def __call__(self, state):
h = state
for layer in self.hidden_layers:
h = F.relu(layer(h))
v = self.v(h)
mu = self.mu(h)
if self.scale_mu:
mu = scale_by_tanh(mu, high=self.action_space.high,
low=self.action_space.low)
mat_diag = F.exp(self.mat_diag(h))
if hasattr(self, 'mat_non_diag'):
mat_non_diag = self.mat_non_diag(h)
tril = lower_triangular_matrix(mat_diag, mat_non_diag)
mat = matmul_v3(tril, tril, transb=True)
else:
mat = F.expand_dims(mat_diag ** 2, axis=2)
return QuadraticActionValue(
mu, mat, v, min_action=self.action_space.low,
max_action=self.action_space.high)
python类expand_dims()的实例源码
def __call__(self, state):
h = self.hidden_layers(state)
v = self.v(h)
mu = self.mu(h)
if self.scale_mu:
mu = scale_by_tanh(mu, high=self.action_space.high,
low=self.action_space.low)
mat_diag = F.exp(self.mat_diag(h))
if hasattr(self, 'mat_non_diag'):
mat_non_diag = self.mat_non_diag(h)
tril = lower_triangular_matrix(mat_diag, mat_non_diag)
mat = matmul_v3(tril, tril, transb=True)
else:
mat = F.expand_dims(mat_diag ** 2, axis=2)
return QuadraticActionValue(
mu, mat, v, min_action=self.action_space.low,
max_action=self.action_space.high)
def predict(self, xs):
"""
batch: list of splitted sentences
"""
xs = [self.extractor.process(x) for x in xs]
batchsize = len(xs)
ws, cs, ls = zip(*xs)
ws = map(self.emb_word, ws)
cs = [F.squeeze(
F.max_pooling_2d(
self.conv_char(
F.expand_dims(
self.emb_char(c), 1)), (l, 1)))
for c, l in zip(cs, ls)]
xs_f = [F.dropout(F.concat([w, c]),
self.dropout_ratio, train=self.train) for w, c in zip(ws, cs)]
xs_b = [x[::-1] for x in xs_f]
cx_f, hx_f, cx_b, hx_b = self._init_state(batchsize)
_, _, hs_f = self.lstm_f(hx_f, cx_f, xs_f, train=self.train)
_, _, hs_b = self.lstm_b(hx_b, cx_b, xs_b, train=self.train)
hs_b = [x[::-1] for x in hs_b]
ys = [self.linear2(F.relu(self.linear1(F.concat([h_f, h_b]))))
for h_f, h_b in zip(hs_f, hs_b)]
return [y.data[1:-1] for y in ys]
def __call__(self, X, ht_enc):
pad = self._kernel_size - 1
WX = self.W(X)
if pad > 0:
WX = WX[..., :-pad]
Vh = self.V(ht_enc)
# copy Vh
# e.g.
# WX = [[[ 0 1 2]
# [ 3 4 5]
# [ 6 7 8]
# Vh = [[11, 12, 13]]
#
# Vh, WX = F.broadcast(F.expand_dims(Vh, axis=2), WX)
#
# WX = [[[ 0 1 2]
# [ 3 4 5]
# [ 6 7 8]
# Vh = [[[ 11 11 11]
# [ 12 12 12]
# [ 13 13 13]
Vh, WX = functions.broadcast(functions.expand_dims(Vh, axis=2), WX)
return self.pool(functions.split_axis(WX + Vh, self.num_split, axis=1))
def _context(self, p, fb_mat, fbe_mat):
batch_size, source_length, _ = fb_mat.data.shape
# {pe,e}_mat: shape = [batch * srclen, atten]
pe_mat = F.reshape(
F.broadcast_to(
F.expand_dims(self.p_e(p), 1),
[batch_size, source_length, self.atten_size]),
[batch_size * source_length, self.atten_size])
e_mat = F.tanh(fbe_mat + pe_mat)
# a_mat: shape = [batch, srclen]
a_mat = F.softmax(F.reshape(self.e_a(e_mat), [batch_size, source_length]))
# q: shape = [batch, 2 * hidden]
q = F.reshape(
F.batch_matmul(a_mat, fb_mat, transa=True),
[batch_size, 2 * self.hidden_size])
return q
def __call__(self, x):
minibatch_size = x.shape[0]
activation = F.reshape(self.t(x), (-1, self.n_kernels, self.kernel_dim))
activation_ex = F.expand_dims(activation, 3)
activation_ex_t = F.expand_dims(F.transpose(activation, (1, 2, 0)), 0)
