# 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])
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