def __init__(self, n_cells, n_x, n_h, initializer=None):
with tf.name_scope("Weight_Matrices"):
params = utils.read_params()
# class variables
self.n_cells = n_cells
if initializer is None:
init = tf.contrib.layers.xavier_initializer()
else:
init = initializer
with tf.name_scope("x_list"):
x_list = []
for x in range(self.n_cells):
with tf.name_scope("y_list"):
y_list = []
for y in range(self.n_cells):
z_list = []
with tf.name_scope("z_list"):
for z in range(self.n_cells):
name = "W_{}{}{}".format(x, y, z)
W = tf.Variable(init([n_x, n_h]),
name=name)
if params["VIS"]["HISTOGRAMS"]:
tf.summary.histogram(name, W)
z_list.append(W)
y_list.append(z_list)
x_list.append(y_list)
self.weight_matrix_grid = x_list
def __init__(self,
n_cells=4,
n_input=1024,
n_hidden_state=128,
initializer=None):
with tf.name_scope("LSTM_Grid"):
if initializer is None:
init = tf.contrib.layers.xavier_initializer()
else:
init = initializer
# parameters for the cell state and input,forget & output gates
self.W = [
Weight_Matrices(
n_cells, n_input, n_hidden_state, initializer=init)
] * 4
self.U = [
tf.Variable(init([3, 3, 3, n_hidden_state, n_hidden_state]),
name="U")
] * 4
self.b = [
tf.Variable(init([n_cells, n_cells, n_cells, n_hidden_state]),
name="b")
] * 4
params = utils.read_params()
if params["VIS"]["HISTOGRAMS"]:
for i in range(4):
tf.summary.histogram("U[{}]".format(i), self.U[i])
tf.summary.histogram("b[{}]".format(i), self.b[i])
def fully_connected_sequence(sequence,
in_units=1024,
out_units=1024,
initializer=None):
with tf.name_scope("fully_connected_sequence"):
if initializer is None:
init = tf.contrib.layers.xavier_initializer()
else:
init = initializer
weights = tf.Variable(init([in_units, out_units]), name="weights")
bias = tf.Variable(init([out_units]), name="bias")
def forward_pass(a):
return tf.nn.bias_add(tf.matmul(a, weights), bias)
ret = tf.map_fn(forward_pass, sequence, name='fully_connected_map')
#ret = map_images(forward_pass, sequence, name='fully_connected_map')
params = utils.read_params()
if params["VIS"]["HISTOGRAMS"]:
tf.summary.histogram("weights", weights)
tf.summary.histogram("bias", bias)
if params["VIS"]["SHAPES"]:
print(ret.shape)
return ret
def voxel(vox, color=None, f_name=None, npimage=False, view=(30, 45)):
assert (vox.ndim == 3)
vox = vox.transpose(2, 0, 1)
color = color.transpose(2, 0, 1)
if color is None or len(np.unique(color)) <= 2:
color = 'red'
else:
color_map = plt.get_cmap('coolwarm')
color = color_map(color)
fig = plt.figure()
ax = fig.gca(projection='3d')
#ax.voxels(vox, facecolors=color, edgecolor='k')
ax.voxels(vox, edgecolor='k')
ax.view_init(view[0], view[1])
if npimage:
return mplfig_to_npimage(fig)
if f_name is not None:
params = utils.read_params()
f_name = os.path.join(params["DIRS"]["OUTPUT"], f_name)
utils.make_prev_dirs(f_name)
fig.savefig(f_name, bbox_inches='tight', dpi=2400)
fig.clf()
plt.close()
return
return fig.show()
def conv_sequence(sequence,
in_featuremap_count,
out_featuremap_count,
initializer=None,
K=3,
S=[1, 1, 1, 1],
D=[1, 1, 1, 1],
P="SAME"):
with tf.name_scope("conv_sequence"):
if initializer is None:
init = tf.contrib.layers.xavier_initializer()
else:
init = initializer
kernel = tf.Variable(init(
[K, K, in_featuremap_count, out_featuremap_count]),
name="kernel")
bias = tf.Variable(init([out_featuremap_count]), name="bias")
