使用Tensorflow 2.0在Tensorboard中可视化Keras模型

Question

我正在学习使用带有Tensorboard 2.0的Tensorboard可视化Keras模型

对于可重现的示例：

inputs = Input(shape = (train_data.shape[1], ))
x1 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(inputs)
x1a = Dropout(0.5)(x1)
x2 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x1a)
x2a = Dropout(0.5)(x2)
x3 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x2a)
x3a = Dropout(0.5)(x3)
x4 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x3a)
x4a = Dropout(0.5)(x4)
x5 = Dense(100, kernel_initializer = 'he_normal', activation = 'elu')(x4a)
predictions = Dense(1)(x5)
model = Model(inputs = inputs, outputs = predictions)

@tf.function
def traceme(x):
    return model(x)


logdir = "log"
writer = tf.summary.create_file_writer(logdir)
tf.summary.trace_on(graph=True, profiler=True)
# Forward pass
traceme(tf.zeros((1, train_data.shape[1])))
with writer.as_default():
    tf.summary.trace_export(name="model_trace", step=0, profiler_outdir=logdir)

您能告诉我如何从这一点开始进行可视化吗？

您的帮助将不胜感激。

Answer 1

您能告诉我我从现在开始如何进行可视化？

在jupyter笔记本中，

%tensorboard --logdir log

https://www.tensorflow.org/tensorboard/r2/graphs#graphs_of_tffunctions

您也可以通过运行此命令从终端执行此操作，

tensorboard --logdir log

并从网络浏览器中打开'localhost:6006'。