def fit(self, X, Y, learning_rate=10e-7, reg=0*10e-22, epochs=120000, show_fig=False):
X, Y = shuffle(X, Y)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
N, D = X.shape
self.W = np.random.randn(D) / np.sqrt(D)
self.b = 0
costs = []
best_validation_error = 1
for i in xrange(epochs):
# forward propagation and cost calculation
pY = self.forward(X)
# gradient descent step
self.W -= learning_rate*(X.T.dot(pY - Y) + reg*self.W)
self.b -= learning_rate*((pY - Y).sum() + reg*self.b)
if i % 20 == 0:
pYvalid = self.forward(Xvalid)
c = sigmoid_cost(Yvalid, pYvalid)
costs.append(c)
e = error_rate(Yvalid, np.round(pYvalid))
print "i:", i, "cost:", c, "error:", e
if e < best_validation_error:
best_validation_error = e
print "best_validation_error:", best_validation_error
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, learning_rate=1e-7, reg=0., epochs=10000, show_fig=False):
X, Y = shuffle(X, Y)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
Tvalid = y2indicator(Yvalid)
X, Y = X[:-1000], Y[:-1000]
N, D = X.shape
K = len(set(Y))
T = y2indicator(Y)
self.W = np.random.randn(D, K) / np.sqrt(D)
self.b = np.zeros(K)
costs = []
best_validation_error = 1
for i in range(epochs):
# forward propagation and cost calculation
pY = self.forward(X)
# gradient descent step
self.W -= learning_rate*(X.T.dot(pY - T) + reg*self.W)
self.b -= learning_rate*((pY - T).sum(axis=0) + reg*self.b)
if i % 10 == 0:
pYvalid = self.forward(Xvalid)
c = cost(Tvalid, pYvalid)
costs.append(c)
e = error_rate(Yvalid, np.argmax(pYvalid, axis=1))
print("i:", i, "cost:", c, "error:", e)
if e < best_validation_error:
best_validation_error = e
print("best_validation_error:", best_validation_error)
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, learning_rate=1e-6, reg=1e-6, epochs=10000, show_fig=False):
X, Y = shuffle(X, Y)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
# Tvalid = y2indicator(Yvalid)
X, Y = X[:-1000], Y[:-1000]
N, D = X.shape
K = len(set(Y))
T = y2indicator(Y)
self.W1 = np.random.randn(D, self.M) / np.sqrt(D)
self.b1 = np.zeros(self.M)
self.W2 = np.random.randn(self.M, K) / np.sqrt(self.M)
self.b2 = np.zeros(K)
costs = []
best_validation_error = 1
for i in range(epochs):
# forward propagation and cost calculation
pY, Z = self.forward(X)
# gradient descent step
pY_T = pY - T
self.W2 -= learning_rate*(Z.T.dot(pY_T) + reg*self.W2)
self.b2 -= learning_rate*(pY_T.sum(axis=0) + reg*self.b2)
# dZ = pY_T.dot(self.W2.T) * (Z > 0) # relu
dZ = pY_T.dot(self.W2.T) * (1 - Z*Z) # tanh
self.W1 -= learning_rate*(X.T.dot(dZ) + reg*self.W1)
self.b1 -= learning_rate*(dZ.sum(axis=0) + reg*self.b1)
if i % 10 == 0:
pYvalid, _ = self.forward(Xvalid)
c = cost2(Yvalid, pYvalid)
costs.append(c)
e = error_rate(Yvalid, np.argmax(pYvalid, axis=1))
print("i:", i, "cost:", c, "error:", e)
if e < best_validation_error:
best_validation_error = e
print("best_validation_error:", best_validation_error)
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, learning_rate=5*10e-7, reg=1.0, epochs=10000, show_fig=False):
X, Y = shuffle(X, Y)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
N, D = X.shape
self.W1 = np.random.randn(D, self.M) / np.sqrt(D + self.M)
self.b1 = np.zeros(self.M)
self.W2 = np.random.randn(self.M) / np.sqrt(self.M)
self.b2 = 0
costs = []
best_validation_error = 1
for i in xrange(epochs):
# forward propagation and cost calculation
pY, Z = self.forward(X)
# gradient descent step
pY_Y = pY - Y
self.W2 -= learning_rate*(Z.T.dot(pY_Y) + reg*self.W2)
self.b2 -= learning_rate*((pY_Y).sum() + reg*self.b2)
# print "(pY_Y).dot(self.W2.T) shape:", (pY_Y).dot(self.W2.T).shape
# print "Z shape:", Z.shape
# dZ = np.outer(pY_Y, self.W2) * (Z > 0)
dZ = np.outer(pY_Y, self.W2) * (1 - Z*Z)
self.W1 -= learning_rate*(X.T.dot(dZ) + reg*self.W1)
self.b1 -= learning_rate*(np.sum(dZ, axis=0) + reg*self.b1)
if i % 20 == 0:
pYvalid, _ = self.forward(Xvalid)
c = sigmoid_cost(Yvalid, pYvalid)
costs.append(c)
e = error_rate(Yvalid, np.round(pYvalid))
print "i:", i, "cost:", c, "error:", e
if e < best_validation_error:
best_validation_error = e
print "best_validation_error:", best_validation_error
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, Xtest, Ytest, pretrain=True, epochs=1, batch_sz=100):
N = len(X)
# greedy layer-wise training of autoencoders
pretrain_epochs = 1
if not pretrain:
pretrain_epochs = 0
current_input = X
for ae in self.hidden_layers:
ae.fit(current_input, epochs=pretrain_epochs)
# create current_input for the next layer
current_input = ae.transform(current_input)
n_batches = N // batch_sz
costs = []
print("supervised training...")
for i in range(epochs):
print("epoch:", i)
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]
self.session.run(
self.train_op,
feed_dict={self.X: Xbatch, self.Y: Ybatch}
)
c, p = self.session.run(
(self.cost, self.prediction),
feed_dict={self.X: Xtest, self.Y: Ytest
})
error = error_rate(p, Ytest)
if j % 10 == 0:
print("j / n_batches:", j, "/", n_batches, "cost:", c, "error:", error)
costs.append(c)
plt.plot(costs)
plt.show()
def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100):
# cast to float32
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
mi = D
for mo in self.hidden_layer_sizes:
h = HiddenLayer(mi, mo)
self.hidden_layers.append(h)
mi = mo
# initialize logistic regression layer
W = init_weights((mo, K))
b = np.zeros(K, dtype=np.float32)
self.W = theano.shared(W)
self.b = theano.shared(b)
self.params = [self.W, self.b]
self.allWs = []
for h in self.hidden_layers:
self.params += h.params
self.allWs.append(h.W)
self.allWs.append(self.W)
X_in = T.matrix('X_in')
targets = T.ivector('Targets')
pY = self.forward(X_in)
cost = -T.mean(T.log(pY[T.arange(pY.shape[0]), targets]))
prediction = self.predict(X_in)
updates = momentum_updates(cost, self.params, mu, learning_rate)
train_op = theano.function(
inputs=[X_in, targets],
outputs=[cost, prediction],
updates=updates,
)
n_batches = N // batch_sz
costs = []
lastWs = [W.get_value() for W in self.allWs]
W_changes = []
print("supervised training...")
for i in range(epochs):
print("epoch:", i)
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
c, p = train_op(Xbatch, Ybatch)
if j % 100 == 0:
print("j / n_batches:", j, "/", n_batches, "cost:", c,
"error:", error_rate(p, Ybatch))
costs.append(c)
# log changes in all Ws
W_change = [
np.abs(W.get_value() - lastW).mean()
for W, lastW in zip(self.allWs, lastWs)
]
W_changes.append(W_change)
lastWs = [W.get_value() for W in self.allWs]
W_changes = np.array(W_changes)
plt.subplot(2, 1, 1)
for i in range(W_changes.shape[1]):
plt.plot(W_changes[:, i], label='layer %s' % i)
plt.legend()
# plt.show()
plt.subplot(2, 1, 2)
plt.plot(costs)
plt.show()
def batch_grad():
#get data and for test and train sets
X, Y = get_normalized_data()
#XTrain = X[:-1000, :]
#YTrain = Y[:-1000]
#YTrain_ind = y2indicator(YTrain)
#XTest = X[-1000:, :]
#YTest = Y[-1000:]
# = y2indicator(YTest)
Y_ind = y2indicator(Y)
batchSz = 500
#Initialize random weights
N, D = X.shape
K = len(set(Y))
M = 300
W1 = np.random.randn(D, M)
b1 = np.random.randn(M)
W2 = np.random.randn(M, K)
b2 = np.random.randn(K)
learning_rate = 10e-5
reg = 0.01
no_batches = int(N / batchSz)
print("No of bathces: ", no_batches)
for i in range(300):
for n in range(no_batches):
#get current batch
XBatch = X[n * batchSz:(n * batchSz + batchSz), :]
YBatch_ind = Y_ind[n * batchSz:(n * batchSz + batchSz), :]
#Forward prop
pY, Z = forward_relu(XBatch, W1, b1, W2, b2)
#Backprop
W2 += learning_rate * (derivative_w2(pY, YBatch_ind, Z) + reg * W2)
b2 += learning_rate * (derivative_b2(pY, YBatch_ind) + reg * b2)
W1 += learning_rate * (
derivative_w1(pY, YBatch_ind, W2, Z, XBatch) + reg * W1)
b1 += learning_rate * (derivative_b1(pY, YBatch_ind, W2, Z) +
reg * b1)
if n % 100 == 0:
#Forward prop
#pY, Z = forward_relu(XBatch, W1, b1, W2, b2)
YBatch = Y[n * batchSz:n * batchSz + batchSz]
P = np.argmax(pY, axis=1)
er = error_rate(P, YBatch)
c = cost(YBatch_ind, pY)
print("Loop: ", i, n, "Error rate: ", er, "Cost: ", c)
pY, Z = forward_relu(X, W1, b1, W2, b2)
p = np.argmax(pY, axis=1)
print("Final training error rate: ", error_rate(p, Y))
XTest = get_test_data()
pY, ZTest = forward_relu(XTest, W1, b1, W2, b2)
YTest = np.argmax(pY, axis=1)
f = open("test_result.csv", "w")
f.write("ImageId,Label\n")
n = YTest.shape[0]
for i in range(n):
f.write(str(i + 1) + "," + str(YTest[i]) + "\n")
f.close()
def main():
max_iter = 10
print_period = 10
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
reg = 0.01
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
M = 300
K = 10
W1_0 = np.random.randn(D, M) / np.sqrt(D)
b1_0 = np.zeros(M)
W2_0 = np.random.randn(M, K) / np.sqrt(M)
b2_0 = np.zeros(K)
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
# 1st moment
mW1 = 0
mb1 = 0
mW2 = 0
mb2 = 0
# 2nd moment
vW1 = 0
vb1 = 0
vW2 = 0
vb2 = 0
# hyperparams
lr0 = 0.001
beta1 = 0.9
beta2 = 0.999
eps = 1e-8
# 1. Adam
loss_adam = []
err_adam = []
t = 1
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), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
# gradients
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg * W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg * b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg * W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1
# new m
mW1 = beta1 * mW1 + (1 - beta1) * gW1
mb1 = beta1 * mb1 + (1 - beta1) * gb1
mW2 = beta1 * mW2 + (1 - beta1) * gW2
mb2 = beta1 * mb2 + (1 - beta1) * gb2
# new v
vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1
vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1
vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2
vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2
# bias correction
correction1 = 1 - beta1**t
hat_mW1 = mW1 / correction1
hat_mb1 = mb1 / correction1
hat_mW2 = mW2 / correction1
hat_mb2 = mb2 / correction1
correction2 = 1 - beta2**t
hat_vW1 = vW1 / correction2
hat_vb1 = vb1 / correction2
hat_vW2 = vW2 / correction2
hat_vb2 = vb2 / correction2
# update t
t += 1
# apply updates to the params
W1 = W1 - lr0 * hat_mW1 / np.sqrt(hat_vW1 + eps)
b1 = b1 - lr0 * hat_mb1 / np.sqrt(hat_vb1 + eps)
W2 = W2 - lr0 * hat_mW2 / np.sqrt(hat_vW2 + eps)
b2 = b2 - lr0 * hat_mb2 / np.sqrt(hat_vb2 + eps)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
loss_adam.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
err = error_rate(pY, Ytest)
err_adam.append(err)
print("Error rate:", err)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 2. RMSprop with momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
loss_rms = []
err_rms = []
# comparable hyperparameters for fair comparison
lr0 = 0.001
mu = 0.9
decay_rate = 0.999
eps = 1e-8
# rmsprop cache
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
# momentum
dW1 = 0
db1 = 0
dW2 = 0
db2 = 0
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), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg * W2
cache_W2 = decay_rate * cache_W2 + (1 - decay_rate) * gW2 * gW2
dW2 = mu * dW2 + (1 - mu) * lr0 * gW2 / (np.sqrt(cache_W2) + eps)
W2 -= dW2
gb2 = derivative_b2(Ybatch, pYbatch) + reg * b2
cache_b2 = decay_rate * cache_b2 + (1 - decay_rate) * gb2 * gb2
db2 = mu * db2 + (1 - mu) * lr0 * gb2 / (np.sqrt(cache_b2) + eps)
b2 -= db2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg * W1
cache_W1 = decay_rate * cache_W1 + (1 - decay_rate) * gW1 * gW1
dW1 = mu * dW1 + (1 - mu) * lr0 * gW1 / (np.sqrt(cache_W1) + eps)
W1 -= dW1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1
cache_b1 = decay_rate * cache_b1 + (1 - decay_rate) * gb1 * gb1
db1 = mu * db1 + (1 - mu) * lr0 * gb1 / (np.sqrt(cache_b1) + eps)
b1 -= db1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
loss_rms.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
err = error_rate(pY, Ytest)
err_rms.append(err)
print("Error rate:", err)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
plt.plot(loss_adam, label='adam')
plt.plot(loss_rms, label='rmsprop')
plt.legend()
plt.show()
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
# normalize X first
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
print "Performing logistic regression..."