def conv2d(x):
return tf.nn.bias_add(
tf.nn.conv2d(x,
kernel,
S,
padding=P,
dilations=D,
name="conv2d"), bias)
ret = tf.map_fn(conv2d, sequence, name="conv2d_map")
#ret = map_images(conv2d, sequence, name='conv2d_map')
tf.add_to_collection("feature_maps", ret)
# visualization code
params = utils.read_params()
image_count = params["VIS"]["IMAGE_COUNT"]
if params["VIS"]["KERNELS"]:
kern_1 = tf.concat(tf.unstack(kernel, axis=-1), axis=-1)
kern_2 = tf.transpose(kern_1, [2, 0, 1])
kern_3 = tf.expand_dims(kern_2, -1)
tf.summary.image("2d kernel", kern_3, max_outputs=image_count)
if params["VIS"]["FEATURE_MAPS"]:
feature_map_1 = tf.concat(tf.unstack(ret, axis=4), axis=2)
feature_map_2 = tf.concat(tf.unstack(feature_map_1, axis=1),
axis=2)
feature_map_3 = tf.expand_dims(feature_map_2, -1)
tf.summary.image("feature_map",
feature_map_3,
max_outputs=image_count)
if params["VIS"]["HISTOGRAMS"]:
tf.summary.histogram("kernel", kernel)
tf.summary.histogram("bias", bias)
if params["VIS"]["SHAPES"]:
print(ret.shape)
return ret
def conv_vox(vox,
in_featurevoxel_count,
out_featurevoxel_count,
K=3,
S=[1, 1, 1, 1, 1],
D=[1, 1, 1, 1, 1],
initializer=None,
P="SAME"):
# deconvolution
with tf.name_scope("conv_vox"):
if initializer is None:
init = tf.contrib.layers.xavier_initializer()
else:
init = initializer
kernel = tf.Variable(init(
[K, K, K, in_featurevoxel_count, out_featurevoxel_count]),
name="kernel")
bias = tf.Variable(init([out_featurevoxel_count]), name="bias")
ret = tf.nn.bias_add(
tf.nn.conv3d(vox, kernel, S, padding=P, dilations=D,
name="conv3d"), bias)
tf.add_to_collection("feature_voxels", ret)
# visualization code
params = utils.read_params()
image_count = params["VIS"]["IMAGE_COUNT"]
if params["VIS"]["KERNELS"]:
kern_1 = tf.concat(tf.unstack(kernel, axis=-1), axis=-1)
kern_2 = tf.transpose(kern_1, [3, 0, 1, 2])
kern_3 = tf.expand_dims(kern_2, -1)
kern_4 = tf.concat(tf.unstack(kern_3, axis=1), axis=1)
tf.summary.image("3d kernel", kern_4, max_outputs=image_count)
if params["VIS"]["VOXEL_SLICES"]:
vox_slice_1 = tf.unstack(ret, axis=4)[1]
vox_slice_2 = tf.split(vox_slice_1, 4, axis=3)
vox_slice_3 = tf.concat(vox_slice_2, axis=1)
vox_slice_4 = tf.concat(tf.unstack(vox_slice_3, axis=-1), axis=2)
vox_slice_5 = tf.expand_dims(vox_slice_4, -1)
tf.summary.image("vox_slices",
vox_slice_5,
max_outputs=image_count)
if params["VIS"]["FEATURE_VOXELS"]:
tf.summary.tensor_summary("feature_voxels", ret[0, :, :, :, 0])
if params["VIS"]["HISTOGRAMS"]:
tf.summary.histogram("kernel", kernel)
tf.summary.histogram("bias", bias)
if params["VIS"]["SHAPES"]:
print(ret.shape)
return ret
def create_video(obj_id="02691156_131db4a650873babad3ab188d086d4db"):
params = utils.read_params()
out_dir = params["DIRS"]["OUTPUT"]
model_dir = params["SESSIONS"]["LONGEST"]
epoch_count = utils.get_latest_epoch_index(model_dir) + 1
x, _ = dataset.load_obj_id(obj_id)
for i in range(epoch_count):
net = network.Network_restored("{}/epoch_{}".format(model_dir, i))
yp = net.predict(x)
voxel_binary(yp[0], f_name="{}/{}/frame_{}".format(out_dir, obj_id, i))
def save_im(im, f_name=None, ndarray=False):
fig = plt.figure()
if ndarray:
fig.set_tight_layout(True)
fig.canvas.draw()
ret = np.array(fig.canvas.renderer._renderer)
fig.clf()
plt.close()
return ret
if f_name is not None:
