batch_sz + batch_sz), ]
if len(Xbatch) == batch_sz:
session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
# Due to RAM limitations we need to have a
# fixed size input, so we have this ugly total
# cost and prediction computation.
test_cost = 0
prediction = np.zeros(len(Xtest))
for k in range(len(Xtest) // batch_sz):
Xtestbatch = Xtest[k * batch_sz:(k * batch_sz +
batch_sz), ]
Ytestbatch = Ytest_ind[k * batch_sz:(k * batch_sz +
batch_sz), ]
test_cost += session.run(cost,
feed_dict={
X: Xtestbatch,
T: Ytestbatch
})
prediction[k *
batch_sz:(k * batch_sz +
batch_sz)] = session.run(
predict_op,
feed_dict={X: Xtestbatch})
err = error_rate(prediction, Ytest)
print("Cost / err at iteration i = %d,\
j = %d: %.3f / %.3f" % (i, j, test_cost, err))
LL.append(test_cost)
print("Elapsed time:", (datetime.now() - t0))
plt.plot(LL)
plt.show()
# In[3]: train, test = get_data() # 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 # print len(Ytrain) 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) # gradient descent params max_iter = 6 print_period = 10 N = Xtrain.shape[0] batch_sz = 500 n_batches = N // batch_sz # limit samples since input will always have to be same size # you could also just do N = N / batch_sz * batch_sz
def main():
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 = convolve_flatten(train['X'].astype(np.float32))
Ytrain = train['y'].flatten() - 1
Xtrain, Ytrain = shuffle(Xtrain, Ytrain)
Ytrain_ind = y2indicator(Ytrain)
Xtest = convolve_flatten(test['X'].astype(np.float32))
Ytest = test['y'].flatten() - 1
Ytest_ind = y2indicator(Ytest)
# gradient descent params
max_iter = 20
print_period = 10
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
# initial weights
M1 = 1000 # hidden layer size
M2 = 500
K = 10
W1_init = np.random.randn(D, M1) / np.sqrt(D + M1)
b1_init = np.zeros(M1)
W2_init = np.random.randn(M1, M2) / np.sqrt(M1 + M2)
b2_init = np.zeros(M2)
W3_init = np.random.randn(M2, K) / np.sqrt(M2 + K)
b3_init = np.zeros(K)
# define variables and expressions
X = tf.placeholder(tf.float32, shape=(None, D), name='X')
T = tf.placeholder(tf.float32, shape=(None, K), name='T')
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
Z1 = tf.nn.relu(tf.matmul(X, W1) + b1)
Z2 = tf.nn.relu(tf.matmul(Z1, W2) + b2)
Yish = tf.matmul(Z2, W3) + b3
cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(logits=Yish, labels=T))
train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99,
momentum=0.9).minimize(cost)
# we'll use this to calculate the error rate
predict_op = tf.argmax(Yish, 1)
LL = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_sz:(j * batch_sz + batch_sz), ]
Ybatch = Ytrain_ind[j * batch_sz:(j * batch_sz + batch_sz), ]
session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
test_cost = session.run(cost,
feed_dict={
X: Xtest,
T: Ytest_ind
})
prediction = session.run(predict_op, feed_dict={X: Xtest})
err = error_rate(prediction, Ytest)
print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" %
(i, j, test_cost, err))
LL.append(test_cost)
plt.plot(LL)
plt.show()
def main():
# step 1: load the data, transform as needed
train, test = get_data()
# 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)
# step 2: 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 * 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 range(max_iter):
for j in range(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 main():
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 = convolve_flatten(train['X'].astype(np.float32))
Ytrain = train['y'].flatten() - 1
Xtrain, Ytrain = shuffle(Xtrain, Ytrain)
Ytrain_ind = y2indicator(Ytrain)
Xtest = convolve_flatten(test['X'].astype(np.float32))