Xtrain = X[:-1000, ]
Ytrain = Y[:-1000]
Xtest = X[-1000:, ]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(200):
p_y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for full GD:", datetime.now() - t0
# 2. stochastic
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(
1): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in xrange(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n, :].reshape(1, D)
y = tmpY[n, :].reshape(1, 10)
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (N / 2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for SGD:", datetime.now() - t0
# 3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in xrange(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches / 2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for batch GD:", datetime.now() - t0
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
# Update T
t += 1
# Apply Update to parameters
W1 = W1 - lr0 * hat_mW1 / np.sqrt(hat_vW1 + eps)
b1 = b1 - lr0 * hat_mb1 / np.sqrt(hat_vb1 + eps)
W2 = W2 - lr0 * hat_mW2 / np.sqrt(hat_vW2 + eps)
b1 = b2 - lr0 * hat_mb2 / np.sqrt(hat_vb2 + eps)
if j % print_period == 0:
pY, _ = forward(X_test, W1, b1, W2, b2)
l = cost(pY, Y_test_ind)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
err = error_rate(pY, Y_test)
err_adam.append(err)
print("Eror rate: ", err)
pY, _ = forward(X_test, W1, b1, W2, b2)
print("Final error rate: ", error_rate(pY, Y_test))
# 2. RMSprop with momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
loss_rms = []
err_rms = []
def fit(self,
X,
Y,
Xtest,
Ytest,
pretrain=True,
learning_rate=0.01,
mu=0.99,
reg=0.1,
epochs=1,
batch_sz=100):
# greedy layer-wise training of autoencoders
pretrain_epochs = 1
if not pretrain:
pretrain_epochs = 0
current_input = X
for ae in self.hidden_layers:
ae.fit(current_input, epochs=pretrain_epochs)
# create current_input for the next layer
current_input = ae.hidden_op(current_input)
# initialize logistic regression layer
N = len(Y)
K = len(set(Y))
W0 = init_weights((self.hidden_layers[-1].M, K))
self.W = theano.shared(W0, "W_logreg")
self.b = theano.shared(np.zeros(K), "b_logreg")
self.params = [self.W, self.b]
for ae in self.hidden_layers:
self.params += ae.forward_params
# for momentum
self.dW = theano.shared(np.zeros(W0.shape), "dW_logreg")
self.db = theano.shared(np.zeros(K), "db_logreg")
self.dparams = [self.dW, self.db]
for ae in self.hidden_layers:
self.dparams += ae.forward_dparams
X_in = T.matrix('X_in')
targets = T.ivector('Targets')
pY = self.forward(X_in)
# squared_magnitude = [(p*p).sum() for p in self.params]
# reg_cost = T.sum(squared_magnitude)
cost = -T.mean(T.log(pY[T.arange(pY.shape[0]),
targets])) #+ reg*reg_cost
prediction = self.predict(X_in)
cost_predict_op = theano.function(
inputs=[X_in, targets],
outputs=[cost, prediction],
)
updates = [(p, p + mu * dp - learning_rate * T.grad(cost, p))
for p, dp in zip(self.params, self.dparams)
] + [(dp, mu * dp - learning_rate * T.grad(cost, p))
for p, dp in zip(self.params, self.dparams)]
# updates = [(p, p - learning_rate*T.grad(cost, p)) for p in self.params]
train_op = theano.function(
inputs=[X_in, targets],
updates=updates,
)
n_batches = N / batch_sz
costs = []
print "supervised training..."
for i in xrange(epochs):
print "epoch:", i
X, Y = shuffle(X, Y)
for j in xrange(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
train_op(Xbatch, Ybatch)
the_cost, the_prediction = cost_predict_op(Xtest, Ytest)
error = error_rate(the_prediction, Ytest)
print "j / n_batches:", j, "/", n_batches, "cost:", the_cost, "error:", error
costs.append(the_cost)
plt.plot(costs)
plt.show()
def fit(self,
X,
Y,
learning_rate=1e-2,
mu=0.99,
decay=0.999,
reg=1e-3,
epochs=10,
batch_sz=100,
show_fig=False):
K = len(set(Y)) # won't work later b/c we turn it into indicator
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = y2indicator(Y).astype(np.float32)
# Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
Yvalid_flat = np.argmax(Yvalid, axis=1) # for calculating error rate
X, Y = X[:-1000], Y[:-1000]
# initialize hidden layers
N, D = X.shape
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W, b = init_weight_and_bias(M1, K)
self.W = tf.Variable(W.astype(np.float32))
self.b = tf.Variable(b.astype(np.float32))
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# set up theano functions and variables
tfX = tf.placeholder(tf.float32, shape=(None, D), name='X')
tfT = tf.placeholder(tf.float32, shape=(None, K), name='T')
act = self.forward(tfX)
rcost = reg * sum([tf.nn.l2_loss(p) for p in self.params])
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=act,
labels=tfT)) + rcost
prediction = self.predict(tfX)
train_op = tf.train.RMSPropOptimizer(learning_rate,
decay=decay,
momentum=mu).minimize(cost)
n_batches = N // batch_sz
costs = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
session.run(train_op, feed_dict={tfX: Xbatch, tfT: Ybatch})
if j % 20 == 0:
c = session.run(cost,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
costs.append(c)
p = session.run(prediction,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
e = error_rate(Yvalid_flat, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c,
"error rate:", e)
# TODO: ask lazy programmer how to make a score function.
# For this lecture: https://www.udemy.com/data-science-deep-learning-in-theano-tensorflow/learn/v4/t/lecture/5228492?start=0
if show_fig:
plt.plot(costs)
plt.show()
def main():
max_iter = 10
print_period = 10
X, Y = get_normalized_data()
reg = 0.01
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
M = 300
K = 10
W1_0 = np.random.randn(D, M) / np.sqrt(D)
b1_0 = np.zeros(M)
W2_0 = np.random.randn(M, K) / np.sqrt(M)
b2_0 = np.zeros(K)
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
# 1st moment
mW1 = 0
mb1 = 0
mW2 = 0
mb2 = 0
# 2nd moment
vW1 = 0
vb1 = 0
vW2 = 0
vb2 = 0
# hyperparams
lr0 = 0.001
beta1 = 0.9
beta2 = 0.999
eps = 1e-8
# 1. Adam
loss_adam = []
err_adam = []
t = 1
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
# gradients
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
# new m
mW1 = beta1 * mW1 + (1 - beta1) * gW1
mb1 = beta1 * mb1 + (1 - beta1) * gb1
mW2 = beta1 * mW2 + (1 - beta1) * gW2
mb2 = beta1 * mb2 + (1 - beta1) * gb2
# new v
vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1
vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1
vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2
vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2
# bias correction
correction1 = 1 - beta1 ** t
hat_mW1 = mW1 / correction1
hat_mb1 = mb1 / correction1
hat_mW2 = mW2 / correction1
hat_mb2 = mb2 / correction1
correction2 = 1 - beta2 ** t
hat_vW1 = vW1 / correction2
hat_vb1 = vb1 / correction2
hat_vW2 = vW2 / correction2
hat_vb2 = vb2 / correction2
# update t
t += 1
# apply updates to the params
W1 = W1 - lr0 * hat_mW1 / np.sqrt(hat_vW1 + eps)
b1 = b1 - lr0 * hat_mb1 / np.sqrt(hat_vb1 + eps)
W2 = W2 - lr0 * hat_mW2 / np.sqrt(hat_vW2 + eps)
b2 = b2 - lr0 * hat_mb2 / np.sqrt(hat_vb2 + eps)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
loss_adam.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
err = error_rate(pY, Ytest)
err_adam.append(err)
print("Error rate:", err)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 2. RMSprop with momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
loss_rms = []
err_rms = []
# comparable hyperparameters for fair comparison
lr0 = 0.001
mu = 0.9
decay_rate = 0.999
eps = 1e-8
# rmsprop cache
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
# momentum
dW1 = 0
db1 = 0
dW2 = 0
db2 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2
dW2 = mu * dW2 + (1 - mu) * lr0 * gW2 / (np.sqrt(cache_W2) + eps)
W2 -= dW2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2
db2 = mu * db2 + (1 - mu) * lr0 * gb2 / (np.sqrt(cache_b2) + eps)
b2 -= db2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1
dW1 = mu * dW1 + (1 - mu) * lr0 * gW1 / (np.sqrt(cache_W1) + eps)
W1 -= dW1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1
db1 = mu * db1 + (1 - mu) * lr0 * gb1 / (np.sqrt(cache_b1) + eps)
b1 -= db1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
loss_rms.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
err = error_rate(pY, Ytest)
err_rms.append(err)
print("Error rate:", err)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
plt.plot(loss_adam, label='adam')
plt.plot(loss_rms, label='rmsprop')
plt.legend()
plt.show()
def batch_grad():
#get data and for test and train sets
X, Y = get_normalized_data()
#XTrain = X[:-1000, :]
#YTrain = Y[:-1000]
#YTrain_ind = y2indicator(YTrain)
#XTest = X[-1000:, :]
#YTest = Y[-1000:]
# = y2indicator(YTest)
Y_ind = y2indicator(Y)
batchSz = 500
#Initialize random weights
N, D = X.shape
K = len(set(Y))
M = 300
W1 = np.random.randn(D, M)
b1 = np.random.randn(M)
W2 = np.random.randn(M, K)
b2 = np.random.randn(K)
learning_rate = 10e-5
no_batches = int(N / batchSz)
print("No of bathces: ", no_batches)
for i in range(300):
for n in range(no_batches):
#get current batch
XBatch = X[n * batchSz:(n * batchSz + batchSz), :]
#YBatch = Y[n*batchSz:n*batchSz + batchSz]
YBatch_ind = Y_ind[n * batchSz:(n * batchSz + batchSz), :]
#Forward prop
pY, Z = forward_relu(XBatch, W1, b1, W2, b2)
#Backprop
W2 += learning_rate * derivative_w2(pY, YBatch_ind, Z)
b2 += learning_rate * derivative_b2(pY, YBatch_ind)
W1 += learning_rate * derivative_w1(pY, YBatch_ind, W2, Z, XBatch)
b1 += learning_rate * derivative_b1(pY, YBatch_ind, W2, Z)
if n % 100 == 0:
#Forward prop
#pY, Z = forward_relu(XBatch, W1, b1, W2, b2)
YBatch = Y[n * batchSz:n * batchSz + batchSz]
P = np.argmax(pY, axis=1)
er = error_rate(P, YBatch)
c = cost(YBatch_ind, pY)
print("Loop: ", i, n, "Error rate: ", er, "Cost: ", c)
# pY, Z = forward_prop(XTrain, W1, b1, W2, b2)
# P = np.argmax(pY, axis=1)
# print("Final training error rate: ", error_rate(P, YTrain))
#
# pY, Z = forward_prop(XTest, W1, b1, W2, b2)
# P = np.argmax(pY, axis=1)
# print("Final testing error rate: ", error_rate(P, YTest))
pY, Z = forward_relu(X, W1, b1, W2, b2)
p = np.argmax(pY, axis=1)
print("Final Final training error rate: ", error_rate(p, Y))
def main():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(50):
p_y = forward(Xtrain, W, b)
W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)
b += lr*(gradb(Ytrain_ind, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for full GD:", datetime.now() - t0)
# 2. stochastic
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(50): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in range(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n,:].reshape(1,D)
y = tmpY[n,:].reshape(1,10)
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for SGD:", datetime.now() - t0)
# 3. batch
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N // batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j*batch_sz:(j*batch_sz + batch_sz),:]
y = tmpY[j*batch_sz:(j*batch_sz + batch_sz),:]
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for batch GD:", datetime.now() - t0)
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def main():
# 3 scenarios
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
max_iter = 15
print_period = 10
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
lr = 0.0001
reg = 0.001
# Xtrain = X[:-1000, ]
# Ytrain = Y[:-1000]
# Xtest = X[-1000:, ]
# Ytest = Y[-1000:, ]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = int(N / batch_sz)
M = 300
K = 10
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
# Batch
losses_batch = []
error_batch = []
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), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
W2 -= lr * (derivative_w2(Z, Ybatch, pYbatch) + reg * W2)
b2 -= lr * (derivative_b2(Ybatch, pYbatch) + reg * b2)
W1 -= lr * (derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) +
reg * W1)
b1 -= lr * (derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1)
# A = ' '
# A = u"\n| |\n|----------------------| \n(\\__/) || \n(• v •) || \n / D"
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_batch.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
# print(
# u"|----------------------|\n| | \n Costo
# en i=%d, j=%d: \n %.6f" % (i, j, l) + A)
e = error_rate(pY, Ytest)
error_batch.append(e)
print("Ratio de error:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate: ", error_rate(pY, Ytest))
# Momentum
W1 = W1_0.copy()
b1 = b1.copy()
W2 = W2.copy()
b2 = b2.copy()
losses_rms = []
errors_rms = []
lr0 = 0.001
cacheW2 = 0
cacheb2 = 0
cacheW1 = 0
cacheb1 = 0
decay_rate = 0.99
eps = 0.000001
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), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# gradients
gW2 = (derivative_w2(Z, Ybatch, pYbatch) + reg * W2)
gb2 = (derivative_b2(Ybatch, pYbatch) + reg * b2)
gW1 = (derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg * W1)
gb1 = (derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1)
# caches
cacheW2 = decay_rate * cacheW2 + (1 - decay_rate) * gW2 * gW2
cacheb2 = decay_rate * cacheb2 + (1 - decay_rate) * gb2 * gb2
cacheW1 = decay_rate * cacheW1 + (1 - decay_rate) * gW1 * gW1
cacheb1 = decay_rate * cacheb1 + (1 - decay_rate) * gb1 * gb1
W2 -= lr0 * gW2 / (np.sqrt(cacheW2) + eps)
b2 -= lr0 * gb2 / (np.sqrt(cacheb2) + eps)
W1 -= lr0 * gW1 / (np.sqrt(cacheW1) + eps)
b1 -= lr0 * gb1 / (np.sqrt(cacheb1) + eps)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_rms.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_rms.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate: ", error_rate(pY, Ytest))
plt.plot(losses_batch, label='batch')
plt.plot(losses_rms, label='rmsprop')
plt.legend()
plt.show()
def fit(self,
X,
Y,
Xtest,
Ytest,
pretrain=True,
train_head_only=False,
learning_rate=0.1,
mu=0.99,
reg=0.0,
epochs=1,
batch_sz=100):
# cast to float32
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
reg = np.float32(reg)
# greedy layer-wise training of autoencoders
pretrain_epochs = 2
if not pretrain:
pretrain_epochs = 0
current_input = X
for ae in self.hidden_layers:
ae.fit(current_input, epochs=pretrain_epochs)
# create current_input for the next layer
current_input = ae.hidden_op(current_input)
# initialize logistic regression layer
N = len(Y)
K = len(set(Y))
W0 = init_weights((self.hidden_layers[-1].M, K))
self.W = theano.shared(W0, "W_logreg")
self.b = theano.shared(np.zeros(K, dtype=np.float32), "b_logreg")
self.params = [self.W, self.b]
if not train_head_only:
for ae in self.hidden_layers:
self.params += ae.forward_params
X_in = T.matrix('X_in')
targets = T.ivector('Targets')
pY = self.forward(X_in)
squared_magnitude = [(p * p).sum() for p in self.params]
reg_cost = T.sum(squared_magnitude)
cost = -T.mean(T.log(pY[T.arange(pY.shape[0]),
targets])) + reg * reg_cost
prediction = self.predict(X_in)
cost_predict_op = theano.function(
inputs=[X_in, targets],
outputs=[cost, prediction],
)
updates = momentum_updates(cost, self.params, mu, learning_rate)
train_op = theano.function(
inputs=[X_in, targets],
updates=updates,
)
n_batches = N // batch_sz
costs = []
print("supervised training...")