activation_ex, activation_ex_t = F.broadcast(activation_ex, activation_ex_t)
diff = activation_ex - activation_ex_t
xp = chainer.cuda.get_array_module(x.data)
eps = F.expand_dims(xp.eye(minibatch_size, dtype=xp.float32), 1)
eps = F.broadcast_to(eps, (minibatch_size, self.n_kernels, minibatch_size))
sum_diff = F.sum(abs(diff), axis=2)
sum_diff = F.broadcast_to(sum_diff, eps.shape)
abs_diff = sum_diff + eps
minibatch_features = F.sum(F.exp(-abs_diff), 2)
return F.concat((x, minibatch_features), axis=1)
def __call__(self, x):
xp = chainer.cuda.get_array_module(x.data)
batchsize = x.shape[0]
if self.train_weights == False and self.initial_T is not None:
self.T.W.data = self.initial_T
M = F.reshape(self.T(x), (-1, self.num_kernels, self.ndim_kernel))
M = F.expand_dims(M, 3)
M_T = F.transpose(M, (3, 1, 2, 0))
M, M_T = F.broadcast(M, M_T)
norm = F.sum(abs(M - M_T), axis=2)
eraser = F.broadcast_to(xp.eye(batchsize, dtype=x.dtype).reshape((batchsize, 1, batchsize)), norm.shape)
c_b = F.exp(-(norm + 1e6 * eraser))
o_b = F.sum(c_b, axis=2)
if self.train_weights == False:
self.initial_T = self.T.W.data
return F.concat((x, o_b), axis=1)
def __call__(self, v, h, label):
v_t = self.vertical_conv_t(v)
v_s = self.vertical_conv_s(v)
to_vertical_t = self.v_to_h_conv_t(v_t)
to_vertical_s = self.v_to_h_conv_s(v_s)
# v_gate = self.vertical_gate_conv(v)
# label bias is added to both vertical and horizontal conv
# here we take only shape as it should be the same
label = F.broadcast_to(F.expand_dims(F.expand_dims(self.label(label), -1), -1), v_t.shape)
v_t, v_s = v_t + label, v_s + label
v = F.tanh(v_t) * F.sigmoid(v_s)
h_t = self.horizontal_conv_t(h)
h_s = self.horizontal_conv_s(h)
h_t, h_s = h_t + to_vertical_t + label, h_s + to_vertical_s + label
h = self.horizontal_output(F.tanh(h_t) * F.sigmoid(h_s))
return v, h
def __call__(self, x):
xp = chainer.cuda.get_array_module(x.data)
batchsize = x.shape[0]
if self.train_weights == False and self.initial_T is not None:
self.T.W.data = self.initial_T
M = F.reshape(self.T(x), (-1, self.num_kernels, self.ndim_kernel))
M = F.expand_dims(M, 3)
M_T = F.transpose(M, (3, 1, 2, 0))
M, M_T = F.broadcast(M, M_T)
norm = F.sum(abs(M - M_T), axis=2)
eraser = F.broadcast_to(xp.eye(batchsize, dtype=x.dtype).reshape((batchsize, 1, batchsize)), norm.shape)
c_b = F.exp(-(norm + 1e6 * eraser))
o_b = F.sum(c_b, axis=2)
if self.train_weights == False:
self.initial_T = self.T.W.data
return F.concat((x, o_b), axis=1)
def __call__(self, x):
xp = chainer.cuda.get_array_module(x.data)
batchsize = x.shape[0]
if self.train_weights == False and self.initial_T is not None:
self.T.W.data = self.initial_T
M = F.reshape(self.T(x), (-1, self.num_kernels, self.ndim_kernel))
M = F.expand_dims(M, 3)
M_T = F.transpose(M, (3, 1, 2, 0))
M, M_T = F.broadcast(M, M_T)
norm = F.sum(abs(M - M_T), axis=2)
eraser = F.broadcast_to(xp.eye(batchsize, dtype=x.dtype).reshape((batchsize, 1, batchsize)), norm.shape)
c_b = F.exp(-(norm + 1e6 * eraser))
o_b = F.sum(c_b, axis=2)
if self.train_weights == False:
self.initial_T = self.T.W.data
return F.concat((x, o_b), axis=1)
predictive_autoencoder.py 文件源码
项目：Multitask-and-Transfer-Learning
作者: AI-ON
项目源码
文件源码
阅读 29