params = utils.read_params()
f_name = os.path.join(params["DIRS"]["OUTPUT"], f_name)
utils.make_prev_dirs(f_name)
plt.imsave(f_name, im)
plt.clf()
plt.close()
return plt.imshow(im)
def save_loss(loss_arr, loss_type):
loss_ndarr = np.array(loss_arr)
save_dir = net.get_cur_epoch_dir()
np.save("{}/{}_loss.npy".format(save_dir, loss_type), loss_ndarr)
def plot_loss(loss_arr, loss_type):
loss_ndarr = np.array(loss_arr)
save_dir = net.get_cur_epoch_dir()
plt.plot(loss_ndarr.flatten())
plt.savefig("{}/{}_loss.png".format(save_dir, loss_type),
bbox_inches="tight")
plt.close()
# params on disk
print(utils.read_params()["TRAIN"])
# get preprocessed data
data, label = dataset.load_preprocessed_dataset()
# init network
net = network.Network()
# net.init_parameters()
params = net.get_params()
train_params = params["TRAIN"]
# split dataset
X_train, y_train, X_val, y_val, X_test, y_test = dataset.train_val_test_split(
data, label)
save_dataset_split()
def retrain(self, params=None):
# read params
if params is None:
self.params = utils.read_params()
else:
self.params = params
self.CREATE_TIME = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
self.MODEL_DIR = "{}/model_{}".format(
self.params["DIRS"]["MODELS_LOCAL"], self.CREATE_TIME)
utils.make_dir(self.MODEL_DIR)
with open(self.MODEL_DIR + '/params.json', 'w') as f:
json.dump(self.params, f)
#with tf.get_default_graph() as graph:
#with self.graph.as_default() as graph:
#graph = self.sess.graph
graph = tf.get_default_graph()
#with tf.Graph().as_default():
if True:
self.X = graph.get_tensor_by_name('Data/Placeholder:0')
self.Y_onehot = graph.get_tensor_by_name('Labels/Placeholder:0')
self.LR = graph.get_tensor_by_name('LearningRate/Placeholder:0')
# TODO: make to read network name instead of hard coding
#self.logits = graph.get_tensor_by_name('SENet_Decoder/conv_vox/BiasAdd:0')
freeze_layer = graph.get_tensor_by_name(
'SENet_Decoder/conv_vox/BiasAdd:0')
#freeze_layer = graph.get_tensor_by_name('SENet_Decoder/block_seresnet_decoder_4/relu_vox_1/relu:0')
# stop graident and make is identity
freeze_layer = tf.stop_gradient(freeze_layer)
self.logits = decoder.conv_vox(freeze_layer, 2, 2)
#hidden_state = graph.get_tensor_by_name('Recurrent_module/while/Exit:0')
## decoder
#print("decoder")
#if isinstance(hidden_state, tuple):
# hidden_state = hidden_state[0]
#if self.params["TRAIN"]["DECODER_MODE"] == "DILATED":
# de = decoder.Dilated_Decoder(hidden_state)
#elif self.params["TRAIN"]["DECODER_MODE"] == "RESIDUAL":
# de = decoder.Residual_Decoder(hidden_state)
#elif self.params["TRAIN"]["DECODER_MODE"] == "SERESNET":
# de = decoder.SENet_Decoder(hidden_state)
#else:
# de = decoder.Simple_Decoder(hidden_state)
#self.logits = de.out_tensor
print("loss")
#self.softmax = graph.get_tensor_by_name('Loss_Voxel_Softmax/clip_by_value:0')
#self.loss = graph.get_tensor_by_name('Loss_Voxel_Softmax/Mean:0')
if self.params["TRAIN"]["LOSS_FCN"] == "FOCAL_LOSS":
voxel_loss = loss.Focal_Loss(self.Y_onehot, self.logits)
self.softmax = voxel_loss.pred
else:
voxel_loss = loss.Voxel_Softmax(self.Y_onehot, self.logits)
self.softmax = voxel_loss.softmax
self.loss = voxel_loss.loss
tf.summary.scalar("loss", self.loss)
# misc
print("misc")
t = tf.constant(self.params["TRAIN"]["TIME_STEP_COUNT"])
#self.step_count = graph.get_tensor_by_name('misc_2/step_count:0')
