keras+TensorBoard实现训练可视化

# 引入Tensorboard
from keras.callbacks import TensorBoard
	
tbCallBack = TensorBoard(log_dir='./logs',  # log 目录
                 histogram_freq=0,  # 按照何等频率（epoch）来计算直方图，0为不计算
#                  batch_size=32,     # 用多大量的数据计算直方图
                 write_graph=True,  # 是否存储网络结构图
                 write_grads=True, # 是否可视化梯度直方图
                 write_images=True,# 是否可视化参数
                 embeddings_freq=0, 
                 embeddings_layer_names=None, 
                 embeddings_metadata=None)
	
model.fit(...inputs and parameters..., callbacks=[tbCallBack])

• 通过引入tensorboard加入了回调函数的功能。 它将在训练期间运行并输出可用于张量板的文件。如果您想要在训练的过程中可视化，请在terminal终端输入

    
  tensorboard --logdir=./logs 
  

• 然后在浏览器中访问http://localhost:6006

• 完整代码如下：
• 由keras/examples/mnist_mlp.py示例代码修改:

from __future__ import print_function

import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import RMSprop

# 引入Tensorboard
from keras.callbacks import TensorBoard

batch_size = 128
num_classes = 10
epochs = 20

# the data, split between train and test sets
(x_train, y_train), (x_test, y_test) = mnist.load_data()

x_train = x_train.reshape(60000, 784)
x_test = x_test.reshape(10000, 784)
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')

# convert class vectors to binary class matrices
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)

model = Sequential()
model.add(Dense(512, activation='relu', input_shape=(784,)))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(num_classes, activation='softmax'))

model.summary()

model.compile(loss='categorical_crossentropy',
              optimizer=RMSprop(),
              metrics=['accuracy'])

tbCallBack = TensorBoard(log_dir='./logs',  # log 目录
                 histogram_freq=0,  # 按照何等频率（epoch）来计算直方图，0为不计算
#                  batch_size=32,     # 用多大量的数据计算直方图
                 write_graph=True,  # 是否存储网络结构图
                 write_grads=True, # 是否可视化梯度直方图
                 write_images=True,# 是否可视化参数
                 embeddings_freq=0, 
                 embeddings_layer_names=None, 
                 embeddings_metadata=None)

history = model.fit(x_train, y_train,
                    batch_size=batch_size,
                    epochs=epochs,
                    verbose=1,
                    validation_data=(x_test, y_test),
                    callbacks=[tbCallBack])
score = model.evaluate(x_test, y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1])