寻找张量流模型的准确性
finding accuracy of tensorflow model
在使用 sigmoid 函数训练这个简单的线性模型后,我试图找出准确性:
import numpy as np
import tensorflow as tf
import _pickle as cPickle
with open("var_x.txt", "rb") as fp: # Unpickling
var_x = cPickle.load(fp)
with open("var_y.txt", "rb") as fp: # Unpickling
var_y = cPickle.load(fp)
with open("var_x_test.txt", "rb") as fp: # Unpickling
var_x_test = cPickle.load(fp)
with open("var_y_test.txt", "rb") as fp: # Unpickling
var_y_test = cPickle.load(fp)
def model_fn(features, labels, mode):
# Build a linear model and predict values
W = tf.get_variable("W", [4], dtype=tf.float64)
b = tf.get_variable("b", [1], dtype=tf.float64)
y = tf.sigmoid( tf.reduce_sum(W*features['x']) + b)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=y)
loss = tf.reduce_sum(tf.square(y - labels))
global_step = tf.train.get_global_step()
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = tf.group(optimizer.minimize(loss),
tf.assign_add(global_step, 1))
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=y,
loss=loss,
train_op=train)
estimator = tf.estimator.Estimator(model_fn=model_fn)
x_train = np.array(var_x)
y_train = np.array(var_y)
x_test = np.array(var_x_test)
y_test = np.array(var_y_test)
input_fn = tf.estimator.inputs.numpy_input_fn(
{"x": x_train}, y_train, batch_size=4, num_epochs=60, shuffle=True)
estimator.train(input_fn=input_fn, steps=1000)
test_input_fn= tf.estimator.inputs.numpy_input_fn(
x ={"x":np.array(x_test)},
y=np.array(y_test),
num_epochs=1,
shuffle=False
)
accuracy_score = estimator.evaluate(input_fn=test_input_fn["accuracy"])
print(accuracy_score)
但是字典没有 "accuracy" 键。我如何找到它?另外,如何使用 tensorboard 跟踪每一步后的准确性?
先谢谢了,tensorflow教程讲解的很烂
accuracy_score = estimator.evaluate(input_fn=test_input_fn)
print(accuracy_score["loss"])
你可以像上面的方法一样得到loss的准确性。
您需要使用 tf.metrics.accuracy
在 model_fn
中自行创建准确度,并将其传递给 eval_metric_ops
,函数将返回该准确度。
def model_fn(features, labels, mode):
# define model...
y = tf.nn.sigmoid(...)
predictions = tf.cast(y > 0.5, tf.int64)
eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels, predictions)}
#...
return tf.estimator.EstimatorSpec(mode=mode, train_op=train_op,
loss=loss, eval_metric_ops=eval_metric_ops)
然后 estimator.evaluate()
的输出将包含一个准确性密钥,该密钥将保存在验证集上计算的准确性。
metrics = estimator.evaluate(test_input_fn)
print(metrics['accuracy'])
test_results = {}
test_results['model'] = model.evaluate(
test_features, test_labels, verbose=0)
print(f" Accuracy: {test_results}")
在使用 sigmoid 函数训练这个简单的线性模型后,我试图找出准确性:
import numpy as np
import tensorflow as tf
import _pickle as cPickle
with open("var_x.txt", "rb") as fp: # Unpickling
var_x = cPickle.load(fp)
with open("var_y.txt", "rb") as fp: # Unpickling
var_y = cPickle.load(fp)
with open("var_x_test.txt", "rb") as fp: # Unpickling
var_x_test = cPickle.load(fp)
with open("var_y_test.txt", "rb") as fp: # Unpickling
var_y_test = cPickle.load(fp)
def model_fn(features, labels, mode):
# Build a linear model and predict values
W = tf.get_variable("W", [4], dtype=tf.float64)
b = tf.get_variable("b", [1], dtype=tf.float64)
y = tf.sigmoid( tf.reduce_sum(W*features['x']) + b)
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.estimator.EstimatorSpec(mode=mode, predictions=y)
loss = tf.reduce_sum(tf.square(y - labels))
global_step = tf.train.get_global_step()
optimizer = tf.train.GradientDescentOptimizer(0.01)
train = tf.group(optimizer.minimize(loss),
tf.assign_add(global_step, 1))
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=y,
loss=loss,
train_op=train)
estimator = tf.estimator.Estimator(model_fn=model_fn)
x_train = np.array(var_x)
y_train = np.array(var_y)
x_test = np.array(var_x_test)
y_test = np.array(var_y_test)
input_fn = tf.estimator.inputs.numpy_input_fn(
{"x": x_train}, y_train, batch_size=4, num_epochs=60, shuffle=True)
estimator.train(input_fn=input_fn, steps=1000)
test_input_fn= tf.estimator.inputs.numpy_input_fn(
x ={"x":np.array(x_test)},
y=np.array(y_test),
num_epochs=1,
shuffle=False
)
accuracy_score = estimator.evaluate(input_fn=test_input_fn["accuracy"])
print(accuracy_score)
但是字典没有 "accuracy" 键。我如何找到它?另外,如何使用 tensorboard 跟踪每一步后的准确性?
先谢谢了,tensorflow教程讲解的很烂
accuracy_score = estimator.evaluate(input_fn=test_input_fn)
print(accuracy_score["loss"])
你可以像上面的方法一样得到loss的准确性。
您需要使用 tf.metrics.accuracy
在 model_fn
中自行创建准确度,并将其传递给 eval_metric_ops
,函数将返回该准确度。
def model_fn(features, labels, mode):
# define model...
y = tf.nn.sigmoid(...)
predictions = tf.cast(y > 0.5, tf.int64)
eval_metric_ops = {'accuracy': tf.metrics.accuracy(labels, predictions)}
#...
return tf.estimator.EstimatorSpec(mode=mode, train_op=train_op,
loss=loss, eval_metric_ops=eval_metric_ops)
然后 estimator.evaluate()
的输出将包含一个准确性密钥,该密钥将保存在验证集上计算的准确性。
metrics = estimator.evaluate(test_input_fn)
print(metrics['accuracy'])
test_results = {}
test_results['model'] = model.evaluate(
test_features, test_labels, verbose=0)
print(f" Accuracy: {test_results}")