def make_updater_convnet_theano():
X = TT.tensor4("X") # so shapes can be inferred
y = TT.ivector("y")
np.random.seed(0)
stepsize = TT.scalar("stepsize")
layer1 = SpatialConvolutionTheano(1, 32, kernelshape=(3,3), pad=(0,0),
weight_init=nn.IIDGaussian(std=.1))
conv1 = nn.rectify(layer1(X))
pool1 = theano.tensor.signal.downsample.max_pool_2d(conv1, ds=(3,3), st=(2,2))
layer2 = SpatialConvolutionTheano(32, 32, kernelshape=(3,3), pad=(0,0),
weight_init=nn.IIDGaussian(std=.1))
conv2 = nn.rectify(layer2(pool1))
pool2 = theano.tensor.signal.downsample.max_pool_2d(conv2, ds=(3,3), st=(2,2))
d0,d1,d2,d3 = pool2.shape
flatlayer = pool2.reshape([d0,d1*d2*d3])
nfeats = 800 # theano doens't know how to calculate shapes before compiling
# the function, so this needs to be computed by hand
layer3 = AffineTheano(nfeats, 10)
ip1 = layer3(flatlayer)
logprobs = logsoftmax_theano(ip1)
loss = -logprobs[TT.arange(X.shape[0]), y].mean()
params = [layer1.weight, layer1.bias, layer2.weight, layer2.bias, layer3.weight, layer3.bias]
gparams = TT.grad(loss, params)
updates = [(p, p-stepsize*gp) for (p, gp) in zip(params, gparams)]
return theano.function([X,y, stepsize], loss, updates=updates, allow_input_downcast=True)
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--profile",action="store_true")
parser.add_argument("--unittest",action="store_true")
parser.add_argument("--epochs",type=int,default=10)
args = parser.parse_args()
batchsize = 64
Xshape = (batchsize, 3, 32, 32)
X = cgt.tensor4("X", fixed_shape = Xshape)
y = cgt.vector("y", fixed_shape = (batchsize,), dtype='i4')
conv1 = nn.SpatialConvolution(3, 32, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=1e-4))(X)
relu1 = nn.rectify(conv1)
pool1 = nn.max_pool_2d(relu1, kernelshape=(3,3), stride=(2,2))
conv2 = nn.SpatialConvolution(32, 32, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=0.01))(pool1)
relu2 = nn.rectify(conv2)
pool2 = nn.max_pool_2d(relu2, kernelshape=(3,3), stride=(2,2))
conv3 = nn.SpatialConvolution(32, 64, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=0.01))(pool2)
pool3 = nn.max_pool_2d(conv3, kernelshape=(3,3), stride=(2,2))
relu3 = nn.rectify(pool3)
d0,d1,d2,d3 = relu3.shape
flatlayer = relu3.reshape([d0,d1*d2*d3])
nfeats = cgt.infer_shape(flatlayer)[1]
ip1 = nn.Affine(nfeats, 10)(flatlayer)
logprobs = nn.logsoftmax(ip1)
loss = -logprobs[cgt.arange(batchsize), y].mean()
params = nn.get_parameters(loss)
updates = rmsprop_updates(loss, params, stepsize=1e-3)
train = cgt.function(inputs=[X, y], outputs=[loss], updates=updates)
if args.profile: cgt.profiler.start()
data = fetch_dataset("http://rll.berkeley.edu/cgt-data/cifar10.npz")
Xtrain = data["X_train"]
ytrain = data["y_train"]
print fmt_row(10, ["Epoch","Train NLL","Train Err","Test NLL","Test Err","Epoch Time"])
for i_epoch in xrange(args.epochs):
for start in xrange(0, Xtrain.shape[0], batchsize):
tstart = time.time()
end = start+batchsize
print train(Xtrain[start:end], ytrain[start:end]), time.time()-tstart
if start > batchsize*5: break
# elapsed = time.time() - tstart
# trainerr, trainloss = computeloss(Xtrain[:len(Xtest)], ytrain[:len(Xtest)])
# testerr, testloss = computeloss(Xtest, ytest)
# print fmt_row(10, [i_epoch, trainloss, trainerr, testloss, testerr, elapsed])
if args.profile:
cgt.profiler.print_stats()
return
if args.unittest:
break
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--profile",action="store_true")
parser.add_argument("--unittest",action="store_true")
parser.add_argument("--epochs",type=int,default=10)
args = parser.parse_args()
batchsize = 64
Xshape = (batchsize, 3, 32, 32)
X = cgt.tensor4("X", fixed_shape = Xshape)
y = cgt.vector("y", fixed_shape = (batchsize,), dtype='i4')
conv1 = nn.SpatialConvolution(3, 32, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=1e-4))(X)
relu1 = nn.rectify(conv1)
pool1 = nn.max_pool_2d(relu1, kernelshape=(3,3), stride=(2,2))
conv2 = nn.SpatialConvolution(32, 32, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=0.01))(relu1)
relu2 = nn.rectify(conv2)
pool2 = nn.max_pool_2d(relu2, kernelshape=(3,3), stride=(2,2))
conv3 = nn.SpatialConvolution(32, 64, kernelshape=(5,5), pad=(2,2),
weight_init=nn.IIDGaussian(std=0.01))(pool2)
pool3 = nn.max_pool_2d(conv3, kernelshape=(3,3), stride=(2,2))
relu3 = nn.rectify(pool3)
d0,d1,d2,d3 = relu3.shape
flatlayer = relu3.reshape([d0,d1*d2*d3])
nfeats = cgt.infer_shape(flatlayer)[1]
ip1 = nn.Affine(nfeats, 10)(flatlayer)
logprobs = nn.logsoftmax(ip1)
loss = -logprobs[cgt.arange(batchsize), y].mean()
params = nn.get_parameters(loss)
updates = rmsprop_updates(loss, params, stepsize=1e-3)
train = cgt.function(inputs=[X, y], outputs=[loss], updates=updates)
if args.profile: cgt.profiler.start()
data = np.load("/Users/joschu/Data/cifar-10-batches-py/cifar10.npz")
Xtrain = data["X_train"]
ytrain = data["y_train"]
print fmt_row(10, ["Epoch","Train NLL","Train Err","Test NLL","Test Err","Epoch Time"])
for i_epoch in xrange(args.epochs):
for start in xrange(0, Xtrain.shape[0], batchsize):
tstart = time.time()
end = start+batchsize
print train(Xtrain[start:end], ytrain[start:end]), time.time()-tstart
if start > batchsize*5: break
# elapsed = time.time() - tstart
# trainerr, trainloss = computeloss(Xtrain[:len(Xtest)], ytrain[:len(Xtest)])
# testerr, testloss = computeloss(Xtest, ytest)
# print fmt_row(10, [i_epoch, trainloss, trainerr, testloss, testerr, elapsed])
if args.profile:
cgt.profiler.print_stats()
return
if args.unittest:
break
def dense_model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
X = nn.dropout(X, p_drop_input)
h = nn.rectify(cgt.dot(X, w_h))
h = nn.dropout(h, p_drop_hidden)
h2 = nn.rectify(cgt.dot(h, w_h2))
h2 = nn.dropout(h2, p_drop_hidden)
py_x = nn.softmax(cgt.dot(h2, w_o))
return py_x
def dense_model(X, w_h, w_h2, w_o, p_drop_input, p_drop_hidden):
X = nn.dropout(X, p_drop_input)
h = nn.rectify(cgt.dot(X, w_h))
h = nn.dropout(h, p_drop_hidden)
h2 = nn.rectify(cgt.dot(h, w_h2))
h2 = nn.dropout(h2, p_drop_hidden)
py_x = nn.softmax(cgt.dot(h2, w_o))
return py_x
def build_fc_return_loss(X, y):
"""
Build fully connected network and return loss
"""
np.random.seed(0)
h1 = nn.rectify(nn.Affine(28 * 28, 256, weight_init=nn.IIDGaussian(std=0.1))(X))
h2 = nn.rectify(nn.Affine(256, 256, weight_init=nn.IIDGaussian(std=0.1))(h1))
logprobs = nn.logsoftmax(nn.Affine(256, 10, weight_init=nn.IIDGaussian(std=0.1))(h2))
neglogliks = -logprobs[cgt.arange(X.shape[0]), y]
loss = neglogliks.mean()
return loss