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def __call__(self, x, action):
h1 = F.relu(self.conv1(x))
index = F.expand_dims(np.array(action, dtype=np.int32), axis=0)
h2 = F.relu(self.embed_conv2d(index, x))
h = F.concat((h1, h2), axis=1) # Glue together the action convolutions
h = F.relu(self.conv2(h))
h = F.relu(self.conv3(h))
h = F.relu(self.conv_gru1(h))
h_img = F.relu(self.deconv1(h))
h_img = self.deconv2(h_img)
h_action = F.relu(self.linear1(h))
h_action = self.linear2(h_action)
return h_img, h_action
predictive_autoencoder.py 文件源码
项目：Multitask-and-Transfer-Learning
作者: AI-ON
项目源码
文件源码
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def __call__(self, x_image, t_image, x_action, t_action):
self.y_image, self.y_action = self.predictor(x_image, x_action)
predicted_action = self.action_meaning(
F.argmax(self.y_action, axis=1).data[0])
real_action = self.action_meaning(t_action)
if predicted_action != real_action:
print("Predicted action:", predicted_action,
"it was actually", real_action)
image_loss = F.mean_squared_error(self.y_image, t_image)
self.error_mask = normalize_2d(F.squared_error(self.y_image, t_image))
action_loss = F.softmax_cross_entropy(
self.y_action,
F.expand_dims(np.array(t_action, dtype=np.int32), axis=0),
)
print('Image loss', image_loss.data, ', Action loss:', action_loss.data)
return self.weight * image_loss + (1.0 - self.weight) * action_loss
def scale_by_tanh(x, low, high):
xp = cuda.get_array_module(x.data)
scale = (high - low) / 2
scale = xp.expand_dims(xp.asarray(scale, dtype=np.float32), axis=0)
mean = (high + low) / 2
mean = xp.expand_dims(xp.asarray(mean, dtype=np.float32), axis=0)
return F.tanh(x) * scale + mean
def update_on_policy(self, statevar):
assert self.t_start < self.t
if not self.disable_online_update:
next_values = {}
for t in range(self.t_start + 1, self.t):
next_values[t - 1] = self.past_values[t]
if statevar is None:
next_values[self.t - 1] = chainer.Variable(
self.xp.zeros_like(self.past_values[self.t - 1].data))
else:
with state_kept(self.model):
_, v = self.model(statevar)
next_values[self.t - 1] = v
log_probs = {t: self.past_action_distrib[t].log_prob(
self.xp.asarray(self.xp.expand_dims(a, 0)))
for t, a in self.past_actions.items()}
self.online_batch_losses.append(self.compute_loss(
t_start=self.t_start, t_stop=self.t,
rewards=self.past_rewards,
values=self.past_values,
next_values=next_values,
log_probs=log_probs))
if len(self.online_batch_losses) == self.batchsize:
loss = chainerrl.functions.sum_arrays(
self.online_batch_losses) / self.batchsize
self.update(loss)
self.online_batch_losses = []
self.init_history_data_for_online_update()
def forward(self, ws, cs, ls, dep_ts=None):
batchsize = len(ws)
xp = chainer.cuda.get_array_module(ws[0])
ws = map(self.emb_word, ws)
cs = [F.squeeze(
F.max_pooling_2d(
self.conv_char(
F.expand_dims(
self.emb_char(c), 1)), (int(l[0]), 1)))
for c, l in zip(cs, ls)]
xs_f = [F.dropout(F.concat([w, c]),
self.dropout_ratio, train=self.train) for w, c in zip(ws, cs)]
xs_b = [x[::-1] for x in xs_f]
cx_f, hx_f, cx_b, hx_b = self._init_state(xp, batchsize)
_, _, hs_f = self.lstm_f(hx_f, cx_f, xs_f, train=self.train)
_, _, hs_b = self.lstm_b(hx_b, cx_b, xs_b, train=self.train)
