def get_generator(self, meanx, z0, y_1hot):
''' specify generator G0, gen_x = G0(z0, h1) '''
"""
#z0 = theano_rng.uniform(size=(self.args.batch_size, 16)) # uniform noise
gen0_layers = [LL.InputLayer(shape=(self.args.batch_size, 50), input_var=z0)] # Input layer for z0
gen0_layers.append(nn.batch_norm(LL.DenseLayer(nn.batch_norm(LL.DenseLayer(gen0_layers[0], num_units=128, W=Normal(0.02), nonlinearity=nn.relu)),
num_units=128, W=Normal(0.02), nonlinearity=nn.relu))) # embedding, 50 -> 128
gen0_layer_z_embed = gen0_layers[-1]
#gen0_layers.append(LL.InputLayer(shape=(self.args.batch_size, 256), input_var=real_fc3)) # Input layer for real_fc3 in independent training, gen_fc3 in joint training
gen0_layers.append(LL.InputLayer(shape=(self.args.batch_size, 10), input_var=y_1hot)) # Input layer for real_fc3 in independent training, gen_fc3 in joint training
gen0_layer_fc3 = gen0_layers[-1]
gen0_layers.append(LL.ConcatLayer([gen0_layer_fc3,gen0_layer_z_embed], axis=1)) # concatenate noise and fc3 features
gen0_layers.append(LL.ReshapeLayer(nn.batch_norm(LL.DenseLayer(gen0_layers[-1], num_units=256*5*5, W=Normal(0.02), nonlinearity=T.nnet.relu)),
(self.args.batch_size,256,5,5))) # fc
gen0_layers.append(nn.batch_norm(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,256,10,10), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))) # deconv
gen0_layers.append(nn.batch_norm(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,128,14,14), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=nn.relu))) # deconv
gen0_layers.append(nn.batch_norm(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,128,28,28), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))) # deconv
gen0_layers.append(nn.Deconv2DLayer(gen0_layers[-1], (self.args.batch_size,3,32,32), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=T.nnet.sigmoid)) # deconv
gen_x_pre = LL.get_output(gen0_layers[-1], deterministic=False)
gen_x = gen_x_pre - meanx
# gen_x_joint = LL.get_output(gen0_layers[-1], {gen0_layer_fc3: gen_fc3}, deterministic=False) - meanx
return gen0_layers, gen_x
"""
gen_x_layer_z = LL.InputLayer(shape=(self.args.batch_size, self.args.z0dim), input_var=z0) # z, 20
# gen_x_layer_z_embed = nn.batch_norm(LL.DenseLayer(gen_x_layer_z, num_units=128), g=None) # 20 -> 64
gen_x_layer_y = LL.InputLayer(shape=(self.args.batch_size, 10), input_var=y_1hot) # conditioned on real fc3 activations
gen_x_layer_y_z = LL.ConcatLayer([gen_x_layer_y,gen_x_layer_z],axis=1) #512+256 = 768
gen_x_layer_pool2 = LL.ReshapeLayer(nn.batch_norm(LL.DenseLayer(gen_x_layer_y_z, num_units=256*5*5)), (self.args.batch_size,256,5,5))
gen_x_layer_dconv2_1 = nn.batch_norm(nn.Deconv2DLayer(gen_x_layer_pool2, (self.args.batch_size,256,10,10), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))
gen_x_layer_dconv2_2 = nn.batch_norm(nn.Deconv2DLayer(gen_x_layer_dconv2_1, (self.args.batch_size,128,14,14), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=nn.relu))
gen_x_layer_dconv1_1 = nn.batch_norm(nn.Deconv2DLayer(gen_x_layer_dconv2_2, (self.args.batch_size,128,28,28), (5,5), stride=(2, 2), padding = 'half',
W=Normal(0.02), nonlinearity=nn.relu))
gen_x_layer_x = nn.Deconv2DLayer(gen_x_layer_dconv1_1, (self.args.batch_size,3,32,32), (5,5), stride=(1, 1), padding = 'valid',
W=Normal(0.02), nonlinearity=T.nnet.sigmoid)
# gen_x_layer_x = dnn.Conv2DDNNLayer(gen_x_layer_dconv1_2, 3, (1,1), pad=0, stride=1,
# W=Normal(0.02), nonlinearity=T.nnet.sigmoid)
gen_x_layers = [gen_x_layer_z, gen_x_layer_y, gen_x_layer_y_z, gen_x_layer_pool2, gen_x_layer_dconv2_1,
gen_x_layer_dconv2_2, gen_x_layer_dconv1_1, gen_x_layer_x]
gen_x_pre = LL.get_output(gen_x_layer_x, deterministic=False)
gen_x = gen_x_pre - meanx
return gen_x_layers, gen_x
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