def dense_model3(X, w_h, w_h2, w_h3, w_o, p_drop_input, p_drop_hidden):
X = nn.dropout(X, p_drop_input)
h = nn.rectify(cgt.dot(X, w_h))
h = nn.dropout(h, p_drop_hidden[0])
h2 = nn.rectify(cgt.dot(h, w_h2))
h2 = nn.dropout(h2, p_drop_hidden[1])
h3 = nn.rectify(cgt.dot(h2, w_h3))
h3 = nn.dropout(h3, p_drop_hidden[2])
py_x = nn.softmax(cgt.dot(h3, w_o))
return py_x
def build_fc_return_loss(X, y):
"""
Build fully connected network and return loss
"""
np.random.seed(0)
h1 = nn.rectify(
nn.Affine(28 * 28, 256, weight_init=nn.IIDGaussian(std=.1))(X))
h2 = nn.rectify(
nn.Affine(256, 256, weight_init=nn.IIDGaussian(std=.1))(h1))
logprobs = nn.logsoftmax(
nn.Affine(256, 10, weight_init=nn.IIDGaussian(std=.1))(h2))
neglogliks = -logprobs[cgt.arange(X.shape[0]), y]
loss = neglogliks.mean()
return loss
def build_convnet_return_loss(X, y):
np.random.seed(0)
conv1 = nn.rectify(
nn.SpatialConvolution(1, 32, kernelshape=(3, 3), pad=(0, 0), weight_init=nn.IIDGaussian(std=0.1))(X)
)
pool1 = nn.max_pool_2d(conv1, kernelshape=(3, 3), stride=(2, 2))
conv2 = nn.rectify(
nn.SpatialConvolution(32, 32, kernelshape=(3, 3), pad=(0, 0), weight_init=nn.IIDGaussian(std=0.1))(pool1)
)
pool2 = nn.max_pool_2d(conv2, kernelshape=(3, 3), stride=(2, 2))
d0, d1, d2, d3 = pool2.shape
flatlayer = pool2.reshape([d0, d1 * d2 * d3])
nfeats = cgt.infer_shape(flatlayer)[1]
logprobs = nn.logsoftmax(nn.Affine(nfeats, 10)(flatlayer))
loss = -logprobs[cgt.arange(X.shape[0]), y].mean()
return loss
def make_updater_fc_theano():
X = TT.matrix("X")
y = TT.ivector("y")
np.random.seed(0)
stepsize = TT.scalar("stepsize")
layer1 = AffineTheano(28 * 28, 256, weight_init=nn.IIDGaussian(std=0.1))
h1 = nn.rectify(layer1(X))
layer2 = AffineTheano(256, 256, weight_init=nn.IIDGaussian(std=0.1))
h2 = nn.rectify(layer2(h1))
logprobs = logsoftmax_theano(AffineTheano(256, 10, weight_init=nn.IIDGaussian(std=0.1))(h2))
neglogliks = -logprobs[TT.arange(X.shape[0]), y]
loss = neglogliks.mean()
params = [layer1.weight, layer1.bias, layer2.weight, layer2.bias]
gparams = TT.grad(loss, params)
updates = [(p, p - stepsize * gp) for (p, gp) in zip(params, gparams)]
return theano.function([X, y, stepsize], loss, updates=updates, allow_input_downcast=True)
def make_updater_convnet_theano():
X = TT.tensor4("X") # so shapes can be inferred
y = TT.ivector("y")
np.random.seed(0)
stepsize = TT.scalar("stepsize")
layer1 = SpatialConvolutionTheano(1,
32,
kernelshape=(3, 3),
pad=(0, 0),
weight_init=nn.IIDGaussian(std=.1))
conv1 = nn.rectify(layer1(X))
pool1 = theano.tensor.signal.downsample.max_pool_2d(conv1,
ds=(3, 3),
st=(2, 2))
layer2 = SpatialConvolutionTheano(32,
32,
kernelshape=(3, 3),
pad=(0, 0),
weight_init=nn.IIDGaussian(std=.1))
conv2 = nn.rectify(layer2(pool1))
pool2 = theano.tensor.signal.downsample.max_pool_2d(conv2,
ds=(3, 3),
st=(2, 2))
d0, d1, d2, d3 = pool2.shape
flatlayer = pool2.reshape([d0, d1 * d2 * d3])
nfeats = 800 # theano doens't know how to calculate shapes before compiling
# the function, so this needs to be computed by hand
layer3 = AffineTheano(nfeats, 10)
ip1 = layer3(flatlayer)
logprobs = logsoftmax_theano(ip1)
loss = -logprobs[TT.arange(X.shape[0]), y].mean()
params = [
layer1.weight, layer1.bias, layer2.weight, layer2.bias,
