我已经以提供的鲍鱼为例,并确保我已经理解了。。。。我想是的。但作为我正在进行的另一个估算项目是产生总垃圾-我已经尝试添加张量板,所以我可以理解发生了什么。
基本代码是https://www.tensorflow.org/extend/estimators
我增加了一个会话和一个作家
# 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)
最后添加了
writer.close()
当我运行代码时。。。我在/tmp/ab_tf中得到一个数据文件,该文件不为空。但它的大小也只有139字节。。。这意味着什么都没写。。。。
当我用张量板打开时-没有数据。
我做错什么了?
感谢任何意见。。。
实际上,您不需要为估计器设置摘要编写器。 摘要日志将写入估计器的model_dir。
假设估计器的model_dir为'./tmp/model', 您可以使用tensorboard--logdir=./tmp/model查看摘要
我想和你做同样的事。我终于发现您需要将model_dir作为参数传递给类构造函数,如下所示:
您可以在TensorFlow API中看到这一点:https://www.tensorflow.org/api_docs/python/tf/contrib/learn/Estimator
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