#self.print = graph.get_tensor_by_name('misc_2/Print:0')
with tf.name_scope("misc"):
self.step_count = tf.Variable(0,
trainable=False,
name="step_count")
self.print = tf.Print(self.loss,
[self.step_count, self.loss, t])
# optimizer
print("optimizer")
#optimizer = graph.get_tensor_by_name('Adam:0')
if self.params["TRAIN"]["OPTIMIZER"] == "ADAM":
optimizer = tf.train.AdamOptimizer(
learning_rate=self.LR,
epsilon=self.params["TRAIN"]["ADAM_EPSILON"],
name='NewAdam')
tf.summary.scalar("adam_learning_rate", optimizer._lr)
else:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=self.LR)
tf.summary.scalar("learning_rate", optimizer._learning_rate)
grads_and_vars = optimizer.compute_gradients(self.loss)
self.apply_grad = optimizer.apply_gradients(
grads_and_vars, global_step=self.step_count)
## metric
print("metrics")
with tf.name_scope("newmetrics"):
Y = tf.argmax(self.Y_onehot, -1)
predictions = tf.argmax(self.softmax, -1)
acc, acc_op = tf.metrics.accuracy(Y, predictions)
rms, rms_op = tf.metrics.root_mean_squared_error(
self.Y_onehot, self.softmax)
iou, iou_op = tf.metrics.mean_iou(Y, predictions, 2)
self.metrics_op = tf.group(acc_op, rms_op, iou_op)
tf.summary.scalar("accuracy", acc)
tf.summary.scalar("rmse", rms)
tf.summary.scalar("iou", iou)
# initalize
# config=tf.ConfigProto(log_device_placement=True)
print("setup")
self.summary_op = tf.summary.merge_all()
#self.sess = tf.InteractiveSession()
def initialize_uninitialized(sess):
global_vars = tf.global_variables()
is_not_initialized = sess.run(
[tf.is_variable_initialized(var) for var in global_vars])
not_initialized_vars = [
v for (v, f) in zip(global_vars, is_not_initialized)
if not f
]
print("-->", len(global_vars), len(is_not_initialized),
len(not_initialized_vars))
if len(not_initialized_vars):
sess.run(tf.variables_initializer(not_initialized_vars))
initialize_uninitialized(self.sess)
print('trainable vars:', len(tf.trainable_variables()))
self.sess.run(tf.local_variables_initializer())
# summaries
print("summaries")
if self.params["MODE"] == "TEST":
self.test_writer = tf.summary.FileWriter(
"{}/test".format(self.MODEL_DIR), self.sess.graph)
else:
self.train_writer = tf.summary.FileWriter(
"{}/train".format(self.MODEL_DIR), self.sess.graph)
self.val_writer = tf.summary.FileWriter(
"{}/val".format(self.MODEL_DIR), self.sess.graph)
def __init__(self, params=None):
# read params
if params is None:
self.params = utils.read_params()
else:
self.params = params
if self.params["TRAIN"]["INITIALIZER"] == "XAVIER":
init = tf.contrib.layers.xavier_initializer()
else:
init = tf.random_normal_initializer()
self.CREATE_TIME = datetime.now().strftime("%Y-%m-%d_%H:%M:%S")
self.MODEL_DIR = "{}/model_{}".format(
self.params["DIRS"]["MODELS_LOCAL"], self.CREATE_TIME)
utils.make_dir(self.MODEL_DIR)
with open(self.MODEL_DIR + '/params.json', 'w') as f:
json.dump(self.params, f)
# place holders
with tf.name_scope("Data"):
self.X = tf.placeholder(tf.float32, [None, None, None, None, None])
with tf.name_scope("Labels"):
if "64" in self.params["TRAIN"]["DECODER_MODE"]:
self.Y_onehot = tf.placeholder(tf.float32,
[None, 64, 64, 64, 2])
else:
self.Y_onehot = tf.placeholder(tf.float32,
[None, 32, 32, 32, 2])
with tf.name_scope("LearningRate"):
self.LR = tf.placeholder(tf.float32, [])
print("Initializing Network")
