def gen(Z, Y, w, w2, w3, wx):
yb = Y.dimshuffle(0, 1, 'x', 'x')
Z = T.concatenate([Z, Y], axis=1)
h = relu(batchnorm(T.dot(Z, w)))
h = T.concatenate([h, Y], axis=1)
h2 = relu(batchnorm(T.dot(h, w2)))
h2 = h2.reshape((h2.shape[0], ngf*2, npx_, npx_))
h2 = conv_cond_concat(h2, yb)
h3 = relu(batchnorm(deconv(h2, w3, subsample=(2, 2), border_mode=(2, 2))))
h3 = conv_cond_concat(h3, yb)
x = sigmoid(deconv(h3, wx, subsample=(2, 2), border_mode=(2, 2)))
return x
def discrim(X, Y, w, w2, w3, wy):
yb = Y.dimshuffle(0, 1, 'x', 'x')
X = conv_cond_concat(X, yb)
h = lrelu(dnn_conv(X, w, subsample=(2, 2), border_mode=(2, 2)))
h = conv_cond_concat(h, yb)
h2 = lrelu(batchnorm(dnn_conv(h, w2, subsample=(2, 2), border_mode=(2, 2))))
h2 = T.flatten(h2, 2)
h2 = T.concatenate([h2, Y], axis=1)
h3 = lrelu(batchnorm(T.dot(h2, w3)))
h3 = T.concatenate([h3, Y], axis=1)
y = sigmoid(T.dot(h3, wy))
return y
def gen(Z, Y, w, w2, w3, wx):
yb = Y.dimshuffle(0, 1, 'x', 'x')
Z = T.concatenate([Z, Y], axis=1)
h = relu(batchnorm(T.dot(Z, w)))
h = T.concatenate([h, Y], axis=1)
h2 = relu(batchnorm(T.dot(h, w2)))
h2 = h2.reshape((h2.shape[0], ngf * 2, temp, temp))
h2 = conv_cond_concat(h2, yb)
h3 = relu(batchnorm(deconv(h2, w3, subsample=(2, 2), border_mode=(2, 2))))
h3 = conv_cond_concat(h3, yb)
x = sigmoid(deconv(h3, wx, subsample=(2, 2), border_mode=(2, 2)))
return x
def discrim(X, Y, w, w2, w3, wy):
yb = Y.dimshuffle(0, 1, 'x', 'x')
X = conv_cond_concat(X, yb)
h = lrelu(dnn_conv(X, w, subsample=(2, 2), border_mode=(2, 2)))
h = conv_cond_concat(h, yb)
h2 = lrelu(batchnorm(dnn_conv(h, w2, subsample=(2, 2),
border_mode=(2, 2))))
h2 = T.flatten(h2, 2)
h2 = T.concatenate([h2, Y], axis=1)
h3 = lrelu(batchnorm(T.dot(h2, w3)))
h3 = T.concatenate([h3, Y], axis=1)
y = sigmoid(T.dot(h3, wy))
return y
def gen(Z, Y):
yb = Y.dimshuffle(0, 1, 'x', 'x')
Z = T.concatenate([Z, Y], axis=1)
h = relu(batchnorm(T.dot(Z, gw), g=gg, b=gb))
h = h.reshape((h.shape[0], ngf * 4, 4, 4))
h = conv_cond_concat(h, yb)
h2 = relu(
batchnorm(deconv(h, gw2, subsample=(2, 2), border_mode=(2, 2)),
g=gg2,
b=gb2))
h2 = conv_cond_concat(h2, yb)
h3 = relu(
batchnorm(deconv(h2, gw3, subsample=(2, 2), border_mode=(2, 2)),
g=gg3,
b=gb3))
h3 = conv_cond_concat(h3, yb)
x = tanh(deconv(h3, gw4, subsample=(2, 2), border_mode=(2, 2)))
return x
def gen(Z, Y, w, w2, w3, wx):
print '\n@@@@ gen()'
printVal('Z', Z) # matrix
#printVal( 'Y', Y ) # matrix
printVal('w', w) # matrix