hs_b = [x[::-1] for x in hs_b]
hs = [F.concat([h_f, h_b]) for h_f, h_b in zip(hs_f, hs_b)]
dep_ys = [self.biaffine_arc(
F.elu(F.dropout(self.arc_dep(h), 0.32, train=self.train)),
F.elu(F.dropout(self.arc_head(h), 0.32, train=self.train))) for h in hs]
if dep_ts is not None:
heads = dep_ts
else:
heads = [F.argmax(y, axis=1) for y in dep_ys]
cat_ys = [
self.biaffine_tag(
F.elu(F.dropout(self.rel_dep(h), 0.32, train=self.train)),
F.elu(F.dropout(self.rel_head(
F.embed_id(t, h, ignore_label=IGNORE)), 0.32, train=self.train))) \
for h, t in zip(hs, heads)]
return cat_ys, dep_ys
def forward(self, ws, cs, ls):
"""
xs [(w,s,p,y), ..., ]
w: word, c: char, l: length, y: label
"""
batchsize = len(ws)
# cs: [(sentence length, max word length)]
ws = map(self.emb_word, ws)
# ls: [(sentence length, char dim)]
# before conv: (sent len, 1, max word len, char_size)
# after conv: (sent len, char_size, max word len, 1)
# after max_pool: (sent len, char_size, 1, 1)
cs = [F.squeeze(
F.max_pooling_2d(
self.conv_char(
F.expand_dims(
self.emb_char(c), 1)), (l, 1)))
for c, l in zip(cs, ls)]
# [(sentence length, (word_dim + char_dim))]
xs_f = [F.dropout(F.concat([w, c]),
self.dropout_ratio, train=self.train) for w, c in zip(ws, cs)]
xs_b = [x[::-1] for x in xs_f]
cx_f, hx_f, cx_b, hx_b = self._init_state(batchsize)
_, _, hs_f = self.lstm_f(hx_f, cx_f, xs_f, train=self.train)
_, _, hs_b = self.lstm_b(hx_b, cx_b, xs_b, train=self.train)
hs_b = [x[::-1] for x in hs_b]
# ys: [(sentence length, number of category)]
hs = [F.concat([h_f, h_b]) for h_f, h_b in zip(hs_f, hs_b)]
cat_ys = [self.linear_cat2(F.relu(self.linear_cat1(h))) for h in hs]
dep_ys = [self.biaffine(
F.relu(F.dropout(self.linear_dep(h), 0.32, train=self.train)),
F.relu(F.dropout(self.linear_head(h), 0.32, train=self.train))) for h in hs]
return cat_ys, dep_ys
def get_greedy_action(Q, obs, show_f=False):
xp = Q.xp
obs = xp.expand_dims(xp.asarray(obs, dtype=np.float32), 0)
with chainer.no_backprop_mode():
f = Q.feature(obs)
q = Q.predict(f)[0]
#q = Q(obs).data[0]
if show_f:
show_feature(f)
return int(xp.argmax(q))
def mean_clipped_loss(y, t):
# Add an axis because F.huber_loss only accepts arrays with ndim >= 2
y = F.expand_dims(y, axis=-1)
t = F.expand_dims(t, axis=-1)
return F.sum(F.huber_loss(y, t, 1.0)) / y.shape[0]
def get_greedy_action(Q, obs):
xp = Q.xp
obs = xp.expand_dims(xp.asarray(obs, dtype=np.float32), 0)
with chainer.no_backprop_mode():
q = Q(obs).data[0]
return int(xp.argmax(q))
def mean_clipped_loss(y, t):
# Add an axis because F.huber_loss only accepts arrays with ndim >= 2
y = F.expand_dims(y, axis=-1)
t = F.expand_dims(t, axis=-1)
return F.sum(F.huber_loss(y, t, 1.0)) / y.shape[0]
def _attend(self, p):
p = self.xh(p)
p = F.expand_dims(p, 1)
p = F.broadcast_to(p, self.shape2)
h = F.tanh(self.h + p)
shape3 = (self.batchsize * self.src_len, self.dim_hid)
h_reshaped = F.reshape(h, shape3)
weight_reshaped = self.hw(h_reshaped)
weight = F.reshape(weight_reshaped, (self.batchsize, self.src_len, 1))
weight = F.where(self.mask, weight, self.minf)
attention = F.softmax(weight)
return attention
def __call__(self, x):
return functions.expand_dims(x, self.axis)
def forward_one_step(self, X, ht_enc):
pad = self._kernel_size - 1
