def test_batcher_can_reinstate_dropout_mask():
batcher = kayak.Batcher(5, 10)
X = kayak.Inputs(np.ones((10,10)))
Y = kayak.Dropout(X, batcher=batcher)
assert not np.all(Y.value == np.ones((10, 10)))
batcher.test_mode()
print "Y value", Y.value
assert np.all(Y.value == np.ones((10, 10)))
def test_batcher_updates_dropout():
batcher = kayak.Batcher(5, 10)
X = kayak.Inputs(np.random.randn(10,10))
Y = kayak.Dropout(X, batcher=batcher)
val1 = Y.value
batcher.next()
val2 = Y.value
assert not np.all(val1 == val2)
def test_dropout_clears_value_cache():
X = kayak.Inputs(np.random.randn(10,10))
Y = kayak.Dropout(X)
Z = kayak.MatSum(Y, axis=1)
val1 = Z.value
Y.draw_new_mask()
val2 = Z.value
assert not np.all(val1 == val2)
assert np.all(Z.value == Z.value)
def test_alldropout_values():
npr.seed(2)
# Drop everything out.
np_X = npr.randn(10,20)
X = kayak.Parameter(np_X)
Y = kayak.Dropout(X, drop_prob=1.0)
assert np.all(Y.value == 0.0)
def test_nondropout_values():
npr.seed(1)
# First sanity check: don't actually drop anything out.
# Make sure we get everything back.
np_X = npr.randn(10,20)
X = kayak.Parameter(np_X)
Y = kayak.Dropout(X, drop_prob=0.0)
assert np.all(close_float(Y.value, np_X))
def test_alldropout_grad():
npr.seed(5)
np_X = npr.randn(10,20)
X = kayak.Parameter(np_X)
Y = kayak.Dropout(X, drop_prob=1.0)
Z = kayak.MatSum(Y)
Z.value
assert Z.grad(X).shape == np_X.shape
assert kayak.util.checkgrad(X, Z) < MAX_GRAD_DIFF
def test_dropout_values():
# Drop some things out.
npr.seed(3)
for ii in xrange(NUM_TRIALS):
prob = npr.rand()
scale = 1.0 / (1.0 - prob)
np_X = npr.randn(5,6)
X = kayak.Parameter(np_X)
Y = kayak.Dropout(X, drop_prob=prob)
Y.value
assert np.all(np.logical_xor(Y.value == 0.0, close_float(Y.value, scale*np_X)))
def test_dropout_grad():
# Drop some things out.
npr.seed(6)
for ii in xrange(NUM_TRIALS):
prob = npr.rand()
scale = 1.0 / (1.0 - prob)
np_X = npr.randn(5,6)
X = kayak.Parameter(np_X)
Y = kayak.Dropout(X, drop_prob=prob)
Z = kayak.MatSum(Y)
Z.value
assert Z.grad(X).shape == np_X.shape
assert kayak.util.checkgrad(X, Z) < MAX_GRAD_DIFF
def train(inputs, targets, batch_size, learn_rate, momentum, l1_weight,
l2_weight, dropout, improvement_thresh):
# Create a batcher object.
batcher = kayak.Batcher(batch_size, inputs.shape[0])
# Inputs and targets need access to the batcher.
X = kayak.Inputs(inputs, batcher)
T = kayak.Targets(targets, batcher)
# Put some dropout regularization on the inputs
H = kayak.Dropout(X, dropout)
# Weights and biases, with random initializations.
W = kayak.Parameter(0.1 * npr.randn(inputs.shape[1], 10))
B = kayak.Parameter(0.1 * npr.randn(1, 10))
