如何解决Tensorboard 仅显示包含所有数据的两个标量
所以我正在使用 Tensorboard,它将所有数据一起显示(验证损失和正常等),这非常混乱,我无法使用它。
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Activation,Flatten,Conv2D,MaxPooling2D,Reshape
from tensorflow.keras.callbacks import TensorBoard
import time
import pickle
import numpy as np
from tensorflow.compat.v1 import ConfigProto
from tensorflow.compat.v1 import InteractiveSession
config = ConfigProto()
config.gpu_options.allow_growth = True
session = InteractiveSession(config=config)
#tensorboard = TensorBoard(log_dir='logs/{}'.format(NAME))
pickle_in = open("X.pickle","rb")
X = pickle.load(pickle_in)
pickle_in = open("y.pickle","rb")
y = pickle.load(pickle_in)
X=np.array(X/255.0)
y=np.array(y)
dense_layers = [0,1,2]
layer_sizes = [32,64,128]
conv_layers = [1,2,3]
for dense_layer in dense_layers:
for layer_size in layer_sizes:
for conv_layer in conv_layers:
NAME = "{}-conv-{}-nodes-{}-dense-{}".format(conv_layer,layer_size,dense_layer,int(time.time()))
file_writer = tf.train.SummaryWriter('/logs/{}.format(NAME)',sess.graph)
print(NAME)
model = Sequential()
model.add(Reshape((100,100,1)))
model.add(Conv2D(64,(3,3),input_shape = X.shape[1:]))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
for l in range(conv_layer-1):
model.add(Conv2D(64,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size=(2,2)))
model.add(Flatten())
for l in range(dense_layer):
model.add(Dense(layer_size))
model.add(Activation("relu"))
# model.add(Dense(64))
# model.add(Activation("relu"))
model.add(Dense(1))
model.add(Activation("sigmoid"))
model.compile(loss="binary_crossentropy",optimizer="adam",metrics=["accuracy"])
model.fit(X,y,batch_size=5,epochs=10,validation_split=0.1,callbacks=[file_writer])
有人可以帮忙吗?我不明白我做错了什么,或者这是否正常!因为我看过多个教程,其中所有内容都有标量。提前致谢!!!!
解决方法
这可能不是您问题的直接答案,但您是否尝试过 Aim?
它很容易上手,集成/使用也非常简单。 Aim 的主要超能力是您可以一次比较大量实验。
import aim
aim.set_params(hyperparam_dict,name='hparams')
aim.track(metric_value,name='metric_name',epoch=epoch_number)
免责声明:我是贡献者。