for i in range(epochs):
print("epoch:", i)
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
train_op(Xbatch, Ybatch)
the_cost, the_prediction = cost_predict_op(Xtest, Ytest)
error = error_rate(the_prediction, Ytest)
print("j / n_batches:", j, "/", n_batches, "cost:", the_cost,
"error:", error)
costs.append(the_cost)
plt.plot(costs)
plt.show()
def fit(self,
X,
Y,
Xvalid,
Yvalid,
learning_rate=1e-3,
mu=0.99,
decay=0.999,
reg=1e-3,
epoches=10,
batch_sz=100,
show_fig=False):
# step1. get data
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = y2indicator(Y).astype(np.int32)
Xvalid = Xvalid.astype(np.float32)
Yvalid_vector = Yvalid.astype(np.int32)
Yvalid = y2indicator(Yvalid).astype(np.int32)
# step1.1 initialize each layer and parameters(with tf.Variable) of NN and keep them in a list
N, D = X.shape
M1 = D
K = Y.shape[1]
self.hidden_layers = [] # for saving HiddenLayer object
count = 0
for M2 in self.hidden_layer_size: # 這邊做出第一層~ 倒數第二層 hidden layer 的HiddenLayer object
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W, b = init_weight_and_bias(M1, K) # 最後輸出層的 weight
self.W = tf.Variable(W.astype(np.float32))
self.b = tf.Variable(b.astype(np.float32))
# collect all the parameters that we are going to use grediant descent
self.params = [self.W, self.b]
for layer in self.hidden_layers:
self.params += layer.params
# step1.2 tf.palceholder
tfX = tf.placeholder(tf.float32, shape=(None, D), name="X")
tfT = tf.placeholder(tf.float32, shape=(None, K), name="T")
# step2. model
act = self.forward(
tfX) # 最後不經過softmax喔，也不通過其他的activation fun(因為tf 就是這樣要求的)
# step3. cost function
rcost = reg * sum([tf.nn.l2_loss(p) for p in self.params])
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits_v2(logits=act,
labels=tfT)) + rcost
prediction_op = self.predict(tfX)
# step4. solver
traiin_op = tf.train.RMSPropOptimizer(learning_rate=learning_rate,
momentum=mu,
decay=decay).minimize(cost)
init = tf.global_variables_initializer()
n_batches = N // batch_sz
costs = []
with tf.Session() as sess:
sess.run(init)
for i in range(epoches):
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j + 1) * batch_sz, ]
Ybatch = Y[j * batch_sz:(j + 1) * batch_sz, ]
sess.run(traiin_op, feed_dict={tfX: Xbatch, tfT: Ybatch})
if j % 50 == 0:
cost_val = sess.run(cost,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
costs.append(cost_val)
preds = sess.run(prediction_op,
feed_dict={tfX: Xvalid})
err = error_rate(Yvalid_vector, preds)
print("i:", i, "j:", j, "nb:", n_batches, "cost:",
cost_val, "error rate:", err)
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, lr=10e-5, mu=0.99, reg=10e-7, decay=0.99999, eps=10e-3, batch_sz=30, epochs=100, show_fig=True):
lr = np.float32(lr)
mu = np.float32(mu)
reg = np.float32(reg)
decay = np.float32(decay)
eps = np.float32(eps)
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
# initialize convpool layers
N, c, d, d = X.shape
mi = c
outw = d
outh = d
self.convpool_layers = []
for mo, fw, fh in self.convpool_layer_sizes:
layer = ConvPoolLayer(mi, mo, fw, fh)
self.convpool_layers.append(layer)
outw = (outw - fw + 1) / 2
outh = (outh - fh + 1) / 2
mi = mo
# initialize mlp layers
K = len(set(Y))
self.hidden_layers = []
M1 = self.convpool_layer_sizes[-1][0]*outw*outh # size must be same as output of last convpool layer
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
# logistic regression layer
W, b = init_weight_and_bias(M1, K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for c in self.convpool_layers:
self.params += c.params
for h in self.hidden_layers:
self.params += h.params
# for momentum
dparams = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params]
# for rmsprop
cache = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params]
# set up theano functions and variables
thX = T.tensor4('X', dtype='float32')
thY = T.ivector('Y')
pY = self.forward(thX)
rcost = reg*T.sum([(p*p).sum() for p in self.params])
cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
prediction = self.predict(thX)
cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction])
# updates = [
# (c, decay*c + (np.float32(1)-decay)*T.grad(cost, p)*T.grad(cost, p)) for p, c in zip(self.params, cache)
# ] + [
# (p, p + mu*dp - lr*T.grad(cost, p)/T.sqrt(c + eps)) for p, c, dp in zip(self.params, cache, dparams)
# ] + [
# (dp, mu*dp - lr*T.grad(cost, p)/T.sqrt(c + eps)) for p, c, dp in zip(self.params, cache, dparams)
# ]
# momentum only
updates = [
(p, p + mu*dp - lr*T.grad(cost, p)) for p, dp in zip(self.params, dparams)
] + [
(dp, mu*dp - lr*T.grad(cost, p)) for p, dp in zip(self.params, dparams)
]
train_op = theano.function(
inputs=[thX, thY],
updates=updates
)
n_batches = N / batch_sz
costs = []
for i in xrange(epochs):
X, Y = shuffle(X, Y)
for j in xrange(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]
train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print "i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e
if show_fig:
plt.plot(costs)
plt.show()
def main():
max_iter = 20 # make it 30 for sigmoid
print_period = 10
X, Y = get_normalized_data()
lr = 0.00004
reg = 0.01
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N / batch_sz
M = 300
K = 10
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
# 1. const
# cost = -16
LL_batch = []
CR_batch = []
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
b2 -= lr*(derivative_b2(Ybatch, pYbatch) + reg*b2)
W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_batch.append(ll)
print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)
err = error_rate(pY, Ytest)
CR_batch.append(err)
print "Error rate:", err
pY, _ = forward(Xtest, W1, b1, W2, b2)
print "Final error rate:", error_rate(pY, Ytest)
# 2. RMSprop
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
LL_rms = []
CR_rms = []
lr0 = 0.001 # if you set this too high you'll get NaN!
cache_W2 = 0
cache_b2 = 0
cache_W1 = 0
cache_b1 = 0
decay_rate = 0.999
eps = 0.0000000001
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2
W2 -= lr0 * gW2 / (np.sqrt(cache_W2) + eps)
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2
b2 -= lr0 * gb2 / (np.sqrt(cache_b2) + eps)
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1
W1 -= lr0 * gW1 / (np.sqrt(cache_W1) + eps)
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1
b1 -= lr0 * gb1 / (np.sqrt(cache_b1) + eps)
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_rms.append(ll)
print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)
err = error_rate(pY, Ytest)
CR_rms.append(err)
print "Error rate:", err
pY, _ = forward(Xtest, W1, b1, W2, b2)
print "Final error rate:", error_rate(pY, Ytest)
plt.plot(LL_batch, label='const')
plt.plot(LL_rms, label='rms')
plt.legend()
plt.show()
def main():
#Get train and test data
XTrain, YTrain = get_train_data()
YTrain_ind = y2indicator(YTrain)
XTrain = reshape(XTrain)
XTest, YTest = get_test_data()
YTest_ind = y2indicator(YTest)
XTest = reshape(XTest)
N,K = YTrain_ind.shape
M=100
lr = np.float32(0.000001)
reg = np.float32(0.01)
mu = np.float32(0.99)
poolsize = (2,2)
batch_sz = 500
no_batches = int(N/batch_sz)
#Initial random weight values
W1_shape = (20, 3, 5, 5)
W1_init = init_filter(W1_shape, poolsize)
b1_init = np.zeros([W1_shape[0]])
W2_shape = (50, 20, 5, 5)
W2_init = init_filter(W2_shape, poolsize)
b2_init = np.zeros([W2_shape[0]])
W3_init = np.random.randn(W2_shape[0]*5*5, M)/np.sqrt(W2_shape[0]*5*5 + M)
b3_init = np.zeros([M])
W4_init = np.random.randn(M,K)/np.sqrt(M+K)
b4_init = np.zeros([K])
#Create theano variables
X = T.tensor4('X', dtype='float32') #inputs
Y = T.matrix('Y')
W1 = theano.shared(W1_init.astype(np.float32), 'W1') #Weights
b1 = theano.shared(b1_init.astype(np.float32), 'b1')
W2 = theano.shared(W2_init.astype(np.float32), 'W2')
b2 = theano.shared(b2_init.astype(np.float32), 'b2')
W3 = theano.shared(W3_init.astype(np.float32), 'W3')
b3 = theano.shared(b3_init.astype(np.float32), 'b3')
W4 = theano.shared(W4_init.astype(np.float32), 'W4')
b4 = theano.shared(b4_init.astype(np.float32), 'b4')
dW1 = theano.shared(np.zeros(W1_init.shape, dtype=np.float32)) #Momentum variables
db1 = theano.shared(np.zeros(b1_init.shape, dtype=np.float32))
dW2 = theano.shared(np.zeros(W2_init.shape, dtype=np.float32))
db2 = theano.shared(np.zeros(b2_init.shape, dtype=np.float32))
dW3 = theano.shared(np.zeros(W3_init.shape, dtype=np.float32))
db3 = theano.shared(np.zeros(b3_init.shape, dtype=np.float32))
dW4 = theano.shared(np.zeros(W4_init.shape, dtype=np.float32))
db4 = theano.shared(np.zeros(b4_init.shape, dtype=np.float32))
#Forward prop equations
Z1 = convpool(X, W1, b1) #2 Conv-pool layer
Z2 = convpool(Z1, W2, b2)
Z3 = relu(Z2.flatten(ndim=2).dot(W3) + b3) #Fully connected NN
P = T.nnet.softmax(Z3.dot(W4) + b4)
#Cost and prediction equations
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(P)).sum() + reg_cost
pred = T.argmax(P, axis=1)
#Update Weights
W1_update = W1 + mu*dW1 + lr*T.grad(cost, W1)
b1_update = b1 + mu*db1 + lr*T.grad(cost,b1)
W2_update = W2 + mu*dW2 + lr*T.grad(cost, W2)
b2_update = b2 + mu*db2 + lr*T.grad(cost,b2)
W3_update = W3 + mu*dW3 + lr*T.grad(cost, W3)
b3_update = b3 + mu*db3 + lr*T.grad(cost,b3)
W4_update = W4 + mu*dW4 + lr*T.grad(cost, W4)
b4_update = b4 + mu*db4 + lr*T.grad(cost,b4)
#Gradient updates for momentum
dW1_update = mu*dW1 + lr*T.grad(cost, W1)
db1_update = mu*db1 + lr*T.grad(cost, b1)
dW2_update = mu*dW2 + lr*T.grad(cost, W2)
db2_update = mu*db2 + lr*T.grad(cost, b2)
dW3_update = mu*dW3 + lr*T.grad(cost, W3)
db3_update = mu*db3 + lr*T.grad(cost, b3)
dW4_update = mu*dW4 + lr*T.grad(cost, W4)
db4_update = mu*db4 + lr*T.grad(cost, b4)
#Train function
train = theano.function(
inputs=[X,Y],
updates=[ (W1, W1_update),
(b1, b1_update),
(W2, W2_update),
(b2, b2_update),
(W3, W3_update),
(b3, b3_update),
(W4, W4_update),
(b4, b4_update),
(dW1, dW1_update),
(db1, db1_update),
(dW2, dW2_update),
(db2, db2_update),
(dW3, dW3_update),
(db3, db3_update),
(dW4, dW4_update),
(db4, db4_update),
])
#Get cost and prediction function
get_res = theano.function(
inputs=[X,Y],
outputs=[cost,pred])
#Run batch gradient descent
costs = []
for i in range(400):
for n in range(no_batches):
#get current batches
XBatch = XTrain[n*batch_sz:(n*batch_sz + batch_sz), :]
YBatch_ind = YTrain_ind[n*batch_sz:(n*batch_sz + batch_sz), :]
#Forward prop
train(XBatch, YBatch_ind)
if(n%200 == 0):
#YBatch = YTrain[n*batch_sz:(n*batch_sz + batch_sz)]
c, P = get_res(XTest, YTest_ind)
er = error_rate(P, YTest)
print("Iteration: ", i, "Cost: ", c, "Error rate: ", er)
def score(self,X,Y):
prediction = self.forward(X)
return (1 - error_rate(Y,prediction))
def main():
# compare 3 scenarios:
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
max_iter = 20 # make it 30 for sigmoid
print_period = 10
X, Y = get_normalized_data()
lr = 0.00004
reg = 0.01
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N / batch_sz
M = 300
K = 10
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
# 1. batch
# cost = -16
LL_batch = []
CR_batch = []
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
b2 -= lr*(derivative_b2(Ybatch, pYbatch) + reg*b2)
W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_batch.append(ll)
print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)
err = error_rate(pY, Ytest)
CR_batch.append(err)
print "Error rate:", err
pY, _ = forward(Xtest, W1, b1, W2, b2)
print "Final error rate:", error_rate(pY, Ytest)
# 2. batch with momentum
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
LL_momentum = []
CR_momentum = []
mu = 0.9
dW2 = 0
db2 = 0
dW1 = 0
db1 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
dW2 = mu*dW2 - lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
W2 += dW2
db2 = mu*db2 - lr*(derivative_b2(Ybatch, pYbatch) + reg*b2)
b2 += db2
dW1 = mu*dW1 - lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
W1 += dW1
db1 = mu*db1 - lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
b1 += db1
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_momentum.append(ll)
print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)
err = error_rate(pY, Ytest)
CR_momentum.append(err)
print "Error rate:", err
pY, _ = forward(Xtest, W1, b1, W2, b2)
print "Final error rate:", error_rate(pY, Ytest)
# 3. batch with Nesterov momentum
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
LL_nest = []
CR_nest = []
mu = 0.9
dW2 = 0
db2 = 0
dW1 = 0
db1 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
dW2 = mu*mu*dW2 - (1 + mu)*lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
W2 += dW2
db2 = mu*mu*db2 - (1 + mu)*lr*(derivative_b2(Ybatch, pYbatch) + reg*b2)
b2 += db2
dW1 = mu*mu*dW1 - (1 + mu)*lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
W1 += dW1
db1 = mu*mu*db1 - (1 + mu)*lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
b1 += db1
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_nest.append(ll)
print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll)
err = error_rate(pY, Ytest)
CR_nest.append(err)
print "Error rate:", err
pY, _ = forward(Xtest, W1, b1, W2, b2)
print "Final error rate:", error_rate(pY, Ytest)
plt.plot(LL_batch, label="batch")
plt.plot(LL_momentum, label="momentum")
plt.plot(LL_nest, label="nesterov")
plt.legend()
plt.show()
def main():
# compare 2 scenarios:
# 1. batch GD with RMSProp and momentum
# 2. Adam GD
max_iter = 20
print_period = 10
X, Y = get_normalized_data()
reg = 0.01
Xtrain, Ytrain = X[:-1000, :], Y[:-1000]
Xtest, Ytest = X[-1000:, :], Y[-1000:]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
M = 300 # number of hidden layer units
K = len(set(Ytrain))
batch_size = 500
n_batches = N // batch_size
# randomly initialize weights:
W1_0 = np.random.randn(D, M) / np.sqrt(D)
b1_0 = np.zeros(M)
W2_0 = np.random.randn(M, K) / np.sqrt(M)
b2_0 = np.zeros(K)
# 1. batch GD with RMSProp and momentum:
print('\nperforming batch GD with RMSProp and momentum...')