# Nothing fancy here: inputs times weights, plus bias, then softmax.
Y = kayak.LogSoftMax(kayak.ElemAdd(kayak.MatMult(H, W), B))
# The training loss is negative multinomial log likelihood.
loss = kayak.MatAdd(kayak.MatSum(kayak.LogMultinomialLoss(Y, T)),
kayak.L2Norm(W, l2_weight), kayak.L1Norm(W, l1_weight))
# Use momentum for the gradient-based optimization.
mom_grad_W = np.zeros(W.shape)
best_loss = np.inf
best_epoch = -1
# Loop over epochs.
for epoch in range(100):
# Track the total loss.
total_loss = 0.0
# Loop over batches -- using batcher as iterator.
for batch in batcher:
# Draw new random dropouts
H.draw_new_mask()
# Compute the loss of this minibatch by asking the Kayak
# object for its value and giving it reset=True.
total_loss += loss.value
# Now ask the loss for its gradient in terms of the
# weights and the biases -- the two things we're trying to
# learn here.
grad_W = loss.grad(W)
grad_B = loss.grad(B)
# Use momentum on the weight gradient.
mom_grad_W *= momentum
mom_grad_W += (1.0 - momentum) * grad_W
# Now make the actual parameter updates.
W.value -= learn_rate * mom_grad_W
B.value -= learn_rate * grad_B
print("Epoch: %d, total loss: %f" % (epoch, total_loss))
if not np.isfinite(total_loss):
print("Training diverged. Returning constraint violation.")
break
if total_loss < best_loss:
best_epoch = epoch
else:
if (epoch - best_epoch) > improvement_thresh:
print("Has been %d epochs without improvement. Aborting." %
(epoch - best_epoch))
break
# After we've trained, we return a sugary little function handle
# that makes things easy. Basically, what we're doing here is
# simply replacing the inputs in the above defined graph and then
# running through it to produce the outputs.
# The point here is that we wind up with a function
# handle the can be called with a numpy object and it produces the
# target values for novel data, using the parameters we just learned.
def predict(x):
X.value = x
H.reinstate_units()
return Y.value
return predict
def kayak_mlp(X, y):
"""
Kayak implementation of a mlp with relu hidden layers and dropout
"""
# Create a batcher object.
batcher = kayak.Batcher(batch_size, X.shape[0])
# count number of rows and columns
num_examples, num_features = np.shape(X)
X = kayak.Inputs(X, batcher)
T = kayak.Targets(y, batcher)
# ----------------------------- first hidden layer -------------------------------
# set up weights for our input layer
# use the same scheme as our numpy mlp
input_range = 1.0 / num_features**(1 / 2)
weights_1 = kayak.Parameter(0.1 * np.random.randn(X.shape[1], layer1_size))
bias_1 = kayak.Parameter(0.1 * np.random.randn(1, layer1_size))
# linear combination of weights and inputs
hidden_1_input = kayak.ElemAdd(kayak.MatMult(X, weights_1), bias_1)
# apply activation function to hidden layer
hidden_1_activation = kayak.HardReLU(hidden_1_input)
# apply a dropout for regularization
hidden_1_out = kayak.Dropout(hidden_1_activation,
layer1_dropout,
batcher=batcher)
# ----------------------------- output layer -----------------------------------
weights_out = kayak.Parameter(0.1 * np.random.randn(layer1_size, 9))
bias_out = kayak.Parameter(0.1 * np.random.randn(1, 9))
# linear combination of layer2 output and output weights
out = kayak.ElemAdd(kayak.MatMult(hidden_1_out, weights_out), bias_out)
# apply activation function to output
yhat = kayak.SoftMax(out)
# ----------------------------- loss function -----------------------------------
loss = kayak.MatAdd(kayak.MatSum(kayak.L2Loss(yhat, T)),
kayak.L2Norm(weights_1, layer1_l2))
# Use momentum for the gradient-based optimization.
mom_grad_W1 = np.zeros(weights_1.shape)
mom_grad_W2 = np.zeros(weights_out.shape)
# Loop over epochs.
plot_loss = np.ones((iterations, 2))
for epoch in xrange(iterations):
# Track the total loss.
total_loss = 0.0
for batch in batcher:
# Compute the loss of this minibatch by asking the Kayak
# object for its value and giving it reset=True.
total_loss += loss.value
# Now ask the loss for its gradient in terms of the
# weights and the biases -- the two things we're trying to
# learn here.
grad_W1 = loss.grad(weights_1)
grad_B1 = loss.grad(bias_1)
grad_W2 = loss.grad(weights_out)
grad_B2 = loss.grad(bias_out)