layer3.weight, layer3.bias
]
gparams = TT.grad(loss, params)
updates = [(p, p - stepsize * gp) for (p, gp) in zip(params, gparams)]
return theano.function([X, y, stepsize],
loss,
updates=updates,
allow_input_downcast=True)
def convnet_model(X, w, w2, w3, w4, w_o, p_drop_conv, p_drop_hidden):
l1a = nn.rectify(nn.conv2d(X, w, kernelshape=(3,3), pad=(1,1)))
l1 = nn.max_pool_2d(l1a, kernelshape=(2, 2), stride=(2,2))
l1 = nn.dropout(l1, p_drop_conv)
l2a = nn.rectify(nn.conv2d(l1, w2, kernelshape=(3,3), pad=(1,1)))
l2 = nn.max_pool_2d(l2a, kernelshape=(2, 2), stride=(2,2))
l2 = nn.dropout(l2, p_drop_conv)
l3a = nn.rectify(nn.conv2d(l2, w3, kernelshape=(3,3), pad=(1,1)))
l3b = nn.max_pool_2d(l3a, kernelshape=(2, 2), stride=(2,2))
batchsize,channels,rows,cols = l3b.shape
l3 = cgt.reshape(l3b, [batchsize, channels*rows*cols])
l3 = nn.dropout(l3, p_drop_conv)
l4 = nn.rectify(cgt.dot(l3, w4))
l4 = nn.dropout(l4, p_drop_hidden)
pyx = nn.softmax(cgt.dot(l4, w_o))
return pyx
def convnet_model(X, w, w2, w3, w4, w_o, p_drop_conv, p_drop_hidden):
l1a = nn.rectify(nn.conv2d(X, w, kernelshape=(3, 3), pad=(1, 1)))
l1 = nn.max_pool_2d(l1a, kernelshape=(2, 2), stride=(2, 2))
l1 = nn.dropout(l1, p_drop_conv)
l2a = nn.rectify(nn.conv2d(l1, w2, kernelshape=(3, 3), pad=(1, 1)))
l2 = nn.max_pool_2d(l2a, kernelshape=(2, 2), stride=(2, 2))
l2 = nn.dropout(l2, p_drop_conv)
l3a = nn.rectify(nn.conv2d(l2, w3, kernelshape=(3, 3), pad=(1, 1)))
l3b = nn.max_pool_2d(l3a, kernelshape=(2, 2), stride=(2, 2))
batchsize, channels, rows, cols = l3b.shape
l3 = cgt.reshape(l3b, [batchsize, channels * rows * cols])
l3 = nn.dropout(l3, p_drop_conv)
l4 = nn.rectify(cgt.dot(l3, w4))
l4 = nn.dropout(l4, p_drop_hidden)
pyx = nn.softmax(cgt.dot(l4, w_o))
return pyx
def make_updater_fc_theano():
X = TT.matrix("X")
y = TT.ivector("y")
np.random.seed(0)
stepsize = TT.scalar("stepsize")
layer1 = AffineTheano(28 * 28, 256, weight_init=nn.IIDGaussian(std=.1))
h1 = nn.rectify(layer1(X))
layer2 = AffineTheano(256, 256, weight_init=nn.IIDGaussian(std=.1))
h2 = nn.rectify(layer2(h1))
logprobs = logsoftmax_theano(
AffineTheano(256, 10, weight_init=nn.IIDGaussian(std=.1))(h2))
neglogliks = -logprobs[TT.arange(X.shape[0]), y]
loss = neglogliks.mean()
params = [layer1.weight, layer1.bias, layer2.weight, layer2.bias]
gparams = TT.grad(loss, params)
updates = [(p, p - stepsize * gp) for (p, gp) in zip(params, gparams)]
return theano.function([X, y, stepsize],
loss,
updates=updates,
allow_input_downcast=True)
def __init__(self, n_actions):
Serializable.__init__(self, n_actions)
cgt.set_precision('double')
n_in = 128
o_no = cgt.matrix("o_no",fixed_shape=(None,n_in))
a_n = cgt.vector("a_n",dtype='i8')
q_n = cgt.vector("q_n")
oldpdist_np = cgt.matrix("oldpdists")
nhid, nhid2 = 64, 64
h0 = (o_no - 128.0)/128.0
d0 = nn.dropout(h1, .2)
h1 = nn.rectify(nn.Affine(128,nhid,weight_init=nn.IIDGaussian(std=.1))(d0))
d1 = nn.dropout(h1, .2)
h2 = nn.rectify(nn.Affine(nhid,nhid2,weight_init=nn.IIDGaussian(std=.1))(d1))
# d2 = nn.dropout(h2, .2)
probs_na = nn.softmax(nn.Affine(nhid2,n_actions,weight_init=nn.IIDGaussian(std=0.01))(d2))
logprobs_na = cgt.log(probs_na)
b = cgt.size(o_no, 0)
logps_n = logprobs_na[cgt.arange(b), a_n]