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
LL_rm = []
CR_rm = []
# hyperparams:
lr0 = 0.001
#lr0 = 0.0001
mu = 0.9
decay = 0.999
eps = 10e-9
# momentum (velocity terms):
dW1 = 0
db1 = 0
dW2 = 0
db2 = 0
# rms-prop cache (with no bias correction):
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
t0 = datetime.now()
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_size:(j + 1) * batch_size, :]
Ybatch = Ytrain_ind[j * batch_size:(j + 1) * batch_size, :]
p_Ybatch, Z = forward(Xbatch, W1, b1, W2, b2)
#print(Z.shape, p_Ybatch.shape, Ybatch.shape)
#print('First batch cost:', cost(p_Ybatch, Ybatch))
# updates:
# (note: we utilize a bit different version of momentum)
gW2 = (derivative_W2(Z, Ybatch, p_Ybatch) + reg * W2)
cache_W2 = decay * cache_W2 + (1 - decay) * gW2 * gW2
dW2 = mu * dW2 + (1 - mu) * lr0 * gW2 / (np.sqrt(cache_W2 + eps))
W2 -= dW2
#dW2 = mu*dW2 - lr0*gW2 / (np.sqrt(cache_W2) + eps)
#W2 += dW2
gb2 = (derivative_b2(Ybatch, p_Ybatch) + reg * b2)
cache_b2 = decay * cache_b2 + (1 - decay) * gb2 * gb2
db2 = mu * db2 + (1 - mu) * lr0 * gb2 / (np.sqrt(cache_b2 + eps))
b2 -= db2
#db2 = mu*db2 - lr0*gb2 / (np.sqrt(cache_b2) + eps)
#b2 += db2
gW1 = (derivative_W1(Xbatch, Z, Ybatch, p_Ybatch, W2) + reg * W1)
cache_W1 = decay * cache_W1 + (1 - decay) * gW1 * gW1
dW1 = mu * dW1 + (1 - mu) * lr0 * gW1 / (np.sqrt(cache_W1 + eps))
W1 -= dW1
#dW1 = mu*dW1 - lr0*gW1 / (np.sqrt(cache_W1) + eps)
#W1 += dW1
gb1 = (derivative_b1(Z, Ybatch, p_Ybatch, W2) + reg * b1)
cache_b1 = decay * cache_b1 + (1 - decay) * gb1 * gb1
db1 = mu * db1 + (1 - mu) * lr0 * gb1 / (np.sqrt(cache_b1 + eps))
b1 -= db1
#db1 = mu*db1 - lr0*gb1 / (np.sqrt(cache_b1) + eps)
#b1 += db1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
#print('pY:', pY)
ll = cost(pY, Ytest_ind)
LL_rm.append(ll)
print('\ni: %d, j: %d, cost: %.6f' % (i, j, ll))
error = error_rate(pY, Ytest)
CR_rm.append(error)
print('error rate:', error)
dt1 = datetime.now() - t0
pY, _ = forward(Xtest, W1, b1, W2, b2)
print('\nFinal error rate:', error_rate(pY, Ytest))
print('Elapsed time for batch GD with RMSProp and momentum:', dt1)
# plot the cost
plt.plot(LL_rm)
plt.title('Cost for batch GD with RMSProp and momentum')
plt.show()
# 2. Adam optimizer
print('\nperforming Adam optimizer...')
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
# hyperparams:
lr = 0.001
beta1 = 0.9
beta2 = 0.999
eps = 10e-9
# 1st moment:
mW1 = 0
mb1 = 0
mW2 = 0
mb2 = 0
# 2nd moment:
vW1 = 0
vb1 = 0
vW2 = 0
vb2 = 0
LL_adam = []
CR_adam = []
t0 = datetime.now()
t = 1 # index; used instead of j, because j starts with 0
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_size:(j + 1) * batch_size, :]
Ybatch = Ytrain_ind[j * batch_size:(j + 1) * batch_size, :]
p_Ybatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates:
# gradients:
gW2 = derivative_W2(Z, Ybatch, p_Ybatch) + reg * W2
gb2 = derivative_b2(Ybatch, p_Ybatch) + reg * b2
gW1 = derivative_W1(Xbatch, Z, Ybatch, p_Ybatch, W2) + reg * W1
gb1 = derivative_b1(Z, Ybatch, p_Ybatch, W2) + reg * b1
# 1st moment:
mW2 = beta1 * mW2 + (1 - beta1) * gW2
mb2 = beta1 * mb2 + (1 - beta1) * gb2
mW1 = beta1 * mW1 + (1 - beta1) * gW1
mb1 = beta1 * mb1 + (1 - beta1) * gb1
# 2nd moment:
vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2
vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2
vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1
vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1
# bias correction:
mW2_bc = mW2 / (1 - beta1**t)
mb2_bc = mb2 / (1 - beta1**t)
mW1_bc = mW1 / (1 - beta1**t)
mb1_bc = mb1 / (1 - beta1**t)
vW2_bc = vW2 / (1 - beta2**t)
vb2_bc = vb2 / (1 - beta2**t)
vW1_bc = vW1 / (1 - beta2**t)
vb1_bc = vb1 / (1 - beta2**t)
# weights and biases (parameters):
W2 = W2 - lr * mW2_bc / np.sqrt(vW2_bc + eps)
b2 = b2 - lr * mb2_bc / np.sqrt(vb2_bc + eps)
W1 = W1 - lr * mW1_bc / np.sqrt(vW1_bc + eps)
b1 = b1 - lr * mb1_bc / np.sqrt(vb1_bc + eps)
t += 1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
ll = cost(pY, Ytest_ind)
LL_adam.append(ll)
print('\ni: %d, j: %d, cost: %.6f' % (i, j, ll))
error = error_rate(pY, Ytest)
CR_adam.append(error)
print('error rate:', error)
dt2 = datetime.now() - t0
pY, _ = forward(Xtest, W1, b1, W2, b2)
print('\nFinal error rate:', error_rate(pY, Ytest))
print('Elapsed time for Adam optimizer:', dt2)
# plot the cost
plt.plot(LL_adam)
plt.title('Cost for Adam optimizer')
plt.show()
# plot costs from the two experiments together:
plt.plot(LL_rm, label='RMSProp with momentum')
plt.plot(LL_adam, label='Adam optimizer')
plt.title('Cost')
plt.legend()
plt.show()
def fit(self, X, Y, learning_rate=1e-3, mu=0.9, decay=0.9, reg=0, eps=1e-10, epochs=100, batch_sz=30, show_fig=False):
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
decay = np.float32(decay)
reg = np.float32(reg)
eps = np.float32(eps)
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
# initialize hidden layers
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W, b = init_weight_and_bias(M1, K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# set up theano functions and variables
thX = T.fmatrix('X')
thY = T.ivector('Y')
pY = self.th_forward(thX)
rcost = reg*T.sum([(p*p).sum() for p in self.params])
cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
prediction = self.th_predict(thX)
# actual prediction function
self.predict_op = theano.function(inputs=[thX], outputs=prediction)
cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction])
updates = rmsprop(cost, self.params, learning_rate, mu, decay, eps)
train_op = theano.function(
inputs=[thX, thY],
updates=updates
)
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]
train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def main():
train, test = get_data()
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)
# cost_val = 0
# err = 0 ### test
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()
def main():
X, Y, _, _ = get_transformed_data()
X = X[:, :300]
# normalize X first
mu = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mu) / std
print "Performing logistic regression..."
Xtrain = X[:-1000,]
Ytrain = Y[:-1000]
Xtest = X[-1000:,]
Ytest = Y[-1000:]
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(200):
p_y = forward(Xtrain, W, b)
W += lr*(gradW(Ytrain_ind, p_y, Xtrain) - reg*W)
b += lr*(gradb(Ytrain_ind, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 10 == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for full GD:", datetime.now() - t0
# 2. stochastic
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in xrange(1): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in xrange(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n,:].reshape(1,D)
y = tmpY[n,:].reshape(1,10)
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if n % (N/2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for SGD:", datetime.now() - t0
# 3. batch
W = np.random.randn(D, 10) / 28
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N / batch_sz
t0 = datetime.now()
for i in xrange(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in xrange(n_batches):
x = tmpX[j*batch_sz:(j*batch_sz + batch_sz),:]
y = tmpY[j*batch_sz:(j*batch_sz + batch_sz),:]
p_y = forward(x, W, b)
W += lr*(gradW(y, p_y, x) - reg*W)
b += lr*(gradb(y, p_y) - reg*b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if j % (n_batches/2) == 0:
err = error_rate(p_y_test, Ytest)
print "Cost at iteration %d: %.6f" % (i, ll)
print "Error rate:", err
p_y = forward(Xtest, W, b)
print "Final error rate:", error_rate(p_y, Ytest)
print "Elapsted time for batch GD:", datetime.now() - t0
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def fit(self, X, Y, learning_rate=10e-7, mu=0.99, decay=0.999, reg=10e-12, eps=10e-10, epochs=400, batch_sz=100, show_fig=False):
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
decay = np.float32(decay)
reg = np.float32(reg)
eps = np.float32(eps)
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
# initialize hidden layers
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W, b = init_weight_and_bias(M1, K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# for momentum
dparams = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params]
# for rmsprop
cache = [theano.shared(np.zeros(p.get_value().shape, dtype=np.float32)) for p in self.params]
# set up theano functions and variables
thX = T.fmatrix('X')
thY = T.ivector('Y')
pY = self.forward(thX)
rcost = reg*T.sum([(p*p).sum() for p in self.params])
cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
prediction = self.predict(thX)
cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction])
updates = [
(c, decay*c + (np.float32(1)-decay)*T.grad(cost, p)*T.grad(cost, p)) for p, c in zip(self.params, cache)
] + [
(p, p + mu*dp - learning_rate*T.grad(cost, p)/T.sqrt(c + eps)) for p, c, dp in zip(self.params, cache, dparams)
] + [
(dp, mu*dp - learning_rate*T.grad(cost, p)/T.sqrt(c + eps)) for p, c, dp in zip(self.params, cache, dparams)
]
# momentum only
# updates = [
# (p, p + mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, dparams)
# ] + [
# (dp, mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, dparams)
# ]
train_op = theano.function(
inputs=[thX, thY],
updates=updates
)
n_batches = N / batch_sz
costs = []
for i in xrange(epochs):
X, Y = shuffle(X, Y)
for j in xrange(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]
train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print "i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e
if show_fig:
plt.plot(costs)
plt.show()
def main():
# compare 5 scenarios:
# 1. batch SGD with constant learning rate
# 2. batch SGD with RMSProp
# 3. batch SGD with AdaGrad
# 4. batch SGD with exponential decay
np.random.seed(2)
max_iter = 20
print_period = 10
X, Y = get_normalized_data()
lr = 0.00004
reg = 0.01
Xtrain, Ytrain = X[:-1000, :], Y[:-1000]
Xtest, Ytest = X[-1000:, :], Y[-1000:]
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
M = 300 # number of hidden layer units
K = len(set(Ytrain))
batch_size = 500
n_batches = N // batch_size
# randomly initialize weights:
W1 = np.random.randn(D, M) / np.sqrt(D)
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
# save initial weights:
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
# 1. batch SGD with constant learning rate:
LL_batch = []
CR_batch = []
t0 = datetime.now()
print('\nperforming batch SGD with constant learning rate...')
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_size:(j + 1) * batch_size, :]
Ybatch = Ytrain_ind[j * batch_size:(j + 1) * batch_size, :]
p_Ybatch, Z = forward(Xbatch, W1, b1, W2, b2)
#print(Z.shape, p_Ybatch.shape, Ybatch.shape)
#print('First batch cost:', cost(p_Ybatch, Ybatch))
# updates:
W2 -= lr * (derivative_W2(Z, Ybatch, p_Ybatch) + reg * W2)
b2 -= lr * (derivative_b2(Ybatch, p_Ybatch) + reg * b2)
W1 -= lr * (derivative_W1(Xbatch, Z, Ybatch, p_Ybatch, W2) +
reg * W1)
b1 -= lr * (derivative_b1(Z, Ybatch, p_Ybatch, W2) + reg * b1)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
#print('pY:', pY)
ll = cost(pY, Ytest_ind)
LL_batch.append(ll)
print('\ni: %d, j: %d, cost: %.6f' % (i, j, ll))
error = error_rate(pY, Ytest)
CR_batch.append(error)
print('error rate:', error)
dt1 = datetime.now() - t0
pY, _ = forward(Xtest, W1, b1, W2, b2)
final_err1 = error_rate(pY, Ytest)
# plot the cost
#plt.plot(LL_batch)
#plt.title('Cost for batch GD with const lr')
#plt.show()
# 2. batch GD with RMSProp:
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
LL_RMSProp = []
CR_RMSProp = []
lr0 = 0.001 # initial learning rate
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
decay = 0.999
eps = 10e-10
t0 = datetime.now()
print('\nperforming batch SGD with RMSProp...')
