def define_graph(self):
with tf.variable_scope("Graph", reuse=tf.AUTO_REUSE):
self.in_data = tf.placeholder(self._dtype,
shape=(self._batch, self._in_seq,
self._h, self._w, self._in_c),
name="input")
self.gt_data = tf.placeholder(self._dtype,
shape=(self._batch, self._out_seq,
self._h, self._w, 1),
name="gt")
self.global_step = tf.Variable(0, trainable=False)
with tf.device('/device:GPU:0'):
encoder_net = Encoder(self._batch,
self._in_seq,
gru_filter=c.ENCODER_GRU_FILTER,
gru_in_chanel=c.ENCODER_GRU_INCHANEL,
conv_kernel=c.CONV_KERNEL,
conv_stride=c.CONV_STRIDE,
h2h_kernel=c.H2H_KERNEL,
i2h_kernel=c.I2H_KERNEL,
height=self._h,
width=self._w)
for i in range(self._in_seq):
encoder_net.rnn_encoder(self.in_data[:, i, ...])
states = encoder_net.rnn_states
with tf.device('/device:GPU:1'):
forecaster_net = Forecaster(
self._batch,
self._out_seq,
gru_filter=c.DECODER_GRU_FILTER,
gru_in_chanel=c.DECODER_GRU_INCHANEL,
deconv_kernel=c.DECONV_KERNEL,
deconv_stride=c.DECONV_STRIDE,
h2h_kernel=c.H2H_KERNEL,
i2h_kernel=c.I2H_KERNEL,
rnn_states=states,
height=self._h,
width=self._w)
for i in range(self._out_seq):
forecaster_net.rnn_forecaster()
pred = tf.concat(forecaster_net.pred, axis=1)
with tf.variable_scope("loss"):
gt = self.gt_data
weights = get_loss_weight_symbol(pred)
self.result = pred
self.mse = weighted_mse(pred, gt, weights)
self.mae = weighted_mae(pred, gt, weights)
self.gdl = gdl_loss(pred, gt)
loss = c.L1_LAMBDA * self.mse + c.L2_LAMBDA * self.mae + c.GDL_LAMBDA * self.gdl
self.optimizer = tf.train.AdamOptimizer(c.LR).minimize(
loss, global_step=self.global_step)
tf.summary.scalar('mse', self.mse)
tf.summary.scalar('mae', self.mae)
tf.summary.scalar('gdl', self.gdl)
tf.summary.scalar('combine_loss', loss)
self.summary = tf.summary.merge_all()
def define_graph(self):
with tf.variable_scope("Graph", reuse=tf.AUTO_REUSE):
self.in_data = tf.placeholder(self._dtype,
shape=(self._batch, self._in_seq,
self._h, self._w, self._in_c),
name="input")
self.gt_data = tf.placeholder(self._dtype,
shape=(self._batch, self._out_seq,
self._h, self._w, 1),
name="gt")
self.global_step = tf.Variable(0, trainable=False)
with tf.device('/device:GPU:0'):
encoder_net = Encoder(self._batch,
self._in_seq,
gru_filter=c.ENCODER_GRU_FILTER,
gru_in_chanel=c.ENCODER_GRU_INCHANEL,
conv_kernel=c.CONV_KERNEL,
conv_stride=c.CONV_STRIDE,
h2h_kernel=c.H2H_KERNEL,
i2h_kernel=c.I2H_KERNEL,
height=self._h,
width=self._w)
if c.SEQUENCE_MODE:
for i in range(self._in_seq):
encoder_net.rnn_encoder_step(self.in_data[:, i, ...])
else:
encoder_net.rnn_encoder(self.in_data)
states = encoder_net.rnn_states
with tf.device('/device:GPU:1'):
forecaster_net = Forecaster(
self._batch,
self._out_seq,
gru_filter=c.DECODER_GRU_FILTER,
gru_in_chanel=c.DECODER_GRU_INCHANEL,
deconv_kernel=c.DECONV_KERNEL,
deconv_stride=c.DECONV_STRIDE,
h2h_kernel=c.H2H_KERNEL,
i2h_kernel=c.I2H_KERNEL,
rnn_states=states,
height=self._h,
width=self._w)
if c.SEQUENCE_MODE:
for i in range(self._out_seq):
forecaster_net.rnn_forecaster_step()
pred = tf.concat(forecaster_net.pred, axis=1)
else:
forecaster_net.rnn_forecaster()
pred = forecaster_net.pred
with tf.variable_scope("loss"):
gt = self.gt_data
weights = get_loss_weight_symbol(pred)
self.result = pred
self.mse = tf.reduce_mean(tf.square(pred - gt))
self.mae = weighted_mae(pred, gt, weights)
self.gdl = gdl_loss(pred, gt)
self.d_loss = self.result
if c.ADVERSARIAL:
self.d_pred = tf.placeholder(
self._dtype,
(self._batch, self._out_seq, self._h, self._w, 1))
self.d_loss = tf.reduce_mean(tf.square(self.d_pred - gt))
self.loss = tf.cond(self.global_step > self.adv_involve,
lambda: c.L1_LAMBDA * self.mae \
+ c.L2_LAMBDA * self.mse \
+ c.GDL_LAMBDA * self.gdl \
+ c.ADV_LAMBDA * self.d_loss,
lambda: c.L1_LAMBDA * self.mae \
+ c.L2_LAMBDA * self.mse \
+ c.GDL_LAMBDA * self.gdl
)
# self.loss = c.L1_LAMBDA * self.mae \
# + c.L2_LAMBDA * self.mse \
# + c.GDL_LAMBDA * self.gdl \
# + c.ADV_LAMBDA * self.d_loss
else:
self.loss = c.L1_LAMBDA * self.mae + c.L2_LAMBDA * self.mse + c.GDL_LAMBDA * self.gdl
self.d_loss = self.loss
self.optimizer = tf.train.AdamOptimizer(c.LR).minimize(
self.loss, global_step=self.global_step)
self.summary = tf.summary.merge([
tf.summary.scalar('mse', self.mse),
tf.summary.scalar('mae', self.mae),
tf.summary.scalar('gdl', self.gdl),
tf.summary.scalar('combine_loss', self.loss)
])