Ytest = test['y'].flatten() - 1
Ytest_ind = y2indicator(Ytest)
# gradient descent params
max_iter = 20
print_period = 10
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
# initial weights
M1 = 1000 # hidden layer size
M2 = 500
K = 10
W1_init = np.random.randn(D, M1) / np.sqrt(D + M1)
b1_init = np.zeros(M1)
W2_init = np.random.randn(M1, M2) / np.sqrt(M1 + M2)
b2_init = np.zeros(M2)
W3_init = np.random.randn(M2, K) / np.sqrt(M2 + K)
b3_init = np.zeros(K)
# define variables and expressions
X = tf.placeholder(tf.float32, shape=(None, D), name='X')
T = tf.placeholder(tf.float32, shape=(None, K), name='T')
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
Z1 = tf.nn.relu( tf.matmul(X, W1) + b1 )
Z2 = tf.nn.relu( tf.matmul(Z1, W2) + b2 )
Yish = tf.matmul(Z2, W3) + b3
cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(
logits=Yish,
labels=T
)
)
train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost)
# we'll use this to calculate the error rate
predict_op = tf.argmax(Yish, 1)
LL = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]
Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]
session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
test_cost = session.run(cost, feed_dict={X: Xtest, T: Ytest_ind})
prediction = session.run(predict_op, feed_dict={X: Xtest})
err = error_rate(prediction, Ytest)
print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err))
LL.append(test_cost)
plt.plot(LL)
plt.show()
def main():
train, test = get_data()
Xtrain = rearrange(train['X'])
# train['y'] has shape (N,1) and vals ranging 1:10; need shape (N,) and ranging 0:9
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)
# grad. desc. params
max_iter = 20
print_period = 10
N = Xtrain.shape[0]
batch_sz = 500
n_batches = N // batch_sz
# make num samples divisible by batch_sz so all batches are same sz
Xtrain = Xtrain[:73000, ]
Ytrain = Ytrain[:73000]
Xtest = Xtest[:26000, ]
Ytest = Ytest[:26000]
Ytest_ind = Ytest_ind[:26000, ]
# initial weights
M = 500 # neurons in final layer
K = 10 # num classes
pool_sz = (2, 2)
W1_shape = (
5, 5, 3, 20
) # filter shape (width, height, num_color_channel, num_feature_maps(or filters))
W1_init = init_filter(W1_shape,
pool_sz) # pass in pool_sz for normalization
b1_init = np.zeros(W1_shape[-1], dtype=np.float32)
W2_shape = (5, 5, 20, 50)
W2_init = init_filter(W2_shape, pool_sz)
b2_init = np.zeros(W2_shape[-1], dtype=np.float32)
# vanilla NN weights
W3_init = np.random.randn(W2_shape[-1] * 8 * 8, M) / np.sqrt(
W2_shape[-1] * 8 * 8 + M) # 8 factor is result of
# final convolution (2 convpool layers 32x32--> 16x16 --> 8x8 output_sz)
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)
X = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X')
T = tf.placeholder(tf.float32, shape=(batch_sz, K), name='T')
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
W4 = tf.Variable(W4_init.astype(np.float32))
b4 = tf.Variable(b4_init.astype(np.float32))
Z1 = convpool(X, W1, b1)
Z2 = convpool(Z1, W2, b2)
Z2_shape = Z2.get_shape().as_list()
Z2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])
]) # reshape data to input to ANN layer
Z3 = tf.nn.relu(tf.matmul(Z2r, W3) + b3)
Yish = tf.matmul(Z3, W4) + b4
cost = tf.reduce_sum( # sums all elements in matrix
tf.nn.softmax_cross_entropy_with_logits(
# computes softmax with logits and labels
logits=Yish,
labels=T))
train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99,
momentum=0.9).minimize(cost)
predict_op = tf.argmax(Yish, 1)
t0 = datetime.now()
LL = []
init = tf.global_variables_initializer()
with tf.Session() as sess:
sess.run(init)
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_sz:(j * batch_sz + batch_sz), ]
