keras中调用tensorboard：from keras.callbacks import TensorBoard

from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from keras.callbacks import TensorBoard

def model(optimizer="adam"):
    #create model
    model = Sequential()
    model.add(Dense(input_dim=4,units=12,activation="relu"))
    model.add(Dense(units=8,activation="relu"))
    model.add(Dense(units=1,activation="sigmoid"))
    #compile model
    model.compile(loss="mse",optimizer=optimizer,metrics=["accuracy"],)
    return model
#######################################################################################
#create data
np.random.seed(seed=10)
X = np.random.randn(100,4)
y = np.random.randn(100)

#split data in train dataset and test dataset
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)

#using wrappers to create sklearn interface

model = KerasRegressor(build_fn=model,epochs=10,batch_size=5)

#training
#引入Tensorboard画图
model.fit(X_train,y_train,validation_split=0.3,
          callbacks=[TensorBoard(log_dir="H:/1/",histogram_freq=1)])
#predicting
y_pred = model.predict(X_test)
#evalution
print("mse:"+str(mean_squared_error(y_test,y_pred)))

启动：tensorboard --logdir="H:/1/"