surr = (logps_n*q_n).mean()
kl = (oldpdist_np * cgt.log(oldpdist_np/probs_na)).sum(axis=1).mean()
params = nn.get_parameters(surr)
gradsurr = cgt.grad(surr, params)
flatgrad = cgt.concatenate([p.flatten() for p in gradsurr])
lam = cgt.scalar()
penobj = surr - lam * kl
self._f_grad_lagrangian = cgt.function([lam, oldpdist_np, o_no, a_n, q_n],
cgt.concatenate([p.flatten() for p in cgt.grad(penobj,params)]))
self.f_pdist = cgt.function([o_no], probs_na)
self.f_probs = cgt.function([o_no], probs_na)
self.f_surr_kl = cgt.function([oldpdist_np, o_no, a_n, q_n], [surr, kl])
self.f_gradlogp = cgt.function([oldpdist_np, o_no, a_n, q_n], flatgrad)
self.pc = ParamCollection(params)
def build_convnet_return_loss(X, y):
np.random.seed(0)
conv1 = nn.rectify(
nn.SpatialConvolution(1,
32,
kernelshape=(3, 3),
pad=(0, 0),
weight_init=nn.IIDGaussian(std=.1))(X))
pool1 = nn.max_pool_2d(conv1, kernelshape=(3, 3), stride=(2, 2))
conv2 = nn.rectify(
nn.SpatialConvolution(32,
32,
kernelshape=(3, 3),
pad=(0, 0),
weight_init=nn.IIDGaussian(std=.1))(pool1))
pool2 = nn.max_pool_2d(conv2, kernelshape=(3, 3), stride=(2, 2))
d0, d1, d2, d3 = pool2.shape
flatlayer = pool2.reshape([d0, d1 * d2 * d3])
nfeats = cgt.infer_shape(flatlayer)[1]
logprobs = nn.logsoftmax(nn.Affine(nfeats, 10)(flatlayer))
loss = -logprobs[cgt.arange(X.shape[0]), y].mean()
return loss
def make_deep_rrnn_rot_relu(size_input, size_mem, n_layers, size_output,
size_batch_in, k_in, k_h):
inputs = [cgt.matrix() for i_layer in xrange(n_layers + 1)]
outputs = []
print 'input_size: ', size_input
for i_layer in xrange(n_layers):
prev_h = inputs[
i_layer +
1] # note that inputs[0] is the external input, so we add 1
x = inputs[0] if i_layer == 0 else outputs[i_layer - 1]
size_x = size_input if i_layer == 0 else size_mem
size_batch = prev_h.shape[0]
xform_h_param = nn.TensorParam((2 * k_h, size_mem), name="rotxform")
xform_h_non = xform_h_param.weight
xform_h_non.props["is_rotation"] = True
xform_h_norm = cgt.norm(xform_h_non, axis=1, keepdims=True)
xform_h = cgt.broadcast('/', xform_h_non, xform_h_norm, "xx,x1")
add_in_lin = nn.Affine(size_x, size_mem)(x)
add_in_relu = nn.rectify(add_in_lin)
prev_h_scaled = nn.scale_mag(prev_h)
h_in_added = prev_h_scaled + add_in_relu
inters_h = [h_in_added]
colon = slice(None, None, None)
for i in xrange(2 * k_h):
inter_in = inters_h[-1]
r_cur = xform_h[i, :]
#r_cur = cgt.subtensor(xform_h, [i, colon])
r_cur_2_transpose = cgt.reshape(r_cur, (size_mem, 1))
r_cur_2 = cgt.reshape(r_cur, (1, size_mem))
ref_cur = cgt.dot(cgt.dot(inter_in, r_cur_2_transpose), r_cur_2)
inter_out = inter_in - 2 * ref_cur
inters_h.append(inter_out)
next_h = inters_h[-1]
outputs.append(next_h)
category_activations = nn.Affine(size_mem, size_output,
name="pred")(outputs[-1])
logprobs = nn.logsoftmax(category_activations)
outputs.append(logprobs)
#print 'len outputs:', len(outputs)
#print 'len inputs:', len(inputs)
return nn.Module(inputs, outputs)
def make_deep_rrnn_rot_relu(size_input, size_mem, n_layers, size_output, size_batch_in, k_in, k_h):
inputs = [cgt.matrix() for i_layer in xrange(n_layers+1)]
outputs = []
print 'input_size: ', size_input
for i_layer in xrange(n_layers):
prev_h = inputs[i_layer+1] # note that inputs[0] is the external input, so we add 1