Ybatch = Ytrain_ind[j * batch_sz:(j * batch_sz + batch_sz)]
if len(Xbatch) == batch_sz:
sess.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
# due to RAM limiations, we need to have fixed size input
# as a result, need total clost and pred computation
test_cost = 0
prediction = np.zeros(len(Xtest))
# since tf var X is expecting input of batch_sz, need to loop throug Xtest
# in iterations of batch_sz
for k in range(len(Xtest) // batch_sz):
Xtestbatch = Xtest[k * batch_sz:(k * batch_sz +
batch_sz), ]
Ytestbatch = Ytest_ind[k * batch_sz:(k * batch_sz +
batch_sz)]
test_cost += sess.run(cost,
feed_dict={
X: Xtestbatch,
T: Ytestbatch
})
prediction[k *
batch_sz:(k * batch_sz +
batch_sz)] = sess.run(
predict_op,
feed_dict={X: Xtestbatch})
err = error_rate(prediction, Ytest)
print(
"Cost / err at iteration i=%d, j=%d: %.3f / %.3f" %
(i, j, test_cost, err))
LL.append(test_cost)
print("Elapsed time:", (datetime.now() - t0))
plt.plot(LL)
plt.show()
def main():
train, test = get_data()
# 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
# print len(Ytrain)
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)
# gradient descent params
max_iter = 6
print_period = 10
N = Xtrain.shape[0]
batch_sz = 500
n_batches = N // batch_sz
# limit samples since input will always have to be same size
# you could also just do N = N / batch_sz * batch_sz
Xtrain = Xtrain[:73000,]
Ytrain = Ytrain[:73000]
Xtest = Xtest[:26000,]
Ytest = Ytest[:26000]
Ytest_ind = Ytest_ind[:26000,]
# print "Xtest.shape:", Xtest.shape
# print "Ytest.shape:", Ytest.shape
# initial weights
M = 500
K = 10
poolsz = (2, 2)
W1_shape = (5, 5, 3, 20) # (filter_width, filter_height, num_color_channels, num_feature_maps)
W1_init = init_filter(W1_shape, poolsz)
b1_init = np.zeros(W1_shape[-1], dtype=np.float32) # one bias per output feature map
W2_shape = (5, 5, 20, 50) # (filter_width, filter_height, old_num_feature_maps, num_feature_maps)
W2_init = init_filter(W2_shape, poolsz)
b2_init = np.zeros(W2_shape[-1], dtype=np.float32)
# vanilla ANN weights
W3_init = np.random.randn(W2_shape[-1]*8*8, M) / np.sqrt(W2_shape[-1]*8*8 + 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 variables and expressions
# using None as the first shape element takes up too much RAM unfortunately
X = tf.placeholder(tf.float32, shape=(batch_sz, 32, 32, 3), name='X')
T = tf.placeholder(tf.float32, shape=(batch_sz, K), name='T')
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
W4 = tf.Variable(W4_init.astype(np.float32))
b4 = tf.Variable(b4_init.astype(np.float32))
Z1 = convpool(X, W1, b1)
Z2 = convpool(Z1, W2, b2)
Z2_shape = Z2.get_shape().as_list()
Z2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])])
Z3 = tf.nn.relu( tf.matmul(Z2r, W3) + b3 )
Yish = tf.matmul(Z3, W4) + b4
cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(
logits=Yish,
labels=T
)
)
train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99, momentum=0.9).minimize(cost)
# we'll use this to calculate the error rate
predict_op = tf.argmax(Yish, 1)
t0 = datetime.now()
LL = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j*batch_sz:(j*batch_sz + batch_sz),]
Ybatch = Ytrain_ind[j*batch_sz:(j*batch_sz + batch_sz),]
if len(Xbatch) == batch_sz:
session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
# due to RAM limitations we need to have a fixed size input
# so as a result, we have this ugly total cost and prediction computation
test_cost = 0
prediction = np.zeros(len(Xtest))
for k in range(len(Xtest) // batch_sz):
Xtestbatch = Xtest[k*batch_sz:(k*batch_sz + batch_sz),]
Ytestbatch = Ytest_ind[k*batch_sz:(k*batch_sz + batch_sz),]
test_cost += session.run(cost, feed_dict={X: Xtestbatch, T: Ytestbatch})