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_size:(j + 1) * batch_size, :]
Ybatch = Ytrain_ind[j * batch_size:(j + 1) * batch_size, :]
p_Ybatch, Z = forward(Xbatch, W1, b1, W2, b2)
#print(Z.shape, p_Ybatch.shape, Ybatch.shape)
#print('First batch cost:', cost(p_Ybatch, Ybatch))
# updates:
gW2 = (derivative_W2(Z, Ybatch, p_Ybatch) + reg * W2)
cache_W2 = decay * cache_W2 + (1 - decay) * gW2 * gW2
W2 -= lr0 * gW2 / np.sqrt(cache_W2 + eps)
gb2 = (derivative_b2(Ybatch, p_Ybatch) + reg * b2)
cache_b2 = decay * cache_b2 + (1 - decay) * gb2 * gb2
b2 -= lr0 * gb2 / np.sqrt(cache_b2 + eps)
gW1 = (derivative_W1(Xbatch, Z, Ybatch, p_Ybatch, W2) + reg * W1)
cache_W1 = decay * cache_W1 + (1 - decay) * gW1 * gW1
W1 -= lr0 * gW1 / np.sqrt(cache_W1 + eps)
gb1 = (derivative_b1(Z, Ybatch, p_Ybatch, W2) + reg * b1)
cache_b1 = decay * cache_b1 + (1 - decay) * gb1 * gb1
b1 -= lr0 * gb1 / np.sqrt(cache_b1 + eps)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
#print('pY:', pY)
ll = cost(pY, Ytest_ind)
LL_RMSProp.append(ll)
print('\ni: %d, j: %d, cost: %.6f' % (i, j, ll))
error = error_rate(pY, Ytest)
CR_RMSProp.append(error)
print('error rate:', error)
dt2 = datetime.now() - t0
pY, _ = forward(Xtest, W1, b1, W2, b2)
final_err2 = error_rate(pY, Ytest)
# plot the cost
#plt.plot(LL_RMSProp)
#plt.title('Cost for batch SGD with RMSProp')
#plt.show()
# 3. batch SGD with AdaGrad:
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
LL_AdaGrad = []
CR_AdaGrad = []
lr0 = 0.01 # initial learning rate
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
eps = 10e-10
t0 = datetime.now()
print('\nperforming batch SGD with AdaGrad...')
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j * batch_size:(j + 1) * batch_size, :]
Ybatch = Ytrain_ind[j * batch_size:(j + 1) * batch_size, :]
p_Ybatch, Z = forward(Xbatch, W1, b1, W2, b2)
#print(Z.shape, p_Ybatch.shape, Ybatch.shape)
#print('First batch cost:', cost(p_Ybatch, Ybatch))
# updates:
gW2 = (derivative_W2(Z, Ybatch, p_Ybatch) + reg * W2)
cache_W2 = cache_W2 + gW2 * gW2
W2 -= lr0 * gW2 / np.sqrt(cache_W2 + eps)
gb2 = (derivative_b2(Ybatch, p_Ybatch) + reg * b2)
cache_b2 = cache_b2 + gb2 * gb2
b2 -= lr0 * gb2 / np.sqrt(cache_b2 + eps)
gW1 = (derivative_W1(Xbatch, Z, Ybatch, p_Ybatch, W2) + reg * W1)
cache_W1 = cache_W1 + gW1 * gW1
W1 -= lr0 * gW1 / np.sqrt(cache_W1 + eps)
gb1 = (derivative_b1(Z, Ybatch, p_Ybatch, W2) + reg * b1)
cache_b1 = cache_b1 + gb1 * gb1
b1 -= lr0 * gb1 / np.sqrt(cache_b1 + eps)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
#print('pY:', pY)
ll = cost(pY, Ytest_ind)
LL_AdaGrad.append(ll)
print('\ni: %d, j: %d, cost: %.6f' % (i, j, ll))
error = error_rate(pY, Ytest)
CR_AdaGrad.append(error)
print('error rate:', error)
dt3 = datetime.now() - t0
pY, _ = forward(Xtest, W1, b1, W2, b2)
final_err3 = error_rate(pY, Ytest)
# plot the cost
#plt.plot(LL_AdaGrad)
#plt.title('Cost for batch SGD with AdaGrad')
#plt.show()
'''
# 4. batch SGD with exponential decay:
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
LL_exp = []
CR_exp = []
lr0 = 0.0004 # initial learning rate
k = 1e-7
t = 0 # initial log
lr = lr0
t0 = datetime.now()
print('\nperforming batch SGD with lr exponential decay...')
for i in range(max_iter):
for j in range(n_batches):
Xbatch = Xtrain[j*batch_size:(j+1)*batch_size, :]
Ybatch = Ytrain_ind[j*batch_size:(j+1)*batch_size, :]
p_Ybatch, Z = forward(Xbatch, W1, b1, W2, b2)
#print(Z.shape, p_Ybatch.shape, Ybatch.shape)
#print('First batch cost:', cost(p_Ybatch, Ybatch))
# updates:
gW2 = (derivative_W2(Z, Ybatch, p_Ybatch) + reg*W2)
W2 -= lr*gW2
gb2 = (derivative_b2(Ybatch, p_Ybatch) + reg*b2)
b2 -= lr*gb2
gW1 = (derivative_W1(Xbatch, Z, Ybatch, p_Ybatch, W2) + reg*W1)
W1 -= lr*gW1
gb1 = (derivative_b1(Z, Ybatch, p_Ybatch, W2) + reg*b1)
b1 -= lr*gb1
# decrease the learning rate
lr = lr0 * np.exp(-k*t)
t += 1
if j % print_period == 0:
print('current learning rate:', lr)
pY, _ = forward(Xtest, W1, b1, W2, b2)
#print('pY:', pY)
ll = cost(pY, Ytest_ind)
LL_exp.append(ll)
print('\ni: %d, j: %d, cost: %.6f' % (i, j, ll))
error = error_rate(pY, Ytest)
CR_exp.append(error)
print('error rate:', error)
dt4 = datetime.now() - t0
pY, _ = forward(Xtest, W1, b1, W2, b2)
print('\nFinal error rate:', error_rate(pY, Ytest))
print('Elapsed time for batch SGD with lr exponential decay:', dt4)
# plot the cost
#plt.plot(LL_exp)
#plt.title('Cost for batch SGD with lr exponential decay')
#plt.show()
'''
print('\nBatch SGD with constant learning rate:')
print('final error rate:', final_err1)
print('elapsed time:', dt1)
print('\nBatch SGD with RMSProp:')
print('final error rate:', final_err2)
print('elapsed time:', dt2)
print('\nBatch SGD with AdaGrad:')
print('final error rate:', final_err3)
print('elapsed time:', dt3)
# plot the costs together:
plt.plot(LL_batch, label='const_lr')
plt.plot(LL_RMSProp, label='RMSProp')
plt.plot(LL_AdaGrad, label='AdaGrad')
#plt.plot(LL_exp, label='lr_exp_decay')
plt.legend()
plt.show()
def fit(self,
X,
Y,
Xtest,
Ytest,
pretrain=True,
learning_rate=0.01,
mu=.99,
reg=0 * .1,
epochs=1,
batch_sz=100,
show_fig=False):
pretrain_epochs = 1
if not pretrain:
pretrain_epochs = 0
current_input = X
for ae in self.hidden_layers:
ae.fit(current_input, epochs=pretrain_epochs)
current_input = ae.hidden_op(current_input)
N = len(Y)
K = len(set(Y))
W0 = init_weight(self.hidden_layers[-1].M, K)
self.W = theano.shared(W0, 'W_logreg')
self.b = theano.shared(np.zeros(K), 'b_logreg')
self.params = [self.W, self.b]
for ae in self.hidden_layers:
self.params += ae.forward_params
self.dW = theano.shared(np.zeros(W0.shape), 'dW_logreg')
self.db = theano.shared(np.zeros(K), 'db_logreg')
self.dparams = [self.dW, self.db]
for ae in self.hidden_layers:
self.dparams += ae.forward_dparams
X_in = T.matrix('X_in')
targets = T.ivector('Targets')
pY = self.forward(X_in)
# squared_magnitude = [(p*p) for p in self.params]
# reg_cost= T.sum(squared_magnitude)
cost = -T.mean(T.log(pY[T.arange(pY.shape[0]), targets])) #+ reg_cost
prediction = self.predict(X_in)
cost_predict_op = theano.function(inputs=[X_in, targets],
outputs=[cost, prediction])
updates = [(p, p + mu * dp - learning_rate * T.grad(cost, p))
for p, dp in zip(self.params, self.dparams)
] + [(dp, mu * dp - learning_rate * T.grad(cost, p))
for p, dp in zip(self.params, self.dparams)]
train_op = theano.function(
inputs=[X_in, targets],
updates=updates,
)
n_batches = N / batch_sz
costs = []
print 'supervised training'
for i in xrange(epochs):
print "epoch:", i
X, Y = shuffle(X, Y)
for j in xrange(n_batches):
Xbatch = X[j * batch_sz:(j + 1) * batch_sz]
Ybatch = Y[j * batch_sz:(j + 1) * batch_sz]
train_op(Xbatch, Ybatch)
the_cost, the_prediction = cost_predict_op(Xtest, Ytest)
error = error_rate(the_prediction, Ytest)
print "i:%d\tj:%d\tnb:%d\tcost:%.6f\terror:%.3f\t" % (
i, j, n_batches, the_cost, error)
costs.append(the_cost)
if show_fig:
plt.plot(costs)
plt.show()
def main():
max_iter = 20 # make it 30 for sigmoid
print_period = 10
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
lr = 0.00004
reg = 0.01
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
M = 300
K = 10
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
# 1. const
# cost = -16
LL_batch = []
CR_batch = []
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), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
W2 -= lr * (derivative_w2(Z, Ybatch, pYbatch) + reg * W2)
b2 -= lr * (derivative_b2(Ybatch, pYbatch) + reg * b2)
W1 -= lr * (derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) +
reg * W1)
b1 -= lr * (derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1)
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_batch.append(ll)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll))
err = error_rate(pY, Ytest)
CR_batch.append(err)
print("Error rate:", err)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 2. RMSprop
W1 = np.random.randn(D, M) / 28
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
LL_rms = []
CR_rms = []
lr0 = 0.001 # if you set this too high you'll get NaN!