#pp = preprocessor.Preprocessor(self.X) # here
#X_preprocessed = pp.out_tensor # here
X_preprocessed = self.X # (n_batch, n_views, 127, 127, 3)
n_batchsize = tf.shape(X_preprocessed)[0]
# switch batch <-> nviews
X_preprocessed = tf.transpose(X_preprocessed, [1, 0, 2, 3, 4])
# encoder
print("encoder")
if self.params["TRAIN"]["ENCODER_MODE"] == "DILATED":
en = encoder.Dilated_Encoder(X_preprocessed)
elif self.params["TRAIN"]["ENCODER_MODE"] == "RESIDUAL":
en = encoder.Residual_Encoder(X_preprocessed)
elif self.params["TRAIN"]["ENCODER_MODE"] == "SERESNET":
en = encoder.SENet_Encoder(X_preprocessed)
else:
en = encoder.Simple_Encoder(X_preprocessed)
encoded_input = en.out_tensor
# switch batch <-> nviews
encoded_input = tf.transpose(encoded_input, [1, 0, 2])
X_preprocessed = tf.transpose(X_preprocessed, [1, 0, 2, 3, 4])
# visualize transformation of input state to voxel
if self.params["VIS"]["ENCODER_PROCESS"]:
with tf.name_scope("misc"):
feature_maps = tf.get_collection("feature_maps")
fm_list = []
for fm in feature_maps:
fm_slice = fm[0, 0, :, :, 0]
#fm_shape = fm_slice.get_shape().as_list()
fm_shape = tf.shape(fm_slice)
fm_slice = tf.pad(fm_slice,
[[0, 0], [127 - fm_shape[0], 0]])
fm_list.append(fm_slice)
fm_img = tf.concat(fm_list, axis=0)
tf.summary.image("feature_map_list",
tf.expand_dims(tf.expand_dims(fm_img, -1), 0))
# recurrent_module
print("recurrent_module")
with tf.name_scope("Recurrent_module"):
rnn_mode = self.params["TRAIN"]["RNN_MODE"]
n_cell = self.params["TRAIN"]["RNN_CELL_NUM"]
n_hidden = self.params["TRAIN"]["RNN_HIDDEN_SIZE"]
if rnn_mode == "LSTM":
rnn = recurrent_module.LSTM_Grid(initializer=init)
hidden_state = (
tf.zeros([n_batchsize, n_cell, n_cell, n_cell, n_hidden],
name="zero_hidden_state"),
tf.zeros([n_batchsize, n_cell, n_cell, n_cell, n_hidden],
name="zero_cell_state"))
else:
rnn = recurrent_module.GRU_Grid(initializer=init)
hidden_state = tf.zeros(
[n_batchsize, n_cell, n_cell, n_cell, n_hidden],
name="zero_hidden_state")
#n_timesteps = self.params["TRAIN"]["TIME_STEP_COUNT"]
n_timesteps = np.shape(X_preprocessed)[1]
# feed a limited seqeuence of images
if isinstance(n_timesteps, int) and n_timesteps > 0:
for t in range(n_timesteps):
hidden_state = rnn.call(encoded_input[:, t, :],
hidden_state)
else: # feed an arbitray seqeuence of images
n_timesteps = tf.shape(X_preprocessed)[1]
t = tf.constant(0)
def condition(h, t):
return tf.less(t, n_timesteps)
def body(h, t):
h = rnn.call(encoded_input[:, t, :], h)
t = tf.add(t, 1)
return h, t
hidden_state, t = tf.while_loop(condition, body,
(hidden_state, t))
# decoder
print("decoder")
if isinstance(hidden_state, tuple):
hidden_state = hidden_state[0]
if self.params["TRAIN"]["DECODER_MODE"] == "DILATED":
de = decoder.Dilated_Decoder(hidden_state)
elif self.params["TRAIN"]["DECODER_MODE"] == "RESIDUAL":
de = decoder.Residual_Decoder(hidden_state)
elif self.params["TRAIN"]["DECODER_MODE"] == "RESIDUAL64":
de = decoder.Residual_Decoder64(hidden_state)
elif self.params["TRAIN"]["DECODER_MODE"] == "SERESNET":
de = decoder.SENet_Decoder(hidden_state)
elif self.params["TRAIN"]["DECODER_MODE"] == "SERESNET64":
de = decoder.SENet_Decoder64(hidden_state)
else:
de = decoder.Simple_Decoder(hidden_state)
self.logits = de.out_tensor
# visualize transformation of hidden state to voxel
if self.params["VIS"]["DECODER_PROCESS"]:
with tf.name_scope("misc"):
feature_voxels = tf.get_collection("feature_voxels")
fv_list = []
for fv in feature_voxels:
fv_slice = fv[0, :, :, 0, 0]
fv_shape = fv_slice.get_shape().as_list()
if "64" in self.params["TRAIN"]["DECODER_MODE"]:
fv_slice = tf.pad(fv_slice,
[[0, 0], [64 - fv_shape[0], 0]])
else:
fv_slice = tf.pad(fv_slice,
[[0, 0], [32 - fv_shape[0], 0]])
fv_list.append(fv_slice)
fv_img = tf.concat(fv_list, axis=0)
tf.summary.image("feature_voxel_list",
tf.expand_dims(tf.expand_dims(fv_img, -1), 0))
# loss
print("loss")
if self.params["TRAIN"]["LOSS_FCN"] == "FOCAL_LOSS":
voxel_loss = loss.Focal_Loss(self.Y_onehot, self.logits)
self.softmax = voxel_loss.pred
elif self.params["TRAIN"]["LOSS_FCN"] == "WEIGHTED_SOFTMAX":
voxel_loss = loss.Weighted_Voxel_Softmax(self.Y_onehot,
self.logits)
self.softmax = voxel_loss.softmax
elif self.params["TRAIN"]["LOSS_FCN"] == "SOFTMAX":
voxel_loss = loss.Voxel_Softmax(self.Y_onehot, self.logits)
self.softmax = voxel_loss.softmax
else:
print("WRONG LOSS FUNCTION. CHECK LOSS")
os.abort()
self.loss = voxel_loss.loss
tf.summary.scalar("loss", self.loss)
# misc
print("misc")
with tf.name_scope("misc"):
self.step_count = tf.Variable(0,
trainable=False,
name="step_count")
self.print = tf.Print(self.loss, [self.step_count, self.loss, t])
# optimizer
print("optimizer")
if self.params["TRAIN"]["OPTIMIZER"] == "ADAM":
optimizer = tf.train.AdamOptimizer(
learning_rate=self.LR,
epsilon=self.params["TRAIN"]["ADAM_EPSILON"])
#learning_rate=self.params["TRAIN"]["ADAM_LEARN_RATE"], epsilon=self.params["TRAIN"]["ADAM_EPSILON"])
tf.summary.scalar("adam_learning_rate", optimizer._lr)
else:
optimizer = tf.train.GradientDescentOptimizer(
learning_rate=self.LR)
#learning_rate=self.params["TRAIN"]["GD_LEARN_RATE"])
tf.summary.scalar("learning_rate", optimizer._learning_rate)
grads_and_vars = optimizer.compute_gradients(self.loss)
self.apply_grad = optimizer.apply_gradients(
grads_and_vars, global_step=self.step_count)
# metric
print("metrics")
with tf.name_scope("metrics"):
Y = tf.argmax(self.Y_onehot, -1)
predictions = tf.argmax(self.softmax, -1)
acc, acc_op = tf.metrics.accuracy(Y, predictions)
rms, rms_op = tf.metrics.root_mean_squared_error(
self.Y_onehot, self.softmax)
iou, iou_op = tf.metrics.mean_iou(Y, predictions, 2)
self.metrics_op = tf.group(acc_op, rms_op, iou_op)
tf.summary.scalar("accuracy", acc)
tf.summary.scalar("rmse", rms)
tf.summary.scalar("iou", iou)
# initalize
# config=tf.ConfigProto(log_device_placement=True)
print("setup")
self.summary_op = tf.summary.merge_all()
self.sess = tf.InteractiveSession()
if self.params["MODE"] == "DEBUG":
self.sess = tf_debug.TensorBoardDebugWrapperSession(
self.sess,
"nat-oitwireless-inside-vapornet100-c-15126.Princeton.EDU:6064"
)
# summaries
print("summaries")
if self.params["MODE"] == "TEST":
self.test_writer = tf.summary.FileWriter(
"{}/test".format(self.MODEL_DIR), self.sess.graph)
else:
self.train_writer = tf.summary.FileWriter(
"{}/train".format(self.MODEL_DIR), self.sess.graph)
self.val_writer = tf.summary.FileWriter(
"{}/val".format(self.MODEL_DIR), self.sess.graph)
# initialize
print("initialize")
tf.global_variables_initializer().run()
tf.local_variables_initializer().run()
print('trainable vars:', len(tf.trainable_variables()))
print("...done!")