tensorflow可视化Keras框架中Tensorboard使用示例

log_dir='./logs',  # 默认保存在当前文件夹下的logs文件夹之下
histogram_freq=0,
batch_size=32,
write_graph=True,  #默认是True，默认是显示graph的。
write_grads=False,
write_images=False,
update_freq='epoch'

import numpy as np
from keras.layers import Input, Dense, Dropout, Activation,Conv2D,MaxPool2D,Flatten
from keras.datasets import mnist
from keras.models import Model
from keras.utils import to_categorical
from keras.callbacks import TensorBoard
if __name__=="__main__":
    (x_train,y_train),(x_test,y_test) = mnist.load_data()
    x_train=np.expand_dims(x_train,axis=-1)
    x_test=np.expand_dims(x_test,axis=-1)
    y_train=to_categorical(y_train,num_classes=10)
    y_test=to_categorical(y_test,num_classes=10)
    batch_size=128
    epochs=10
    inputs = Input([28,28,1])
    x = Conv2D(32, (5,5), activation='relu')(inputs)
    x = Conv2D(64, (5,5), activation='relu')(x)   
    x = MaxPool2D(pool_size=(2,2))(x)
    x = Flatten()(x)    
    x = Dense(128, activation='relu')(x)
    x = Dropout(0.5)(x)
    x = Dense(10, activation='softmax')(x)
    model = Model(inputs,x)
    model.compile(loss='categorical_crossentropy', optimizer="adam",metrics=['acc']) 
    Tensorboard= TensorBoard(log_dir="./model", histogram_freq=1,write_grads=True)
    history=model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, shuffle=True, validation_split=0.2,callbacks=[Tensorboard])