我正在使用这个自定义回归量，并且无法使用精选列值打印预测。
我只能打印我的生成器值而不能打印整个矩阵。
如何打印矩阵？这是代码。在this site的代码中有一个小修改。
由于
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Regression using the DNNRegressor Estimator."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import imports85 # pylint: disable=g-bad-import-order
STEPS = 1000
PRICE_NORM_FACTOR = 1000
def my_dnn_regression_fn(features, labels, mode, params):
"""A model function implementing DNN regression for a custom Estimator."""
# Extract the input into a dense layer, according to the feature_columns.
top = tf.feature_column.input_layer(features, params["feature_columns"])
# Iterate over the "hidden_units" list of layer sizes, default is [20].
for units in params.get("hidden_units", [20]):
# Add a hidden layer, densely connected on top of the previous layer.
top = tf.layers.dense(inputs=top, units=units, activation=tf.nn.relu)
# Connect a linear output layer on top.
output_layer = tf.layers.dense(inputs=top, units=1)
# Reshape the output layer to a 1-dim Tensor to return predictions
predictions = tf.squeeze(output_layer, 1)
if mode == tf.estimator.ModeKeys.PREDICT:
# In `PREDICT` mode we only need to return predictions.
return tf.estimator.EstimatorSpec(
mode=mode, predictions={"price": predictions})
# Calculate loss using mean squared error
average_loss = tf.losses.mean_squared_error(labels, predictions)
# Pre-made estimators use the total_loss instead of the average,
# so report total_loss for compatibility.
batch_size = tf.shape(labels)[0]
total_loss = tf.to_float(batch_size) * average_loss
if mode == tf.estimator.ModeKeys.TRAIN:
optimizer = params.get("optimizer", tf.train.AdamOptimizer)
optimizer = optimizer(params.get("learning_rate", None))
train_op = optimizer.minimize(
loss=average_loss, global_step=tf.train.get_global_step())
return tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
# In evaluation mode we will calculate evaluation metrics.
assert mode == tf.estimator.ModeKeys.EVAL
# Calculate root mean squared error
rmse = tf.metrics.root_mean_squared_error(labels, predictions)
# Add the rmse to the collection of evaluation metrics.
eval_metrics = {"rmse": rmse}
return tf.estimator.EstimatorSpec(
mode=mode,
# Report sum of error for compatibility with pre-made estimators
loss=total_loss,
eval_metric_ops=eval_metrics)
def main(argv):
"""Builds, trains, and evaluates the model."""
assert len(argv) == 1
(train, test) = imports85.dataset()
# Switch the labels to units of thousands for better convergence.
def normalize_price(features, labels):
return features, labels / PRICE_NORM_FACTOR
train = train.map(normalize_price)
test = test.map(normalize_price)
# Build the training input_fn.
def input_train():
return (
# Shuffling with a buffer larger than the data set ensures
# that the examples are well mixed.
train.shuffle(1000).batch(128)
# Repeat forever
.repeat().make_one_shot_iterator().get_next())
# Build the validation input_fn.
def input_test():
return (test.shuffle(1000).batch(128)
.make_one_shot_iterator().get_next())
# The first way assigns a unique weight to each category. To do this you must
# specify the category's vocabulary (values outside this specification will
# receive a weight of zero). Here we specify the vocabulary using a list of
# options. The vocabulary can also be specified with a vocabulary file (using
# `categorical_column_with_vocabulary_file`). For features covering a
# range of positive integers use `categorical_column_with_identity`.
body_style_vocab = ["hardtop", "wagon", "sedan", "hatchback", "convertible"]
body_style = tf.feature_column.categorical_column_with_vocabulary_list(
key="body-style", vocabulary_list=body_style_vocab)
make = tf.feature_column.categorical_column_with_hash_bucket(
key="make", hash_bucket_size=50)
feature_columns = [
tf.feature_column.numeric_column(key="curb-weight"),
tf.feature_column.numeric_column(key="highway-mpg"),
# Since this is a DNN model, convert categorical columns from sparse
# to dense.
# Wrap them in an `indicator_column` to create a
# one-hot vector from the input.
tf.feature_column.indicator_column(body_style),
# Or use an `embedding_column` to create a trainable vector for each
# index.
tf.feature_column.embedding_column(make, dimension=3),
]
# Build a custom Estimator, using the model_fn.
# `params` is passed through to the `model_fn`.
model = tf.estimator.Estimator(
model_fn=my_dnn_regression_fn,
params={
"feature_columns": feature_columns,
"learning_rate": 0.001,
"optimizer": tf.train.AdamOptimizer,
"hidden_units": [20, 20]
},
model_dir="resultados")
# Train the model.
model.train(input_fn=input_train, steps=STEPS)
# Evaluate how the model performs on data it has not yet seen.
eval_result = model.evaluate(input_fn=input_test)
pred_result = model.predict(input_fn = input_test,
predict_keys=None,
hooks=None,
checkpoint_path=None)
# Print the Root Mean Square Error (RMSE).
print("\n" + 80 * "*")
print("\nRMS error for the test set: ${:.0f}"
.format(PRICE_NORM_FACTOR * eval_result["rmse"]))
print(list(pred_result))
print()
if __name__ == "__main__":
# The Estimator periodically generates "INFO" logs; make these logs visible.
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(main=main)
答案 0 :(得分：0)
您可以使用2功能，
get_variable_names()
get_variable_value(name)
以下列方式打电话给他们，
model.get_variable_names()
get_variable_value(name)
因此，您可能想要检索的模型矩阵是唯一的方法。请参阅documentation。
中的更多内容