# Use momentum on the weight gradients.
mom_grad_W1 = momentum * mom_grad_W1 + (1.0 - momentum) * grad_W1
mom_grad_W2 = momentum * mom_grad_W2 + (1.0 - momentum) * grad_W2
# Now make the actual parameter updates.
weights_1.value -= learn_rate * mom_grad_W1
bias_1.value -= learn_rate * grad_B1
weights_out.value -= learn_rate * mom_grad_W2
bias_out.value -= learn_rate * grad_B2
# save values into table to print learning curve at the end of trianing
plot_loss[epoch, 0] = epoch
plot_loss[epoch, 1] = total_loss
print epoch, total_loss
#pyplot.plot(plot_loss[:,0], plot_loss[:,1], linewidth=2.0)
#pyplot.show()
def compute_predictions(x):
X.data = x
batcher.test_mode()
return yhat.value
return compute_predictions
batcher = kayak.Batcher(batch_size, N)
# Build network.
kyk_inputs = kayak.Inputs(X, batcher)
# Labels.
kyk_targets = kayak.Targets(Y, batcher)
# First layer weights and biases.
kyk_W1 = kayak.Parameter(npr.randn(D, H1))
kyk_B1 = kayak.Parameter(npr.randn(1, H1))
# First layer weight mult plus biases, then nonlinearity.
kyk_H1 = kayak.Dropout(kayak.HardReLU(
kayak.ElemAdd(kayak.MatMult(kyk_inputs, kyk_W1), kyk_B1)),
drop_prob=0.5,
batcher=batcher)
# Second layer weights and bias.
kyk_W2 = kayak.Parameter(npr.randn(H1, P))
kyk_B2 = kayak.Parameter(npr.randn(1, P))
# Second layer multiplication.
kyk_out = kayak.Dropout(kayak.HardReLU(
kayak.ElemAdd(kayak.MatMult(kyk_H1, kyk_W2), kyk_B2)),
drop_prob=0.5,
batcher=batcher)
# Elementwise Loss.
kyk_el_loss = kayak.L2Loss(kyk_out, kyk_targets)
def train(inputs, targets):
# Create a batcher object.
batcher = kayak.Batcher(batch_size, inputs.shape[0])
# Inputs and targets need access to the batcher.
X = kayak.Inputs(inputs, batcher)
T = kayak.Targets(targets, batcher)
# First-layer weights and biases, with random initializations.
W1 = kayak.Parameter(0.1 * npr.randn(inputs.shape[1], layer1_sz))
B1 = kayak.Parameter(0.1 * npr.randn(1, layer1_sz))
# First hidden layer: ReLU + Dropout
H1 = kayak.Dropout(kayak.HardReLU(kayak.ElemAdd(kayak.MatMult(X, W1), B1)),
layer1_dropout,
batcher=batcher)
# Second-layer weights and biases, with random initializations.
W2 = kayak.Parameter(0.1 * npr.randn(layer1_sz, layer2_sz))
B2 = kayak.Parameter(0.1 * npr.randn(1, layer2_sz))
# Second hidden layer: ReLU + Dropout
H2 = kayak.Dropout(kayak.HardReLU(kayak.ElemAdd(kayak.MatMult(H1, W2),
B2)),
layer2_dropout,
batcher=batcher)
# Output layer weights and biases, with random initializations.
W3 = kayak.Parameter(0.1 * npr.randn(layer2_sz, 10))
B3 = kayak.Parameter(0.1 * npr.randn(1, 10))
# Output layer.
Y = kayak.LogSoftMax(kayak.ElemAdd(kayak.MatMult(H2, W3), B3))
# The training loss is negative multinomial log likelihood.
loss = kayak.MatSum(kayak.LogMultinomialLoss(Y, T))
# Use momentum for the gradient-based optimization.
mom_grad_W1 = np.zeros(W1.shape)
mom_grad_W2 = np.zeros(W2.shape)
mom_grad_W3 = np.zeros(W3.shape)
# Loop over epochs.
for epoch in xrange(10):
# Track the total loss.
total_loss = 0.0
# Loop over batches -- using batcher as iterator.
for batch in batcher:
# Compute the loss of this minibatch by asking the Kayak
# object for its value and giving it reset=True.
total_loss += loss.value
# Now ask the loss for its gradient in terms of the
# weights and the biases -- the two things we're trying to
# learn here.
grad_W1 = loss.grad(W1)
grad_B1 = loss.grad(B1)
grad_W2 = loss.grad(W2)
grad_B2 = loss.grad(B2)
grad_W3 = loss.grad(W3)
grad_B3 = loss.grad(B3)
# Use momentum on the weight gradients.
mom_grad_W1 = momentum * mom_grad_W1 + (1.0 - momentum) * grad_W1
mom_grad_W2 = momentum * mom_grad_W2 + (1.0 - momentum) * grad_W2
mom_grad_W3 = momentum * mom_grad_W3 + (1.0 - momentum) * grad_W3
# Now make the actual parameter updates.
W1.value -= learn_rate * mom_grad_W1
B1.value -= learn_rate * grad_B1
W2.value -= learn_rate * mom_grad_W2
B2.value -= learn_rate * grad_B2
W3.value -= learn_rate * mom_grad_W3
B3.value -= learn_rate * grad_B3
print epoch, total_loss
# After we've trained, we return a sugary little function handle
# that makes things easy. Basically, what we're doing here is
# handing the output object (not the loss!) a dictionary where the
# key is the Kayak input object 'X' (that is the features being
# used here for logistic regression) and the value in that
# dictionary is being determined by the argument to the lambda
# expression. The point here is that we wind up with a function
# handle the can be called with a numpy object and it produces the
# target values for novel data, using the parameters we just learned.
def compute_predictions(x):
X.data = x
batcher.test_mode()
return Y.value
return compute_predictions
def train(inputs, targets, batch_size, learn_rate, momentum, l1_weight, l2_weight, dropout):
# Create a batcher object.
batcher = kayak.Batcher(batch_size, inputs.shape[0])
# Inputs and targets need access to the batcher.
X = kayak.Inputs(inputs, batcher)
T = kayak.Targets(targets, batcher)
# Weights and biases, with random initializations.
W = kayak.Parameter( 0.1*npr.randn( inputs.shape[1], 10 ))
B = kayak.Parameter( 0.1*npr.randn(1,10) )
# Nothing fancy here: inputs times weights, plus bias, then softmax.
dropout_layer = kayak.Dropout(X, dropout, batcher=batcher)
Y = kayak.LogSoftMax( kayak.ElemAdd( kayak.MatMult(dropout_layer, W), B ) )
# The training loss is negative multinomial log likelihood.
loss = kayak.MatAdd(kayak.MatSum(kayak.LogMultinomialLoss(Y, T)),
kayak.L2Norm(W, l2_weight),
kayak.L1Norm(W, l1_weight))
# Use momentum for the gradient-based optimization.
mom_grad_W = np.zeros(W.shape)
# Loop over epochs.
for epoch in xrange(10):
# Track the total loss and the overall gradient.
total_loss = 0.0
total_grad_W = np.zeros(W.shape)
# Loop over batches -- using batcher as iterator.
for batch in batcher:
# Compute the loss of this minibatch by asking the Kayak
# object for its value and giving it reset=True.
total_loss += loss.value
# Now ask the loss for its gradient in terms of the
# weights and the biases -- the two things we're trying to
# learn here.
grad_W = loss.grad(W)
grad_B = loss.grad(B)
# Use momentum on the weight gradient.
mom_grad_W = momentum*mom_grad_W + (1.0-momentum)*grad_W
# Now make the actual parameter updates.
W.value -= learn_rate * mom_grad_W
B.value -= learn_rate * grad_B
# Keep track of the gradient to see if we're converging.
total_grad_W += grad_W
#print epoch, total_loss, np.sum(total_grad_W**2)
# After we've trained, we return a sugary little function handle
# that makes things easy. Basically, what we're doing here is
# handing the output object (not the loss!) a dictionary where the
# key is the Kayak input object 'X' (that is the features being
# used here for logistic regression) and the value in that
# dictionary is being determined by the argument to the lambda
# expression. The point here is that we wind up with a function
# handle the can be called with a numpy object and it produces the
# target values for novel data, using the parameters we just learned.
def compute_predictions(x):
X.data = x
batcher.test_mode()
return Y.value
return compute_predictions