WX = self.W(X)[..., -pad-1, None]
Vh = self.V(ht_enc)
Vh, WX = functions.broadcast(functions.expand_dims(Vh, axis=2), WX)
return self.pool(functions.split_axis(WX + Vh, self.num_split, axis=1))
def check_forward(self, x_data):
x = chainer.Variable(x_data)
y = functions.expand_dims(x, self.axis)
self.assertEqual(y.data.shape, self.out_shape)
y_expect = numpy.expand_dims(cuda.to_cpu(x_data), self.axis)
self.assertEqual(y.data.dtype, self.dtype)
numpy.testing.assert_array_equal(cuda.to_cpu(y.data), y_expect)
def check_backward(self, x_data):
x = chainer.Variable(x_data)
y = functions.expand_dims(x, self.axis)
y.grad = y.data
y.backward()
gradient_check.assert_allclose(x.data, x.grad, atol=0, rtol=0)
def test_invalid_dim(self):
x = chainer.Variable(self.x)
with self.assertRaises(chainer.utils.type_check.InvalidType):
functions.expand_dims(x, self.x.ndim + 1)
with self.assertRaises(chainer.utils.type_check.InvalidType):
functions.expand_dims(x, -self.x.ndim - 2)
def _encode(self, x_list):
batch_size = len(x_list[0])
source_length = len(x_list)
# Encoding
fc = bc = f = b = _zeros((batch_size, self.hidden_size))
i_list = [self.x_i(_mkivar(x)) for x in x_list]
f_list = []
b_list = []
for i in i_list:
fc, f = F.lstm(fc, self.i_f(i) + self.f_f(f))
f_list.append(f)
for i in reversed(i_list):
bc, b = F.lstm(bc, self.i_b(i) + self.b_b(b))
b_list.append(b)
b_list.reverse()
# Making concatenated matrix
# {f,b}_mat: shape = [batch, srclen, hidden]
f_mat = F.concat([F.expand_dims(f, 1) for f in f_list], 1)
b_mat = F.concat([F.expand_dims(b, 1) for b in b_list], 1)
# fb_mat: shape = [batch, srclen, 2 * hidden]
fb_mat = F.concat([f_mat, b_mat], 2)
# fbe_mat: shape = [batch * srclen, atten]
fbe_mat = self.fb_e(
F.reshape(fb_mat, [batch_size * source_length, 2 * self.hidden_size]))
return fb_mat, fbe_mat, fc, bc, f_list[-1], b_list[0]
def __call__(self, x):
return functions.expand_dims(x, self.axis)
def __call__(self, chars):
xp = self.xp
if not isinstance(chars, (tuple, list)):
chars = [chars]
lengths = [len(_chars) for _chars in chars]
n_words = len(lengths)
pad_width = self._pad_width
char_ids = F.PadSequence(length=max(lengths) + pad_width,
padding=self._pad_id).forward(chars)[0]
left_pads = xp.full((n_words, pad_width), self._pad_id, xp.int32)
char_ids = xp.concatenate((left_pads, xp.array(char_ids)), axis=1)
"""note: cupy does not have `inf`."""
mask = xp.full(char_ids.shape, np.inf)
for i, length in enumerate(lengths):
mask[i, pad_width:pad_width + length] = 0
mask = xp.expand_dims(mask, axis=2)
xs = self.embed(char_ids)
xs = F.dropout(xs, self._dropout)
C = self.conv(F.expand_dims(xs, axis=1))
C = F.transpose(F.squeeze(C, axis=3), (0, 2, 1))
"""
assert C.shape == (n_words,
pad_width + max(lengths) + pad_width,
self.out_size)
"""
ys = F.max(C - mask, axis=1)
return ys
def kl_div(mu1, lv1, lv2):
# KL Divergence between given normal and prior at N(0, sigma_2)
# Prior assumes mean at zero
# lns2 - lns1 + (s2^2 + (u1 - u2)**2)/ 2s2**2 - 0.5
if len(lv1.shape) == 2:
lv1 = F.expand_dims(lv1, 0)
mu1 = F.expand_dims(mu1, 0)
lv2 = F.broadcast_to(lv2, lv1.shape)
v12 = F.exp(lv1)**2.0
v22 = F.exp(lv2)**2.0
return lv2 - lv1 + .5 * v12 / v22 + .5 * mu1**2. / v22 - .5