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
decay_rate = 0.999
eps = 1e-10
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), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg * W2
cache_W2 = decay_rate * cache_W2 + (1 - decay_rate) * gW2 * gW2
W2 -= lr0 * gW2 / (np.sqrt(cache_W2) + eps)
gb2 = derivative_b2(Ybatch, pYbatch) + reg * b2
cache_b2 = decay_rate * cache_b2 + (1 - decay_rate) * gb2 * gb2
b2 -= lr0 * gb2 / (np.sqrt(cache_b2) + eps)
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg * W1
cache_W1 = decay_rate * cache_W1 + (1 - decay_rate) * gW1 * gW1
W1 -= lr0 * gW1 / (np.sqrt(cache_W1) + eps)
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1
cache_b1 = decay_rate * cache_b1 + (1 - decay_rate) * gb1 * gb1
b1 -= lr0 * gb1 / (np.sqrt(cache_b1) + eps)
if j % print_period == 0:
# calculate just for LL
pY, _ = forward(Xtest, W1, b1, W2, b2)
# print "pY:", pY
ll = cost(pY, Ytest_ind)
LL_rms.append(ll)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll))
err = error_rate(pY, Ytest)
CR_rms.append(err)
print("Error rate:", err)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
plt.plot(LL_batch, label='const')
plt.plot(LL_rms, label='rms')
plt.legend()
plt.show()
def fit(self,
X,
Y,
learning_rate=10e-4,
mu=0.99,
decay=0.999,
reg=10e-3,
epochs=400,
batch_sz=128,
show_fig=False):
K = len(set(Y))
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = y2indicator(Y).astype(np.float32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
Yvalid_flat = np.argmax(Yvalid, axis=1)
X, Y = X[:-1000], Y[:-1000]
# intialize hidden layers
N, D = X.shape
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2 #output of last layer is input of next
count += 1
# initaliz params of output layers
W, b = init_weight_and_bias(M1, K)
self.W = tf.Variable(W.astype(np.float32))
self.b = tf.Variable(b.astype(np.float32))
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
tfX = tf.placeholder(tf.float32, shape=(None, D), name='X')
tfT = tf.placeholder(tf.float32, shape=(None, K), name='T')
act = self.forward(tfX)
rcost = reg * sum([tf.nn.l2_loss(p) for p in self.params])
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=act,
labels=tfT)) + rcost
predction = self.predict(tfX)
train_op = tf.train.RMSPropOptimizer(learning_rate,
decay=decay,
momentum=mu).minimize(cost)
n_batches = int(N / batch_sz)
costs = []
init = tf.initialize_all_variables()
with tf.Session() as session:
session.run(init)
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
session.run(train_op, feed_dict={tfX: Xbatch, tfT: Ybatch})
if j % 20 == 0:
c = session.run(cost,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
costs.append(c)
p = session.run(predction,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
e = error_rate(Yvalid_flat, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c,
"error_rate", e)
if show_fig:
plt.plot(costs)
plt.show()
def fit(self,
X,
Y,
lr=1e-3,
mu=0.99,
reg=1e-3,
decay=0.99999,
eps=1e-10,
batch_sz=30,
epochs=3,
show_fig=True):
lr = np.float32(lr)
mu = np.float32(mu)
reg = np.float32(reg)
decay = np.float32(decay)
eps = np.float32(eps)
K = len(set(Y))
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = y2indicator(Y).astype(np.float32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
Yvalid_flat = np.argmax(Yvalid, axis=1) # for calculating error rate
# initialize convpool layers
N, width, height, c = X.shape
mi = c
outw = width
outh = height
self.convpool_layers = []
for mo, fw, fh in self.convpool_layer_sizes:
layer = ConvPoolLayer(mi, mo, fw, fh)
self.convpool_layers.append(layer)
outw = outw // 2
outh = outh // 2
mi = mo
# initialize mlp layers
self.hidden_layers = []
M1 = self.convpool_layer_sizes[-1][
0] * outw * outh # size must be same as output of last convpool layer
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
# logistic regression layer
W, b = init_weight_and_bias(M1, K)
self.W = tf.Variable(W, 'W_logreg')
self.b = tf.Variable(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.convpool_layers:
self.params += h.params
for h in self.hidden_layers:
self.params += h.params
# set up tensorflow functions and variables
tfX = tf.placeholder(tf.float32,
shape=(None, width, height, c),
name='X')
tfY = tf.placeholder(tf.float32, shape=(None, K), name='Y')
act = self.forward(tfX)
rcost = reg * sum([tf.nn.l2_loss(p) for p in self.params])
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=act,
labels=tfY)) + rcost
prediction = self.predict(tfX)
train_op = tf.train.RMSPropOptimizer(lr, decay=decay,
momentum=mu).minimize(cost)
n_batches = N // batch_sz
costs = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
session.run(train_op, feed_dict={tfX: Xbatch, tfY: Ybatch})
if j % 20 == 0:
c = session.run(cost,
feed_dict={
tfX: Xvalid,
tfY: Yvalid
})
costs.append(c)
p = session.run(prediction,
feed_dict={
tfX: Xvalid,
tfY: Yvalid
})
e = error_rate(Yvalid_flat, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c,
"error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def main():
#Get train and test data
XTrain, YTrain = get_train_data()
YTrain_ind = y2indicator(YTrain)
XTrain = reshape(XTrain)
XTest, YTest = get_test_data()
YTest_ind = y2indicator(YTest)
XTest = reshape(XTest)
N, K = YTrain_ind.shape
lr = np.float32(0.001)
mu = np.float32(0.99)
reg = np.float32(0.01)
poolsz = (2, 2)
M = 100
batch_sz = 500
no_batches = int(N / batch_sz)
#Initial random weights
W1_shape = (5, 5, 3, 20)
W1_init = init_filter(W1_shape, poolsz)
b1_init = np.zeros([W1_shape[3]])
W2_shape = (5, 5, 25, 50)
W2_init = init_filter(W2_shape, poolsz)
b2_init = np.zeros([W2_shape[3]])
W3_init = np.random.randn(W2_shape[3] * 8 * 8,
M) / np.sqrt(W2_shape[3] * 8 * 8 + M)
b3_init = np.zeros([M])
W4_init = np.random.randn(M, K) / np.sqrt(M + K)
b4_init = np.zeros([K])
#Tensorflow variables
X = tf.placeholder(name='X', dtype='float32', shape=(batch_sz, 32, 32, 3))
Y = tf.placeholder(name='Y', dtype='float32', shape=(batch_sz, K))
W1 = tf.Variable(W1_init.astype(np.float32), name='W1')
b1 = tf.Variable(b1_init.astype(np.float32), name='b1')
W2 = tf.Variable(W2_init.astype(np.float32), name='W2')
b2 = tf.Variable(b2_init.astype(np.float32), name='b2')
W3 = tf.Variable(W3_init.astype(np.float32), name='W3')
b3 = tf.Variable(b3_init.astype(np.float32), name='b3')
W4 = tf.Variable(W4_init.astype(np.float32), name='W4')
b4 = tf.Variable(b4_init.astype(np.float32), name='b4')
#Forward prop
Z1 = convpool(X, W1, b1)
Z2 = convpool(Z1, W2, b2)
Z2_shape = Z2.get_shape().as_list()
Z2_flat = tf.reshape(Z2, [Z2_shape[0], np.prod(Z2_shape[1:])])
Z3 = tf.nn.relu(tf.matmul(Z2_flat, W3) + b3)
pY = tf.matmul(Z3, W4) + b4
#Cost and prediction
cost = tf.reduce_sum(
tf.nn.softmax_cross_entropy_with_logits(logits=pY, labels=Y))
#Train function
train = tf.train.RMSPropOptimizer(lr, decay=0.99,
momentum=mu).minimize(cost)
#Get prediction
pred = tf.argmax(pY, axis=1)
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(100):
for n in range(no_batches):
#get current batches
XBatch = XTrain[n * batch_sz:(n * batch_sz + batch_sz), :]
YBatch_ind = YTrain_ind[n * batch_sz:(n * batch_sz +
batch_sz), :]
#Forward prop
session.run(train, feed_dict={X: XBatch, Y: YBatch_ind})
if (n % 200 == 0):
YBatch = YTrain[n * batch_sz:(n * batch_sz + batch_sz)]
c = session.run(cost, feed_dict={X: XBatch, Y: YBatch_ind})
P = session.run(pred, feed_dict={X: XBatch})
er = error_rate(P, YBatch)
print("Iteration: ", i, "Cost: ", c, "Error rate: ", er)
def main():
max_iter = 20
print_period = 20
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
Ytrain_ind = y2indicator(Ytrain) # Target of train data
Ytest_ind = y2indicator(Ytest) # Target of test data
lr = 0.00004
reg = 0.01
N, D = Xtrain.shape
M = 300
K = 10
np.random.seed(123)
W1 = np.random.randn(D, M) / np.sqrt(D)
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
batch_sz = 500
n_batches = N // batch_sz # 82
# save initial weights
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
# 1. learning rate = constant
losses_batch = []
errors_batch = []
for i in range(max_iter):
# Xtrain, Ytrain_ind = shuffle(Xtrain, Ytrain_ind)
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),] # Target of each batch
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
b2 -= lr*(derivative_b2(Ybatch, pYbatch) + reg*b2)
W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_batch.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_batch.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 2. RMSprop
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
losses_rms = []
errors_rms = []
'''
in RMSprop you can use a bigger lr,
but if you set this too high you'll get NaN!
if you use the same learning rate within RMSprop and General method, there is only slight difference between them.
'''
lr0 = 0.001
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
decay_rate = 0.999
eps = 1e-10
for i in range(max_iter):
# Xtrain, Ytrain_ind = shuffle(Xtrain, Ytrain_ind)
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),] # Target of each batch
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# # update
# cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*np.square(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
# W2 -= lr0 / (np.sqrt(cache_W2) + eps) *(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
# cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*np.square(derivative_b2(Ybatch, pYbatch) + reg*b2)
# b2 -= lr0 / (np.sqrt(cache_b2) + eps) *(derivative_b2(Ybatch, pYbatch) + reg*b2)
# cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*np.square(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2))
# W1 -= lr0 / (np.sqrt(cache_W1) + eps) *(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
# cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*np.square(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
# b1 -= lr0 / (np.sqrt(cache_b1) + eps) *(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
# updates
# 更聰明的寫法，是把上面式子中，會重複計算到的部分提出來計算並指派給變數，讓它只計算一次，這樣會加速
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
cache_W2 = decay_rate*cache_W2 + (1 - decay_rate)*gW2*gW2
W2 -= lr0 * gW2 / (np.sqrt(cache_W2) + eps)
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
cache_b2 = decay_rate*cache_b2 + (1 - decay_rate)*gb2*gb2
b2 -= lr0 * gb2 / (np.sqrt(cache_b2) + eps)
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
cache_W1 = decay_rate*cache_W1 + (1 - decay_rate)*gW1*gW1
W1 -= lr0 * gW1 / (np.sqrt(cache_W1) + eps)
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
cache_b1 = decay_rate*cache_b1 + (1 - decay_rate)*gb1*gb1
b1 -= lr0 * gb1 / (np.sqrt(cache_b1) + eps)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_rms.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_rms.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
plt.plot(losses_batch, label='contant')
plt.plot(losses_rms, label='RMSprop')
plt.legend()
plt.show()
def main(): ''' RMSprop is a form adaptative learning rate which decreases over time ''' max_iter = 20 #for RelU #max_iter = 30 #for sigmoid print_period = 10 X, Y = get_normalized_data() lr = 0.0004 reg = 0.01 Xtrain = X[:-1000,] Ytrain = Y[:-1000] Xtest = X[-1000:,] Ytest = Y[-1000:] Ytrain_ind = y2indicator(Ytrain) Ytest_ind = y2indicator(Ytest) N, D = Xtrain.shape batch_sz = 500 n_batches = N / batch_sz M =300 K=10 #1. batch SGD W1 = np.random.randn(D, M) / 28 b1 = np.zeros(M) W2 = np.random.randn(M, K) / np.sqrt(M) b2 = np.zeros(K) LL_batch = [] CR_batch = [] for i in xrange(max_iter): for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:((j+1)*batch_sz), :] Ybatch = Ytrain_ind[j*batch_sz:((j+1)*batch_sz), :] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2) b2 -= lr*(derivative_b2(Ybatch,pYbatch) + reg*b2) W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1) b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1) if j % print_period ==0: pY, _ = forward(Xtest, W1, b1, W2, b2) ll = cost(pY, Ytest_ind) LL_batch.append(ll) print "Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) err = error_rate(pY, Ytest) CR_batch.append(err) print "Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) print "Final error rate:", error_rate(pY, Ytest) #2. RMSProp W1 = np.random.randn(D, M) / 28 b1 = np.zeros(M) W2 = np.random.randn(M, K) / np.sqrt(M) b2 = np.zeros(K) LL_rms = [] CR_rms = [] lr0 = 0.001 cache_W2 = 0 cache_b2 = 0 cache_W1 = 0 cache_b1 = 0 decay_rate = 1 - 1e-5 eps = 1e-10 for i in xrange(max_iter): for j in xrange(n_batches): Xbatch = Xtrain[j*batch_sz:((j+1)*batch_sz), :] Ybatch = Ytrain_ind[j*batch_sz:((j+1)*batch_sz), :] pYbatch, Z = forward(Xbatch, W1, b1, W2, b2) #updates gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2 cache_W2 = decay_rate*cache_W2 + (1-decay_rate)*gW2*gW2 W2 -= lr0*gW2 /(np.sqrt(cache_W2) + eps) gb2 = derivative_b2(Ybatch,pYbatch) + reg*b2 cache_b2 = decay_rate*cache_b2 + (1-decay_rate)*gb2*gb2 b2 -= lr0*gb2 /(np.sqrt(cache_b2) + eps) gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1 cache_W1 = decay_rate*cache_W1 + (1-decay_rate)*gW1*gW1 W1 -= lr0*gW1 /(np.sqrt(cache_W1) + eps) gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1 cache_b1 = decay_rate*cache_b1 + (1-decay_rate)*gb1*gb1 b1 -= lr0*gb1 /(np.sqrt(cache_b1) + eps) if j % print_period ==0: pY, _ = forward(Xtest, W1, b1, W2, b2) ll = cost(pY, Ytest_ind) LL_rms.append(ll) print "RMS Cost at iteration i=%d, j=%d: %.6f" % (i, j, ll) err = error_rate(pY, Ytest) CR_rms.append(err) print "RMS Error rate:", err pY, _ = forward(Xtest, W1, b1, W2, b2) print "RMS Final error rate:", error_rate(pY, Ytest) plt.plot(LL_batch, label='batch') plt.plot(LL_rms, label='rms') plt.legend() plt.show()
def score(self, X, Y):
prediction = self.predict(X)
return 1 - error_rate(Y, prediction)
def fit(self,
X,
Y,
learning_rate=10e-7,
mu=0.99,
decay=0.999,
reg=10e-12,
eps=10e-10,
epochs=400,
batch_sz=100,
show_fig=False):
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
decay = np.float32(decay)
reg = np.float32(reg)
eps = np.float32(eps)
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
# initialize hidden layers
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W, b = weights_and_bias_init(M1, K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# for momentum
dparams = [
theano.shared(np.zeros(p.get_value().shape, dtype=np.float32))
for p in self.params
]
# for rmsprop
cache = [
theano.shared(np.zeros(p.get_value().shape, dtype=np.float32))
for p in self.params
]
# set up theano functions and variables
thX = T.fmatrix('X')
thT = T.ivector('T')
Y = self.th_forward(thX)
rcost = reg * T.sum([(p * p).sum() for p in self.params])
cost = -T.mean(T.log(Y[T.arange(thT.shape[0]), thT])) + rcost
prediction = self.th_predict(thX)
# actual prediction function
self.predict_op = theano.function(inputs=[thX], outputs=prediction)
cost_predict_op = theano.function(inputs=[thX, thT],
outputs=[cost, prediction])
updates = [
(c, decay * c +
(np.float32(1) - decay) * T.grad(cost, p) * T.grad(cost, p))
for p, c in zip(self.params, cache)
] + [
(p,
p + mu * dp - learning_rate * T.grad(cost, p) / T.sqrt(c + eps))
for p, c, dp in zip(self.params, cache, dparams)
] + [(dp, mu * dp - learning_rate * T.grad(cost, p) / T.sqrt(c + eps))
for p, c, dp in zip(self.params, cache, dparams)]
# momentum only
# updates = [
# (p, p + mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, dparams)
# ] + [
# (dp, mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, dparams)
# ]
train_op = theano.function(inputs=[thX, thT], updates=updates)
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c,