printVal('w2', w2) # matrix
printVal('w3', w3) # tensor
printVal('wx', wx) # tensor
# Yの要素の並びの入れ替え。数字の引数は、次元番号。'x' は ブロードキャスト
# 並び替えの前後で、全体の要素数は変わらない。
yb = Y.dimshuffle(0, 1, 'x', 'x')
# yb は４次元テンソル
#printVal('yb', yb)
# 行列 Z と Y を結合（横方向）
Z = T.concatenate([Z, Y], axis=1) # matrix
# Z*w(Full Connect) をバッチ正規化して、ReLU 適用
tmp_a = T.dot(Z, w) # dot(matrix, matrix)->matrix
printVal('dot(Z,w) -> tmp_a', tmp_a)
h = relu(batchnorm(T.dot(Z, w))) #CCC
h = T.concatenate([h, Y], axis=1) #CCC
printVal('h', h) # matrix
h2 = relu(batchnorm(T.dot(h, w2))) #CCC
printVal('h2', h2) #h2:matrix
h2r = h2.reshape((h2.shape[0], GEN_NUM_FILTER * 2, 7, 7)) #CCC
printVal('h2r', h2r) #h2r:tensor
h2ry = conv_cond_concat(h2r, yb) #
printVal('h2ry', h2ry) #h2:tensor
# デコンボリューション:論文によれば、空間プーリングの代わりに適用する
d = deconv(h2ry, w3, subsample=(2, 2), border_mode=(2, 2))
printVal('d', d)
#h3 = relu(batchnorm(deconv(h2, w3, subsample=(2, 2), border_mode=(2, 2))))
h3 = relu(batchnorm(d))
h3 = conv_cond_concat(h3, yb)
x = sigmoid(deconv(h3, wx, subsample=(2, 2), border_mode=(2, 2)))
return x, h2
def gen(Z, Y, w, w2, w3, wx):
#Z: (nbatch, nz) = (128, 100)
#Y: (nbatch, ny) = (128, 10)
#w: (nz+ny, ngfc) = (110, 1024)
#w2: (ngfc+ny, ngf*2*7*7) = (1024+10, 64*2*7*7) = (1034, 6272)
#w3: (ngf*2+ny, ngf, 5, 5) = (128+10, 64, 5, 5 ) = (138, 64, 5, 5)
#wx: (ngf+ny, nc, 5, 5) = (64+10, 1, 5, 5) = (74, 1, 5, 5)
print '\n@@@@ gen()'
printVal( 'Y', Y )
printVal( 'w', w ) #matrix
printVal( 'w2', w2 ) #matrix
printVal( 'w3', w3 ) #tensor
printVal( 'wx', wx ) #tensor
# Yの要素の並びの入れ替え。数字の引数は、次元番号。'x' は ブロードキャスト
#(G1)
yb = Y.dimshuffle(0, 1, 'x', 'x')
# yb は４次元テンソル
printVal('yb', yb)
# 行列 Z と Y を結合（横方向）:いわゆる Conditional GAN の形にする。
#(G2)
Z = T.concatenate([Z, Y], axis=1) # Z: (128, 110)
#(G3)
# Z*w をバッチ正規化して、ReLU 適用
t1 = T.dot(Z, w) #full connect : t1: (128, 1024)
printVal('t1', t1)
h = relu(
batchnorm(
t1
)
)
# h: (128, 1024)
#(G4)
h = T.concatenate([h, Y], axis=1) # h: (128, 1034)
#(G5)
h2 = relu(
batchnorm(
T.dot(h, w2) #full connect
)
)
#(G6)
h2 = h2.reshape((h2.shape[0], ngf*2, 7, 7))
#(G7)
h3 = conv_cond_concat(h2, yb) #XXX
printVal( 'h2', h2 )
#(G8)デコンボリューション:論文によれば、空間プーリングの代わりに適用する
d = deconv(h3, w3, subsample=(2, 2), border_mode=(2, 2))
#h3 = relu(batchnorm(deconv(h2, w3, subsample=(2, 2), border_mode=(2, 2))))
#(G9)
h4 = relu(
batchnorm(d)
)
#(G10)
h5 = conv_cond_concat(h4, yb)
#(G11)
x = sigmoid(
deconv(h5, wx, subsample=(2, 2), border_mode=(2, 2)
)
)
return x