prediction[k*batch_sz:(k*batch_sz + batch_sz)] = session.run(
predict_op, feed_dict={X: Xtestbatch})
err = error_rate(prediction, Ytest)
print("Cost / err at iteration i=%d, j=%d: %.3f / %.3f" % (i, j, test_cost, err))
LL.append(test_cost)
print("Elapsed time:", (datetime.now() - t0))
plt.plot(LL)
plt.show()
def main():
train, test = get_data()
# 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
# print len(Ytrain)
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)
# gradient descent params
max_iter = 6
print_period = 10
N = Xtrain.shape[0]
batch_sz = 500
n_batches = N // batch_sz
Xtrain = Xtrain[:73000, ]
Ytrain = Ytrain[:73000, ]
Xtest = Xtest[:26000, ]
Ytest = Ytest[:26000, ]
M = 500
K = 10
poolsz = (2, 2)
# W1
# (filter_width, filter_height, num_color_channels, num_feature_maps)
W1_shape = (5, 5, 3, 20)
W1_init = init_filter(W1_shape, poolsz)
b1_init = np.zeros(W1_shape[-1], dtype=np.float32)
# W2
# (filter_width, filter_height, num_color_channels, num_feature_maps)
W2_shape = (5, 5, 20, 50)
W2_init = init_filter(W2_shape, poolsz)
b2_init = np.zeros(W2_shape[-1], dtype=np.float32)
# W3. FeedForward Network
W3_init = np.random.randn(W2_shape[-1] * 8 * 8,
M) / np.sqrt(W2_shape[-1] * 8 * 8 + 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)
# Tensorflow variables
# using None as the first shape element takes up too much RAM unfortunately
# Init X
X = tf.placeholder(tf.float32,
shape=(batch_sz, Xtrain.shape[1], Xtrain.shape[2],
Xtrain.shape[3]))
# Init T
T = tf.placeholder(tf.float32, shape=(batch_sz, K), name='T')
# Init Weights and Biases
W1 = tf.Variable(W1_init.astype(np.float32))
b1 = tf.Variable(b1_init.astype(np.float32))
W2 = tf.Variable(W2_init.astype(np.float32))
b2 = tf.Variable(b2_init.astype(np.float32))
W3 = tf.Variable(W3_init.astype(np.float32))
b3 = tf.Variable(b3_init.astype(np.float32))
W4 = tf.Variable(W4_init.astype(np.float32))
b4 = tf.Variable(b4_init.astype(np.float32))
# FeedForward operation
Z1 = convpool(X, W1, b1)
Z2 = convpool(Z1, W2, b2)
Z2_shape = Z2.get_shape().as_list()
# Reshape to [N, W*H*C]
Z2r = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])])
# Output to 2nd convpool layer, flattened
# and multplied with FeedForward layer
Z3 = tf.nn.relu(tf.matmul(Z2r, W3) + b3)
# Output of FF layer, multiplied with Y output layer
Yish = tf.matmul(Z3, W4) + b4
cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(logits=Yish, labels=T))
train_op = tf.train.RMSPropOptimizer(0.0001, decay=0.99,
momentum=0.9).minimize(cost)
# we'll use this to calculate the error rate
predict_op = tf.argmax(Yish, 1)
t0 = datetime.now()
LL = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_sz:(j * batch_sz + batch_sz), ]
Ybatch = Ytrain_ind[j * batch_sz:(j * batch_sz + batch_sz), ]
if len(Xbatch) == batch_sz:
session.run(train_op, feed_dict={X: Xbatch, T: Ybatch})
if j % print_period == 0:
# due to RAM limitations we need to have a fixed size input
# so as a result, we have this ugly total cost and prediction computation
test_cost = 0
prediction = np.zeros(len(Xtest))
for k in range(len(Xtest) // batch_sz):
Xtestbatch = Xtest[k * batch_sz:(k * batch_sz +
batch_sz), ]
Ytestbatch = Ytest_ind[k * batch_sz:(k * batch_sz +
batch_sz), ]
# Accumulate test cost here
test_cost += session.run(cost,
feed_dict={
X: Xtestbatch,
T: Ytestbatch
})
# Only assign part of the prediction
prediction[k * batch_sz:(k * batch_sz +
batch_sz)] = session.run(
predict_op,
feed_dict={
X: Xtestbatch,
T: Ytestbatch
})
err = error_rate(prediction, Ytest)
print(
"Cost / err at iteration i=%d, j=%d: %.3f / %.3f" %
(i, j, test_cost, err))
LL.append(test_cost)
print("Elapsed time:", (datetime.now() - t0))
plt.plot(LL)
plt.show()