"error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def score(self, X, Y):
prediction = self.predict(X)
return 1 - error_rate(Y, prediction)
def main():
# compare 3 scenarios:
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
max_iter = 20 # make it 30 for sigmoid
print_period = 50
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
lr = 0.00004
reg = 0.01
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
M = 300
K = 10
W1 = np.random.randn(D, M) / np.sqrt(D)
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
# save initial weights
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
# 1. batch
losses_batch = []
errors_batch = []
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# updates
W2 -= lr*(derivative_w2(Z, Ybatch, pYbatch) + reg*W2)
b2 -= lr*(derivative_b2(Ybatch, pYbatch) + reg*b2)
W1 -= lr*(derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1)
b1 -= lr*(derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1)
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_batch.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_batch.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 2. batch with momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
losses_momentum = []
errors_momentum = []
mu = 0.9
dW2 = 0
db2 = 0
dW1 = 0
db1 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
# update velocities
dW2 = mu*dW2 - lr*gW2
db2 = mu*db2 - lr*gb2
dW1 = mu*dW1 - lr*gW1
db1 = mu*db1 - lr*gb1
# updates
W2 += dW2
b2 += db2
W1 += dW1
b1 += db1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_momentum.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_momentum.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 3. batch with Nesterov momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
losses_nesterov = []
errors_nesterov = []
mu = 0.9
vW2 = 0
vb2 = 0
vW1 = 0
vb1 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
# v update
vW2 = mu*vW2 - lr*gW2
vb2 = mu*vb2 - lr*gb2
vW1 = mu*vW1 - lr*gW1
vb1 = mu*vb1 - lr*gb1
# param update
W2 += mu*vW2 - lr*gW2
b2 += mu*vb2 - lr*gb2
W1 += mu*vW1 - lr*gW1
b1 += mu*vb1 - lr*gb1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_nesterov.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_nesterov.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
plt.plot(losses_batch, label="batch")
plt.plot(losses_momentum, label="momentum")
plt.plot(losses_nesterov, label="nesterov")
plt.legend()
plt.show()
def main():
max_iter = 10
print_period = 10
X_train, X_test, Y_train, Y_test = get_normalized_data()
reg = 0.01
Y_train_ind = y2indicator(Y_train)
Y_test_ind = y2indicator(Y_test)
N, D = X_train.shape
batch_sz = 500
n_batches = N // batch_sz
M = 300
K = 10
W1_0 = np.random.randn(D, M) / np.sqrt(D)
b1_0 = np.zeros(M)
W2_0 = np.random.randn(M, K) / np.sqrt(K)
b2_0 = np.zeros(K)
# .1 Adam
W1 = W1_0.copy()
W2 = W2_0.copy()
b1 = b1_0.copy()
b2 = b2_0.copy()
losses_adam = []
errors_adam = []
# 1st moment
mW1 = 0
mW2 = 0
mb1 = 0
mb2 = 0
# 2nd moment
vW1 = 0
vW2 = 0
vb1 = 0
vb2 = 0
# Hyperparams
eps = 1e-8
lr = 0.001
beta1 = 0.9
beta2 = 0.999
t = 1
for i in range(max_iter):
for j in range(n_batches):
Xbatch = X_train[j * batch_sz:(j * batch_sz + batch_sz), ]
Ybatch = Y_train_ind[j * batch_sz:(j * batch_sz + batch_sz), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
# gradients
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg * W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg * b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg * W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1
# new m
mW1 = beta1 * mW1 + (1 - beta1) * gW1
mb1 = beta1 * mb1 + (1 - beta1) * gb1
mW2 = beta1 * mW2 + (1 - beta1) * gW2
mb2 = beta1 * mb2 + (1 - beta1) * gb2
# new v
vW2 = beta2 * vW2 + (1 - beta2) * gW2 * gW2
vb2 = beta2 * vb2 + (1 - beta2) * gb2 * gb2
vW1 = beta2 * vW1 + (1 - beta2) * gW1 * gW1
vb1 = beta2 * vb1 + (1 - beta2) * gb1 * gb1
# bias correction
correction1 = 1 - beta1 ** t
mW1_hat = mW1 / correction1
mb1_hat = mb1 / correction1
mW2_hat = mW2 / correction1
mb2_hat = mb2 / correction1
#
correction2 = 1 - beta2 ** t
vb2_hat = vb2 / correction2
vb1_hat = vb1 / correction2
vW2_hat = vW2 / correction2
vW1_hat = vW1 / correction2
t += 1
# weights
W1 = W1 - lr * mW1_hat / np.sqrt(vW1_hat + eps)
b1 = b1 - lr * mb1_hat / np.sqrt(vb1_hat + eps)
W2 = W2 - lr * mW2_hat / np.sqrt(vW2_hat + eps)
b2 = b2 - lr * mb2_hat / np.sqrt(vb2_hat + eps)
if j % print_period == 0:
pY, _ = forward(X_test, W1, b1, W2, b2)
l = cost(pY, Y_test_ind)
losses_adam.append(l)
print(f'Adam Cost at iteration i={i}, j={j} : {l}')
e = error_rate(pY, Y_test)
errors_adam.append(e)
print("error_rate", e)
pY, _ = forward(X_test, W1, b1, W2, b2)
adam_error = error_rate(pY, Y_test)
# 3. RMSProp with momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
losses_rms = []
errors_rms = []
# comparable hyper parameters for fair
lr0 = 0.001
mu = 0.9
decay_rate = 0.999
eps = 1e-8
# rmsprop cache
cache_W2 = 1
cache_b2 = 1
cache_W1 = 1
cache_b1 = 1
# momentum
dW1 = 0
db1 = 0
dW2 = 0
db2 = 0
for i in range(max_iter):
for j in range(n_batches):
Xbatch = X_train[j * batch_sz:(j * batch_sz + batch_sz), ]
Ybatch = Y_train_ind[j * batch_sz:(j * batch_sz + batch_sz), ]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg * W2
cache_W2 = decay_rate * cache_W2 + (1 - decay_rate) * gW2 * gW2
dW2 = mu * dW2 + (1 - mu) * lr0 * gW2 / (np.sqrt(cache_W2) + eps)
W2 -= dW2
gb2 = derivative_b2(Ybatch, pYbatch) + reg * b2
cache_b2 = decay_rate * cache_b2 + (1 - decay_rate) * gb2 * gb2
db2 = mu * db2 + (1 - mu) * lr0 * gb2 / (np.sqrt(cache_b2) + eps)
b2 -= db2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg * W1
cache_W1 = decay_rate * cache_W1 + (1 - decay_rate) * gW1 * gW1
dW1 = mu * dW1 + (1 - mu) * lr0 * gW1 / (np.sqrt(cache_W1) + eps)
W1 -= dW1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg * b1
cache_b1 = decay_rate * cache_b1 + (1 - decay_rate) * gb1 * gb1
db1 = mu * db1 + (1 - mu) * lr0 * gb1 / (np.sqrt(cache_b1) + eps)
b1 -= db1
if j % print_period == 0:
pY, _ = forward(X_test, W1, b1, W2, b2)
l = cost(pY, Y_test_ind)
losses_rms.append(l)
print(f'Cost at iteration i={i}, j={j} : {l}')
err = error_rate(pY, Y_test)
errors_rms.append(err)
print("Error rate:", err)
pY, _ = forward(X_test, W1, b1, W2, b2)
rms_error = error_rate(pY, Y_test)
print(f"Final RMSProp error rate: {rms_error}")
print(f"Final Adam error rate: {adam_error}")
plt.plot(losses_adam, label='batch cost')
plt.plot(losses_rms, label='RMSProp cost')
plt.legend()
plt.show()
def fit(self,
X,
Y,
lr=1e-3,
mu=0.99,
reg=1e-3,
decay=0.99999,
eps=1e-10,
batch_sz=30,
epochs=3,
show_fig=True):
lr = np.float32(lr)
mu = np.float32(mu)
reg = np.float32(reg)
decay = np.float32(decay)
eps = np.float32(eps)
# A cross validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
# initialize convpool layers
N, c, width, height = X.shape
mi = c
outw = width
outh = height
self.convpool_layers = []
for mo, fw, fh in self.convpool_layer_sizes:
layer = ConvPoolLayer(mi, mo, fw, fh)
self.convpool_layers.append(layer)
outw = (outw - fw + 1) // 2
outh = (outh - fh + 1) // 2
mi = mo
# initialize mlp layers
K = len(set(Y))
self.hidden_layers = []
M1 = self.convpool_layer_sizes[-1][
0] * outw * outh # size must be same as output of last convpool layer
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
# logistic regression layer
W, b = init_weight_and_bias(M1, K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for c in self.convpool_layers:
self.params += c.params
for h in self.hidden_layers:
self.params += h.params
# set up theano functions and variables
thX = T.tensor4('X', dtype='float32')
thY = T.ivector('Y')
pY = self.forward(thX)
rcost = reg * T.sum([(p * p).sum() for p in self.params])
cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
prediction = self.th_predict(thX)
cost_predict_op = theano.function(inputs=[thX, thY],
outputs=[cost, prediction])
updates = rmsprop(cost, self.params, lr, mu, decay, eps)
train_op = theano.function(inputs=[thX, thY],
outputs=cost,
updates=updates)
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j * batch_sz + batch_sz)]
Ybatch = Y[j * batch_sz:(j * batch_sz + batch_sz)]
train_c = train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print("i:", i, "j:", j, "nb:", n_batches, "train cost:",
train_c, "cost:", c, "error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, Xtest, Ytest, pretrain=True, learning_rate=0.01, mu=0.99, reg=0.1, epochs=1, batch_sz=100):
# greedy layer-wise training of autoencoders
pretrain_epochs = 1
if not pretrain:
pretrain_epochs = 0
current_input = X
for ae in self.hidden_layers:
ae.fit(current_input, epochs=pretrain_epochs)
# create current_input for the next layer
current_input = ae.hidden_op(current_input)
# initialize logistic regression layer
N = len(Y)
K = len(set(Y))
W0 = init_weights((self.hidden_layers[-1].M, K))
self.W = theano.shared(W0, "W_logreg")
self.b = theano.shared(np.zeros(K), "b_logreg")
self.params = [self.W, self.b]
for ae in self.hidden_layers:
self.params += ae.forward_params
# for momentum
self.dW = theano.shared(np.zeros(W0.shape), "dW_logreg")
self.db = theano.shared(np.zeros(K), "db_logreg")
self.dparams = [self.dW, self.db]
for ae in self.hidden_layers:
self.dparams += ae.forward_dparams
X_in = T.matrix('X_in')
targets = T.ivector('Targets')
pY = self.forward(X_in)
# squared_magnitude = [(p*p).sum() for p in self.params]
# reg_cost = T.sum(squared_magnitude)
cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) ) #+ reg*reg_cost
prediction = self.predict(X_in)
cost_predict_op = theano.function(
inputs=[X_in, targets],
outputs=[cost, prediction],
)
updates = [
(p, p + mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, self.dparams)
] + [
(dp, mu*dp - learning_rate*T.grad(cost, p)) for p, dp in zip(self.params, self.dparams)
]
# updates = [(p, p - learning_rate*T.grad(cost, p)) for p in self.params]
train_op = theano.function(
inputs=[X_in, targets],
updates=updates,
)
n_batches = N // batch_sz
costs = []
print("supervised training...")
for i in range(epochs):
print("epoch:", i)
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]
train_op(Xbatch, Ybatch)
the_cost, the_prediction = cost_predict_op(Xtest, Ytest)
error = error_rate(the_prediction, Ytest)
print("j / n_batches:", j, "/", n_batches, "cost:", the_cost, "error:", error)
costs.append(the_cost)
plt.plot(costs)
plt.show()
def fit(self,
X,
Y,
learning_rate=1e-4,
mu=0.9,
decay=0.9,
epochs=15,
batch_sz=100,
display_cost=False,
save_params=False):
# set evarything to np.float32 to enable tf computation running correctly
learning_rate = np.float32(learning_rate)
mu = np.float32(mu)
decay = np.float32(decay)
# create a vailidation set:
X, Y = shuffle(X, Y)
Xvalid, Yvalid = X[-1000:, ], Y[-1000:]
X, Y = X[:-1000, ], Y[:-1000]
# initialize hidden layers:
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
M1 = D
count = 0
# iterate the self.hidden_layer_sizes list through M1 variable:
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
# the last_hidden_layer-output_layer weights and bias:
W, b = init_weight_and_bias(M1, K)
self.W = tf.Variable(W, name='W%s' % count)
self.b = tf.Variable(b, name='b%s' % count)
# collect all the network's parameters:
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.parameters
# define tensorflow placeholders:
tfX = tf.placeholder(tf.float32, shape=(None, D), name='X')
tfT = tf.placeholder(tf.int32, shape=(None, ), name='T')
# the logits ouputs of the network:
Y_logits = self.forward_train(tfX)
# define the expression for cost:
cost = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=Y_logits,
labels=tfT))
# define the tensorflow train function:
train_op = tf.train.RMSPropOptimizer(learning_rate,
decay=decay,
momentum=mu).minimize(cost)
predict_op = self.predict(tfX)
# validation cost will be calculated separately since nothing will be dropped
Y_logits_valid = self.forward_predict(tfX)
cost_valid = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(
logits=Y_logits_valid, labels=tfT))
n_batches = N // batch_sz
costs = []
init = tf.global_variables_initializer()
with tf.Session() as session:
# initialize all tf variables:
print('\nInitializing variables...')
session.run(init)
print('\nPerforming batch SGD with RMSProp and momentum...')
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j * batch_sz:(j + 1) * batch_sz, :]
Ybatch = Y[j * batch_sz:(j + 1) * batch_sz]
session.run(train_op, feed_dict={tfX: Xbatch, tfT: Ybatch})
if j % 20 == 0:
c = session.run(cost_valid,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
costs.append(c)
prediction = session.run(predict_op,
feed_dict={
tfX: Xvalid,
tfT: Yvalid
})
#print(prediction)
error = error_rate(Yvalid, prediction)
print('\ni: %d, j: %d, cost: %.6f, error: %.6f' %
(i, j, c, error))
# make the final prediction:
prediction = session.run(predict_op, feed_dict={tfX: Xvalid})
final_error = error_rate(Yvalid, prediction)
if save_params:
for h in self.hidden_layers:
p_type = 'W'
for p in h.parameters:
p = p.eval()
#print(type(p))
#print(p.shape)
name = p_type + str(h.id)
np.save(name, p)
p_type = 'b'
# last hidden layer - output layer parameters:
np.save('W%s' % count, self.W.eval())
np.save('b%s' % count, self.b.eval())
if display_cost:
plt.plot(costs)
plt.show()
def fit(self, X, Y, lr=10e-4, mu=0.99, reg=10e-4, decay=0.99999, eps=10e-3, batch_sz=30, epochs=3, show_fig=True):
lr = np.float32(lr)
mu = np.float32(mu)
reg = np.float32(reg)
decay = np.float32(decay)
eps = np.float32(eps)
K = len(set(Y))
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = y2indicator(Y).astype(np.float32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
Yvalid_flat = np.argmax(Yvalid, axis=1) # for calculating error rate
# initialize convpool layers
N, d, d, c = X.shape
mi = c
outw = d
outh = d
self.convpool_layers = []
for mo, fw, fh in self.convpool_layer_sizes:
layer = ConvPoolLayer(mi, mo, fw, fh)
self.convpool_layers.append(layer)
outw = outw / 2
outh = outh / 2
mi = mo
# initialize mlp layers
self.hidden_layers = []
M1 = self.convpool_layer_sizes[-1][0]*outw*outh # size must be same as output of last convpool layer
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
# logistic regression layer
W, b = init_weight_and_bias(M1, K)
self.W = tf.Variable(W, 'W_logreg')
self.b = tf.Variable(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for h in self.convpool_layers:
self.params += h.params
for h in self.hidden_layers:
self.params += h.params
# set up tensorflow functions and variables
tfX = tf.placeholder(tf.float32, shape=(None, d, d, c), name='X')
tfY = tf.placeholder(tf.float32, shape=(None, K), name='Y')
act = self.forward(tfX)
rcost = reg*sum([tf.nn.l2_loss(p) for p in self.params])
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(act, tfY)) + rcost
prediction = self.predict(tfX)
train_op = tf.train.RMSPropOptimizer(lr, decay=decay, momentum=mu).minimize(cost)
n_batches = N / batch_sz
costs = []
init = tf.initialize_all_variables()
with tf.Session() as session:
session.run(init)
for i in xrange(epochs):
X, Y = shuffle(X, Y)
for j in xrange(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]
session.run(train_op, feed_dict={tfX: Xbatch, tfY: Ybatch})
if j % 20 == 0:
c = session.run(cost, feed_dict={tfX: Xvalid, tfY: Yvalid})
costs.append(c)
p = session.run(prediction, feed_dict={tfX: Xvalid, tfY: Yvalid})
e = error_rate(Yvalid_flat, p)
print "i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e
if show_fig:
plt.plot(costs)
plt.show()
def main():
Xtrain, Xtest, Ytrain, Ytest = get_transformed_data()
print("Performing logistic regression...")