x = inputs[0] if i_layer==0 else outputs[i_layer-1]
size_x = size_input if i_layer==0 else size_mem
size_batch = prev_h.shape[0]
xform_h_param = nn.TensorParam((2 * k_h, size_mem), name="rotxform")
xform_h_non = xform_h_param.weight
xform_h_non.props["is_rotation"] = True
xform_h_norm = cgt.norm(xform_h_non, axis=1, keepdims=True)
xform_h = cgt.broadcast('/', xform_h_non, xform_h_norm, "xx,x1")
add_in_lin = nn.Affine(size_x, size_mem)(x)
add_in_relu = nn.rectify(add_in_lin)
prev_h_scaled = nn.scale_mag(prev_h)
h_in_added = prev_h_scaled + add_in_relu
inters_h = [h_in_added]
colon = slice(None, None, None)
for i in xrange(2 * k_h):
inter_in = inters_h[-1]
r_cur = xform_h[i, :]
#r_cur = cgt.subtensor(xform_h, [i, colon])
r_cur_2_transpose = cgt.reshape(r_cur, (size_mem, 1))
r_cur_2 = cgt.reshape(r_cur, (1, size_mem))
ref_cur = cgt.dot(cgt.dot(inter_in, r_cur_2_transpose), r_cur_2)
inter_out = inter_in - 2 * ref_cur
inters_h.append(inter_out)
next_h = inters_h[-1]
outputs.append(next_h)
category_activations = nn.Affine(size_mem, size_output,name="pred")(outputs[-1])
logprobs = nn.logsoftmax(category_activations)
outputs.append(logprobs)
#print 'len outputs:', len(outputs)
#print 'len inputs:', len(inputs)
return nn.Module(inputs, outputs)
def __init__(self, num_features=None, num_hidden=100):
stepsize = 0.01
# with shape (batchsize, ncols)
X = cgt.matrix("X", fixed_shape=(1, num_features))
# y: a symbolic variable representing the rewards, which are integers
y = cgt.scalar("y", dtype='float64')
hid1 = nn.rectify(
nn.Affine(num_features, num_hidden, weight_init=nn.IIDGaussian(std=.1), bias_init=nn.Constant(1))(X)
)
# One final fully-connected layer, and then a linear activation output for reward
output = nn.Affine(num_hidden, 1, weight_init=nn.IIDGaussian(std=.1), bias_init=nn.Constant(1))(hid1)
abs_deviation = cgt.abs(output - y).mean()
params = nn.get_parameters(abs_deviation)
gparams = cgt.grad(abs_deviation, params)
updates = [(p, p-stepsize*gp) for (p, gp) in zip(params, gparams)]
self.predictor = cgt.function([X], output)
self.updater = cgt.function([X, y], abs_deviation, updates=updates)
def __init__(self, num_features=None, num_hidden=100):
stepsize = 0.01
# with shape (batchsize, ncols)
X = cgt.matrix("X", fixed_shape=(1, num_features))
# y: a symbolic variable representing the rewards, which are integers
y = cgt.scalar("y", dtype='float64')
hid1 = nn.rectify(
nn.Affine(num_features,
num_hidden,
weight_init=nn.IIDGaussian(std=.1),
bias_init=nn.Constant(1))(X))
# One final fully-connected layer, and then a linear activation output for reward
output = nn.Affine(num_hidden,
1,
weight_init=nn.IIDGaussian(std=.1),
bias_init=nn.Constant(1))(hid1)
abs_deviation = cgt.abs(output - y).mean()
params = nn.get_parameters(abs_deviation)
gparams = cgt.grad(abs_deviation, params)
updates = [(p, p - stepsize * gp) for (p, gp) in zip(params, gparams)]
self.predictor = cgt.function([X], output)
self.updater = cgt.function([X, y], abs_deviation, updates=updates)
Xtrain, Xtest, ytrain, ytest = load_mnist(onehot=False)
# shuffle the data
np.random.seed(42)
sortinds = np.random.permutation(Xtrain.shape[0])
Xtrain = Xtrain[sortinds]
ytrain = ytrain[sortinds]
