我已经采用了所提供的鲍鱼示例,并确保我已经理解了它……好吧,我想我理解了。但作为我正在从事的另一个估算器项目正在产生总垃圾 - 我试图添加张量板,所以我可以理解发生了什么。
基本代码是 https://www.tensorflow.org/extend/estimators
我添加了一个 Session 和一个 writer
# Set model params
model_params = {"learning_rate": 0.01}
with tf.Session () as sess:
# Instantiate Estimator
nn = tf.contrib.learn.Estimator(model_fn=model_fn, params=model_params)
writer = tf.summary.FileWriter ( '/tmp/ab_tf' , sess.graph)
nn.fit(x=training_set.data, y=training_set.target, steps=5000)
# Score accuracy
ev = nn.evaluate(x=test_set.data, y=test_set.target, steps=1)
And added 1 line in the model_fn function so it looks like this...
def model_fn(features, targets, mode, params):
"""Model function for Estimator."""
# Connect the first hidden layer to input layer
# (features) with relu activation
first_hidden_layer = tf.contrib.layers.relu(features, 49)
# Connect the second hidden layer to first hidden layer with relu
second_hidden_layer = tf.contrib.layers.relu(first_hidden_layer, 49)
# Connect the output layer to second hidden layer (no activation fn)
output_layer = tf.contrib.layers.linear(second_hidden_layer, 1)
# Reshape output layer to 1-dim Tensor to return predictions
predictions = tf.reshape(output_layer, [-1])
predictions_dict = {"ages": predictions}
# Calculate loss using mean squared error
loss = tf.losses.mean_squared_error(targets, predictions)
# Calculate root mean squared error as additional eval metric
eval_metric_ops = {
"rmse": tf.metrics.root_mean_squared_error(
tf.cast(targets, tf.float64), predictions)
}
train_op = tf.contrib.layers.optimize_loss(
loss=loss,
global_step=tf.contrib.framework.get_global_step(),
learning_rate=params["learning_rate"],
optimizer="SGD")
tf.summary.scalar('Loss',loss)
return model_fn_lib.ModelFnOps(
mode=mode,
predictions=predictions_dict,
loss=loss,
train_op=train_op,
eval_metric_ops=eval_metric_ops)
终于加了一个
writer.close()
当我运行代码时...我在/tmp/ab_tf 中得到一个数据文件,这个文件不是空的。但它的大小也只有 139 个字节……这意味着没有任何内容被写入……
当我用张量板打开它时 - 没有数据。
我做错了什么?
感谢任何输入...
最佳答案
实际上,您不需要为估算器设置摘要编写器。 摘要日志将写入估算器的 model_dir。
假设您的估算器 model_dir 是“./tmp/model”, 您可以使用 tensorboard --logdir=./tmp/model 查看摘要
关于python - 如何将 Tensorboard 添加到 Tensorflow 估算器进程,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/43908432/