N, D = Xtrain.shape
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
# 1. full
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(50):
p_y = forward(Xtrain, W, b)
W += lr * (gradW(Ytrain_ind, p_y, Xtrain) - reg * W)
b += lr * (gradb(Ytrain_ind, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for full GD:", datetime.now() - t0)
# 2. stochastic
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL_stochastic = []
lr = 0.0001
reg = 0.01
t0 = datetime.now()
for i in range(
50): # takes very long since we're computing cost for 41k samples
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for n in range(min(N, 500)): # shortcut so it won't take so long...
x = tmpX[n, :].reshape(
1, D
) # in this case, x is a vector, shape=(D,) ，為了讓 feature and target 可以計算 forward and Weights 要做reshape
y = tmpY[n, :].reshape(
1, 10
) # y is a vector, need to convert into metrix for y2indicator calculation
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_stochastic.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for SGD:", datetime.now() - t0)
# 3. batch
W = np.random.randn(D, 10) / np.sqrt(D)
b = np.zeros(10)
LL_batch = []
lr = 0.0001
reg = 0.01
batch_sz = 500
n_batches = N // batch_sz
t0 = datetime.now()
for i in range(50):
tmpX, tmpY = shuffle(Xtrain, Ytrain_ind)
for j in range(n_batches):
x = tmpX[j * batch_sz:(j * batch_sz + batch_sz), :]
y = tmpY[j * batch_sz:(j * batch_sz + batch_sz), :]
p_y = forward(x, W, b)
W += lr * (gradW(y, p_y, x) - reg * W)
b += lr * (gradb(y, p_y) - reg * b)
p_y_test = forward(Xtest, W, b)
ll = cost(p_y_test, Ytest_ind)
LL_batch.append(ll)
if i % 1 == 0:
err = error_rate(p_y_test, Ytest)
if i % 10 == 0:
print("Cost at iteration %d: %.6f" % (i, ll))
print("Error rate:", err)
p_y = forward(Xtest, W, b)
print("Final error rate:", error_rate(p_y, Ytest))
print("Elapsted time for batch GD:", datetime.now() - t0)
x1 = np.linspace(0, 1, len(LL))
plt.plot(x1, LL, label="full")
x2 = np.linspace(0, 1, len(LL_stochastic))
plt.plot(x2, LL_stochastic, label="stochastic")
x3 = np.linspace(0, 1, len(LL_batch))
plt.plot(x3, LL_batch, label="batch")
plt.legend()
plt.show()
def fit(self, X, Y, learning_rate=0.01, mu=0.99, epochs=30, batch_sz=100):
N, D = X.shape
K = len(set(Y))
self.hidden_layers = []
mi = D
for mo in self.hidden_layer_sizes:
h = HiddenLayer(mi, mo)
self.hidden_layers.append(h)
mi = mo
# initialize logistic regression layer
W = init_weights((mo, K))
b = np.zeros(K)
self.W = theano.shared(W)
self.b = theano.shared(b)
self.params = [self.W, self.b]
self.allWs = []
for h in self.hidden_layers:
self.params += h.params
self.allWs.append(h.W)
self.allWs.append(self.W)
X_in = T.matrix('X_in')
targets = T.ivector('Targets')
pY = self.forward(X_in)
cost = -T.mean( T.log(pY[T.arange(pY.shape[0]), targets]) )
prediction = self.predict(X_in)
# cost_predict_op = theano.function(
# inputs=[X_in, targets],
# outputs=[cost, prediction],
# )
dparams = [theano.shared(p.get_value()*0) for p in self.params]
grads = T.grad(cost, self.params)
updates = [
(p, p + mu*dp - learning_rate*g) for p, dp, g in zip(self.params, dparams, grads)
] + [
(dp, mu*dp - learning_rate*g) for dp, g in zip(dparams, grads)
]
train_op = theano.function(
inputs=[X_in, targets],
outputs=[cost, prediction],
updates=updates,
)
n_batches = N / batch_sz
costs = []
lastWs = [W.get_value() for W in self.allWs]
W_changes = []
print "supervised training..."
for i in xrange(epochs):
print "epoch:", i
X, Y = shuffle(X, Y)
for j in xrange(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz + batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz + batch_sz)]
c, p = train_op(Xbatch, Ybatch)
if j % 100 == 0:
print "j / n_batches:", j, "/", n_batches, "cost:", c, "error:", error_rate(p, Ybatch)
costs.append(c)
# log changes in all Ws
W_change = [np.abs(W.get_value() - lastW).mean() for W, lastW in zip(self.allWs, lastWs)]
W_changes.append(W_change)
lastWs = [W.get_value() for W in self.allWs]
W_changes = np.array(W_changes)
plt.subplot(2,1,1)
for i in xrange(W_changes.shape[1]):
plt.plot(W_changes[:,i], label='layer %s' % i)
plt.legend()
# plt.show()
plt.subplot(2,1,2)
plt.plot(costs)
plt.show()
def main():
# compare 3 scenarios:
# 1. batch SGD
# 2. batch SGD with momentum
# 3. batch SGD with Nesterov momentum
max_iter = 20 # make it 30 for sigmoid
print_period = 50
Xtrain, Xtest, Ytrain, Ytest = get_normalized_data()
lr = 0.00004
reg = 0.01
Ytrain_ind = y2indicator(Ytrain)
Ytest_ind = y2indicator(Ytest)
N, D = Xtrain.shape
batch_sz = 500
n_batches = N // batch_sz
M = 300
K = 10
W1 = np.random.randn(D, M) / np.sqrt(D)
b1 = np.zeros(M)
W2 = np.random.randn(M, K) / np.sqrt(M)
b2 = np.zeros(K)
# save initial weights
W1_0 = W1.copy()
b1_0 = b1.copy()
W2_0 = W2.copy()
b2_0 = b2.copy()
# 1. batch
losses_batch = []
errors_batch = []
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# print "first batch cost:", cost(pYbatch, Ybatch)
# gradients
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
# updates
W2 -= lr*gW2
b2 -= lr*gb2
W1 -= lr*gW1
b1 -= lr*gb1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_batch.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_batch.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 2. batch with momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
losses_momentum = []
errors_momentum = []
mu = 0.9
dW2 = 0
db2 = 0
dW1 = 0
db1 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# gradients
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
# update velocities
dW2 = mu*dW2 - lr*gW2
db2 = mu*db2 - lr*gb2
dW1 = mu*dW1 - lr*gW1
db1 = mu*db1 - lr*gb1
# updates
W2 += dW2
b2 += db2
W1 += dW1
b1 += db1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_momentum.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_momentum.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
# 3. batch with Nesterov momentum
W1 = W1_0.copy()
b1 = b1_0.copy()
W2 = W2_0.copy()
b2 = b2_0.copy()
losses_nesterov = []
errors_nesterov = []
mu = 0.9
vW2 = 0
vb2 = 0
vW1 = 0
vb1 = 0
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),]
pYbatch, Z = forward(Xbatch, W1, b1, W2, b2)
# updates
gW2 = derivative_w2(Z, Ybatch, pYbatch) + reg*W2
gb2 = derivative_b2(Ybatch, pYbatch) + reg*b2
gW1 = derivative_w1(Xbatch, Z, Ybatch, pYbatch, W2) + reg*W1
gb1 = derivative_b1(Z, Ybatch, pYbatch, W2) + reg*b1
# v update
vW2 = mu*vW2 - lr*gW2
vb2 = mu*vb2 - lr*gb2
vW1 = mu*vW1 - lr*gW1
vb1 = mu*vb1 - lr*gb1
# param update
W2 += mu*vW2 - lr*gW2
b2 += mu*vb2 - lr*gb2
W1 += mu*vW1 - lr*gW1
b1 += mu*vb1 - lr*gb1
if j % print_period == 0:
pY, _ = forward(Xtest, W1, b1, W2, b2)
l = cost(pY, Ytest_ind)
losses_nesterov.append(l)
print("Cost at iteration i=%d, j=%d: %.6f" % (i, j, l))
e = error_rate(pY, Ytest)
errors_nesterov.append(e)
print("Error rate:", e)
pY, _ = forward(Xtest, W1, b1, W2, b2)
print("Final error rate:", error_rate(pY, Ytest))
plt.plot(losses_batch, label="batch")
plt.plot(losses_momentum, label="momentum")
plt.plot(losses_nesterov, label="nesterov")
plt.legend()
plt.show()
def fit(self, X, Y, learning_rate=1e-2, mu=0.99, decay=0.999, reg=1e-3, epochs=10, batch_sz=100, show_fig=False):
K = len(set(Y)) # won't work later b/c we turn it into indicator
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = y2indicator(Y).astype(np.float32)
# Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
Yvalid_flat = np.argmax(Yvalid, axis=1) # for calculating error rate
X, Y = X[:-1000], Y[:-1000]
# initialize hidden layers
N, D = X.shape
self.hidden_layers = []
M1 = D
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
W, b = init_weight_and_bias(M1, K)
self.W = tf.Variable(W.astype(np.float32))
self.b = tf.Variable(b.astype(np.float32))
# collect params for later use
self.params = [self.W, self.b]
for h in self.hidden_layers:
self.params += h.params
# set up theano functions and variables
tfX = tf.placeholder(tf.float32, shape=(None, D), name='X')
tfT = tf.placeholder(tf.float32, shape=(None, K), name='T')
act = self.forward(tfX)
rcost = reg*sum([tf.nn.l2_loss(p) for p in self.params])
cost = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(
logits=act,
labels=tfT
)
) + rcost
prediction = self.predict(tfX)
train_op = tf.train.RMSPropOptimizer(learning_rate, decay=decay, momentum=mu).minimize(cost)
n_batches = N // batch_sz
costs = []
init = tf.global_variables_initializer()
with tf.Session() as session:
session.run(init)
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]
session.run(train_op, feed_dict={tfX: Xbatch, tfT: Ybatch})
if j % 20 == 0:
c = session.run(cost, feed_dict={tfX: Xvalid, tfT: Yvalid})
costs.append(c)
p = session.run(prediction, feed_dict={tfX: Xvalid, tfT: Yvalid})
e = error_rate(Yvalid_flat, p)
print("i:", i, "j:", j, "nb:", n_batches, "cost:", c, "error rate:", e)
if show_fig:
plt.plot(costs)
plt.show()
def fit(self, X, Y, lr=1e-3, mu=0.99, reg=1e-3, decay=0.99999, eps=1e-10, batch_sz=30, epochs=3, show_fig=True):
lr = np.float32(lr)
mu = np.float32(mu)
reg = np.float32(reg)
decay = np.float32(decay)
eps = np.float32(eps)
# make a validation set
X, Y = shuffle(X, Y)
X = X.astype(np.float32)
Y = Y.astype(np.int32)
Xvalid, Yvalid = X[-1000:], Y[-1000:]
X, Y = X[:-1000], Y[:-1000]
# initialize convpool layers
N, c, width, height = X.shape
mi = c
outw = width
outh = height
self.convpool_layers = []
for mo, fw, fh in self.convpool_layer_sizes:
layer = ConvPoolLayer(mi, mo, fw, fh)
self.convpool_layers.append(layer)
outw = (outw - fw + 1) // 2
outh = (outh - fh + 1) // 2
mi = mo
# initialize mlp layers
K = len(set(Y))
self.hidden_layers = []
M1 = self.convpool_layer_sizes[-1][0]*outw*outh # size must be same as output of last convpool layer
count = 0
for M2 in self.hidden_layer_sizes:
h = HiddenLayer(M1, M2, count)
self.hidden_layers.append(h)
M1 = M2
count += 1
# logistic regression layer
W, b = init_weight_and_bias(M1, K)
self.W = theano.shared(W, 'W_logreg')
self.b = theano.shared(b, 'b_logreg')
# collect params for later use
self.params = [self.W, self.b]
for c in self.convpool_layers:
self.params += c.params
for h in self.hidden_layers:
self.params += h.params
# set up theano functions and variables
thX = T.tensor4('X', dtype='float32')
thY = T.ivector('Y')
pY = self.forward(thX)
rcost = reg*T.sum([(p*p).sum() for p in self.params])
cost = -T.mean(T.log(pY[T.arange(thY.shape[0]), thY])) + rcost
prediction = self.th_predict(thX)
cost_predict_op = theano.function(inputs=[thX, thY], outputs=[cost, prediction])
updates = rmsprop(cost, self.params, lr, mu, decay, eps)
train_op = theano.function(
inputs=[thX, thY],
outputs=cost,
updates=updates
)
n_batches = N // batch_sz
costs = []
for i in range(epochs):
X, Y = shuffle(X, Y)
for j in range(n_batches):
Xbatch = X[j*batch_sz:(j*batch_sz+batch_sz)]
Ybatch = Y[j*batch_sz:(j*batch_sz+batch_sz)]
train_c = train_op(Xbatch, Ybatch)
if j % 20 == 0:
c, p = cost_predict_op(Xvalid, Yvalid)
costs.append(c)
e = error_rate(Yvalid, p)
print(
"i:", i,
"j:", j,
"nb:", n_batches,
"train cost:", train_c,
"cost:", c,
"error rate:", e
)
if show_fig:
plt.plot(costs)
plt.show()