# Model:
# Two linear/affine layers with a ReLU activation in between
# followed by a logsoftmax.
X = cgt.matrix('X', fixed_shape=(None, 784))
y = cgt.vector('y', dtype='i8')
layer1 = nn.Affine(784, 400, weight_init=nn.XavierNormal())(X)
act1 = nn.rectify(layer1)
layer2 = nn.Affine(400, 400, weight_init=nn.XavierNormal())(act1)
act2 = nn.rectify(layer2)
probs = nn.softmax(nn.Affine(400, 10)(act2))
y_preds = cgt.argmax(probs, axis=1)
cost = -cgt.mean(categorical.loglik(y, probs))
err = cgt.cast(cgt.not_equal(y, y_preds), cgt.floatX).mean()
params = nn.get_parameters(cost)
updates = nn.sgd(cost, params, learning_rate) # train via sgd
# training function
f = cgt.function(inputs=[X, y], outputs=[], updates=updates)
# compute the cost and error
cost_and_err = cgt.function(inputs=[X, y], outputs=[cost, err])
if X.ndim == 4:
X = cgt.reshape(X, [X.shape[0], X.shape[1]*X.shape[2]*X.shape[3]] )
param = layer.inner_product_param
nchanin = infer_shape(X)[1]
Wshape = (param.num_output, nchanin)
Wname = layer.param[0].name or layer.name+":W"
Wval = np.empty(Wshape, dtype=cgt.floatX)
W = name2node[Wname] = cgt.shared(Wval, name=Wname, fixed_shape_mask="all")
bshape = (1, param.num_output)
bname = layer.param[1].name or layer.name+":b"
bval = np.empty(bshape, dtype=cgt.floatX)
b = name2node[bname] = cgt.shared(bval, name=bname, fixed_shape_mask="all")
yname = layer.top[0]
output = [cgt.broadcast("+",X.dot(W), b, "xx,1x") ]
elif layer.type == "ReLU":
output = [nn.rectify(inputs[0])]
elif layer.type == "Softmax":
output = [nn.softmax(inputs[0])]
elif layer.type == "LRN":
# XXX needs params
param = layer.lrn_param
output = [nn.lrn(inputs[0], param.alpha,param.beta, param.local_size)]
elif layer.type == "Concat":
param = layer.concat_param
output = [cgt.concatenate(inputs, param.concat_dim) ]
elif layer.type == "Dropout":
output = [nn.dropout(inputs[0])]
elif layer.type == "SoftmaxWithLoss":
output = [nn.loglik_softmax(inputs[0], inputs[1])]
elif layer.type == "Accuracy":
output = [nn.zero_one_loss(inputs[0], inputs[1])]
ytrain = ytrain[sortinds]
# reshape for convnet
Xtrainimg = Xtrain.reshape(-1, 1, 28, 28)
Xtestimg = Xtest.reshape(-1, 1, 28, 28)
# Model:
# Make it VGG-like
# VGG nets have 3x3 kernels with length 1 padding and max-pooling has all 2s.
#
# VGG is a large model so here well just do a small part of it.
X = cgt.tensor4('X', fixed_shape=(None, 1, 28, 28))
y = cgt.vector('y', dtype='i8')
conv1 = nn.rectify(
nn.SpatialConvolution(1, 32, kernelshape=(3,3), stride=(1,1), pad=(1,1), weight_init=nn.IIDGaussian(std=.1))(X)
)
pool1 = nn.max_pool_2d(conv1, kernelshape=(2,2), stride=(2,2))
conv2 = nn.rectify(
nn.SpatialConvolution(32, 32, kernelshape=(3,3), stride=(1,1), pad=(1,1), weight_init=nn.IIDGaussian(std=.1))(pool1)
)
pool2 = nn.max_pool_2d(conv2, kernelshape=(2,2), stride=(2,2))
d0, d1, d2, d3 = pool2.shape
flat = pool2.reshape([d0, d1*d2*d3])
nfeats = cgt.infer_shape(flat)[1]
probs = nn.softmax(nn.Affine(nfeats, 10)(flat))
cost = -categorical.loglik(y, probs).mean()
y_preds = cgt.argmax(probs, axis=1)
def build_fcn_action_cond_encoder_net(input_shapes, levels=None):
x_shape, u_shape = input_shapes
x_c_dim = x_shape[0]
x1_c_dim = 16
levels = levels or [3]
levels = sorted(set(levels))
X = cgt.tensor4('X', fixed_shape=(None, ) + x_shape)
U = cgt.matrix('U', fixed_shape=(None, ) + u_shape)
# encoding
Xlevels = {}
for level in range(levels[-1] + 1):
if level == 0:
Xlevel = X
else:
if level == 1:
xlevelm1_c_dim = x_c_dim
xlevel_c_dim = x1_c_dim
else:
xlevelm1_c_dim = xlevel_c_dim
xlevel_c_dim = 2 * xlevel_c_dim
Xlevel_1 = nn.rectify(
nn.SpatialConvolution(xlevelm1_c_dim,
xlevel_c_dim,
kernelshape=(3, 3),
pad=(1, 1),
stride=(1, 1),
name='conv%d_1' % level,
weight_init=nn.IIDGaussian(std=0.01))(
Xlevels[level - 1]))
Xlevel_2 = nn.rectify(
nn.SpatialConvolution(
xlevel_c_dim,
xlevel_c_dim,
kernelshape=(3, 3),
pad=(1, 1),
stride=(1, 1),
name='conv%d_2' % level,
weight_init=nn.IIDGaussian(std=0.01))(Xlevel_1))
Xlevel = nn.max_pool_2d(Xlevel_2,
kernelshape=(2, 2),
pad=(0, 0),
stride=(2, 2))
Xlevels[level] = Xlevel
# bilinear
Xlevels_next_pred_0 = {}
Ylevels = OrderedDict()
Ylevels_diff_pred = OrderedDict()
for level in levels:
Xlevel = Xlevels[level]
Xlevel_diff_pred = Bilinear(input_shapes,
b=None,
axis=2,
name='bilinear%d' % level)(Xlevel, U)
Xlevels_next_pred_0[level] = Xlevel + Xlevel_diff_pred
Ylevels[level] = Xlevel.reshape(
(Xlevel.shape[0], cgt.mul_multi(Xlevel.shape[1:])))
Ylevels_diff_pred[level] = Xlevel_diff_pred.reshape(
(Xlevel_diff_pred.shape[0],
cgt.mul_multi(Xlevel_diff_pred.shape[1:])))
# decoding
Xlevels_next_pred = {}
for level in range(levels[-1] + 1)[::-1]:
if level == levels[-1]:
Xlevel_next_pred = Xlevels_next_pred_0[level]
else:
if level == 0:
xlevelm1_c_dim = x_c_dim
elif level < levels[-1] - 1:
xlevel_c_dim = xlevelm1_c_dim
xlevelm1_c_dim = xlevelm1_c_dim // 2
Xlevel_next_pred_2 = SpatialDeconvolution(
xlevel_c_dim,
xlevel_c_dim,
kernelshape=(2, 2),
pad=(0, 0),
stride=(2, 2),
name='upsample%d' % (level + 1),
weight_init=nn.IIDGaussian(std=0.01))(Xlevels_next_pred[
level +
1]) # TODO initialize with bilinear # TODO should rectify?
Xlevel_next_pred_1 = nn.rectify(
SpatialDeconvolution(
xlevel_c_dim,
xlevel_c_dim,
kernelshape=(3, 3),
pad=(1, 1),
stride=(1, 1),
name='deconv%d_2' % (level + 1),
weight_init=nn.IIDGaussian(std=0.01))(Xlevel_next_pred_2))
nonlinearity = nn.rectify if level > 0 else cgt.tanh
Xlevel_next_pred = nonlinearity(
SpatialDeconvolution(
xlevel_c_dim,
xlevelm1_c_dim,
kernelshape=(3, 3),
pad=(1, 1),
stride=(1, 1),
name='deconv%d_1' % (level + 1),
weight_init=nn.IIDGaussian(std=0.01))(Xlevel_next_pred_1))
if level in Xlevels_next_pred_0:
coefs = nn.parameter(nn.init_array(nn.Constant(0.5), (2, )),
name='sum%d.coef' % level)
Xlevel_next_pred = coefs[0] * Xlevel_next_pred + coefs[
1] * Xlevels_next_pred_0[level]
# TODO: tanh should be after sum
Xlevels_next_pred[level] = Xlevel_next_pred
X_next_pred = Xlevels_next_pred[0]
Y = cgt.concatenate(Ylevels.values(), axis=1)
Y_diff_pred = cgt.concatenate(Ylevels_diff_pred.values(), axis=1)
X_diff = cgt.tensor4('X_diff', fixed_shape=(None, ) + x_shape)
X_next = X + X_diff
loss = ((X_next - X_next_pred)**2).mean(axis=0).sum() / 2.
net_name = 'FcnActionCondEncoderNet_levels' + ''.join(
str(level) for level in levels)
input_vars = OrderedDict([(var.name, var) for var in [X, U, X_diff]])
pred_vars = OrderedDict([('Y_diff_pred', Y_diff_pred), ('Y', Y),
('X_next_pred', X_next_pred)])
return net_name, input_vars, pred_vars, loss
Wshape = (param.num_output, nchanin)
Wname = layer.param[0].name or layer.name + ":W"
Wval = np.empty(Wshape, dtype=cgt.floatX)
W = name2node[Wname] = cgt.shared(Wval,
name=Wname,
fixed_shape_mask="all")
bshape = (1, param.num_output)
bname = layer.param[1].name or layer.name + ":b"
bval = np.empty(bshape, dtype=cgt.floatX)
b = name2node[bname] = cgt.shared(bval,
name=bname,
fixed_shape_mask="all")
yname = layer.top[0]
output = [cgt.broadcast("+", X.dot(W), b, "xx,1x")]
elif layer.type == "ReLU":
output = [nn.rectify(inputs[0])]
elif layer.type == "Softmax":
output = [nn.softmax(inputs[0])]
elif layer.type == "LRN":
# XXX needs params
param = layer.lrn_param
output = [
nn.lrn(inputs[0], param.alpha, param.beta, param.local_size)
]
elif layer.type == "Concat":
param = layer.concat_param
output = [cgt.concatenate(inputs, param.concat_dim)]
elif layer.type == "Dropout":
output = [nn.dropout(inputs[0])]
elif layer.type == "SoftmaxWithLoss":
output = [nn.loglik_softmax(inputs[0], inputs[1])]
